Postprandial Dysmetabolism and Non-Alcoholic Fatty Liver Disease
in Relation to Type 2 Diabetes Mellitus and Cardiovascular Risk
The studies presented in this thesis were performed at the Diabetes Center of the
Department of Endocrinology in collaboration with the EMGO Institute and the
Department of Clinical Chemistry, VU University Medical Center, Amsterdam, The
Netherlands, and at the Diabetes Research Center in Hoorn, The Netherlands. Parts of
this work were supported by the Dutch Diabetes Research Foundation (grant no.
2001.00.052) and by funding from Novartis AG, Switzerland.
Financial support by the Dutch Diabetes Research Foundation for the publication of this
thesis is gratefully acknowledged.
Additional financial support for the publication of this thesis was kindly provided by:
Eli Lilly Nederland B.V., GlaxoSmithKline B.V., HemoCue Diagnostics B.V., Novartis
Pharma B.V., Novo Nordisk Farma B.V. and Pfizer B.V, Servier Nederland Farma B.V.
Cover
Cover illustration “Drieluik 2006” by Suzanne Kalf ([email protected]).
Printing
Ponsen & Looijen B.V., Wageningen, The Netherlands.
ISBN 978-90-6464-113-8
NUR 878
© Roger K. Schindhelm, The Netherlands.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, photocopying, or
otherwise, without the permission of the author, or, when appropriate, of the
publishers of the publications.
VRIJE UNIVERSITEIT
Postprandial Dysmetabolism and Non-Alcoholic Fatty Liver Disease
in Relation to Type 2 Diabetes Mellitus and Cardiovascular Risk
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus
prof.dr. L.M. Bouter, in het openbaar te verdedigen
ten overstaan van de promotiecommissie van de faculteit der Geneeskunde
op maandag 21 mei 2007 om 10.45 uur in de aula van de universiteit,
De Boelelaan 1105
door
Roger Karlo Schindhelm
geboren te Kerkrade
promotor: prof.dr. R.J. Heine copromotoren: dr. M. Diamant dr. T. Teerlink
Financial support by the Netherlands Heart Foundation for the publication of this
thesis is gratefully acknowledged.
5
Voor mijn ouders
6
CONTENTS Chapter 1: General introduction and outline of the thesis 9
PART 1 Postprandial dysmetabolism, cardiovascular disease risk
and type 2 diabetes mellitus
Chapter 2: Determinants of postprandial triglyceride and glucose
responses following two consecutive fat-rich or carbohydrate-
rich meals in normoglycemic women and in women with type
2 diabetes: the Hoorn Prandial study.
Submitted
27
Chapter 3: Postprandial glucose and not triglyceride concentrations are
associated with carotid intima media thickness in women with
normal glucose metabolism: the Hoorn Prandial study
Atherosclerosis (in press)
45
Chapter 4: Fasting and postprandial glycoxidative and lipoxidative stress
are increased in women with type 2 diabetes mellitus
Submitted
63
Chapter 5: Comparison of the effect of two consecutive fat-rich and
carbohydrate-rich meals on levels of postprandial plasma
myeloperoxidase in women with and without type 2 diabetes
mellitus
Submitted
79
PART 2 Liver enzymes in relation to the metabolic syndrome, type
2 diabetes mellitus and cardiovascular disease risk
Chapter 6: Alanine aminotransferase as a marker of non-alcoholic fatty
liver disease in relation to type 2 diabetes mellitus and
cardiovascular disease (review).
Diabetes Metab Res Rev 2006;22:437-43.
95
Chapter 7:
Liver alanine aminotransferase, insulin resistance and
endothelial dysfunction in normotriglyceridemic subjects with
type 2 diabetes mellitus.
Eur J Clin Invest 2005;35:369-74.
113
7
Chapter 8: Alanine aminotransferase predicts coronary heart disease
events: a 10-year follow-up of the Hoorn study.
Atherosclerosis 2007;191;391-6.
129
Chapter 9.1: Alanine aminotransferase and the 6-years risk of the
metabolic syndrome in Caucasian men and women: the Hoorn
study.
Diabet Med 2007;24:430-5.
143
Chapter 9.2: No independent association of alanine aminotransferase with
risk of future type 2 diabetes in the Hoorn study (letter).
Diabetes Care 2005;28:2812.
155
Chapter 10.1:
Chapter 10.2:
General discussion - Methodological considerations
General discussion - Pathophysiological considerations
159
169
APPENDIX
Summary
188
Samenvatting voor niet ingewijden
191
Dankwoord
196
List of publications
200
About the author/Over de auteur 203
8
9
Chapter 1
GENERAL INTRODUCTION AND OUTLINE OF THE THESIS
CHAPTER 1
10
DIABETES MELLITUS AND THE METABOLIC SYNDROME
Diabetes mellitus is an endocrine disorder, characterized by a chronic elevation of
blood glucose levels (hyperglycemia). Type 1 diabetes mellitus (less than 10% of all
diabetes mellitus cases) is caused by an immune mediated destruction of the β-
cells resulting in an absolute insulin deficiency. This is the most prevalent form
among children and adolescents. In individuals with type 2 diabetes (DM2),
hyperglycemia is caused by a combination of an impairment of the beta-cells to
release sufficient insulin (β-cell dysfunction) and an impaired metabolic response of
peripheral tissues to circulating insulin (insulin resistance) [1,2]. Deficient action of
insulin on target tissues, either due to insulin resistance or insulin deficiency, not
only leads to hyperglycemia, but also to other metabolic abnormalities, including
abnormalities in fasting and postprandial lipid metabolism (dyslipidemia).
Dyslipidemia associated with DM2 is characterized by hypertriglyceridemia, low
high-density lipoprotein (HDL)-cholesterol, and small and dense low-density
lipoprotein (LDL) particles, which are more atherogenic than the more buoyant
forms [3-5].
In 1988, Reaven described the insulin resistance syndrome or syndrome X [6], now
called the metabolic syndrome. It was originally defined by the presence of
hyperinsulinemia (compensatory for the underlying insulin resistance), varying
degrees of glucose tolerance, hypertriglyceridemia and low plasma HDL cholesterol
concentration [6]. Remarkably, obesity or waist circumference was not part of the
original definition. In the most common working definition, as proposed by the
Adult Treatment Panel III of the National Cholesterol Education Program (NCEP) for
reasons of unification and clinical utility, the metabolic syndrome is characterized
by central obesity, hypertriglyceridemia, low plasma HDL-cholesterol, hypertension
and dysglycemia, i.e. impaired fasting glucose [7].
The prevalence of diabetes has increased substantially over the last decades. An
estimated 30 million people worldwide had diabetes in 1985. For the year 2000,
the World Health Organization has estimated the number of cases of adults with
diabetes to be 171 million and the total number is projected to rise to 366 million
by the year 2030 [8]. The most important factors contributing to this increase in
prevalence are ageing of the population and, in particular, the increasing
prevalence of obesity [9].
INTRODUCTION & OUTLINE
11
Among individuals with DM2, the cardiovascular disease (CVD) morbidity and
mortality is two to four times higher compared to individuals with normal glucose
metabolism and the CVD mortality in individuals with DM2 accounts for as much as
75% of all deaths [10,11]. In the Reykjavik Study, a population-based study with
over 18000 elderly participants, the relative impact of elevated glucose,
triglycerides and systolic blood pressure with regard to fatal and non-fatal CHD was
higher in women than in men [11]. In premenopausal women non-HDL cholesterol
concentrations are lower than in age-matched men, whereas the reverse is true
after menopause [12]. Indeed, postmenopausal women have higher fasting and
postprandial triglyceride levels [13], higher total cholesterol [14,15] and smaller
LDL particles [16], as compared to premenopausal women. This higher relative CVD
risk and the paucity of studies in postmenopausal women prompted us to perform
the postprandial studies described in this thesis in postmenopausal women.
DETERMINANTS OF CARDIOMETABOLIC RISK
New determinants of cardiometabolic risk
There are several conventional risk factors that can explain part of the excess CVD
risk in individuals with DM2. These include high LDL cholesterol, low HDL
cholesterol, hypertension, smoking, hyperglycemia, and high triglyceride
concentrations [17,18]. However, these risk factors, only explain part of the
increased CVD risk in individuals with DM2 [18]. For that reason additional risk
factors or determinants have been explored that might contribute to the excess
CVD risk in individuals with DM2. These include, but are not limited to,
postprandial dysmetabolism [19,20], endothelial dysfunction [21], low-grade
inflammation, non-alcoholic fatty liver disease [22], oxidative stress [23,24], and
advanced glycation end-products [25]. These additional potential determinants are
introduced in the next sections. A risk determinant is defined as a variable that is
directly or indirectly associated with outcome, regardless of the postulated
causality. In case of a risk factor, the association with outcome is presumed to be
causal and in case of a risk predictor, the variable predicts the outcome without
necessarily conveying causality. A risk marker reflects not only the presence of a
risk factor but also some form of susceptibility of the organism to the detrimental
effects of this risk factor. A risk marker is associated with the disease (statistically)
but need not be causally linked and it may be a measure of the disease process
itself [26].
CHAPTER 1
12
Postprandial glucose and triglycerides
Both fasting triglycerides and glucose are established risk factors of CVD in
individuals with and without DM2. A meta-regression analysis of 20 studies with
more than 95 000 people demonstrated a continuous relationship of plasma
glucose levels with incident CVD events [27], and a meta-analyses in over 57 000
individuals of 17 studies found that fasting triglyceride levels were associated with
CVD, also when adjusted for HDL-cholesterol [28]. However, a number of studies
suggest that postprandial or post-load glucose and postprandial triglyceride
concentrations are more strongly associated with some of the CVD risk factors and
mortality than the respective fasting values [29-31]. They debate the causal role of
postprandial glucose in relation to CVD [32], versus simply reflecting the
underlying metabolic abnormalities in for example lipid metabolism. It has also
been suggested that the relation between hyperglycemia and CVD is mediated by
oxidative stress [33]. Ceriello et al. showed a cumulative effect of postprandial
hyperglycemia and postprandial hypertriglyceridemia on oxidative stress
generation in subjects with DM2 [34]. Indeed, specific meal components, such as
fat and carbohydrates may exert acute effects on endothelial function [35], and
enhance the production of reactive oxygen species, which may also contribute to
CVD risk [36]. However, the relative contributions and mechanisms of postprandial
glucose levels and postprandial triglyceride levels with respect to atherosclerosis
and CVD risk are not yet clarified [32,37].
Endothelial functions and dysfunctions
The vascular endothelium is a large autocrine, paracrine and endocrine organ, and
plays an important role in the regulation of vascular homeostasis [21,38,39]. The
normal, healthy endothelium synthesizes and releases numerous substances in
response to a myriad of diverse chemical and physical stimuli. These factors are
important in maintaining and regulating vascular homeostasis, including vascular
tone, smooth muscle cell proliferation, inflammation, and coagulation and
fibrinolysis [40,41]. Endothelium derived nitric oxide (NO) is a key molecule in the
regulation of endothelial functions. NO is synthesized by a stereo-specific reaction
from L-arginine by the enzyme nitric oxide synthase (eNOS) [42,43]. Shear stress
increases the expression of eNOS, whereas asymmetrical dimethylarginine (ADMA)
is an inhibitor of eNOS [44]. The produced NO diffuses through the endothelial cell
membrane into the adjacent smooth muscle cells, and activates soluble guanylate
cyclase, that converts guanosine triphospate (GTP) to cyclic 3’5’-guanosine
INTRODUCTION & OUTLINE
13
monophophate (cGMP) [45], which ultimately decreases intracellular calcium levels
leading to relaxation of the smooth muscle cells and causing vasodilatation. In
addition, NO also inhibits platelet adherence and aggregation, leukocyte adhesion
and proliferation of smooth muscle cells [46]. A disturbance of the endothelial
regulatory mechanisms may lead to endothelial dysfunctioning, which can be
defined as the loss of one or more of these functions [47]. Dysfunctioning of the
vascular endothelium is recognized to be an early step in the development of
atherosclerosis [48-51]. Endothelial function is difficult to assess. An optimal
methodology for studying endothelial function does not exist. Over the last
decades, multiple biochemical markers and invasive and non-invasive tests have
been introduced.
A number of biochemical markers have been identified that reflect endothelial
(dys)function, including soluble adhesion molecules, E-selectin and von Willebrand
factor (vWF). The soluble adhesion molecules, intercellular adhesion molecule 1
(ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) facilitate endothelial
adhesion of circulation leukocytes. These molecules are not endothelium specific;
also other cell types including smooth muscle cells and monocytes express them.
The expression of ICAM-1 and VCAM-1 is increased in response to inflammatory
cytokines [52]. Higher plasma concentrations of VCAM-1 and ICAM-1 have been
reported in DM2 [53,54]. E-selectin is specifically expressed by stimulated
endothelial cells and exposed on their surface. The soluble form of E-selectin may
result from shedding of damaged or activated endothelial cells [55]. High plasma
levels of E-selectin have been observed in obese individuals [56], in hypertensive
patients [57] and in individuals with DM2 [58]. Von Willebrand factor (vWF) is an
endothelial ligand for platelet glycoproteins and is endothelium specific. In case of
endothelial cell injury vWF is released. Major CVD risk factors, including smoking
[59], hypertension [60] and DM2 [61] are associated with increased plasma levels of
vWF. In addition, vWF is a predictor of CVD morbidity and mortality [62,63].
A non-invasive method for measuring endothelial function using high-resolution
vascular ultrasound was first described by Celermajer and colleagues [64]. In this
procedure, the arterial diameter of the brachial artery is subsequently measured in
rest, during reactive hyperemia (leading to flow mediated vasodilatation (FMD), an
endothelium-dependent response) and following sublingual administration of
nitroglycerin (an endothelium independent, nitroglycerin mediated dilatation,
NMD). The responses during reactive hyperemia are measured after a temporary
(usually 4 to 5 minutes) occlusion of the brachial artery using a cuff placed on the
CHAPTER 1
14
forearm [65,66]. FMD is expressed as (percentual) change in diameter after the
stimulus. A higher FMD reflects a “better” endothelial function. [67]. One of the
principal mediators of the FMD response is endothelium-derived NO [68]. The
brachial FMD is closely correlated to coronary endothelial function [46], and
impaired FMD predicts future cardiovascular events [69,70].
Oxidative stress and advanced glycation end-products
Numerous studies have identified hyperglycemia as an important risk factor in the
development of diabetic complications (reviewed in [71]), and several mechanisms
have been proposed [72,73]. These included, but were not limited to altered
lipoprotein metabolism, oxidative stress, dicarbonyl stress, and glycation reactions.
Glycation is the non-enzymatic reaction of glucose and other reducing sugars with
amino groups of proteins. The amino groups of the side chains of arginine and
lysine are the primary targets for this type of posttranslational modification. Over
time, the initial glycation products undergo intramolecular rearrangements and
oxidation reactions (glycoxidation) and ultimately transform into stable so-called
advanced glycation end-products (AGEs) (Figure 1.1). AGE-modification of proteins
can alter or limit their functional or structural properties, which ultimately can lead
to tissue damage as seen in age-related pathologies and diabetes. AGE formation is
promoted by high concentrations of sugars, including glucose, and α-oxoaldehydes
(e.g. 3-deoxyglucosone, methylglyoxal and glyoxal) and enhanced by oxidative
stress [74]. The mechanisms linking hyperglycemia to micro- and macrovascular
complications overlap and interact with each other, e.g. AGE formation and
carbonyl stress may lead to oxidative stress and oxidative stress may enhance AGE
formation [75].
Low-grade inflammation
The insulin resistant state is accompanied by (systemic) low-grade inflammation.
The proinflammatory cytokines include interleukin-6 (IL-6) and tumor necrosis
factor-α (TNF-α). They induce attraction of inflammatory cells and the production of
acute-phase proteins, including C-reactive protein (CRP), by the liver. IL-6 is a
pleiotropic cytokine, required to control the extent of local and systemic
inflammatory responses [76]. IL-6 is increased in hypertension [77], obesity [78],
DM2 [79], and non-alcoholic fatty liver disease (NAFLD) [80]. IL-6 is positively
associated with CVD risk in individuals without DM2 [81].
INTRODUCTION & OUTLINE
15
glucose
Schiff base
Amadori adduct
glucosone
methyglyoxal
glyoxal
glycolaldehyde
methyglyoxal
glyoxal
deoxyglucosones
AGEs and
glycoxidation
products
[O2]
[ONOO-]
[O2]
[O2]
[ONOO-]
[O2]
[O2]
[O2]
glucose
Schiff base
Amadori adduct
glucosone
methyglyoxal
glyoxal
glycolaldehyde
methyglyoxal
glyoxal
deoxyglucosones
AGEs and
glycoxidation
products
[O2]
[ONOO-]
[O2]
[O2]
[ONOO-]
[O2]
[O2]
[O2]
Figure 1.1. Pathways for the formation of advanced glycation end-products (AGE) and AGE-
precursors (adapted from: Thorpe SR, Baynes JW. Amino Acids 2003;25:275-281.)
C-reactive protein (CRP) is an acute phase protein, which is produced by the liver
and is widely used as an indicator of low-grade inflammation. The synthesis of CRP
is largely mediated by cytokines, in particular by IL-6. CVD risk factors that increase
CRP concentrations include obesity [82], hypertension [77], dyslipidemia [83], and
DM2 [83,84]. Some [85], but not all studies [80], have demonstrated an association
of CRP with NAFLD. CRP concentrations are higher in individuals with CVD as
compared to those without CVD. In addition, CRP is positively associated with risk
of CVD, both in patients with DM2 [86,87] and in individuals without DM2 [88]. A
more classical and clinical marker of (low-grade) inflammation is the blood
leukocyte count, which is also associated with CVD events [89,90]. The exact role
of leukocytes in the pathogenesis of CVD is not yet fully elucidated. Postprandial
recruitment and activation of these cells have been suggested to contribute to
endothelial dysfunction [91]. Myeloperoxidase (MPO) is a heme protein which is
abundantly expressed in leukocytes, and released upon activation into phagocytic
vacuoles and in the extra cellular space [92,93], and is regarded as maker of low-
grade systemic inflammation [94]. Several in vitro and in vivo studies have shown
that MPO may interact with the vessel wall by various mechanisms including
binding and transcytosis through endothelial cells, and inflicts damage by
hypochlorous acid generation, nitric oxide oxidation and tyrosine nitration [95].
MPO has been associated with recurrent coronary events [96], and endothelial
dysfunction [97].
CHAPTER 1
16
Non-alcoholic fatty liver disease and liver enzymes
NAFLD includes a wide spectrum of liver pathology, ranging from fatty liver alone
to the more severe non-alcoholic steatohepatitis. It usually has a benign clinical
course, but it may progress to steatohepatitis, fibrosis, and cirrhosis and ultimately
to liver failure. NAFLD resembles alcohol-induced liver disease, but develops in
subjects who are not heavy alcohol consumers and have negative tests for viral and
autoimmune liver diseases [98-102]. This condition was first described in detail by
Ludwig and colleagues in 1980, who reported 20 moderately obese patients with
liver biopsy changes resembling alcohol induced hepatitis, although none of these
patients reported a history of alcohol abuse [103]. Only in recent years, NAFLD has
gained appreciation as a pathogenic factor of insulin resistance and DM2. In
addition, several studies have shown an association between NAFLD and features of
the metabolic syndrome, including dyslipidemia and (visceral) obesity, stressing the
association with insulin resistance as an important feature of NAFLD. Currently,
NAFLD is considered by some authors to be the hepatic component of the
metabolic syndrome [104,105]. In the majority of cases, NAFLD causes
asymptomatic elevation of liver enzyme levels (including alanine aminotransferase
[ALT], aspartate aminotransferase and γ-glutamyltransferase) [106]. Of these liver
enzymes, ALT is most closely related to liver fat accumulation [107], and
consequently ALT has been used as a marker of NAFLD.
The exact mechanisms leading to hepatic triglyceride accumulation are not
completely understood. Insulin resistance is an early important mechanism leading
to an increased free fatty acid (FFA) influx into the liver [108,109], contributing to
hepatic triglyceride accumulation. In addition, the hepatic FFA pool is increased by
de novo lipogenesis [110]. The quantitative importance of the contribution of the
de novo lipogenesis to the hepatic fat pool is disputed. Hyperinsulinemia in
combination with a high FFA flux and hyperglycemia are known to up-regulate
lipogenic transcription factors. In addition, pathways that decrease the hepatic FFA
pool, i.e. both FFA oxidation and efflux of lipids from the liver are impaired. The
increased availability of FFA, glucose and insulin contribute to the increase of
malonyl-CoA by stimulating acetyl-CoA carboxylase that converts acetyl-CoA to
malonyl-CoA. Malonyl-CoA is an inhibitor of carnitine palmitoyl transferase 1 (CPT-
1), required to transport FFA into the mitochondria. The inhibition of CPT-1
prevents FFA oxidation and facilitates accumulation and re-esterification of long-
chain fatty-acyl CoA and subsequent formation of triglycerides [111].
INTRODUCTION & OUTLINE
17
Evidence is accumulating that patients with NAFLD have an increased risk to
develop CVD, but the mechanisms linking NAFLD to CVD are not yet fully
understood.
HOW DO THE VARIOUS CARDIOMETABOLIC RISK DETERMINANTS
RELATE TO EACH OTHER?
The interrelationship of the above mentioned cardiometabolic determinants with
respect to CVD risk is complex. In particular it is not yet possible to infer any
causality for any of these new risk markers. Indeed, NAFLD may be a cause or a
consequence of the metabolic syndrome and postprandial dysmetabolism may
contribute to the development and progression of NAFLD, but also, once a fatty
liver has evolved, postprandial dysmetabolism may aggravate, especially in patients
with DM2. These cardiometabolic determinants are likely to interact and contribute
to the enhanced CVD risk in patients with DM2 and/or individuals with the
metabolic syndrome. A better understanding of these interrelationships and their
relative contribution to CVD is of importance to design strategies, which will lower
CVD risk in populations at risk.
AIMS AND OUTLINE OF THE THESIS
This thesis is divided into two parts.
Part 1 (Chapters 2 to 5) presents the results of studies assessing the responses to
two consecutive fat-rich or carbohydrate-rich meals on the (apo)lipoproteins and
glucose and to study mechanisms that link postprandial glucose and triglyceride
levels to CVD risk in patients with DM2 and individuals with normal glucose
metabolism.
Part 2 (Chapters 6 to 9) focuses on ALT as a marker for NAFLD in relation to CVD,
the metabolic syndrome and DM2.
CHAPTER 1
18
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25
Part 1
POSTPRANDIAL DYSMETABOLISM, CARDIOVASCULAR DISEASE RISK AND TYPE 2 DIABETES MELLITUS
26
27
Chapter 2
DETERMINANTS OF POSTPRANDIAL TRIGLYCERIDE AND GLUCOSE RESPONSES FOLLOWING TWO CONSECUTIVE FAT-RICH OR
CARBOHYDRATE-RICH MEALS IN NORMOGLYCEMIC WOMEN AND IN
WOMEN WITH TYPE 2 DIABETES. THE HOORN PRANDIAL STUDY.
Marjan Alssema1*
Roger K. Schindhelm2*
Jacqueline M. Dekker1
Michaela Diamant2
Giel Nijpels1,3
Tom Teerlink4
Peter G. Scheffer4
Piet J. Kostense1,5
Robert J. Heine1,2
From the 1EMGO Institute, and the Departments of 2Endocrinology/Diabetes Center,
3General Practice, 4Clinical Chemistry and 5Clinical Epidemiology and Biostatistics,
VU University Medical Center, Amsterdam, The Netherlands
*Both authors contributed equally
Submitted
CHAPTER 2
28
ABSTRACT
Background: Both postprandial hyperglycemia and hypertriglyceridemia have been
identified as risk markers for cardiovascular disease. However, the determinants of
the magnitude of postprandial triglyceride and glucose responses are largely
unknown. The objective was to assess potential determinants of postprandial
glucose and triglyceride responses following two consecutive meals in women with
normal glucose metabolism (NGM) and with type 2 diabetes (DM2).
Methods: Post-menopausal women, 76 with NGM and 41 with DM2, received two
consecutive fat-rich meals and carbohydrate-rich meals on separate occasions.
Blood samples were taken before and at t=1, 2, 4, 6 and 8 hours following
breakfast; lunch was given at t=4h.
Results: In NGM women, fasting triglycerides, HbA1c, total cholesterol, and,
inversely, HDL-cholesterol were independently associated with TG-iAUC, and age
and fasting triglycerides were independently associated with glucose-iAUC (GL-
iAUC). In women with DM2, fasting triglycerides were independently associated
with TG-iAUC while HbA1c and fasting glucose were stronger than fasting
triglycerides associated with GL-iAUC. GL-iAUC and TG-iAUC were associated with
each other in women with DM2, but not in NGM.
Conclusions: In conclusion, GL-iAUC and TG-iAUC were correlated with each other
in women with DM2 and fasting triglycerides were associated with GL-iAUC and TG-
iAUC. These findings suggest a common underlying mechanism for postprandial
increments in glucose and triglycerides, especially occurring in women with DM2.
In healthy women, elevated TG-iAUC may be part of a dyslipidemic lipid profile,
whereas the relation between fasting triglycerides and GL-iAUC is suggestive for an
association with insulin resistance.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
29
INTRODUCTION
Already in 1979, Zilversmit postulated that atherogenesis might be a postprandial
phenomenon [1]. Since then, both postprandial hyperglycemia and
hypertriglyceridemia have been identified as risk markers for cardiovascular
disease (CVD) [2,3]. Glucose values two hours following an oral glucose tolerance
test (OGTT) have been shown to be better indicators of CVD risk than fasting
glucose concentration in the general population [4]. We recently demonstrated that
postprandial glucose levels were stronger than the fasting levels, associated with
carotid intima-media thickness in women with normal glucose metabolism (NGM)
[5]. Others have shown that postprandial hypertriglyceridemia is more strongly
related to carotid intima-media thickness than the fasting triglyceride levels [6,7].
The determinants of these postprandial glucose and triglyceride responses are not
well known but might, at least in part, overlap. However, the possible determinants
of postprandial triglyceride and glucose concentrations have not been described in
a single study population before. To date, most studies used a single, often
artificially composed liquid fat and/or carbohydrate load to assess postprandial
responses. Since a first meal can affect the glucose and triglyceride response to a
second meal, we chose to apply two consecutive meals [8,9]. Postmenopausal
women were invited for the present study because in these women, postprandial
triglyceride responses have been shown to be elevated [10], and type 2 diabetes
(DM2) was shown to confer a higher relative risk for CVD as compared to men [11].
In light of the above considerations, we assessed associations of clinical and
biochemical variables with postprandial triglyceride and glucose day profiles in
postmenopausal women with NGM and DM2, in order to investigate whether
postprandial hypertriglyceridemia and hyperglycemia are the result of a similar
underlying mechanism.
SUBJECTS AND METHODS
Study population
The study population has been described in detail previously [5]. In brief, women
with DM2 were randomly selected from the registry of the Diabetes Care System in
the city of Hoorn, the Netherlands. Women with NGM were randomly selected from
the municipal registry of the city of Hoorn. All women were between 50 and 65
years of age, were post-menopausal, non-smokers, had no untreated endocrine
CHAPTER 2
30
disorder other than DM2, did not use HMG-CoA reductase inhibitors, short acting
insulin analogues, peroxisome proliferator-activated receptor-α and/or γ agonists,
oral corticosteroids, or hormone replacement therapy. Women without known DM2
underwent an OGTT and were selected on NGM status (fasting glucose <6.1, 2-hour
post-load glucose <7.8 mmol/L [13]). Women with DM2 were excluded if they had
HbA1c >9.0%. Of the 1,063 women who were invited, a total of 431 of the women
were total non-responders and 258 women were unwilling to participate. A total of
257 women did not meet the inclusion criteria.
Finally, 76 women with NGM and 41 women with DM2 completed the study
protocol. All women gave written informed consent. The study was approved by the
ethics committee of the VU University Medical Center, Amsterdam, the Netherlands.
Measurements
The study consisted of a screening visit and two separate visits for the test-meals
with a minimum interval of one week and a maximum interval of one month
between visits. On the screening visit, blood samples were drawn after a 12-h
overnight fast to determine levels of HbA1c, plasma glucose, total cholesterol,
triglycerides, alanine aminotransferase (ALT) and creatinine. Women who were
selected from the municipal registry underwent an OGTT.
Blood pressure was measured at the left upper arm three times with 5 minutes
intervals using an oscillometric blood pressure measuring device (Collin Press-mate
BP-8800, Colin, Komaki-City, Japan) after a 15-minute supine rest. Weight and
height were measured twice in barefooted participants wearing light clothes only.
BMI was calculated as weight (in kg) divided by the square of height (in m). Waist
circumference was measured twice at the level midway between the lowest rib
margin and the iliac crest, and hip circumference was measured at the widest level
over the greater trochanters. Medical history, medication, (former) smoking and
alcohol use were assessed by a questionnaire [14]. Finally, habitual physical activity
was assessed by the Short Questionnaire to Assess Health-enhancing physical
activity of which reproducibility and relative validity were described previously [15].
On the second and the third visit, women arrived at the test facility in the morning
after an overnight fast. They had abstained from exercise 24 hours before the
study visit. Blood samples were taken before (twice) and at t=1, t=2, t=4, t=6 and
t=8 hours after ingestion of the first test meal. The second test meal was given at
t=4 hour, immediately after the blood sample was taken.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
31
Test meal composition
Postprandial meal responses were examined following the consumption of two
standardized test meals (breakfast and lunch) on two separate occasions, either
with a high fat content or a high carbohydrate content. The fat-rich and the
carbohydrate-rich occasions were performed in random order. Each portion of the
ingredients was weighed before the meal was prepared. The nutrient composition
of the meals was calculated from the Dutch Food Composition Tables [16]. The fat-
rich meals (both breakfast and lunch) consisted of 2 croissants, 10 g of butter, 40
g of fat-rich cheese and 300 ml of fat-rich milk (3349 kJ; 50 g fat; 56 g
carbohydrates and 28 g proteins). The carbohydrate-rich meals (both breakfast and
lunch) consisted of 2 slices of bread, 25 g of marmalade, 30 g of cooked chicken
breast, 50 g of ginger bread and 300 ml of drinkable yogurt enriched with 45 g of
soluble sugars (3261 kJ; 4 g fat, 162 g carbohydrates and 22 g of proteins). Both
meals were eaten within 10 minutes. Apart from the test meals and water (ad
libitum), participants refrained from food and drinks. In addition, physical activity
was limited during the day.
Laboratory analysis
All laboratory analyses were performed at the VU University Medical Center
(department of Clinical Chemistry) in Amsterdam, the Netherlands. Serum total
cholesterol, HDL-cholesterol and triglycerides were measured by enzymatic
colorimetric assays (Roche, Mannheim, Germany). LDL-cholesterol was calculated
according to the Friedewald-formula [17]. ALT was determined by an enzymatic
assay (Roche, Mannheim, Germany) according to the methods proposed by the
International Federation of Clinical Chemistry and Laboratory Medicine [18]. Plasma
glucose concentration was determined with a glucose oxidase method (Granutest,
Merck, Darmstadt, Germany) and HbA1c was measured with cation-exchange
chromatography (Menarini Diagnostics, Florance, Italy). The inter-assay coefficients
of variation for the triglyceride and glucose measurements were <1.8% and <2.2%,
respectively. Immunospecific insulin was measured in serum by an immunometric
assay in which proinsulin does not cross-react (ACS Centaur, Bayer Diagnostics,
Mijdrecht The Netherlands). The inter- and intra-assay coefficients of variation for
insulin were 6% and 3%, respectively.
CHAPTER 2
32
Statistical analyses
Analyses were performed by SPSS for Windows 10 (SPSS Inc. Chicago, IL). Data are
presented as mean values (standard deviation), percentage or, in case of skewed
distribution, as median values (interquartile range) unless otherwise indicated.
Differences in characteristics between NGM and DM2 were tested with t-test for
continuous variables and with χ2-test for dichotomous variables. Postprandial
responses were calculated as incremental area under the curve (iAUC) with the
trapezoid method. Missing values for a given time-point (0.5% of all the glucose
and triglyceride values) were imputed by interpolation. Insulin resistance was
estimated by homeostasis model assessment (HOMA-IR), calculated as (mean
fasting insulin(mU/mL)*mean fasting glucose(mmol/L))/22.5 [19]. Mean glucose
and insulin concentrations were derived from fasting measurements on two
separate study days. To study the associations of possible determinants of
postprandial triglyceride and glucose responses, linear regression analysis was
performed with adjustment for age. As dependent variables, postprandial
triglycerides (iAUC) after the fat-rich meals and postprandial glucose concentrations
(iAUC) after the carbohydrate-rich meals were used. The associations were
expressed as standardized regression coefficients (95% CI) by dividing the
dependent and independent variables by the SD derived from the entire study
population. A regression coefficient of 0.5 means that if the independent variable
increases by 1.0 SD, the dependent variable increases by 0.5 SD. Multivariate
models included age and all variables that were associated with the outcome
variables at level P < 0.10. For all other analysis, we considered a two-sided P-value
<0.05 to indicate statistical significance.
RESULTS
Characteristics of the study population
Clinical and biochemical characteristics of the participants are listed in Table 1.
Figure 1 shows the 8h-time courses of triglyceride and glucose concentrations
following two consecutive fat-rich and two consecutive carbohydrate-rich meals.
Triglyceride-iAUC (TG-iAUC) after the fat-rich meals was similar in the two groups
(P=0.33) (Table2). Following the carbohydrate-rich meals, TG-iAUC was most
marked in women with DM2 compared to NGM women (P=0.01). As expected,
glucose-iAUC (GL-iAUC) and insulin-iAUC after the fat-rich and the carbohydrate-rich
meals was most marked in the women with DM2 (All P<0.01).
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
33
Table 2.1. Clinical and biochemical characteristics of the 117 participants a
NGM DM2
N 76 41
Age (years) 60.1 (4.0) 58.9 (3.7)
Duration of DM2 (years) NA 5 (3-9)
Anthropometry
BMI (kg/m2) 26.3 (3.6) 32.7 (6.0) b
Waist (m) 0.88 (0.10) 1.04 (0.14) b
Hip (m) 1.04 (0.08) 1.12 (0.12) b
Glucose metabolism
HbA1c (%) 5.6 (0.3) 6.6 (0.6) b
Fasting insulin (pmol/L) c 33.2 (25.5-47.5) 82.7 (38.8-122.5) b
HOMA-IR c 1.29 (0.94-1.95) 4.05 (1.92-7.07) b
Lipids (mmol/L)
Total cholesterol 6.0 (0.9) 5.6 (1.0)
HDL-cholesterol 1.80 (0.49) 1.51 (0.34) b
LDL-cholesterol 3.7 (0.9) 3.2 (1.0) b
Blood pressure (mmHg)
Systolic 131 (15) 143 (16) b
Diastolic 72 (8) 79 (7) b
Liver enzyme (U/L)
Alanine aminotransferase 20 (16-26) 27 (20-40) b
Medication (%)
Antihypertensive medication 16 66 b
Blood glucose lowering medication NA 71
Use of insulin NA 17
Lifestyle
Former smoking (%) 49 39
Habitual physical activity (h/week) 35 (23-46) 35 (22-46)
Alcohol > 0 gram/day (%) 78 37 b
NGM: Normal Glucose Metabolism, DM2: Diabetes Mellitus, HOMA-IR: Insulin resistance estimated
by Homeostasis Model Assessment. Differences between groups tested with t-test for continuous
variables and with χ2-test for dichotomous variables. a Data presented as mean values (SD) or
percentages. In case of skewed distribution, data presented as median (interquartile range) and Ln-
transformed values were tested. b P < 0.05. c Subjects using insulin were excluded and fasting
insulin was calculated as mean of two measurements.
CHAPTER 2
34
Fat-rich meals
0 2 4 6 8
Triglycerides (mmol/l)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
B L
Carbohydrate-rich meals
0 2 4 6 8
Triglycerides (mmol/l)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
B L
0 2 4 6 8
Glucose (mmol/l)
0
2
4
6
8
10
12
14
16
B L
0 2 4 6 8
Glucose (mmol/l)
0
2
4
6
8
10
12
14
16
B L
Time (hours)
0 2 4 6 8
Insulin (pmol/l)
0
200
400
600
800
1000
1200 B L
Time (hours)
0 2 4 6 8
Insulin (pmol/l)
0
200
400
600
800
1000
1200B L
Figure 3.1. Triglyceride, glucose and insulin concentrations (mean±SEM) after ingestion of
two consecutive meals. For insulin figures, patients who use insulin (n=7) were excluded.
B: breakfast, L: lunch. NGM: normal glucose metabolism (○), DM2: type 2 diabetes mellitus
(●).
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
35
Table 2.2. Fasting and postprandial glucose and triglyceride levels
after fat-rich meals and after carbohydrate-rich meals a
NGM DM2
Fat-rich meals
Fasting triglycerides
(mmol/L)
1.2 (0.6) 1.7 (0.7) b
TG-iAUC (mmol/L) 0.9 (0.5) 1.0 (0.5)
Fasting glucose (mmol/L) 5.3 (0.4) 7.2 (1.5) b
GL-iAUC (mmol/L) 0.1 (0.5) 0.7 (1.5) b
Carbohydrate-rich meals
Fasting triglycerides
(mmol/L)
1.2 (0.6) 1.7 (0.6) b
TG-iAUC (mmol/L) 0.2 (0.3) 0.3 (0.3) b
Fasting glucose (mmol/L) 5.2 (0.4) 7.1 (1.3) b
GL-iAUC (mmol/L) 0.4 (0.8) 3.6 (2.9) b
NGM: Normal Glucose Metabolism, DM2: Diabetes Mellitus, TG: triglycerides,
iAUC: incremental area under the curve, GL: glucose. TG-iAUC and GL-iAUC
were presented as mean iAUC during the day. a Data presented as mean
values (SD). b T-test P < 0.05.
Associations with postprandial triglycerides
Results of linear regression analyses are presented in Table 3. In the NGM group,
fasting triglycerides were associated with TG-iAUC; also, HbA1c, fasting insulin,
HOMA-IR, total cholesterol were positively associated, whereas HDL-cholesterol and
hip circumference were negatively associated with TG-iAUC. To study whether
potential determinants of TG-iAUC were independent of fasting triglyceride levels,
we made a multivariate model. For women with NGM, we included fasting
triglycerides, hip circumference, HbA1c, fasting insulin, total cholesterol and HDL-
cholesterol and adjusted for age and BMI (R2=0.52), and all these variables, except
fasting insulin (P=0.08) and hip circumference (P=0.07), remained statistically
significantly associated with TG-iAUC. In the DM2 group, fasting triglyceride, HbA1c
and age were the strongest predictors of TG-iAUC. In a multivariate model with
fasting triglycerides, HbA1c and age (R2=0.29) fasting triglycerides was the only
independent determinant of TG-iAUC.
CHAPTER 2
36
Table 2.3. Linear regression analysis of clinical and biochemical parameters with postprandial triglyceride and glucose concentrations
NGM (n=76) DM2 (n=41)
Variables (SD) a
TG-iAUC GL-iAUC TG-iAUC GL-iAUC
Age (3.9 years) 0.04 (-0.18;0.25) 0.07 (-0.003;0.14) b,c 0.40 (0.04;0.75) b -0.04 (-0.46;0.39)
DM2 duration (4.3 years) - - 0.02 (-0.32;0.35) 0.18 (-0.22;0.58)
BMI (5.4 kg m-2) 0.05 (-0.29;0.39) 0.04 (-0.07;0.16) 0.07 (-0.24;0.37) 0.27 (-0.09;0.62)
Waist (0.14 m) d
0.41 (-0.15;0.97) 0.20 (0.01;0.38)b
0.34 (-0.62;1.29) 0.22 (-0.90;1.34)
Hip (0.10 m) d
-0.75 (-1.21;-0.29)b -0.13 (-0.29;0.03) 0.43 (-0.32;1.18) 0.06 (-0.82;0.94)
Fasting glucose (1.2 mmol/L) 0.50 (-0.21;1.21) -0.08 (-0.32;0.16) 0.11 (-0.20;0.41) 0.30 (-0.05;0.65)b
HbA1c (0.7%) 0.77 (0.31;1.22)b,c 0.13 (-0.03;0.29) 0.33 (-0.01;0.67)
b0.86 (0.54;1.18)
b,c
Fasting insulin (ln) (0.67) e 0.39 (0.05;0.74) b 0 (-0.12;0.12) 0.23 (-0.23;0.68) 0.47 (0.001;0.93)
b
HOMA-IR (ln) (0.79) e 0.32 (0.05;0.59) b 0 (-0.09;0.10) 0.18 (-0.16;0.51) 0.42 (0.09;0.74)
b
Total cholesterol (1.0 mmol/L) 0.32 (0.09;0.55) b,c -0.01 (-0.09;0.07) 0.24 (-0.06;0.54) 0.33 (-0.03;0.68)b
HDL-cholesterol (0.47 mmol/L) -0.31 (-0.51;-0.12) b,c -0.10 (-0.17;-0.04) b 0.01 (-0.47;0.50) -0.16 (-0.74;0.42)
Fasting triglycerides (0.6 mmol/L) 0.60 (0.39;0.81)b,c 0.13 (0.05;0.21)
b,c0.40 (0.06;0.73)
b,c0.48 (0.08;0.88) b
ALT (ln) (0.45) 0.09 (-0.16;0.33) 0.01 (-0.08;0.09) 0.03 (-0.30;0.36) 0.31 (-0.07;0.70)
Systolic BP (16 mmHg) f
0.14 (-0.13;0.41) 0.04 (-0.05;0.13) 0.17 (-0.18;0.52) 0.07 (-0.37;0.51)
Alcohol (65 g/week) 0.06 (-0.15;0.27) 0.02 (-0.05;0.09) 0.11 (-0.40;0.62) -0.47 (-1.06;0.12)
Habitual physical activity (18 h/week) 0 (-0.23;0.23) -0.05 (-0.12;0.03) -0.33 (-0.72;0.07) -0.42 (-0.90;0.06)b
NGM: Normal Glucose Metabolism, DM2: Diabetes Mellitus, TG: triglycerides, iAUC: incremental area under the curve, HOMA -IR: Insulin resistance estimated by Homeostasis
Model Assessment, ALT: alanine aminotransferase.
a Standardized regression coefficients (95% CI) adjusted for age in SD per 1 SD increase in independent variable. SD for TG -iAUC 0.5 mmol/L and for GL -iAUC 2.3 mmol/L. b
P < 0.10, variable included in the multivariate model; insulin and not HOMA- -IR were included when both were significant. c variable significant at level P < 0.05 in
multivariate model. dAdditionally adjusted for BMI.
eSubjects using insulin (n=7) were excluded.
fAdditionally adjusted for use of antihypertensive medication.
Table 2.3. Linear regression analysis of clinical and biochemical parameters with postprandial triglyceride and glucose concentrations
NGM (n=76) DM2 (n=41)
Variables (SD) a
TG-iAUC GL-iAUC TG-iAUC GL-iAUC
Age (3.9 years) 0.04 (-0.18;0.25) 0.07 (-0.003;0.14) b,c 0.40 (0.04;0.75) b -0.04 (-0.46;0.39)
DM2 duration (4.3 years) - - 0.02 (-0.32;0.35) 0.18 (-0.22;0.58)
BMI (5.4 kg m-2) 0.05 (-0.29;0.39) 0.04 (-0.07;0.16) 0.07 (-0.24;0.37) 0.27 (-0.09;0.62)
Waist (0.14 m) d
0.41 (-0.15;0.97) 0.20 (0.01;0.38)b
Table 2.3. Linear regression analysis of clinical and biochemical parameters with postprandial triglyceride and glucose concentrations
NGM (n=76) DM2 (n=41)
Variables (SD) a
TG-iAUC GL-iAUC TG-iAUC GL-iAUC
Age (3.9 years) 0.04 (-0.18;0.25) 0.07 (-0.003;0.14) b,c 0.40 (0.04;0.75) b -0.04 (-0.46;0.39)
DM2 duration (4.3 years) - - 0.02 (-0.32;0.35) 0.18 (-0.22;0.58)
BMI (5.4 kg m-2) 0.05 (-0.29;0.39) 0.04 (-0.07;0.16) 0.07 (-0.24;0.37) 0.27 (-0.09;0.62)
Waist (0.14 m) d
0.41 (-0.15;0.97) 0.20 (0.01;0.38)b
0.34 (-0.62;1.29) 0.22 (-0.90;1.34)
Hip (0.10 m) d
-0.75 (-1.21;-0.29)b -0.13 (-0.29;0.03) 0.43 (-0.32;1.18) 0.06 (-0.82;0.94)
Fasting glucose (1.2 mmol/L) 0.50 (-0.21;1.21) -0.08 (-0.32;0.16) 0.11 (-0.20;0.41) 0.30 (-0.05;0.65)b
HbA1c (0.7%) 0.77 (0.31;1.22)b,c 0.13 (-0.03;0.29) 0.33 (-0.01;0.67)
b0.86 (0.54;1.18)
b,c
Fasting insulin (ln) (0.67) e 0.39 (0.05;0.74) b 0 (-0.12;0.12) 0.23 (-0.23;0.68) 0.47 (0.001;0.93)
b
HOMA-IR (ln) (0.79) e 0.32 (0.05;0.59) b 0 (-0.09;0.10) 0.18 (-0.16;0.51) 0.42 (0.09;0.74)
b
Total cholesterol (1.0 mmol/L) 0.32 (0.09;0.55) b,c -0.01 (-0.09;0.07) 0.24 (-0.06;0.54) 0.33 (-0.03;0.68)b
HDL-cholesterol (0.47 mmol/L) -0.31 (-0.51;-0.12) b,c -0.10 (-0.17;-0.04) b 0.01 (-0.47;0.50) -0.16 (-0.74;0.42)
Fasting triglycerides (0.6 mmol/L) 0.60 (0.39;0.81)b,c 0.13 (0.05;0.21)
b,c0.40 (0.06;0.73)
b,c0.48 (0.08;0.88)
0.34 (-0.62;1.29) 0.22 (-0.90;1.34)
Hip (0.10 m) d
-0.75 (-1.21;-0.29)b -0.13 (-0.29;0.03) 0.43 (-0.32;1.18) 0.06 (-0.82;0.94)
Fasting glucose (1.2 mmol/L) 0.50 (-0.21;1.21) -0.08 (-0.32;0.16) 0.11 (-0.20;0.41) 0.30 (-0.05;0.65)b
HbA1c (0.7%) 0.77 (0.31;1.22)b,c 0.13 (-0.03;0.29) 0.33 (-0.01;0.67)
b0.86 (0.54;1.18)
b,c
Fasting insulin (ln) (0.67) e 0.39 (0.05;0.74) b 0 (-0.12;0.12) 0.23 (-0.23;0.68) 0.47 (0.001;0.93)
b
HOMA-IR (ln) (0.79) e 0.32 (0.05;0.59) b 0 (-0.09;0.10) 0.18 (-0.16;0.51) 0.42 (0.09;0.74)
b
Total cholesterol (1.0 mmol/L) 0.32 (0.09;0.55) b,c -0.01 (-0.09;0.07) 0.24 (-0.06;0.54) 0.33 (-0.03;0.68)b
HDL-cholesterol (0.47 mmol/L) -0.31 (-0.51;-0.12) b,c -0.10 (-0.17;-0.04) b 0.01 (-0.47;0.50) -0.16 (-0.74;0.42)
Fasting triglycerides (0.6 mmol/L) 0.60 (0.39;0.81)b,c 0.13 (0.05;0.21)
b,c0.40 (0.06;0.73)
b,c0.48 (0.08;0.88) b
ALT (ln) (0.45) 0.09 (-0.16;0.33) 0.01 (-0.08;0.09) 0.03 (-0.30;0.36) 0.31 (-0.07;0.70)
Systolic BP (16 mmHg) f
0.14 (-0.13;0.41) 0.04 (-0.05;0.13) 0.17 (-0.18;0.52) 0.07 (-0.37;0.51)
Alcohol (65 g/week) 0.06 (-0.15;0.27) 0.02 (-0.05;0.09) 0.11 (-0.40;0.62) -0.47 (-1.06;0.12)
Habitual physical activity (18 h/week) 0 (-0.23;0.23) -0.05 (-0.12;0.03) -0.33 (-0.72;0.07) -0.42 (-0.90;0.06)b
NGM: Normal Glucose Metabolism, DM2: Diabetes Mellitus, TG: triglycerides, iAUC: incremental area under the curve, HOMA -IR: Insulin resistance estimated by Homeostasis
Model Assessment, ALT: alanine aminotransferase.
a Standardized regression coefficients (95% CI) adjusted for age in SD per 1 SD increase in independent variable. SD for TG -iAUC 0.5 mmol/L and for GL -iAUC 2.3 mmol/L. b
P < 0.10, variable included in the multivariate model; insulin and not HOMA- -IR were included when both were significant. c variable significant at level P < 0.05 in
multivariate model. dAdditionally adjusted for BMI.
eSubjects using insulin (n=7) were excluded.
fAdditionally adjusted for use of antihypertensive medication.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
37
Associations with postprandial glucose
Results of the linear regression analyses for fasting and postprandial glucose after
the carbohydrate-rich meals are presented in Table 3. In the NGM group, age, waist
circumference and fasting triglycerides were positively, and HDL-cholesterol was
inversely associated with GL-iAUC. In a multivariate model containing age, waist
circumference, BMI, HDL-cholesterol and fasting triglycerides (R2=0.24), fasting
triglycerides were still associated with GL-iAUC and age became significantly
associated with GL-iAUC (beta=0.07 [0.01;0.14]). In women with DM2, HbA1c,
fasting insulin, HOMA-IR and fasting triglycerides were positively associated with
GL-iAUC. For this reason, fasting insulin was part of the multivariate model, and we
excluded women with DM2 who used insulin (n=7). In this model, only HbA1c
remained associated with GL-iAUC when adjusted for age, fasting glucose, fasting
insulin, total cholesterol, triglycerides and habitual physical activity in a
multivariate model (R2=0.68). Because HbA1c is not considered as a determinant of
GL-iAUC, we also made a multivariate model including the above mentioned
variables except HbA1c. In this model (R2=0.51), fasting glucose was the strongest
determinant of GL-iAUC (beta=0.49 [0.19;0.82]).
Association between postprandial triglyceride and glucose responses
We additionally assessed the age-adjusted association of GL-iAUC with TG-iAUC. In
NGM women, no statistically significant association was found between these
metabolic responses (beta=0.39 [-0.31;1.08]). In contrast, for women with DM2, GL-
iAUC and TG-iAUC were associated (beta=0.26 [0.002;0.53]). The latter association
was in part dependent of fasting triglycerides; the regression coefficient reduced to
0.18 [-0.10;0.45] when fasting triglycerides were added to the model with age and
GL-iAUC.
DISCUSSION
The present study is to our knowledge the first to assess both postprandial
triglyceride and glucose responses at two separate occasions in one study
population. We demonstrated that fasting triglycerides were associated with both
TG-iAUC and GL-iAUC, but other potential determinants of TG-iAUC and GL-iAUC
differed. In spite of similar TG-iAUC responses in NGM and DM2, determinants of
TG-iAUC and also of GL-iAUC clearly differed between women with NGM and DM2.
CHAPTER 2
38
We expected a more markedly prolonged triglyceride response especially after the
second meal in patients with DM2 [20]. The relative lack of exaggerated triglyceride
response in women with DM2 might be the result of the meal-composition. The
substantial amount of carbohydrates in the fat-rich mixed meal might have resulted
in a high insulin response. Indeed, attenuation of the triglyceride response was
previously shown when glucose was added to a liquid fat-load [21]. Also, the
patients with DM2 included in the present study were well-controlled, possibly
contributing to an ameliorated response of the liver to the meal-induced insulin
response. On the other hand, since the women with DM2 were more insulin
resistant than women with NGM (HOMA-IR), an elevated TG-iAUC was expected [22].
Finally, we cannot exclude the possibility that a prolonged triglyceride response in
patients with DM2 might have become evident with a longer observation period
[23].
A common mechanism for elevated postprandial triglyceride and glucose
responses?
Insulin resistance or central obesity, both components of the metabolic syndrome,
might contribute to increased postprandial triglyceride and glucose levels.
Resistance to the suppressive effect of insulin on hepatic VLDL-production results
in exaggerated postprandial triglyceride responses [24,25]. We found that fasting
insulin and HOMA-IR, as a reflection of insulin resistance and in particular hepatic
insulin resistance, were associated with TG-iAUC in women with NGM, but the
association disappeared when fasting triglycerides were added to the model.
Furthermore, no association of HOMA-IR and fasting insulin with TG-iAUC in women
with DM2 was found. However, also fasting triglycerides are a recognized
component of the metabolic syndrome [26], and the association between fasting
triglycerides and TG-iAUC might, at least in part, be the result of underlying hepatic
insulin resistance.
Impaired glucose tolerance has been attributed to a combination of peripheral
insulin resistance and beta-cell dysfunction, whereas impaired fasting glucose is
mainly associated with hepatic insulin resistance and impaired insulin secretion
[27]. This corroborates with our finding that fasting glucose was associated with
GL-iAUC in women with DM2, but not in NGM. The combination of hepatic insulin
resistance and beta-cell dysfunction, reflected by elevated fasting and postprandial
glucose levels, is common in DM2, but not in NGM.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
39
Central obesity as a component of the metabolic syndrome may be the common
mechanism for elevated postprandial triglyceride and glucose concentration. The
increased flux of free fatty acids in central obesity and elevated levels of pro-
inflammatory adipocytokines result in so-called ectopic fat depositions in liver,
muscle and beta-cells contributing to the development of hepatic and peripheral
insulin resistance, and beta-cell dysfunction [28]. Studies in men reported an
association between visceral obesity and TG-iAUC [29,30]. In line with these
observations, a study among obese women showed that abdominal obesity, but not
obesity as such, was associated with TG-iAUC. In the present study, the association
between waist circumference and TG-iAUC was, although not statistically
significant, stronger than the association between BMI and TG-iAUC. Furthermore,
we found that hip circumference was inversely associated with TG-iAUC in women
with NGM. An inverse relationship between larger hip circumference and fasting
triglycerides was previously found in both men and women [31,32], and may be
explained by an enhanced buffering capacity for triglyceride storage which
prevents from fat deposition in other organs [33]. The findings for GL-iAUC are
comparable to those for TG-iAUC; central obesity seemed to be more strongly
associated with elevated postprandial glucose than BMI in NGM women.
The present data also suggest that an elevated postprandial triglyceride response
might be part of a dyslipidemic lipid profile in women with NGM. Independent of
fasting triglycerides, total cholesterol was positively and fasting HDL-cholesterol
concentration was inversely associated with TG-iAUC. It is known that high levels of
triglycerides and low HDL-cholesterol concentrations are associated [34] and
postprandial lipemia might be another feature of this so-called ‘diabetic’
dyslipidemia. An important observation is that TG-iAUC was associated with GL-
iAUC in DM2, but not in NGM. This suggests that the underlying mechanisms, e.g.
hepatic insulin resistance and/or beta-cell dysfunction, is present in DM2, but not
(yet) in NGM. Other potential mechanisms for postprandial triglyceride and glucose
responses. An interesting finding was that HbA1c was strongly associated with TG-
iAUC in women with NGM. It is known that improvement of glycemic control also
improves postprandial triglyceride concentrations in patients with DM2 [35]. This
effect is attributed to the lipoprotein lipase (LPL) mediated clearance of TG-rich
lipoproteins, which is hampered when insulin action and/or secretion is inadequate
[36]. Since we did not measure LPL activity in this study, we cannot verify this
assumption. The proportion of variance explained by the multivariate model for TG-
iAUC is lower for patients with DM2 (R2 = 0.29) compared to women with NGM (R2
= 0.52). In DM2, insulin resistance, only partly reflected by fasting triglycerides
CHAPTER 2
40
possibly plays an important role in increasing postprandial triglycerides, including
the earlier discussed decreased LPL activity. The proportion of variance explained
(R2) by the multivariate model for GL-iAUC was 0.51 for DM2 and 0.24 for the NGM
group. Obviously, the range in GL-iAUC is much smaller in women with NGM.
Furthermore, we did not consider the effect of gut hormones (incretins), rate of
gastric emptying and peripheral insulin resistance on GL-iAUC, which play
important roles in glucose regulation.
Study limitations
A number of potential limitations of this study should be considered. First, the
population consisted of Caucasian post-menopausal women aged 50 to 65, and
caution should be exercised to generalise our findings to other populations.
Second, the cross-sectional design limits us to assess causal relationships. Third,
as a result of the smaller sample size of the DM2 population as compared to the
NGM study population, less associations in the DM2 study population might have
reached statistical significance.
Conclusion
In conclusion, GL-iAUC and TG-iAUC were interrelated in women with DM2 and
fasting triglycerides were associated with both GL-iAUC and TG-iAUC. These
findings suggest, at least partly, a common underlying mechanism for postprandial
increments in glucose and triglycerides, especially occuring in women with DM2. In
healthy women, elevated TG-iAUC may belong to a dyslipidemic lipid profile
whereas fasting triglycerides were related to GL-iAUC, suggestive for an association
with insulin resistance.
ACKNOWLEDGEMENTS
The authors thank Jannet Entius, Tanja Nansink, Lida Ooteman and Marianne
Veeken for their excellent technical assistance, Jolanda Bosman for her support in
organizing the study at the Diabetes Research Center, and all participants for their
contributions. This research was financially supported by a grant from the Dutch
Diabetes Research Foundation (grant no.2001.00.052) and by funding from
Novartis International AG, Switzerland.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
41
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32. Frayn KN. Adipose tissue as a buffer for daily lipid flux. Diabetologia 2002;45:1201-
10.
33. Ginsberg HN, Zhang YL, Hernandez-Ono A. Regulation of plasma triglycerides in
insulin resistance and diabetes. Arch Med Res 2005;36:232-40.
35. Jeppesen J, Zhou MY, Chen YD, Reaven GM. Effect of metformin on postprandial
lipemia in patients with fairly to poorly controlled NIDDM. Diabetes Care
1994;17:1093-9.
36. Ginsberg HN. Efficacy and mechanisms of action of statins in the treatment of
diabetic dyslipidemia. J Clin Endocrinol Metab 2006;91:383-92.
CHAPTER 2
44
45
Chapter 3
POSTPRANDIAL GLUCOSE AND NOT TRIGLYCERIDE CONCENTRATIONS ARE ASSOCIATED WITH CAROTID INTIMA MEDIA THICKNESS IN WOMEN
WITH NORMAL GLUCOSE METABOLISM: THE HOORN PRANDIAL STUDY
Marjan Alssema1
Roger K. Schindhelm2
Jacqueline M. Dekker1
Michaela Diamant2
Piet J. Kostense1,3
Tom Teerlink4
Peter G. Scheffer4
Giel Nijpels1,5
Robert. J. Heine1,2
From the 1EMGO Institute and the Departments of 2Endocrinology/Diabetes Center,
3Clinical Epidemiology and Biostatistics, 4Clinical Chemistry, and 5General Practice,
VU University Medical Center, Amsterdam, The Netherlands.
Atherosclerosis (in press)
Copyright © 2007 Elsevier Ireland Ltd.
Reprinted with permission from Elsevier.
CHAPTER 3
46
ABSTRACT
The present study aimed to compare the associations of postprandial glucose
(ppGL) and postprandial triglycerides (ppTG) with carotid intima media thickness
(cIMT) in women with normal glucose metabolism (NGM) and type 2 diabetes
(DM2). Post-menopausal women (76 with NGM, 78 with DM2), received two
consecutive fat-rich and two consecutive carbohydrate-rich meals on separate
occasions. Blood samples were taken before and 1, 2, 4, 6 and 8 hours following
breakfast; lunch was given at t=4. Ultrasound imaging of the carotid artery was
performed to measure cIMT. In women with NGM, an increase of 1.0 mmol/l
glucose following the fat-rich meals was associated with a 50 µm cIMT increase
(P=0.04), and following the carbohydrate meals, an increase of 1.8 mmol/l glucose
was associated with a 50 µm larger cIMT (P=0.08). These associations were not
explained by classical cardiovascular risk factors. However, no association between
ppGL and cIMT was found in women with DM2 and ppTG were not associated with
cIMT. The association between ppGL and cIMT in normoglycemic women suggests
that ppGL in the normal range is a marker or a risk factor for atherosclerosis.
Postprandial glucose levels might be a better indicator of risk than post-OGTT
glucose levels or triglyceride levels.
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
47
INTRODUCTION
Men with type 2 diabetes mellitus (DM2) have a two- to three-fold and women a
three- to four-fold increased risk of cardiovascular disease (CVD) [1,2]. The
increased risk for CVD in patients with DM2 is only partly explained by the classical
CVD risk factors hypertension, high LDL-cholesterol, low HDL-cholesterol and
smoking [3].
Disturbances in the postprandial metabolism are hypothesized to contribute to the
excess risk of CVD, because of the elevated concentrations of glucose, triglyceride-
enriched lipoproteins and inflammatory markers in the postprandial period [4].
Glucose levels after an oral glucose tolerance test (OGTT) have been shown to be
better indicators of CVD risk than fasting glucose levels in the general population
and in subjects with a family history of DM2 [5-7]. Also, postprandial triglyceride
levels were more strongly related to cIMT than fasting triglyceride levels in both
healthy subjects [8-10] and in patients with DM2 [11,12]. However, the relative
contributions of postprandial glucose (ppGL) levels and postprandial triglyceride
(ppTG) levels to atherosclerosis are not clarified yet.
Most postprandial studies applied a single fat-rich liquid-formula or an OGTT, but
the association of the more physiological meal-related glucose and triglyceride
responses with CVD might be more relevant. Furthermore, during daytime, glucose
excursions following a first meal are known to affect the responses after a second
meal [13]. A cumulative effect of the triglyceride excursions following breakfast
and lunch is likely to occur, especially in subjects with DM2 [14].
The aim of the present study was to investigate the relation of ppGL and ppTG
following two consecutive fat-rich meals or two consecutive carbohydrate-rich
meals on two separate days, to cIMT, and to investigate whether these associations
differ between women with normal glucose metabolism (NGM) and women with
DM2.
CHAPTER 3
48
METHODS
Study population
Women with DM2 (n=522), from the registry of the Diabetes Care System in the city
of Hoorn, the Netherlands, and women who were randomly selected from the
municipal registry of Hoorn (n=541) aged 50-65 years at the beginning of the
study, were invited to participate in the study. Exclusion criteria were non-post-
menopausal status (menses in the last 12 months), smoking, untreated endocrine
disorder other than DM2, use of short acting insulin analogues, use of PPAR-α and -
γ agonists, use of oral corticosteroids, use of hormone replacement therapy,
fasting cholesterol >8.0 mmol/l, fasting triglycerides >4.0 mmol/l, systolic blood
pressure>190 mmHg, liver or renal impairment (liver derived enzymes >2.5 time
the upper limit of the laboratory reference range, creatinine >120 µmol/l). Women
who were selected from the municipal registry underwent a 75-g OGTT to verify
their glucose tolerance status (fasting glucose ≤6.0 mmol/l and 2-hour post-load
glucose <7.8 mmol/l [15]). Women with DM2 were excluded if they had HbA1c
>9.0%. Women with NGM were excluded if they used HMG-CoA reductase inhibitors
(statins), and women with DM2 who used statins were considered as a separate
study group. Of the 1,063 women who were invited, 431 women were complete
non-responders and 258 women were not willing to participate. A total of 220
women did not meet the inclusion criteria. Reasons for exclusion were: smoking
n=65, elevated fasting and/or post-load glucose levels (NGM only) n=33, pre-
menopausal n=28, drop-out n=22, failure to draw blood samples from a canula
n=19, use of hormone replacement therapy n=13, use of short acting insulin
analogues n=12, illness during study participation n=6, elevated triglycerides n=4,
use of PPAR-γ agonists n=4, use of statins (NGM only) n=3, blood pressure n=3,
miscellaneous reasons n=4, cholesterol n=1, creatinine n=1, liver enzymes n=1 and
missing cIMT n=1. Finally, 76 women with NGM and 78 women with DM2
completed the study protocol. All women gave written informed consent. The study
was approved by the ethics committee of the VU University Medical Center.
Screening visit
The study consisted of three separate visits, i.e. a screening visit and two visits for
the test-meals, with a maximum of one month apart. On the screening visit, fasting
blood samples were drawn after a 12-h overnight fast.
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
49
Blood pressure was measured at the left arm three times with 5 minutes intervals
with an oscillometric blood pressure measuring device (Collin Press-mate BP-8800,
Colin, Komaki-City, Japan) after a 15-minute supine rest. Weight and height were
measured twice in barefooted participants wearing light clothes only. BMI was
calculated as weight (in kg) divided by the square of height (in m). Waist
circumference was measured twice at the level midway between the lowest rib
margin and the iliac crest, and hip circumference was measured at the widest level
over the greater trochanters. Medical history, medication, smoking and alcohol
consumption were assessed by a questionnaire [16]. Finally, physical activity was
assessed by the Short Questionnaire to Assess Health-enhancing physical activity
[17].
Ultrasound imaging
A single observer (MA) performed ultrasound imaging with an ultrasound scanner
(350 Series, Pie Medical, Maastricht, The Netherlands), equipped with a 7.5-MHz
linear probe. Measurements were performed in the right common carotid artery at
10 mm proximal to the carotid bulb. Images were registered and analyzed by a
personal computer equipped with vessel wall movement detection software and an
acquisition system (Wall Track System, Pie Medical, Maastricht, the Netherlands).
After a 15-minute supine rest, the artery was visualized in B-mode. After defining
the segment, 10 mm proximal to the carotid bulb, the screen was switched to the
M-mode and data acquisition in real-time presentation on the computer screen was
enabled. Data were obtained by 3 consecutive measurements of 4 seconds each,
triggered by the R-top of a simultaneously recorded ECG [18]. Carotid IMT was
measured from the posterior wall, as the distance from the first to the second
echogenic line [19]. Reproducibility of scanning was assessed in 7 women with
DM2 and in 5 women with NGM, who were examined twice, two weeks apart. The
intra-observer coefficient of variation (=SD of the mean difference / (√2 * pooled
mean) * 100%) for cIMT was 5.2%.
Test meals
All participants received two consecutive fat-rich meals on one occasion and two
consecutive carbohydrate-rich meals on another occasion, in random order. Women
arrived at the test facility in the morning after an overnight fast. Blood samples
were taken before and at t=1, t=2, t=4, t=6 and t=8 hour after ingestion of the first
test meal (breakfast). Lunch was given at t=4, immediately after a blood sample
CHAPTER 3
50
was taken. The nutrient composition of the meals was calculated from the Dutch
Nutrient Database [20]. The fat-rich meals consisted of croissants, butter, cheese
and fat-rich milk (3349 kJ; 50 g fat; 56 g carbohydrates and 28 g proteins). The
carbohydrate-rich meals consisted of bread, marmalade, cooked chicken breast,
ginger bread and drinkable yogurt enriched with soluble carbohydrates (3261 kJ; 4
g fat, 162 g carbohydrates and 22 g proteins). Meals were eaten within 10 minutes.
Apart from the test meals and water (ad libitum), participants refrained from food
and drinks. In addition, throughout the visit, physical activity was limited to a short
walk between two adjacent rooms.
Laboratory analysis
All laboratory analyses were performed at the VU University Medical Center in
Amsterdam, the Netherlands. Serum total cholesterol, HDL-cholesterol and
triglycerides were measured by enzymatic calorimetric assays (Roche, Mannheim,
Germany). The inter-assay coefficients of variation for the triglyceride levels were
2.2% at a mean level of 1.0 mmol/l and 1.8% at a mean level of 1.9 mmol/l. Fasting
LDL-cholesterol was calculated according to the Friedewald-formula [21]. Plasma
glucose levels were determined with a glucose oxidase method (Granutest, Merck,
Darmstadt, Germany) and HbA1c was measured with reversed-phase cation
exchange chromatography (Menarini Diagnostics, Florence, Italy). The inter-assay
coefficients of variation for the glucose levels were 1.2% at a mean level of 4.9
mmol/l and 1.3% at a mean level of 19.0 mmol/l. Specific insulin was measured in
serum by an immunometric assay in which proinsulin did not cross-react (Advia
Centaur, Bayer Diagnostics, Mijdrecht, The Netherlands). The intra- and inter-assay
coefficients of variation for insulin were <4% and <7%, respectively.
Statistical analyses
Analyses were performed in SPSS 12.0.1 for Windows (SPSS Inc. Chicago, IL).
Differences in baseline characteristics between the three groups (NGM, DM2 and
DM2-ST) were tested with ANOVA with post-hoc analyses (LSD) for continuous
variables that were ln-transformed in case of skewed distribution. Dichotomous
variables were tested with χ2-test. Postprandial responses were calculated with the
trapezoid method as total area under the curve (AUC) divided by 8 (hours). Insulin
resistance was estimated by homeostasis model assessment (HOMA-IR), calculated
as (mean fasting insulin (µU/ml) * mean fasting glucose (mmol/l))/22.5 [22]. Means
for glucose and insulin were derived from fasting measurements on two separate
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
51
study days. To study the possible associations between risk factors and cIMT and
between postprandial responses and cIMT, we performed linear regression analysis
with adjustment for age. The associations were expressed as regression
coefficients per 1 SD (of the total study population) increase in independent
variable. We tested for possible interactions of independent variables with DM2 and
use of statins by calculating the p values of the interaction terms. Subsequently,
regression coefficients for ppGL and ppTG were adjusted for fasting levels to
investigate whether fasting levels explained the possible association between
postprandial response and cIMT. To investigate the relative contribution of ppTG
and ppGL to cIMT, we also made a mutually adjusted model for ppTG and ppGL.
Finally, we tested BMI, total cholesterol, fasting insulin, HOMA-IR, systolic blood
pressure, smoking, LDL-cholesterol and HDL-cholesterol as potentially confounding
factors. We considered a two-sided P-value <0.05 to indicate statistical significance.
For interaction terms, we considered P<0.15 statistically significant.
RESULTS
Clinical and biochemical characteristics assessed at the screening visit and fasting
and postprandial glucose and triglycerides were presented separately for women
with NGM, DM2 and women with DM2 using statin medication (DM2-ST, Table 1).
Fasting and postprandial triglycerides and glucose, were similar in the DM2 group
and in the DM2-ST group, but were higher compared to the NGM group (all p<0.01,
Table 3.1). Carotid IMT was increased and CVD was more prevalent in women with
DM2 who were not using statins, but these differences did not reach statistical
significance.
In Table 3.2, results of linear regression analysis of known CVD risk factors with
regard to cIMT are shown. Because no interaction with DM2 status or use of statins
was found for most of the risk factors, we combined the subgroups for these
analyses. Age was associated with cIMT: the estimated beta was 20.2, indicating
that a 4 year difference in age is associated with a 20 µm larger cIMT (P=0.02). Also
systolic blood pressure was associated with cIMT, a difference of 16 mmHg in
systolic blood pressure was associated with a 19 µm difference in cIMT (P=0.04).
However, the latter association became non-significant after adjustment for DM2
status and use of statins.
CHAPTER 3
52
Table 3.1. Population characteristics (N=154)
NGM DM2 DM2-ST
N 76 41 37
Age (years) 60.1 (4.0) 58.9 (3.7) 61.1 (4.0) a
Duration of DM2 (years) - 5 (3-9) 4 (2-9)
BMI (kg/m2) 26.3 (3.6) 32.7 (6.0) b 30.6 (4.6) b
Waist (cm) 88 (10) 104 (14) b 102 (13) b
Hip (cm) 104 (8) 112 (12) b 108 (10) b
2-h post-load glucose (mmol/l) 5.4 (1.0) - -
HbA1c 5.6 (0.3) 6.6 (0.6) b 6.8 (0.7) b
Fasting insulin (pmol/l) c,d 33.2 (25.5-47.5) 82.7 (38.8-122.5) b 82.3 (53.3-107.3) b
HOMA-IR c,d 1.29 (0.94-1.95) 4.05 (1.92-7.07) b 4.33 (2.53-6.61) b
Total cholesterol (mmol/l) c 5.7 (0.8) 5.5 (1.0) 4.4 (0.8) a,b
HDL-cholesterol (mmol/l) c 1.62 (0.42) 1.36 (0.29) b 1.33 (0.29) b
LDL-cholesterol (mmol/l) c 3.5 (0.8) 3.4 (0.9) 2.3 (0.7) a,b
Systolic blood pressure (mmHg) 131 (15) 143 (16) b 134 (16) a
Former smoking (%) 49 39 43
Physical activity (hours/week) 37 (19) 36 (17) 31 (21) b
Alcohol intake >0gram/day (%) 78 37 b 46 b
Antihypertensive medication (%) 16 66 b 62 b
Diabetes treatment (%)
Diet alone - 24 14
Metformin only - 39 35
Sulfonylureas only - 7 14
Metformin and sulfonylureas - 10 19
Metformin and acarbose - 2 0
Use of insulin - 17 19
Meal measurements
Fasting triglycerides (mmol/l) c 1.2 (0.5) 1.7 (0.6) b 1.8 (0.6) b
Fasting glucose (mmol/l) c 5.2 (0.4) 7.1 (1.4) b 7.5 (1.2) b
Fat-rich meals:
PpTG (mmol/l) 2.1 (0.9) 2.7 (1.0) b 2.7 (0.9) b
PpGL (mmol/l) 5.4 (0.5) 7.9 (1.8) b 7.8 (1.6) b
Carbohydrate-rich meals:
PpTG (mmol/l) 1.4 (0.7) 2.0 (0.6) b 2.0 (0.7) b
PpGL (mmol/l) 5.7 (0.8) 10.7 (3.5) b 11.5 (3.6) b
Prevalent cardiovascular disease (%)e 22 34 26
Carotid Intima Media Thickness (µm) 764 (110) 790 (107) 769 (121)
Data as means (SD) or percentages. In case of skewed distribution, medians (interquartile range) are
presented and Ln-transformed values were tested. NGM= normal glucose metabolism, DM2= type 2 diabetes mellitus, DM2-ST= type 2 diabetes mellitus using statins, HOMA-IR=homeostasis model
assessment for insulin resistance, ppTG=postprandial triglycerides, ppGL=postprandial glucose. a
P<0.05 compared to DM2 group, b P<0.05 compared to NGM group, c mean of 2 measurements, d
women using insulin excluded, e prevalent cardiovascular disease defined as ankle-brachial-index
<0.90, ECG abnormalities (1.1-1.3, 4.1-4.3, 5.1-5.3 or 7-1), history of arterial surgery, cerebral
vascular event, angina, amputation, claudication or possible myocardial infarction.
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
53
Table 3.2. Linear regression analysis (beta (95% CI)) of risk factors and carotid Intima Media Thickness (n=154) Variable SD Model 1 Model 2
Age 4.0 years 20.2 (2.6;37.8) a 22.7 (4.8;40.6) a
DM2 (yes versus no) 17.2 (-17.9;52.3) 33.1 (-9.3;75.5)
DM2 duration b 4.7 years 12.7 (-11.1;36.5) 11.0 (-12.5;34.5)
BMI 5.4 kg/m2 11.4 (-6.2;29.0) 7.7 (-13.0;28.3)
Waist c 14.0 cm 13.0 (-31.6;57.6) 16.6 (-30.6;63.8)
Hip c 10.3 cm -8.3 (-45.4;28.8) -8.7 (-47.6;30.3)
2-hour post-load glucose d 1.0 mmol/l 2.1 (-22.8; 27.0) -
HbA1c 0.76 % 12.4 (-5.2;29.9) 16.1 (-10.2;42.3)
Fasting insulin b,e 59 pmol/l 1.7 (-19.2;22.6) -4.1 (-28.8;20.6)
HOMA-IR b,e 3.4 5.0 (-15.3;25.2) -0.1 (-26.8;26.7)
Total cholesterol 1.01 mmol/l 6.7 (-10.9;24.3) 7.4 (-13.6;28.3)
HDL-cholesterol 0.39 mmol/l -8.0 (-25.8;9.8) -5.6 (-24.6;13.5)
LDL-cholesterol 0.93 mmol/l 10.1 (-7.5;27.6) 12.4 (-9.1;33.9)
Systolic blood pressure f 16 mmHg 18.9 (0.6;37.1) a 17.1 (-1.8;35.9)
Former smoking g (yes versus no) 23.5 (-25.6;72.5) -
Alcohol intake b 63 gram/week 11.5 (-6.4;29.4) 17.7 (-1.1;36.6)
Physical activity 19 hours/week -12.3 (-31.7;7.0) -12.0 (-31.5;7.4)
Beta in µm intima media thickness per 1 SD increase in independent variable. DM2= type 2 diabetes
mellitus, HOMA-IR=homeostasis model assessment for insulin resistance. Model 1: Adjusted for age.
Model 2: Adjusted for age, DM2 and use of statins. a P<0.05, b ln-transformed for analyses, c waist and
hip are additionally adjusted for each other and for BMI, d after an OGTT in women with NGM only, e
women using insulin excluded, f additionally adjusted for use of antihypertensive medication, g
interaction with DM2 (P=0.08) and use of statins (P=0.03), betas for women with NGM only. Other betas
were: -50.6 (-117.4;16.1) and 59.2 (-22.4;140.7) for women with DM2 with DM2-ST, respectively.
Results of linear regression analysis of fasting and postprandial glucose and
triglycerides are shown in Table 3. Because we observed different associations of
ppGL with cIMT for women with DM2 and women with NGM after both the fat-rich
(P-value for interaction P=0.12) and the carbohydrate-rich meals (P-value for
interaction P=0.15), results are presented separately for women with NGM and
DM2. We also found an interaction between fasting glucose and use of statin
medication (P=0.07). Therefore, we performed the regression analysis of fasting
glucose with cIMT for women in the DM2-ST group separately.
CHAPTER 3
54
Table 3.3. Associations (beta (95% CI)) of fasting and postprandial glucose and
triglycerides with carotid Intima Media Thickness
NGM (n=76)
Variable (SD) Model 1 Model 1 +
fasting levels
Model 1 +
mutually adjusted a
Fasting
Triglycerides (0.6 mol/l) 2.3 (-26.0;30.7) - -0.8 (-29.9;28.2)
Glucose (1.4 mmol/l) 50.4 (-45.8;146.6) - 51.0 (-48.1;150.0)
Fat meals
PpTG (1.0 mmol/l) 3.9 (-23.1;30.9) 8.6 (-48.5;65.9) -3.7 (-31.0;23.6)
PpGL (1.8 mmol/l) 92.6 (6.1;179.1) 91.8 (-8.0;191.7) 95.7 (5.5;185.9)
Carbohydrate meals
PpTG (0.7 mmol/l) 10.0 (-16.7;36.6) 43.5 (-18.5;105.5) 1.3 (-27.3;29.9)
PpGL (3.7 mmol/l) 98.8 (-13.7;211.3) 89.9 (-35.9;215.7) 96.7 (-26.0;219.3)
DM2 (n=78)
Variable (SD) Model 1 Model 1 +
fasting levels
Model 1 +
mutually adjusted a
Fasting
Triglycerides (0.6 mol/l) -5.6 (-34.1;22.9) - -12.5 (-43.0;17.9)
Glucose (1.4 mmol/l) 38.6 (6.2;71.0) b - 51.6 (16.8;86.5) b
Fat meals
PpTG (1.0 mmol/l) -13.4 (-40.3;13.6) -26.8 (-73.3;19.7) -14.0 (-41.1;13.1)
PpGL (1.8 mmol/l) 8.6 (-18.5;35.6) c -4.6 (-40.1;30.9) 9.5 (-17.6;36.5)
Carbohydrate meals
PpTG (0.7 mmol/l) -10.1 (-39.8;19.6) -20.6 (-81.2;39.9) -12.7 (-42.7;17.2)
PpGL (3.7 mmol/l) 14.4 (-13.1;42.0) c 5.7 (-29.2;40.7) 16.2 (-11.7;44.2)
Beta in µm intima media thickness per 1 SD increase in independent variable. NGM= normal glucose
metabolism, DM2= type 2 diabetes mellitus, ppTG=postprandial triglycerides, ppGL=postprandial
glucose. Model 1: adjusted for age and (DM2 group) use of statins. a Mutually adjusted as follows:
Fasting triglycerides and fasting glucose; ppTG and ppGL following fat-rich meals; ppTG and ppGL
following carbohydrate-rich meals, b Interaction with use of statins (P=0.07) beta for women with
DM2, not using statins only. Beta for DM2-ST-group –13.6 (-62.8;35.5), c Interaction with DM2
P<0.15.
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
55
Fasting levels of glucose were statistically significantly associated with cIMT in
women with DM2. In women with NGM, this association was of similar magnitude,
although not statistically significant (Table 3). After mutual adjustment of fasting
glucose and fasting triglycerides, the association between fasting glucose and cIMT
in women with DM2 became stronger. After adjustment for CVD risk factors, (BMI,
total cholesterol, fasting insulin, HOMA-IR, systolic blood pressure, smoking, LDL-
cholesterol or HDL-cholesterol) the association between fasting glucose and cIMT
remained statistically significant (data not shown).
PpTG after both the test meals was not associated with cIMT in women with NGM
and not in women with DM2. However, PpGL after both the fat-rich meals (p=0.04)
and the carbohydrate-rich meals (borderline significant P=0.08) was associated with
cIMT in women with NGM. A 1.0 mmol/l higher glucose level during the day of the
fat-rich meals was associated with a 50 µm larger cIMT. In addition, after the
carbohydrate meals, a 1.8 mmol/l higher glucose level was associated with a 50 µm
larger cIMT. These associations only slightly changed after adjustment for fasting
glucose or ppTG. Also adjustment for BMI, total cholesterol, fasting insulin, HOMA-
IR, systolic blood pressure, smoking, LDL-cholesterol or HDL-cholesterol as
potentially confounding factors did not alter the associations between ppGL and
cIMT (data not shown). Age-adjusted IMT in quartiles of ppGL for women with NGM
is shown in Figure 3.1.
To further explore the association between ppGL and cIMT, we performed
regression analyses for each of the glucose measurements separately (1,2 and 4
hour) and for ppGL after breakfast only (AUC 0-4 hour) with regard to cIMT.
Although borderline significant, glucose levels at t=1 and t=2 hour after the fat-rich
meal were associated with cIMT (1.0 mmol/l glucose associated with a 19 µm larger
cIMT at t=1 and 32 µm at t=2, both P=0.05). PpGL, after one meal (AUC 0-4 hour)
was associated with cIMT similar to ppGL calculated after two consecutive meals. A
1.0 mmol/l higher glucose level after the fat-rich breakfast was associated with a
44 µm larger cIMT (P=0.03).
CHAPTER 3
56
Fat-rich meals
600
650
700
750
800
850
<5.0 5.0-5.3 5.3-5.7 >5.7
cIMT (µµ µµm)
Carbohydrate-rich meals
600
650
700
750
800
850
<5.2 5.2-5.7 5.7-6.1 >6.1
cIMT (µµ µµm)
Figure 3.1. Age-adjusted cIMT (±SEM) in quartiles of mean postprandial glucose levels (mmol/l, 0-8 hour) following fat-rich meals (upper) and following carbohydrate-rich meals (lower) for women with NGM. P for trend derived from linear regression analyses with postprandial glucose levels as continuous variable: P<0.05 and P=0.08 for fat-rich and carbohydrate-rich meals, respectively.
DISCUSSION
In the present cross-sectional study, we compared the relative contribution of ppGL
and ppTG to cIMT as an early marker of atherosclerosis. We found that ppGL,
irrespective of the meal composition, was associated with cIMT in women with NGM
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
57
but not in women with DM2. The association between ppGL and cIMT remained
unaltered after adjustment for ppTG or other CVD risk factors.
Mild hyperglycemia has previously been discussed as an underestimated
cardiovascular risk factor [23]. However, in the present study, we also included
women with NGM, confirmed by OGTT. Remarkably, only in this normal range of
fasting and postprandial glucose levels, ppGL was associated with cIMT. In the
women with DM2, an association was found with fasting glycemia but not with
ppGL. Possibly, a high biological variation in ppGL has obscured the association
with cIMT in these patients with DM2. Another possibility is that treatment with
glucose lowering medication and dietary intervention in the women with DM2
might have influenced the association between ppGL levels and cIMT [24].
Based on previously published papers, we would have expected an association
between ppTG and cIMT, especially in patients with DM2 [11,12], but also in
healthy subjects [8-10]. Moreover, post-menopausal women have been shown to
have higher postprandial lipemia [25], which could potentially contribute to the
high risk of CVD in these women [26,27]. However, no associations between ppTG
and cIMT were found in the present study. As to ppTG, no differences were found
in a study with persons with and without CVD [28]. In contrast, in another study,
patients with coronary heart disease were found to have higher fasting triglycerides
and higher ppTG than subjects without coronary heart disease [29]. In the
Atherosclerosis Risk in Communities Study, ppTG levels were found to be
associated with early atherosclerosis, but only in non-obese subjects [8] and the
investigators suggested that this association might be explained by high levels of
atherogenic triglyceride-rich lipoprotein remnants in the postprandial state. These
lipoprotein remnants were indeed related to cIMT, independent of ppTG [30]. Since
we did not measure lipoprotein remnant particles in the present study, we cannot
address this question. It should furthermore be considered that gender-specific
studies on the relation between ppTG and cardiovascular risk are scarce. The above
mentioned Atherosclerosis Risk in Communities Study reported an association
between ppTG and cIMT in both men and women, but an association with exercise-
induced myocardial ischemia was only present in men and not in women [8,31].
Because of the increased risk for CVD converted by DM2 in postmenopausal
women, studies in older women are urgently needed.
Our fat-rich meal should be considered as a mixed meal because it contained
carbohydrates, through which beta-cell stimulation will be induced. Our data show
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58
that despite differences in test-meal composition and carbohydrate content (56 g
or 162 g) an association between average ppGL levels and cIMT can be found. We
also found that measuring glucose after one meal was enough to assess the
association between ppGL and cIMT. However, glucose measured two hours after
an OGTT, did not give a similar strong association with cIMT in women with NGM.
This could not be due to limited precision of the single glucose measurement after
the OGTT compared to the multiple postprandial glucose measurements, because
we found a single 2-hour glucose measurement after a meal, although borderline
significant, associated with a relevant increase in cIMT. This leads us to the
conclusion that ppGL following a meal gives additional clinical relevant information
compared to 2-hour post-load glucose following a glucose solution. First of all, we
should consider that the OGTT contained 75 gram glucose whereas the
carbohydrate-rich meal contained 162 gram carbohydrates which takes longer to
digest due to higher carbohydrate load and the more complex molecular structure
of carbohydrates in the meals. Secondly, it has been suggested that the relation
between hyperglycemia and cardiovascular disease is mediated by oxidative stress.
Ceriello et al. showed a cumulative effect of postprandial hyperglycemia and
postprandial hypertriglyceridemia on oxidative stress generation [32]. Although a
cumulative effect was possible after the mixed meal, we cannot confirm this
because we did not find an independent association between ppTG and cIMT.
Carotid IMT is a well-accepted marker for atherosclerosis and cardiovascular risk
[33]. Age and systolic blood pressure were associated with cIMT in our study. The
estimated age-related increase of 50 µm per ten years is similar to that observed in
a European study population [34] and is comparable to the age-effect in a study
among women of the same age [35]. Probably due to limited power, other CVD risk
factors, systolic blood pressure excepted, were not statistically significantly
associated with cIMT. Although the direction of the associations with cIMT were as
expected, it should be noticed that not only atherosclerosis but also other
mechanisms (thrombosis) play a role in development of CVD.
The cross-sectional study design is a potential limitation to our study. We assumed
that cIMT reflects the cumulative effect of previous atherogenic processes.
However, cIMT is a result of dynamic processes over time: progression as well as
regression due to treatment or lifestyle interventions occurs [24]. Especially in the
present population of women with DM2, who were treated intensively, the
treatment-related modification of the cIMT might have led to an underestimation of
the true association between metabolic responses and atherosclerosis.
POSTPRANDIAL METABOLISM AND ATHEROSCLEROSIS
59
The relative paucity of data in women and the disproportionately high risk of CVD
in the present study population prompted us to perform the study. However, we
should be careful in extrapolating the present results to other populations. In
addition, the present study population cannot be viewed as a population-based
cohort of post-menopausal women because from the 24% who agreed to
participate, 40% did not meet the inclusion-criteria or dropped out of the study.
The association of ppGL with cIMT in women with NGM was stronger than that of
ppTG. The association between ppGL and cIMT in women with NGM suggests that
ppGL, in the normal range, is a marker or a risk factor of CVD. Postprandial glucose
measured after a fat-rich or a carbohydrate-rich meal, might give more information
about development of atherosclerosis than 2-hour glucose, measured after an
OGTT.
ACKNOWLEDGEMENTS
This research was financially supported by a grant from the Dutch Diabetes
Research Foundation (grant no.2001.00.052) and by funding from Novartis
International AG, Switserland. The authors thank Jannet Entius, Tanja Nansink, Lida
Ooteman, Marianne Veeken and Jolanda Bosman for excellent assistance in data
collection and organization of the study, and all participants for their contributions.
CHAPTER 3
60
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63
Chapter 4
FASTING AND POSTPRANDIAL GLYCOXIDATIVE AND LIPOXIDATIVE STRESS ARE INCREASED IN WOMEN WITH TYPE 2 DIABETES MELLITUS
Roger K. Schindhelm1
Marjan Alssema2
Peter G. Scheffer3
Michaela Diamant1
Jacqueline M. Dekker2
Rob Barto3
Giel Nijpels2,4
Piet J. Kostens2,5
Robert. J. Heine1,2
Casper G. Schalkwijk6
Tom Teerlink3
From the Departments of 1Endocrinology/Diabetes Center, 3Clinical Chemistry,
4General Practice and 5Clinical Epidemiology and Biostatistics, and the 2EMGO
Institute, VU University Medical Center, Amsterdam, The Netherlands, and from the
6Department of Internal Medicine, Academic Hospital Maastricht, Maastricht, The
Netherlands.
Submitted
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64
ABSTRACT
Objective: We studied acute changes in markers of glycoxidative and lipoxidative
stress, including oxidized low-density-lipoprotein, Nε-(carboxyethyl)-lysine (CEL),
Nε-(carboxymethyl)-lysine (CML) and 3-deoxyglucosone (3DG), following two
consecutive meals.
Research Design and Methods: Post-menopausal women (27 with normal glucose
metabolism [NGM]; 26 with type 2 diabetes [DM2]), received two consecutive fat-
rich meals and two consecutive carbohydrate-rich meals on two occasions. Glucose
and triglyceride concentrations were measured at baseline and 1,2,4,6 and 8h
following breakfast; lunch was given at 4h. Oxidized-LDL-to-LDL-cholesterol ratio
(oxLDL/LDL-C), CEL, CML and 3DG were measured at baseline and at 8h.
Results: Fasting oxLDL/LDL-C, 3DG and CML were higher in women with DM2
compared to women with NGM and were comparable to the postprandial values at
8 hours in NGM. Postprandial rises in oxLDL/LDL-C and 3DG were similar in both
groups. However, oxLDL/LDL-C increased more after the fat-rich meals, whereas
CML and 3DG increased more after the carbohydrate-rich meals. After the fat-rich
meals, the increase in oxLDL/LDL-C was correlated with postprandial triglycerides,
whereas the increase in 3DG was correlated with postprandial glucose.
Conclusions: The acute changes in markers of glycoxidative and lipoxidative
stress in both DM2 and NGM suggest that post-absorptive oxidative stress may
partly underlie the association of postprandial derangements and cardiovascular
risk.
POSTPRANDIAL OXIDATIVE STRESS
65
INTRODUCTION
Patients with type 2 diabetes mellitus (DM2) have an increased risk of
cardiovascular disease (CVD)[1], which can only partly be explained by the classical
CVD risk factors such as hypertension, high LDL-cholesterol, low HDL-cholesterol
and smoking [2]. In post-menopausal women, as compared to men, the relative risk
of CVD conferred by DM2 is even higher [3]. Zilversmit postulated in 1979 that
disturbances in postprandial metabolism may contribute to the excess risk of CVD
[4], due to postprandial elevations of glucose and triglyceride-enriched lipoproteins
[5,6].
Oxidative stress is regarded as a common pathway by which many of the classical
CVD risk factors and postprandial dysmetabolism may initiate and promote
atherosclerosis [7]. Indeed, elevated levels of oxidized LDL (oxLDL) are associated
with an increased risk for CVD [8]. Prolonged exposure to a high-fat diet has been
shown to result in an increase in plasma levels of oxLDL [9]. Another mechanism
that might link postprandial dysmetabolism and the risk of CVD in patients with
DM2 includes the formation of advanced glycation endproducts (AGEs), which are
related to micro- and macrovascular complications [10]. Two of the most studied
AGEs, Nε-(carboxyethyl)lysine (CEL) and Nε-(carboxymethyl)lysine (CML), can be
formed on proteins by both glycoxidation and lipid-peroxidation pathways. α-
Dicarbonyl compounds such as 3-deoxyglucosone (3DG), glyoxal and
methylglyoxal, are reactive intermediates in the formation of AGEs [11]. 3DG is a
relatively stable intermediate, whereas glyoxal and methylglyoxal convert readily
into CML and CEL, respectively. Because the latter are stable compounds, their
quantification may yield more relevant information than quantification of their
reactive dicarbonyl precursors.
Data on acute meal-induced changes in oxLDL, 3DG and CML and CEL are limited.
To date, one study in healthy young men demonstrated an increase in the oxLDL-
to-LDL-cholesterol-ratio after two consecutive fat-rich mixed meals [12], and one
study in patients with coronary artery disease (CAD) found that postprandial oxLDL
was associated with the severity and extent of CAD [13].
In the present study, 26 women with DM2 and 27 women with normal glucose
metabolism (NGM) received two consecutive (breakfast and lunch) fat-rich or
carbohydrate-rich meals, in order to study meal-related changes in oxidative stress
(oxLDL), AGE-compounds (CML and CEL), and a reactive AGE-precursor (3DG). In
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66
addition to the effects of meal composition and the presence of diabetes, we also
studied associations between postprandial changes of these markers and changes
in glucose and triglycerides.
METHODS
Subjects
The present study was a sub-study of a cross-sectional study to assess the effect
and relative contributions of two consecutive fat-rich and carbohydrate-rich meals
on markers of CVD risk in women with NGM (fasting glucose<6.1 mmol/l and 2-h
post-load glucose<7.8 mmol/l) (n=76) and DM2 (n=79). Women with DM2 (n=522),
recruited from the registry of the Diabetes Care System in the city of Hoorn, the
Netherlands, and women who were randomly selected from the municipal registry
of Hoorn (n=541), aged 50-65 years at the beginning of the study, were invited to
participate in the study. Of these 1,063 women, 431 women were complete non-
responders, 258 women were not willing to participate and 220 women did not
meet the inclusion criteria. Inclusion criteria were: post-menopausal status, non-
smoking, no untreated endocrine disorder other than DM2, no use of short acting
insulin analogues, no use of PPAR-α and -γ agonists, no use of oral corticosteroids,
no use of hormone replacement therapy, no statin use (for NGM-subjects only),
HbA1c<9.0%, fasting cholesterol<8.0 mmol/l, fasting triglycerides<4.0 mmol/l,
systolic blood pressure<190 mmHg, no liver impairment or renal impairment. For
the present study, a sub-sample of 26 women with DM2 (no use of statins or
insulin analogues) and 27 women with NGM matched for age, were studied. All
participants gave written informed consent, and the study protocol was approved
by the ethics committee of the VU University Medical Center in Amsterdam.
Study design
The study consisted of a screening visit and two visits for the test-meals. On the
screening visit, fasting blood samples were drawn after a 12h overnight fast. Blood
pressure was measured thrice with an oscillometric blood pressure measuring
device (Collin Press-mate BP-8800, Colin, Komaki-City, Japan) after a 15-minute
supine rest. Weight and height were measured in participants wearing light clothes
only. Smoking and alcohol consumption were assessed by a questionnaire [14].
POSTPRANDIAL OXIDATIVE STRESS
67
Test meals
Postprandial meal responses were examined following two standardized
consecutive test meals (breakfast and lunch). On one day, both test meals were
high in fat content and on the other occasion, both meals were high in
carbohydrate content. Both occasions were performed in random order. The fat-rich
meals (both breakfast and lunch) consisted of 2 croissants, 10 g of butter, 40 g of
high-fat cheese and 300 ml of high-fat milk (3349kJ; 50 g fat; 56 g carbohydrates).
The carbohydrate-rich meals (both breakfast and lunch) consisted of 2 slices of
bread, 25 g of marmalade, 30 g of cooked chicken breast, 50g of ginger bread and
300ml of drinking yogurt fortified with 45 g of soluble carbohydrates (3261kJ; 4 g
fat, 162 g carbohydrates). Both meals were eaten within 10 minutes. Apart from the
test meals and water (ad libitum), participants were not allowed to eat.
Laboratory analysis
Serum total cholesterol, HDL-cholesterol and triglycerides were measured by
enzymatic colorimetric assays (Roche, Mannheim, Germany). Fasting and
postprandial LDL-cholesterol was calculated according to the Friedewald-formula if
triglycerides <4.5 mmol/l [15]. Plasma glucose levels were determined with a
glucose oxidase method (Granutest, Merck, Darmstadt, Germany) and HbA1c was
measured with cation-exchange chromatography (Menarini Diagnostics, Florence,
Italy).
OxLDL was measured in EDTA-plasma in duplicate with a competitive ELISA
(Mercodia, Uppsala, Sweden), with inter-assay and intra-assay coefficients of
variation of 7.8% and 4.8%, respectively. We expressed oxLDL as a ratio per LDL-
cholesterol (oxLDL/LDL-C) as proposed by Holvoet et al [16].
3DG was measured with a newly developed LC-MS/MS method after pre-column
derivatization according to a published procedure [17]. In brief, 0.5 ml of whole
blood was mixed with 1.0 ml of 1.2 mol/l perchloric acid and immediately stored at
-80 ˚C until assayed. After thawing, the samples were centrifuged (5 min at 20,000
g) and 80 µl of the supernatant was mixed with 100 µl of the internal standard
solution (1 µmol/l 2,3-pentanedione dissolved in ethanol). After addition of 20 µl of
the derivatization reagent (10 mmol/l 2,4-dinitrophenylhydrazine dissolved in 1.2
mol/l perchloric acid), the samples were incubated for 16 h at room temperature.
Chromatographic separation was performed on a Xterra MS C18-column (4.6x50
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68
mm; 3.5 µm particle size) from Waters (Milford, MA, USA) using a linear gradient
from 40% to 95% acetonitrile in water over 9 min at a flow rate of 1 ml/min. Mass
transitions of 521.1→431.0 and 521.1→182.1 for 3DG and 459.1→182.1 for the
internal standard were monitored in negative-ion mode. Intra-assay and inter-assay
coefficients of variation for 3DG were 8% and 12%, respectively. Two subjects with
NGM had missing 3DG because of missing blood samples. In a pilot study applying
the same study-protocol, we assessed the time-course of 3DG in 9 DM2 and 8 NGM
subjects and measured 3DG with an HPLC-method with fluorescence detection as
described previously [18]. The highest 3DG concentration was observed at 8 hours
after the first meal [19].
Unbound CML and CEL were measured in EDTA-plasma by HPLC with stable-
isotope-dilution tandem mass spectrometric detection (LC-MS/MS) using a
procedure developed for the analysis of protein-bound CML and CEL [20], with a
modified sample preparation procedure. Briefly, after centrifugation of the plasma
samples (5 min at 20,000 g), 50 µl of the supernatant was mixed with 150 µl of a
combined internal standard solution (containing deuterated CML and CEL) and
proteins were removed by centrifugation (20 min at 15,000 g) in a 10 kDa cut-off
ultrafiltration device (Vivaspin 500 PES from Vivascience AG, Hannover, Germany).
A 90 µl aliquot of the ultrafiltrate was mixed with 10 µl of a 50 mmol/l
nonafluoropentanoic acid solution and 25 µl of this mixture was subjected to LC-
MS/MS analysis as described before [20]. Intra-assay and inter-assay coefficients of
variation for CML were 3% and 4%, respectively and for CEL 5% and 9%, respectively.
To assess the cumulative effect of both consecutive meals we measured oxLDL,
3DG, CML, and CEL at baseline and at 8h.
Statistical analyses
Analyses were performed with SPSS for Windows 11.01 (SPSS Inc., Chicago, IL, USA).
Data are presented as mean values (standard deviation), and in case of a skewed
distribution, as median values (interquartile range). Data were ln-transformed
before testing in case of a skewed distribution. Differences between women with
NGM and DM2 were tested with Student’s t-test for continuous variables and c2-test
for dichotomous variables. Differences between meals were tested with paired
samples t-tests. Postprandial changes in glucose and triglycerides were calculated
as incremental area under the curve (iAUC) by the trapezoid method. Correlations
between postprandial responses were calculated by Spearman’s correlation-
coefficients. We considered a two-sided P<0.05 to indicate statistical significance.
POSTPRANDIAL OXIDATIVE STRESS
69
RESULTS
Characteristics of the study population
The clinical and biochemical characteristics of the participants at the screening visit
are presented in Table 4.1.
Table 4.1. Clinical and biochemical characteristics of the study-population at the screening visit Variables NGM DM2 P-value
N 27 26
Age (years) 60.3 (4.0) 60.4 (3.2) 0.93
BMI (kg/m2) 26.0 (3.1) 31.8 (5.8) <0.001
Fasting glucose (mmol/l) 5.5 (0.3) 7.6 (1.3) <0.001
HbA1c (%) 5.6 (0.3) 6.5 (0.6) <0.001
Total cholesterol (mmol/l) 6.0 (1.0) 5.6 (0.9) 0.69
HDL-cholesterol (mmol/l) 1.93 (0.57) 1.47 (0.30) 0.01
LDL-cholesterol (mmol/l) 3.6 (0.9) 3.3 (0.9) 0.22
Triglycerides (mmol/l)* 0.9 (0.8-1.6) 2.0 (1.4-2.4) <0.001
Systolic blood pressure (mmHg) 125 (12) 143 (16) <0.001
Diastolic blood pressure (mmHg) 69 (8) 79 (7) <0.001
Smoking (former) 56% 39% 0.16
Alcohol-intake (g/week) 78 ± 12 24 ± 7 <0.01
Antihypertensive medication (%) 0 58 <0.001
Diabetes treatment (%)
- diet alone - 35 -
- metformin - 54 -
- sulfonylureas - 19 -
- acarbose - 4 -
Data are mean (standard deviation) or median (interquartile range) in case of skewed data NGM= normal glucose metabolism, DM2= type 2 diabetes mellitus *ln-transformed before analysis
CHAPTER 4
70
Figure 4.1. Time course of triglycerides and glucose concentrations (mean±SD) following
two consecutive fat-rich or carbohydrate-rich meals in women with normal glucose
metabolism (open circles) and type 2 diabetes mellitus (black circles).
Figure 4.1 shows the 8 h-time courses of triglycerides and glucose after two
consecutive fat-rich and after two consecutive carbohydrate-rich meals. At both
meal visits, fasting triglyceride and glucose levels were higher in women with DM2
compared to the women with NGM (both P<0.001). Triglycerides-iAUC was higher
following the fat-rich meals than after the carbohydrate-rich meals in both women
with DM2 and NGM (both P<0.001), but was similar in both groups. Following the
carbohydrate-rich meals, glucose-iAUC was higher in women with DM2 compared to
NGM (P<0.001), whereas this difference was borderline significant after the fat-rich
meals (P=0.06).
LDL-C significantly decreased from baseline to 8h in both groups and after both
meal types (from 3.6±0.8 mmol/l to 3.2±0.7 mmol/l and 3.7±0.8 mmol/l to
3.3±0.7 mmol/l, fat-rich meals and carbohydrate-rich meals, respectively in NGM
and from 3.3±0.7 mmol/l to 2.9±0.7 mmol/l and 3.3±0.7 mmol/l to 3.0±0.7
POSTPRANDIAL OXIDATIVE STRESS
71
mmol/l, fat-rich meals and carbohydrate-rich meals, respectively in DM2 (all
P<0.001).
Glycoxidative and lipoxidative stress markers
The oxLDL/LDL-C, 3DG, CML and CEL values in women with NGM and DM2 at
baseline and at 8h are listed in Table 2. Baseline oxLDL/LDL-C, 3DG and CML were
significantly higher in DM2 compared to NGM. The increase of oxLDL/LDL-C during
the fat-rich meals was higher than the increase during the carbohydrate-rich meals
(17±10% versus 10±7% in NGM and 15±13% versus 7±9% in DM2, both P<0.05). Of
interest, the fasting values of ox-LDL/LDL-C, 3DG and CML in women with DM2
were similar to the postprandial values at 8h in women with NGM (all P>0.1).
Table 4.2. Fasting and postprandial (8 hours) levels of oxLDL/LDL-C, 3DG, CML and CEL in
women with NGM compared to women with DM2*
NGM DM2
Fasting Postprandial Fasting Postprandial
OxLDL/LDL-C (U/mmol) Fat 21.6 (3.8) 25.1 (4.8)‡ 25.4 (4.6)‡ 29.6 (8.7)‡
Carb 21.6 (2.8) 23.7 (3.8)‡ 25.7 (4.3)‡ 27.3 (4.9)‡
3DG (nmol/l) Fat 164 (41) 183 (45)‡ 208 (60)§ 236 (58)‡
Carb 164 (37) 208 (40)§ 210 (49)§ 281 (50)§
CML (nmol/l) Fat 39.6 (10.1) 45.1 (10.6)‡ 52.2 (18.1)‡ 47.9 (14.1)†
Carb 43.3 (10.7) 54.7 (9.4)§ 48.2 (16.2)‡ 55.9 (16.0)§
CEL (nmol/l) Fat 62.7 (31.2) 67.4 (15.1) 67.0 (27.4) 66.0 (16.3)
Carb 56.4 (13.5) 72.1 (12.8) § 65.8 (23.3) 72.9 (17.9)
*Abbrevations: NGM:normal glucose metabolism, DM2:type 2 diabetes mellitus,
Fat:fat-rich meal, Carb:carbohydrate-rich meal, OxLDL/LDL-C:ratio of oxidized LDL to total LDL-cholesterol,
3DG:3-dexoxyglucosone, CML:Nepsilon-(carboxymethyl)-lysine, CEL:Nepsilon-(carboxyethyl)-lysine
†p<0.05 DM2 baseline versus NGM baseline or 8h versus baseline
‡p<0.01 DM2 baseline versus NGM baseline or 8h versus baseline
§p<0.001 DM2 baseline versus NGM baseline or 8h versus baseline
Baseline oxLDL/LDL-C was correlated with fasting triglycerides in women with DM2
(r=0.39, P<0.05), but not in women with NGM. After the fat-rich meals, changes in
oxLDL/LDL-C correlated with changes in triglycerides, either expressed as
triglycerides-iAUC (r=0.32, P<0.05; Figure 4.2a) or as increase over baseline at 8h
(r=0.50, P<0.001).
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72
The mean increase of 3DG after the carbohydrate-rich meals was higher than after
the fat-rich meals (29±21% versus 13±24%, P<0.05 in NGM, and 36±17% versus
15±20%, P<0.001 in DM2). Fasting plasma glucose concentration was correlated
with baseline 3DG in women with DM2 (r=0.50, P<0.01), but not in women with
NGM. Postprandial changes in 3DG correlated to glucose-iAUC after the fat-rich
meals (r=0.37, P<0.01; Figure 4.2b), but not with glucose-iAUC after the
carbohydrate-rich meals. The changes of CML and CEL were neither correlated to
triglycerides-iAUC nor to glucose-iAUC, irrespective of meal composition.
Figure 4.2. Relations between postprandial changes of the oxLDL-to-LDL-cholesterol ratio
and 8h incremental area under the curve for plasma triglycerides (A) and between changes
of 3-deoxyglucosone and 8h incremental area under the curve for glucose (B) after two
consecutive fat-rich meals in women with normal glucose metabolism (open circles) and
type 2 diabetes mellitus (black circles). Strengths of the associations are indicated by
Spearman’s correlation coefficients.
POSTPRANDIAL OXIDATIVE STRESS
73
DISCUSSION
In the present cross-sectional study in postmenopausal women with NGM and DM2,
we studied the postprandial changes of oxLDL/LCL-C, 3DG, CML and CEL, following
two consecutive fat-rich or carbohydrate-rich meals. The main findings are that the
fasting values of oxLDL/LDL-C, 3DG and CML are higher in women with DM2
compared to women with NGM. In women with DM2, the fasting values of
oxLDL/LDL-C, 3DG and CML were similar to the postprandial values at 8h in women
with NGM. Overall, the postprandial rises of oxLDL/LDL-C and 3DG were of similar
magnitude in DM2 and NGM. OxLDL/LDL-C showed a greater increase after the fat-
rich meals, whereas CML and 3DG increased more after the carbohydrate-rich
meals. The increase in oxLDL/LDL-C was correlated with the rise in postprandial
triglycerides, whereas the increase in 3DG was correlated with the postprandial rise
of glucose.
There is increasing evidence that postprandial glucose and triglycerides may play
an important role in macro- and microvascular complications [21]. However, their
relative contributions and the mechanisms leading to the increased CVD risk are
not yet fully understood [5]. Prospective studies have shown that postprandial or
post-load glucose levels are more strongly associated with increased CVD than
fasting glucose [22,23]. Oxidative stress is hypothesized to be an important
pathway linking postprandial glucose and triglyceride responses to CVD [24,25].
Indeed, in a recent study it was found that postprandial oxLDL was a determinant
of the extent of coronary atherosclerosis in patients with CAD [13]. A limitation of
most previous studies is that responses after a single meal were studied, which
might not reflect the “real life” burden of the postprandial state, possibly leading to
an underestimation of the associations between postprandial responses and CVD
risk. A recent study from our group that applied two consecutive meals (breakfast
and lunch) in healthy young men, demonstrated that even slightly increased levels
of triglycerides and glucose were associated with endothelial dysfunction and
increased markers of oxidative stress, including oxLDL [12]. We concluded that
these findings might be relevant for insulin resistant subjects and DM2 individuals
who have a more pronounced (exaggerated and prolonged) postprandial elevation
of glucose and triglycerides, possibly leading to more oxidative stress. Our present
findings support this conjecture, as we found that baseline oxLDL was higher in
DM2 than in NGM, with a greater increase of oxLDL after the fat-rich meals
compared to the carbohydrate-rich meals. Interestingly, although fasting
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74
oxLDL/LDL-C was higher in the DM2 group, we found no difference in the
postprandial increase in oxLDL/LDL-C between NGM and DM2.
There are multiple sources and mechanisms in the formation of AGEs in vivo,
involving both oxidative and non-oxidative reactions of reducing carbohydrates and
other metabolic intermediates with amino acid residues [26]. The AGE precursor
3DG, which is increased in patients with diabetes [27], is formed by non-oxidative
modifications of Amadori adducts and from fructose-3-phosphate [28]. In line with
these observations, we found that 3DG increased more after the carbohydrate-rich
meals than after the fat-rich meals. In addition, postprandial changes in 3DG were
strongly correlated to changes in glucose, but surprisingly, only after the fat-rich
meals, suggesting additional involvement of oxidative mechanisms in its formation.
In patients with type 1 diabetes Beisswenger et al observed an increase in 3DG
within two hours [29], whereas in our pilot study in patients with DM2 we did not
observe a rise in 3DG after the first meal [19].
Some studies have shown that total serum CML is higher in DM2 than in healthy
individuals [30,31], although in other studies no difference was observed [20]. We
chose not to measure total CML and CEL, but rather the unbound forms of these
compounds, that are continuously released during degradation of intracellular
proteins with a rapid turn-over. Because oxidative stress not only enhances
formation of protein-bound CML and CEL, but also triggers proteasomal
degradation of damaged proteins [32,33], the unbound fraction of CML and CEL
may better reflect formation of AGEs by oxidative stress. We observed that CML was
affected by both meal types. However, possibly due to a relatively large biological
variation, as reflected by differences in the baseline CML and CEL values at the two
meal visits, these changes were less consistent than for oxLDL and 3DG. Future
studies should assess whether changes in the protein-bound forms of CML and CEL
are more informative.
The major precursor of CML is glyoxal and oxidative stress seems to enhance the
formation of glyoxal from glucose [34], which is consistent with our finding of a
greater increase in CML after the carbohydrate-rich meals compared to the fat-rich
meals.
POSTPRANDIAL OXIDATIVE STRESS
75
Potential limitations
First, both groups were not matched for BMI, lipids, blood pressure and alcohol
intake, all of which may have independent effects on the parameters measured.
However, we chose not to match for these variables, to avoid selection of relatively
healthy DM2 patients. Second, our study was performed in elderly postmenopausal
women and the changes observed may be different in younger women as well as in
men.
Conclusion
In summary, we found significant elevations of postprandial oxLDL/LDL-C, 3DG and
CML in postmenopausal women with DM2 and NGM. Since postprandial increases in
α-dicarbonyls and associated AGEs are related to cellular damage, therapies aimed
at reducing postprandial glycemia and triglyceridemia, may contribute to the
reduction in micro- and macrovascular complications.
ACKNOWLEDGEMENTS
The Hoorn prandial study was financially supported by funding from Novartis AG
Switzerland and by a grant from the Dutch Diabetes Research Foundation (grant
no.2001.00.052). We thank Danielle van Assema for her assistance in organizing
the pilot-study and Rick Vermue for his excellent technical assistance.
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76
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79
Chapter 5
COMPARISON OF THE EFFECT OF TWO CONSECUTIVE FAT-RICH AND CARBOHYDRATE-RICH MEALS ON POSTPRANDIAL PLASMA
MYELOPEROXIDASE IN WOMEN WITH AND WITHOUT TYPE 2 DIABETES MELLITUS
Roger K. Schindhelm1
Marjan Alssema2
Michaela Diamant1
Tom Teerlink3
Jacqueline M. Dekker2
Astrid Kok3
Piet J. Kostens2,5
Giel Nijpels2,4
Robert. J. Heine1,2
Peter G. Scheffer3
From the Departments of 1Endocrinology/Diabetes Center, 3Clinical Chemistry
4General Practice and 5Clinical Epidemiology and Biostatistics, and the 2EMGO
Institute, VU University Medical Center, Amsterdam, The Netherlands.
Submitted
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80
ABSTRACT
Background: Patients with type 2 diabetes mellitus (DM2) have an increased risk of
cardiovascular disease (CVD). Myeloperoxidase (MPO), expressed in leukocytes, and
released upon activation, is associated with CVD and endothelial dysfunction.
Postprandial leukocyte recruitment and activation with subsequent MPO release
may contribute to atherosclerosis and CVD. We hypothesized that MPO may
increase in the postprandial state, due to postprandial leukocyte recruitment
and/or activation, especially in subjects with DM2.
Methods: One hundred post-menopausal women, aged 50-65 years (66 with
normal glucose metabolism [NGM], and 34 with DM2), received two consecutive fat-
rich meals and two consecutive carbohydrate-rich meals on separate occasions.
Blood samples were taken before (t=0), and at 2,4 and 8 hours following breakfast;
lunch was given at t=4. Plasma MPO-concentration was measured by sandwich
ELISA.
Results: The number of leukocytes in fasting blood samples was higher in DM2
compared with NGM (6.1±1.4 and 5.4±1.2x109/l, respectively; P<0.05). Baseline
MPO concentration did not differ between NGM and DM2 (51.4±12.9 and
54.5±18.4 µg/L, respectively; P=0.39). Baseline MPO was positively associated with
leukocytes (r=0.20; P<0.05) and inversely associated with high-density lipoprotein
cholesterol (r=-0.22; P<0.05). Leukocytes increased from 5.0±1.5 to 6.1±1.5x109/L
and from 5.8±1.4 to 6.6±1.4x109/l in NGM and DM2, respectively (both P<0.01)
following the fat-rich meals. In contrast to our hypothesized increase in MPO, we
found a significant decrease in MPO in NGM (both meal types) and DM2 (fat-rich
meals only).
Conclusions: Our findings provide no support to our initial hypothesis that meal-
induced release of MPO might be a mechanism that contributes to CVD risk.
POSTPRANDIAL MYELOPEROXIDASE
81
INTRODUCTION
Patients with type 2 diabetes mellitus (DM2) have an increased risk of
cardiovascular disease (CVD) [1-3[. Evidence has been accumulating that chronic
low-grade inflammation may be an important factor in the pathogenesis of
atherosclerosis and CVD [4,5]. The number of white blood cells, especially
polymorphonuclear neutrophils, is associated with future CVD events [6,7].
However, the role of leukocytes in the pathogenesis of CVD has not yet been fully
elucidated. It has been demonstrated that the number of leukocytes was increased
between one and six hours after a fat challenge [8]. Postprandial recruitment and
activation of these cells have been suggested to contribute to endothelial
dysfunction [9], which is an early event in the atherosclerotic process. Since
postprandial responses after a fat-rich mixed-meal may be more pronounced and
prolonged in subjects with DM2 than in healthy controls [10], this may result in a
higher postprandial leukocyte recruitment in these patients. This may be even more
the case in postmenopausal women, who have higher postprandial triglyceride
responses compared to premenopausal women [11].
Myeloperoxidase (MPO) is a heme protein which is abundantly expressed in
leukocytes, and released upon activation into phagocytic vacuoles and in the extra
cellular space [12,13]. Several in vitro and in vivo studies have shown that MPO
interacts with the vessel-wall by various mechanisms, including binding and
transcytosis through endothelial cells, and leads to hypochlorous acid generation,
nitric oxide oxidation and tyrosine nitration [14]. MPO has been associated with
coronary artery disease [15], recurrent coronary events [16], heart failure [17] and
endothelial dysfunction [18].
Postprandial leukocyte recruitment and activation with subsequent MPO activation
may thus contribute to atherosclerosis and CVD, especially in patients with DM2
because of the prolonged and exaggerated dysmetabolic state. Based on these
observations, we hypothesized that, in particular in subjects with DM2, MPO would
increase in the postprandial state due to postprandial leukocyte recruitment and/or
activation. Therefore we assessed postprandial plasma MPO after two consecutive
fat-rich or carbohydrate-rich meals in women with DM2 and in women with normal
glucose metabolism (NGM).
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MATERIAL AND METHODS
Subjects and study design
The women included in the present study were part of a cross-sectional study to
assess the effect and relative contributions of two consecutive fat-rich and
carbohydrate-rich meals on markers of CVD risk in postmenopausal women with
NGM (n=76) and DM2 (n=79). Women with DM2 (n=522), recruited from the registry
of the Diabetes Care System in the city of Hoorn, the Netherlands, and women who
were randomly selected from the municipal registry of Hoorn (n=541), aged 50-65
years at the beginning of the study, were invited to participate in the study. Of
these 1,063 women, 431 women were complete non-responders, 258 women were
not willing to participate and 219 women did not meet the inclusion criteria,
yielding 155 eligible participants. Inclusion criteria were: post-menopausal status
(no menses in the last 12 months), non-smoking, no untreated endocrine disorder
other than DM2, no use of short acting insulin analogues, no use of PPAR-α and -γ
agonists, no use of oral corticosteroids, no use of hormone replacement therapy,
no use of HMG-CoA reductase inhibitors (statins) (for NGM only), HbA1c<9.0%,
fasting cholesterol <8.0 mmol/l, fasting triglycerides <4.0 mmol/l, systolic blood
pressure <190 mmHg, no liver impairment or renal impairment. Women who were
selected from the municipal registry underwent a 75-g oral glucose tolerance test
to verify their glucose tolerance status (fasting glucose <6.1 mmol/l and 2-hour
post-load glucose <7.8 mmol/l). In addition to the above-mentioned inclusion
criteria of the main study protocol, for the present analyses we chose not to include
DM2 women using statins (n=38), as statins have been shown to down regulate
MPO gene expression [19,20]. Thus, for the present study 41 women with DM2,
and 76 women with NGM were included. The protocol consisted of three separate
visits, i.e. a screening visit and two visits for the test-meals. Postprandial meal
responses were examined following the consumption of two consecutive
standardized test meals (breakfast and lunch; fat-rich meals [3349 kJ; 50 g fat; 56
g carbohydrates and 28 g proteins] and carbohydrate meals [3261 kJ; 4 g fat, 162 g
carbohydrates and 22 g of proteins]) on two separate occasions and in random
order. Blood samples were taken before and at t=2, 4 and 8 hours following
breakfast for measurement of glucose, triglycerides and MPO. Lunch was given 4
hours after breakfast (t=4). Apart from the test meals and water (ad libitum),
participants were not allowed to eat. In addition, physical activity was limited
during the day. All participants gave informed consent, and the study protocol was
POSTPRANDIAL MYELOPEROXIDASE
83
approved by the ethics committee of the VU University Medical Center in
Amsterdam.
Measurements
At the screening visit, blood pressure was measured at the left arm three times
with 5 minute intervals with an oscillometric blood pressure measuring device
(Collin Press-mate BP-8800, Colin, Komaki-City, Japan) after a 15-minute supine
rest. Waist circumference was measured twice at the level midway between the
lowest rib margin and the iliac crest. Medical history was assessed by a
questionnaire [21].
Laboratory analyses
MPO was determined in duplicate by a sandwich ELISA (Mercodia, Uppsala, Sweden)
in EDTA-plasma using a CODA® automated EIA analyzer (Biorad, San Francisco, CA,
USA). The concentration of MPO was measured in baseline samples, and in samples
that were taken at 2, 4 and 8 hours after breakfast. The intra-assay and inter-assay
coefficients of variation were 3.3% and 5.0%, respectively. Serum total cholesterol,
high-density lipoprotein (HDL) cholesterol and triglycerides were measured by
enzymatic colorimetric assays (Roche, Mannheim, Germany). Low-density
lipoprotein (LDL) cholesterol was calculated according to the Friedewald-formula
[22]. Plasma glucose levels were determined with a glucose oxidase method
(Granutest, Merck, Darmstadt, Germany) and glycated hemoglobin (HbA1c) was
measured with cation exchange chromatography (Menarini Diagnostics, Florence,
Italy). Leukocytes were measured at baseline in all subjects and at 8 hours after
breakfast in a sub-sample (n=43; 29 NGM and 14 DM2) to determine the
postprandial leukocyte response.
Statistical analyses
Analyses were performed in SPSS for Windows 11.0.1 (SPSS Inc. Chicago, IL). Data
are presented as mean values (standard deviation) or in case of skewed
distribution, as median values (interquartile range). Differences in baseline
characteristics between the NGM and DM2 groups were tested with a Students’ t-
test for continuous variables. Postprandial triglyceride, glucose and MPO responses
were calculated as incremental and total area under the curve (iAUC and tAUC,
respectively) with the trapezoid method and expressed as mean incremental or
total concentration by dividing the incremental or total response by 8 hours.
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84
Differences in postprandial MPO responses (mean total and mean incremental area
under the curve) between the fat-rich and the carbohydrate-rich meals were tested
by a paired samples t-test. A one-samples t-test with zero as test-value was used to
test whether the mean incremental area was different from zero (i.e. no change in
mean incremental MPO). Differences in postprandial time-point (2, 4 and 8h) were
tested with a paired-samples t-test compared to baseline. Correlations between
MPO and clinical and biochemical characteristics were expressed as Spearman’s
correlation coefficients. A two-sided P-value<0.05 was considered as statistically
significant.
RESULTS
Characteristics of study population
Subjects with one or more missing values were excluded (n=17) and all statistical
analyses were performed on the remaining 100 subjects (66 with NGM and 34 with
DM2). Subjects who were excluded from the statistical analyses did not significantly
differ with respect to fasting total leukocyte count, triglycerides and glucose (all
P>0.05, for NGM and DM2 separately). The characteristics of the 100 subjects are
presented in Table 5.1. On average, women with DM2 were more obese, had higher
fasting plasma glucose, HbA1c, triglycerides, and blood pressure compared to
women with NGM. No differences were observed in total and LDL-cholesterol,
whereas HDL-cholesterol was lower in DM2.
Postprandial glucose and triglycerides
Figure 1 shows the 8 hour time courses of triglycerides and glucose after two
consecutive fat-rich and after two consecutive carbohydrate-rich meals. The
increase (iAUC) in postprandial triglycerides was similar in women with DM2 and
women with NGM after the fat-rich meals (0.9±0.5 versus 0.9±0.5 mmol/l; P=0.97).
The rise of plasma glucose (iAUC) levels after the fat-rich meals was higher in
women with DM2 compared to the NGM women (0.7±1.5 versus 0.1±0.4 mmol/l,
respectively; P<0.05). Following the carbohydrate-rich meals, postprandial glucose
(iAUC) values were higher in women with DM2, compared to women with NGM
(3.1±2.7 versus 0.4±0.6 mmol/l, respectively; P<0.001).
POSTPRANDIAL MYELOPEROXIDASE
85
Table 5.1. Clinical and biochemical characteristics of the study-population (n = 100)
Variablea NGM DM2 P-value
n 66 34
Age, years 60.5 (4.0) 58.7 (3.7) 0.03
Waist, cm 87.1 (9.8) 102.4 (14.4) <0.001
Fasting glucose, mmol/lb 5.2 (0.3) 7.0 (1.3) <0.001
HbA1c, % 5.5 (0.3) 6.5 (0.6) <0.001
Total cholesterol, mmol/lb 5.7 (0.8) 5.6 (1.0) 0.41
HDL-cholesterol, mmol/lb 1.65 (0.41) 1.40 (0.30) <0.01
LDL-cholesterol, mmol/lb 3.5 (0.8) 3.4 (0.8) 0.40
Triglycerides, mmol/lb, c 1.1 (0.9-1.4) 1.5 (1.2-2.0) <0.001
Systolic blood pressure, mmHg 129 (14) 143 (17) <0.001
Diastolic blood pressure, mmHg 70 (8) 79 (7) <0.001
NGM= normal glucose metabolism, DM2= type 2 diabetes mellitus a Data are mean (standard deviation) b mean of the two meal-visits c ln-transformed before analysis
Figure 5.1. Time course of triglycerides [left panel] and glucose [right panel] (mean ± SEM)
following two consecutive carbohydrate-rich meals (solid line) and two fat-rich meals
(dotted line), given at 0 and 4 hours in individuals with normal glucose metabolism (NGM:
open dots) and patients with type diabetes mellitus (DM2: solid dots)
0 2 4 6 8
0
5
10
15
20
time [hours]
glucose [mmol/L]
0 2 4 6 8
0
1
2
3
4
time [hours]
triglycerides[m
mol/L]
0 2 4 6 8
0
5
10
15
20
time [hours]
glucose [mmol/L]
0 2 4 6 8
0
1
2
3
4
time [hours]
triglycerides[m
mol/L]
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86
Baseline and postprandial leukocytes and MPO
Baseline number of leukocytes was higher in DM2 compared to NGM (6.1±1.4
versus 5.4±1.2 x 109 /l, respectively; P < 0.05). Changes in postprandial leukocyte
counts were measured in a sub-sample of 43 participants (29 NGM and 14 DM2). In
this sub-sample, the number of leukocytes did not significantly increase after the
carbohydrate-rich meals (from 5.0±1.2 to 5.3±1.3 x 109 /l in NGM and from
5.8±1.3 to 6.0±1.4 x 109 /l in DM2; P = 0.07 and P = 0.23, respectively). Following
the fat-rich meals, leukocytes increased from 5.0±1.5 to 6.1±1.5 x 109 /l and from
5.8±1.4 to 6.6±1.4 x 109 /l in NGM and DM2, respectively (both P < 0.01). The
postprandial changes in MPO are presented in Figure 2 and in Table 2. Baseline
MPO (mean value of the two meal visits) was not statistically significantly different
between NGM and DM2 (51.4±12.9 and 54.5±18.4 µg /l respectively; P = 0.39).
Baseline MPO was positively associated with total leukocyte counts (r=0.20; P =
0.04), whereas an inverse association of fasting MPO with fasting HDL-cholesterol
(r=-0.22; P = 0.02) was observed. No significant associations of baseline MPO were
found with age, triglycerides, fasting glucose, and total-cholesterol and systolic and
diastolic blood pressure. MPO-iAUC and MPO-tAUC after both meal types did not
significantly differ between DM2 and NGM, nor did MPO at any of the time-points
differ between DM and NGM (all P > 0.1). MPO-iAUC was significantly different from
zero in NGM (-2.5±5.5 and -2.9±5.3 µg/l, fat-rich meals and carbohydrate-rich
meals respectively; both P<0.01), but not in DM2 (-1.7±5.5 and -1.6±5.2 µg/l; fat-
rich meals and carbohydrate-rich meals respectively; P = 0.08 and P = 0.09
respectively). Furthermore, compared to baseline MPO was significantly lower at 8
hours after breakfast in NGM (both meal types) and DM2 (fat-rich meals only) (all P
< 0.05) (Table 2).
Table 5.2. Fasting and postprandial levels of MPO (mean, SD) in women with NGM and women with DM2a
Time CARB FAT
NGM DM2 NGM DM2
MPO [µg/l] baseline 51.8 (14.0) 55.6 (18.9) 51.0 (13.0) 53.8 (18.4)
2 50.6 (13.1) 52.3 (18.3) b 51.9 (13.8) 52.3 (17.4)
4 48.8 (13.4)b 54.1 (17.2) 49.5 (12.1) 53.2 (17.8)
8 48.0 (13.0)b 53.5 (18.5) 47.2 (12.6)b 50.5 (17.7)b
aAbbrevations: NGM: normal glucose metabolism, DM2: type 2 diabetes mellitus, Fat: fat-rich meal, Carb: carbohydrate-rich meal, MPO: myeloperoxidase b P < 0.05 versus baseline
POSTPRANDIAL MYELOPEROXIDASE
87
0 2 4 6 8
30
40
50
60
70
time [hours]
MPO [µµ µµg/L]
0 2 4 6 8
30
40
50
60
70
time [hours]
MPO [µµ µµg/L]
0 2 4 6 8
30
40
50
60
70
time [hours]
MPO [µµ µµg/L]
0 2 4 6 8
30
40
50
60
70
time [hours]
MPO [µµ µµg/L]
Figure 5.2. Time course of MPO (mean ± SEM) following two consecutive carbohydrate-rich
meals (solid line) [left panel] and two fat-rich meals (dotted line) [right panel], given at 0
and 4 hours in individuals with normal glucose metabolism (NGM: open dots) and patients
with type diabetes mellitus (DM2: solid dots)
DISCUSSION
This is, to the best of our knowledge, the first study to examine postprandial MPO
responses. We found no significant differences in baseline MPO between DM2 and
NGM. Baseline MPO was negatively related to HDL-cholesterol and positively
correlated to leukocyte counts. Total leukocytes increased after the fat-rich meals
in both NGM and DM2, whereas MPO decreased postprandially in NGM (both meals)
and in DM2 (fat-rich meals).
Previous studies have shown that leukocytes increase following a fat-rich meal [9].
In addition, one study suggested that activation markers on monocytes and
neutrophils are increased and related to postprandial changes in triglycerides [8].
Furthermore, activation markers on monocytes and neutrophils were higher in DM2
as compared to healthy controls [23]. These observations suggest that postprandial
leukocyte recruitment and activation may be a mechanism in the relation between
postprandial derangements and increased CVD risk. In addition, a number of
studies suggest that patients with higher MPO levels have an increased CVD risk
[15-18]. In the present study, the number of leukocytes in the fasting blood
samples was higher in DM2 as compared to NGM, but in spite of this, MPO was not
significantly higher in DM2 as compared to NGM. This lack of difference may be
explained by an impaired leukocyte function in DM2. Indeed, a previous study
CHAPTER 5
88
showed lower MPO in lymphocytes and neutrophils in patients with DM2 compared
to healthy controls [24]. In contrast to our initial hypothesis, we found lower MPO
values in the postprandial state after both meal types. Therefore, changes may not
only be meal-induced, but also due to diurnal changes in leukocytes and MPO.
Future studies assessing diurnal MPO course in the fasting state should address
this issue. In addition, because of unavailable plasma samples at 6 hours
postprandial, the present data may have underestimated the true changes in
postprandial MPO.
Of interest, we found a negative correlation between MPO and HDL-cholesterol.
Indeed, MPO binds to apolipoprotein AI of HDL particles and may subsequently be
inactivated or excreted [25]. Furthermore, MPO may be involved in generating
dysfunctional HDL and thus promote atherogenesis by this pathway [26]. In line
with this conjecture, a recent study has shown that MPO is associated with
progression of carotid artery stenosis in patients with low HDL-cholesterol levels
[27]. MPO has also been shown to promote oxidation of LDL particles [28,29],
which may provide another mechanism linking MPO to CVD risk.
We measured MPO in EDTA-plasma instead of heparin-plasma or serum. MPO mass
determined in serum is in general higher than in heparin plasma due to in vitro
release of MPO from activated leukocytes [30]. For heparin plasma it is necessary to
collect the samples on ice before sample processing (i.e. centrifugation) to avoid
MPO release [30]. When EDTA-blood was taken, we observed no changes in MPO
concentrations over a period of 2 hours at room temperature before sample
processing, whereas MPO rose gradually in heparin-plasma as well as in blood
withdrawn in tubes without an added anticoagulant (data not shown). Hence, EDTA-
plasma was chosen for MPO measurements because we assume that this may
reflect the in vivo MPO secretion of leukocytes most precisely. Although Zhang and
coworkers reported a high correlation between MPO mass and MPO activity (r =
0.95) [15], we cannot exclude that postprandial changes in MPO activity are more
pronounced than alterations in MPO concentrations.
In conclusion, we found no increase in MPO concentration despite the postprandial
increase in leukocytes after the fat-rich meals, suggesting that postprandial
leukocyte recruitment is likely to occur, but without a concomitant MPO release in
plasma. Our findings provide no support to the initial hypothesis that meal-induced
release of MPO might be a mechanism that contributes to CVD risk in
POSTPRANDIAL MYELOPEROXIDASE
89
postmenopausal women with NGM or DM2 and therefore other mechanisms may
link postprandial leukocytes to CVD risk.
ACKNOWLEDGEMENTS
This study was financially supported by funding from Novartis AG Switzerland, and
by a grant from the Dutch Diabetes Research Foundation (grant no.2001.00.052).
The myeloperoxidase ELISA kits were generously provided by Mercodia AB,
Uppsala, Sweden. The authors thank Ina Kamphuis, Harry Twaalfhoven and Rick
Vermue for performing the MPO-assays and all participants for their contributions.
The authors have no conflict of interest with respect to this study.
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5. Ross R. Atherosclerosis--an inflammatory disease. N Engl J Med 1999;340:115-26.
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postprandial lipemia in healthy volunteers. Atherosclerosis 2004;177:175-82.
9. Van Oostrom AJ, Sijmonsma TP, Verseyden C, et al. Postprandial recruitment of
neutrophils may contribute to endothelial dysfunction. J Lipid Res 2003;44:576-83.
10. Tushuizen ME, Diamant M, Heine RJ. Postprandial dysmetabolism and cardiovascular
disease in type 2 diabetes. Postgrad Med J 2005;81:1-6.
11. van Beek AP, Ruijter-Heijstek FC, Erkelens DW, de Bruin TW. Menopause is associated
with reduced protection from postprandial lipemia. Arterioscler Thromb Vasc Biol
1999;19:2737-41.
12. Nauseef WM. Insights into myeloperoxidase biosynthesis from its inherited
deficiency. J Mol Med 1998;76:661-8.
13. Winterbourn CC. Comparative reactivities of various biological compounds with
myeloperoxidase-hydrogen peroxide-chloride, and similarity of the oxidant to
hypochlorite. Biochim Biophys Acta 1985;840:204-10.
14. Lau D, Baldus S. Myeloperoxidase and its contributory role in inflammatory vascular
disease. Pharmacol Ther 2006;111:16-26.
15. Zhang R, Brennan ML, Fu X, et al. Association between myeloperoxidase levels and
risk of coronary artery disease. JAMA 2001;286:2136-42.
16. Baldus S, Heeschen C, Meinertz T, et al. Myeloperoxidase serum levels predict risk in
patients with acute coronary syndromes. Circulation 2003;108:1440-5.
17. Tang WH, Brennan ML, Philip K, et al. Plasma myeloperoxidase levels in patients with
chronic heart failure. Am J Cardiol 2006;98:796-9.
18. Vita JA, Brennan ML, Gokce N, et al. Serum myeloperoxidase levels independently
predict endothelial dysfunction in humans. Circulation 2004;110:1134-9.
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19. Zhou T, Zhou Sh, Qi Ss, et al. The effect of atorvastatin on serum myeloperoxidase
and CRP levels in patients with acute coronary syndrome. Clinica Chimica Acta
2006;368:168-72.
20. Kumar AP, Reynolds WF. Statins downregulate myeloperoxidase gene expression in
macrophages. Biochem Biophys Res Commun 2005;331:442-51.
21. Mooy JM, Grootenhuis PA, de Vries H, et al. Prevalence and determinants of glucose
intolerance in a Dutch caucasian population. The Hoorn Study. Diabetes Care
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22. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-
density lipoprotein cholesterol in plasma, without use of the preparative
ultracentrifuge. Clin Chem 1972;18:499-502.
23. Van Oostrom AJ, van Wijk JP, Sijmonsma TP, Rabelink TJ, Castro CM. Increased
expression of activation markers on monocytes and neutrophils in type 2 diabetes.
Neth J Med 2004;62:320-5.
24. Uchimura K, Nagasaka A, Hayashi R, et al. Changes in superoxide dismutase activities
and concentrations and myeloperoxidase activities in leukocytes from patients with
diabetes mellitus. J Diabetes Complications 1999;13:264-70.
25. Zheng L, Nukuna B, Brennan ML, et al. Apolipoprotein A-I is a selective target for
myeloperoxidase-catalyzed oxidation and functional impairment in subjects with
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26. Nicholls SJ, Zheng L, Hazen SL. Formation of dysfunctional high-density lipoprotein by
myeloperoxidase. Trends Cardiovasc Med 2005;15:212-9.
27. Exner M, Minar E, Mlekusch W, et al. Myeloperoxidase predicts progression of carotid
stenosis in states of low high-density lipoprotein cholesterol. J Am Coll Cardiol
2006;47:2212-8.
28. Kostyuk VA, Kraemer T, Sies H, Schewe T. Myeloperoxidase/nitrite-mediated lipid
peroxidation of low-density lipoprotein as modulated by flavonoids. FEBS Lett
2003;537:146-50.
29. Steffen Y, Wiswedel I, Peter D, Schewe T, Sies H. Cytotoxicity of
myeloperoxidase/nitrite-oxidized low-density lipoprotein toward endothelial cells is
due to a high 7beta-hydroxycholesterol to 7-ketocholesterol ratio. Free Radic Biol Med
2006;41:1139-50.
30. Chang PY, Wu TL, Hung CC, et al. Development of an ELISA for myeloperoxidase on
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93
Part 2
LIVER ENZYMES IN RELATION TO METABOLIC SYNDROME, TYPE 2 DIABETES MELLITUS AND CARDIOVASCULAR RISK
94
95
Chapter 6
ALANINE AMINOTRANSFERASE AS A MARKER OF NON-ALCOHOLIC FATTY LIVER DISEASE IN RELATION TO TYPE 2 DIABETES MELLITUS
AND CARDIOVASCULAR DISEASE
Roger K. Schindhelm1
Michaela Diamant1
Jacqueline M. Dekker2
Maarten E. Tushuizen1
Tom Teerlink3
Robert J. Heine1,2
From the Departments of 1Endocrinology/Diabetes Center and 3Clinical Chemistry,
and the 2EMGO Institute, VU University Medical Center, Amsterdam, The
Netherlands
Diabetes Metabolism Research and Reviews 2006;22:437-443
Copyright © 2006 John Wiley & Sons Ltd.
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ABSTRACT
For a long time, hepatic steatosis was believed to be a benign condition. Only
recently, liver steatosis, also termed non-alcoholic fatty liver disease (NAFLD), has
gained much interest. In most cases of NAFLD, a condition regardedas the hepatic
component of the metabolic syndrome, the enzyme alanine aminotransferase (ALT)
is elevated and consequently has been used as a marker for NAFLD. More recently,
several cross-sectional and prospective studies have demonstrated associations of
this liver enzyme with features of the metabolic syndrome and type 2 diabetes
mellitus. This review discusses the biochemical and metabolic properties of ALT, its
applicability as a marker of NAFLD and describes its possible role in the
pathogenesis of the metabolic syndrome and type 2 diabetes mellitus and
subsequent cardiovascular disease. In addition treatment strategies to ameliorate
NAFLD and the associated risks are discussed.
ALANINE AMINOTRANSFERASE, TYPE 2 DIABETES MELLITUS AND CARDIOVASSCULAR DISEASE
97
INTRODUCTION
For a long time, hepatic steatosis was believed to be a benign condition. Only
recently, liver steatosis, also termed non-alcoholic fatty liver disease (NAFLD), has
gained much interest. NAFLD, which is characterized by a wide spectrum of liver
pathology ranging from mere liver steatosis to the more severe non-alcoholic
steatohepatitis, resembles alcohol-induced liver disease, but develops in subjects
who are not heavy alcohol consumers and have negative tests for viral and auto-
immune liver diseases [1–3]. This condition was first described in detail by Ludwig
and colleagues in 1980, who reported 20 moderately obese patients with liver
biopsy changes resembling alcohol-induced hepatitis, although none of these
patients reported a history of alcohol abuse [4]. Only in recent years, NAFLD has
gained appreciation as a pathogenic factor of insulin resistance and type 2 diabetes
mellitus. Furthermore, several studies showed an association between NAFLD and
features of the metabolic syndrome, including dyslipidemia and (visceral) obesity,
stressing the association with insulin resistance as an important feature of NAFLD.
Currently, NAFLD is considered by some authors to be the hepatic component of
the metabolic syndrome [5,6].
In the majority of cases, NAFLD causes asymptomatic elevation of the level of liver
enzymes alanine aminotransferase (ALT), aspartate aminotransferase (AST) and γ-
glutamyltransferase (GGT)) [7]. Of these liver enzymes, ALT is most closely related
to liver fat accumulation [8], and consequently ALT has been used as marker of
NAFLD. GGT may also be elevated in patients with NAFLD, but only limited data on
its occurrence and degree of elevation are available. Furthermore, the correlation of
liver fat measured by proton-MR spectroscopy with GGT is markedly lower than
with ALT [8], possibly because GGT is also produced in other tissues [9,10]. Finally,
to the best of our knowledge no direct validation of GGT with fat content in liver
biopsy specimen has been reported. Several cross-sectional studies have found
associations of ALT with several features of the metabolic syndrome. At present,
ALT is often used in epidemiological studies as a surrogate marker for NAFLD. In
addition, recent prospective epidemiological studies have demonstrated that ALT is
associated with future risk of type 2 diabetes mellitus and the metabolic syndrome
[11–17].
This review discusses the biochemical and metabolic properties of ALT, its
applicability as a marker of NAFLD and describes its possible role in the
pathogenesis of the metabolic syndrome and type 2 diabetes mellitus and
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98
subsequent cardiovascular disease (CVD), as well as discusses the treatment
strategies to ameliorate NAFLD and the associated risks.
BIOCHEMISTRY AND LABORATORY ANALYSIS
ALT catalyses the reversible transamination between L-alanine and α-ketoglutarate
to form pyruvate and L-glutamate and as such has an important role in
gluconeogenesis and amino-acid metabolism (Figure 6.1). The reaction is
reversible, but the equilibrium of the ALT reaction favors the formation of L-
alanine. The vitamin B6 vitamers pyridoxal-5’-phosphate (PLP) and its amino
analogue pyridoxamine-5’-phosphate, function as coenzymes in the amino transfer
reaction. ALT enzyme activity is primarily found in the liver, but its activity,
although much lower, is also found in many other organs including muscle, heart,
kidney, brain and adipose tissue [18-20].
Figure 6.1. Alanine aminotransferase (ALT) catalyses the interconversion of the amino-group from L-
alanine to α-ketoglutarate and forms pyruvate and L-glutamate. Pyridoxal-5’-phosphate (PLP)
functions as a coenzyme in the amino-transfer reaction.
In 1957, Wroblewski and Cabaud described a colorimetric ALT assay based on
measurement of glutamate by paper chromatography [21]. In later assays, the
transamination reaction was coupled to a second reaction involving the reduction
of pyruvate to lactate by lactate dehydrogenase [22]. In this reaction nicotinamide
COO-
|
H _C _ NH2 +
|
CH3
COO-
|
C = O
|
CH2 |
CH2 |
COO-
COO-
|
C = O +
|
CH3
COO-
|
H _C _ NH2
|
CH2 |
CH2 |
COO-
ALT, PLP
L-alanine αααα-ketoglutarate pyruvate L-glutamate
COO-
|
H _C _ NH2 +
|
CH3
COO-
|
C = O
|
CH2 |
CH2 |
COO-
COO-
|
C = O +
|
CH3
COO-
|
H _C _ NH2
|
CH2 |
CH2 |
COO-
ALT, PLP
L-alanine αααα-ketoglutarate pyruvate L-glutamate
ALANINE AMINOTRANSFERASE, TYPE 2 DIABETES MELLITUS AND CARDIOVASSCULAR DISEASE
99
adenine dinucleotide (NADH) is oxidized to NAD+ and the decrease in NADH is
assessed by measuring the absorption at 340 or 334 nm. In the past, comparison
between ALT activities reported by different laboratories was hampered by the fact
that assay temperatures ranging from 25 to 37 ˚C were used, leading to large
inter-laboratory differences in reported ALT activities and reference values. The
International Federation of Clinical Chemistry and Laboratory Medicine established
in 2002 a reference system for the measurement of enzyme activity of clinically
important enzymes, including ALT, to be measured at 37 ˚C [23]. Levels of 10 to
45 U/l are considered as normal although reference values may still vary among
laboratories [24]. ALT activity is stable for 24 hours in whole blood, with a marked
decrease thereafter. In serum, ALT enzyme activity is stable for three days at room
temperature and for three weeks in the refrigerator, but a marked decrease is
observed after repeated freezing and thawing [25-28]. It is therefore strongly
advisable not to determine ALT activity in frozen samples, as a falsely decreased
ALT activity may be found. Day-to-day variations of 10-30% have been observed
[29]. Moreover, significant diurnal variations have been reported in both healthy
subjects and in patients with chronic liver disease, necessitating standardized
blood sampling times when comparing enzyme values in clinical practice and
research [30].
Early biochemical studies have suggested the existence of two isoforms of ALT in
humans. Based on the amino acid sequence of liver ALT [31], Sohocki and
colleagues have cloned the human ALT1 located on human chromosome 8q24.3
[32]. Recently, Yang and colleagues cloned the second isoform ALT2 that was
mapped to the human chromosome 16q12.1 and demonstrated that this isoform
was mainly expressed in muscle and adipose tissue [33]. The expression of ALT2 in
adipose tissue may indicate that this isoform may contribute to the homeostasis of
fatty acid metabolism and storage. In fatty livers of obese mice, Jadhao and
colleagues demonstrated that murine ALT2 gene expression was induced 2-fold
whereas expression of murine ALT1 remained the same, and the total hepatic ALT
enzyme activity was elevated by 30% [34]. These observations indicate that ALT2
may be responsible for the increased ALT activity in hepatic steatosis. In the
pathogenesis of NAFLD, the role of different isoforms in humans has not been
studied yet. With the standard ALT assay used in clinical practice, enzyme activity
of both ALT isoforms is measured.
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100
ALT AND FATTY LIVER
Fatty liver content can be assessed with different methods. Direct measurement of
hepatic fat by means of liver biopsies, which is regarded as the “gold standard”
[35], although questionable because of the unknown distribution of fatty
infiltration in the liver, has only limited applicability in epidemiological and clinical
studies, because of the elaborate and invasive nature and associated risks of this
technique. Furthermore, no consensus exists on the standardization of diagnostic
criteria bases on liver histology, although only recently a scoring system for NAFLD
has been proposed [36]. Imaging techniques, such as ultrasound, computed
tomography and proton magnetic resonance spectroscopy (1H-MRS), are non-
invasive and can be used rather than biopsies [37,38]. Ultrasound of the liver is
another non-invasive technique which can be used to indicate the presence or
absence of fatty infiltration of the liver, however, at present, this technique at its
best can provide only semi-quantitative data with regard to NAFLD. However, non-
invasive techniques cannot differentiate between fatty liver alone or steatohepatitis. 1H-MRS has been validated against direct determination of triglyceride content in
human liver biopsies [39], and may be used for clinical purposes, but has limited
applicability in larger populations, because of logistics and high costs. In more
recent epidemiological studies, ALT has been used as a surrogate marker for liver
fat accumulation. This was first described by Nanji and colleagues in 1986, who
reported an association of liver enzymes (i.e. the ALT to AST ratio) with fatty liver in
obese patients [40]. Studies assessing the specificity and sensitivity of ALT as
marker of NAFLD are limited [41,42]. Prati and colleagues have suggested new cut-
off values for ALT in order to facilitate the identification of subjects with NAFLD
[43]. However, since cut-off values depend on the assay used, this proposal has
been questioned by others [24,44]. One study has assessed the correlation of ALT
with 1H-MRS and found a modest, but significant correlation (r=0.5) [8]. We found a
similar correlation between liver fat accumulation measured by 1H-MRS and ALT
(r=0.58; P<0.001) in 36 healthy men and men with the metabolic syndrome with
and without hyperglycemia (type 2 diabetes mellitus) (M.E. Tushuizen et al.,
unpublished data).
In order to avoid confounding by other factors that might cause ALT elevation, a
detailed history on alcohol intake is mandatory to exclude those patients with ALT
elevation caused by alcohol-abuse, and to address alcohol-intake as a possible
confounder or effect-modifier. Viral and auto-immune liver disease and hepatotoxic
medication should be considered as well. With these limitations in mind, ALT is an
ALANINE AMINOTRANSFERASE, TYPE 2 DIABETES MELLITUS AND CARDIOVASSCULAR DISEASE
101
acceptable marker for hepatic steatosis in epidemiological studies; its use for
diagnostic and monitoring purposes in clinical care needs to be further delineated.
ALT AND INCIDENT METABOLIC SYNDROME AND TYPE 2 DIABETES
MELLITUS
The putative role of the liver in the pathogenesis of type 2 diabetes mellitus has
gained much interest and several cross-sectional and prospective studies have
shown associations of ALT with type 2 diabetes mellitus and the metabolic
syndrome. Several cross-sectional studies have demonstrated that ALT is related to
features of the metabolic syndrome and type 2 diabetes mellitus [7,45-48]. Clark
and colleagues for example found in their analysis of The National Health and
Nutrition Examination Survey (NHANES) that up to 31% of the elevated
aminotransferase activity could be explained by high alcohol consumption,
hepatitis B or C infection and/or high transferrin saturation. In the remaining 69%,
the elevated ALT activity was significantly associated with higher body mass index,
waist circumference, triglycerides, fasting insulin and lower HDL-cholesterol.
Additionally, in women, this elevation was associated with type 2 diabetes mellitus
and hypertension [7]. In one study it was demonstrated that ALT can be normal in
uncomplicated obesity [49], but this does not guarantee absence of NAFLD, as
normal ALT levels have been observed in patients with NAFLD, diagnosed by liver
biopsy, which is regarded as the gold standard in the diagnosis of NAFLD [50].
Several studies addressed the prospective relation of ALT with the metabolic
syndrome and type 2 diabetes mellitus (Table 6.1). Nakanishi and co-workers found
that of the liver enzymes studied, ALT was associated with future risk of metabolic
syndrome in middle-aged Japanese men, but used body mass index instead of
waist circumference to define the metabolic syndrome [16]. Hanley and co-workers
studied the relation of four different liver enzymes (including ALT) with the
development of the metabolic syndrome in a multiethnic cohort and demonstrated
that ALT was associated with an increased risk of incident metabolic syndrome
[17]. In patients with type 2 diabetes mellitus, elevated serum ALT enzyme activity
is more frequently observed than in the general population [54,55]. Some [11-15],
but not all studies [16,51-53] have demonstrated independent and significant
associations of ALT with incident type 2 diabetes mellitus. In 1988, Ohlson and
colleagues demonstrated that baseline ALT was a predictor of incident type 2
diabetes mellitus after 13.5 years of follow-up in a cohort of 766 Swedish men,
with a significant four-fold risk for those men in the upper quintile compared to
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102
those in the lower quintile. In the final multivariate model, ALT was a predictor of
incident type 2 diabetes mellitus in 451 Pima Indians after an average of 6.9 years,
after adjustment for fasting blood glucose levels, body mass index, bilirubin,
systolic blood pressure, uric acid and a family history of diabetes [11]. Vozarova
and colleagues found that higher ALT (upper compared to lower decile) was a
significant and independent predictor of incident type 2 diabetes mellitus with a
two-fold increase risk, after adjustment for age, sex, body fat, insulin sensitivity
and acute insulin response [12]. Other studies, mainly performed in men,
confirmed these earlier reports [13,14], while recent population-based studies
could not demonstrate independent associations of ALT with risk of type 2 diabetes
mellitus [52,53]. The observed association between ALT and incident type 2
diabetes mellitus in these reports may thus be explained by the fact that these
studies were performed in high-risk populations and may not be representative for
the general population. An alternative explanation is that the applied models over-
adjust for potential mediating variables, such as insulin and 2h glucose, that might
be directly related to liver fat and/or for risk factors clustered in the metabolic
syndrome. More population-based studies and a better understanding of the
pathophysiology are needed to confirm and understand the predictive value of ALT
in relation to the metabolic syndrome and type 2 diabetes mellitus.
ALT AND CARDIOVASCULAR DISEASE
Several studies have demonstrated associations between NAFLD or ALT and
markers of atherosclerosis or inflammation. Previously, we reported an association
of ALT with endothelial dysfunction, measured as flow-mediated vasodilatation
[56], and Kerner and colleagues found a relation of ALT with C-reactive protein
[57]. Only a limited number of studies have addressed the prospective relation of
ALT with cardiovascular disease and mortality. Arndt and colleagues studied the
association of ALT with all-cause mortality in 8,043 male construction workers and
found no significant association [58]. A recent study by Nakamura and co-workers
found a positive association of ALT with all-cause mortality in Japanese men and
women, but only for those with a body mass index below the median (22.7 kg/m2)
[59]. In the Hoorn Study, a population-based cohort of Caucasian men and women
aged 50 to 75 years, we studied the association of ALT at baseline with all-cause
mortality, incident cardiovascular and coronary heart disease events and found a
significant association of ALT with coronary heart disease after adjustment for the
other components of the metabolic syndrome and traditional risk factors [60].
ALANINE AMINOTRANSFERASE, T
YPE 2 DIABETES M
ELLITUS AND CARDIOVASSCULAR DISEASE
103
Table 5.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS 633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS 5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
Table 5.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS
Table 6.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS 633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS
633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS 5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
Table 5.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS
Table 5.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS 633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS
633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS 5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
Table 5.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS
Table 6.1. Prospective studies assessing the relation of ALT and incident metabolic syndrome and type 2 diabetes mellitus
First Author Cohort Subjects Age Sex (%male) Follow -up, y Relative Risk Comparison Groups
Metabolic Syndrome
Nakanishi[16] Japanese men 2957 35-59 100% 7.0 1.42 (0.95 -2.11) Upper versus lower quintile
Hanley[17] IRAS 633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS
633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS 5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS
633 40-69 44% 5.2 2.12 (1.10 -4.09) Upper versus lower quartile
Type 2 diabetes mellitus
Ohlson[11] Swedish men 54 100% 13.5 3.9 (1.4 -11.1) Upper versus lower quintile
Vozar ova[12] Pima Indians 451 18-50 61% 6.9 1.9 (1.1 -3.3) Upper versus lower decile
Sattar[13] WOSCOPS 5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-
5974 45-64 100% 5.6 2.04 (1.16 -3.58) Upper versus lower quartile
Hanley[14] IRAS 906 40-69 44% 5.2 2.00 (1.22 -3.38) Upper versus lower three quartiles
Wann amethee[15] British Regional Heart
Study
3500 40-59 100% 5.0 1.91 (1.01 -3.60) Upper versus lower quartile
Nakanishi[16] Japanese men 3620 35-59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
59 100% 7.0 2.07 (1.07 -3.98) Upper versus lower quintile
Nannipieri[51] Mexico City Diabetes
Study
1233 35-64 39% 7.0 No significant association Upper quartile vs. lower quartiles
Schindhelm[52] Hoorn Study 1289 50-75 46% 6.4 1.18 (0.66 –2.11) Upper tertile versus lower tertile
Andre[53] DESIR Study 2071
2130
30-65 100%
0%
3.0 1.5 (0.6 -30)
0.7 (0.2 -2.1)
Upper ver sus lower quartile
CHAPTER 6
104
PATHOGENIC MECHANISMS
To date, an elevated ALT is considered a consequence of hepatocyte damage due
to NAFLD. However, the measured plasma elevations of ALT may also be a
consequence of high systemic ALT2 isoform levels that is largely derived from
adipose tissue in obesity and insulin resistance, as has been observed in mice [34].
Insulin resistance, increased pro-inflammatory cytokine production, oxidative stress
and mitochondrial dysfunction leading to hepatocyte damage/destruction, all have
been posed as important pathophysiological mechanisms [61]. Indeed a recent
study reported on the association of ALT and GGT with markers of inflammation
and oxidative stress [62].
Adiponectin, released by adipose tissue has been implied in the pathogenesis of
NAFLD. Obese subjects and patients with type 2 diabetes mellitus have lower levels
of adiponectin compared to healthy controls. Yokoyama and colleagues found an
inverse association of ALT, AST and GGT with adiponectin levels in Japanese male
workers. These associations were independent of age, body mass index, insulin
resistance, serum triglycerides and total cholesterol [63]. Aygun and co-workers
found that adiponectin levels were lower in patients with biopsy proven NAFLD and
elevated liver enzymes, compared to patients with NAFLD and normal liver enzymes
and to those without NAFLD [64]. The exact mechanisms, by which adiponectin is
related to NAFLD are not fully elucidated and need further clarification.
Insulin resistance and visceral obesity have a major impact on regulatory processes
of the (postprandial) lipoprotein and glucose metabolism. The normal suppression
of lipolysis by insulin is impaired in the insulin resistant state and this leads to an
elevated release of free fatty acids from the adipose tissue and subsequent
accumulation in hepatocytes, further enhancing hepatic steatosis and contributing
to increased risk of atherothrombotic disease in subjects with NAFLD and insulin
resistance. Dietary fat composition also seems to influence the degree of hepatic
steatosis [65], although the majority of triglycerides arise from free fatty acids,
derived from adipose tissue lipolysis [66]. It has been demonstrated that a fatty
liver is insulin resistant, resulting in an elevated glucose and very low-density
lipoprotein production [12]. Furthermore, cytokines released from the (visceral)
adipose tissue, including interleukin-6 and tumor necrosis factor-alpha, both
associated with decreased hepatic insulin sensitivity [67], may further enhance fatty
infiltration and decrease hepatocyte integrity.
ALANINE AMINOTRANSFERASE, TYPE 2 DIABETES MELLITUS AND CARDIOVASCULAR DISEASE
105
Another explanation might be up-regulation of ALT enzyme activity. Among the
amino acids, alanine is the most effective precursor for gluconeogenesis [68].
Gluconeogenesis is increased in subjects with type 2 diabetes mellitus [69], due to
increased substrate delivery (e.g. alanine), and an increased conversion of alanine
to glucose [70]. This increased conversion might contribute to the increased ALT
activity, as previously observed in alloxan-diabetic rats [71].
ALT might thus be up-regulated as a compensatory response to the impaired
hepatic insulin signaling or, alternatively, may leak more easily out of the
hepatocytes as a consequence of fatty infiltrations and subsequent damage.
THERAPEUTIC INTERVENTIONS
Therapeutic approaches, including diets and exercise, leading to weight loss and
improvement of the cardiovascular disease risk profile, have also been shown to
lower liver fat content.. These interventions concomitantly improve insulin
sensitivity and/or reduce oxidative stress. Ueno and colleagues compared the
effect of restricted diet and exercise in a group of 15 obese subjects during 3
months to that of no lifestyle changes in a control group of 10 obese subjects.
They found a decrease in biochemical markers of NAFLD including ALT, total
cholesterol and fasting blood glucose. This was confirmed by liver biopsies
demonstrating significant lower fat in the treatment group compared to the control
group [72]. Similar results were found in a recent study by Suzuki and co-workers,
who studied the effect of diet and exercise induced weight reduction on the change
of serum ALT and found that subjects with more than 5% weight reduction and
persistence of this weight reduction showed improvement of serum ALT [73].
Hickman and colleagues assessed the effect of weight loss in patients with chronic
liver disease. In addition to improvements in quality of life and insulin sensitivity,
they also demonstrated that ALT reduction was correlated with the amount of
weight loss (r=0.35; P=0.04) after 15 months. ALT remained significantly lower in
those patients who maintained their reduced weight [74]. Although these life-style
interventions seem promising in the short term, long term effects have not yet
been established and might not be sufficient in patients with multiple
cardiovascular disease risk factors, such as subjects with the metabolic syndrome
and/or type 2 diabetes mellitus.
Several pharmacological agents, including metformin and peroxisome proliferator-
activated receptor-γ (PPAR-γ) agonists have been used in the treatment of NAFLD.
Only a limited number of studies addressed the effect of metformin and/or PPAR-γ
CHAPTER 6
106
agonists on ALT. An open-labeled trial of 20 mg/kg metformin for one year,
demonstrated only a transient improvement of liver enzymes [75]. Tiikkainen and
co-workers compared the effect of 2 g of metformin to 8 mg of the PPAR-γ agonists
rosiglitazone on liver fat content and hepatic insulin sensitivity in a 16 week
double-blind randomized trial in 20 subjects. They found that rosiglitazone
decreased liver fat by 51%, whereas no significant decrease was observed in the
metformin treated subjects [76]. In contrast, Bugianesi and co-workers found that 2
g of metformin lead to more ALT normalization (i.e. below the reference range)
than either vitamin E or diet induced weight reduction in 55 subjects in an 12
months open-labeled randomized trail. Metformin induced a decrease of 50% in
liver fat and a decrease of inflammation and necrosis, using paired liver biopsies
[77]. Unfortunately, in this study no paired biopsies were performed in the subjects
who were randomized to vitamin E or the weight reduction intervention, thus the
level of liver histology improvement in the treatment group could not be compared
with the effect in the control group. Sanyal and co-workers compared the effect of
vitamin E versus vitamin E and the PPAR-γ agonists pioglitazone (30 mg/day) in a
randomized clinical trial with 10 patients with non-alcoholic steatohepatitis in each
treatment arm. They found that vitamin E reduced the amount of hepatic steatosis,
whereas the combination of vitamin E with pioglitazone also improved liver
histology [78]. In a recently published study, Ding and co-workers demonstrated
that 60 days administration of a glucagon-like peptide-1 (GLP-1) receptor agonists,
an incretin-mimetic known to promote insulin production and secretion, reduced
hepatic steatosis in ob/ob mice [79]. Studies in humans assessing the effect of GLP-
1 receptor agonists on hepatic steatosis are needed to confirm these results and
assess the applicability of these agents for the treatment of hepatic steatosis. Long-
term effects and reduction of morbidity and mortality of the above mentioned
treatment modalities need further evaluation to achieve optimal treatment
strategies.
CONCLUSIONS
In epidemiological studies, ALT seems an appropriate marker of fatty liver disease,
if known factors that cause ALT elevation have been addressed properly (e.g
alcohol consumption and hepatitis B and C infections). ALT enzyme activity and
NAFLD are associated with cardiovascular risk factors and treatment of NAFLD
should be aimed at reducing hepatic fat content and reduce the concomitant CVD
risk. Moreover, ALT might provide guidance in clinical practice for the choice
between the different treatment options and to monitor the effect of therapy.
ALANINE AMINOTRANSFERASE, TYPE 2 DIABETES MELLITUS AND CARDIOVASCULAR DISEASE
107
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113
Chapter 7
LIVER ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION IN NORMOTRIGLYCERIDEMIC SUBJECTS
WITH TYPE 2 DIABETES MELLITUS
Roger K. Schindhelm1
Michaela Diamant1
Stephan J. L. Bakker2
Rob A. J. M. van Dijk3
Peter G. Scheffer4
Tom Teerlink4
Piet J. Kostense5
Robert J. Heine1
From the Departments of 1Endocrinology/Diabetes Center, 3Radiology, 4Clinical
Chemistry and 5Clinical Epidemiology and Biostatistics, VU University Medical
Center, Amsterdam, The Netherlands and the 2Department of Internal Medicine,
University Hospital Groningen, Groningen, The Netherlands.
European Journal Clinical Investigation 2005;35:369-74
Copyright © 2005 Blackwell Publishing Ltd.
Reprinted with permission.
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ABSTRACT
Background Plasma levels of liver transaminases, including alanine
aminotransferase (ALT), are elevated in most cases of nonalcoholic fatty liver
disease (NAFLD). Elevated ALT levels are associated with insulin resistance, and
subjects with NAFLD have features of the metabolic syndrome that confer high-risk
cardiovascular disease. Alanine aminotransferase predicts the development of type
2 diabetes (DM2) in subjects with the metabolic syndrome. However, the role of
elevated ALT levels in subjects with overt DM2 has yet not been explored.
Materials and methods In a cross-sectional study, 64 normotriglyceridemic
subjects with DM2 were studied with regard to the relation between liver
transaminases with whole-body insulin sensitivity, measured with the euglycemic
hyperinsulinemic clamp and with brachial artery flow-mediated dilation (FMD) as a
marker of endothelial dysfunction.
Results On average, patients were normotriglyceridemic (plasma triglycerides
1.3±0.4 mmol/l) and had good glycemic control (HbA1c 6.2±0.8%). The mean ALT
level was 15.0±7.5 U/l, and the mean aspartate aminotransferase concentration
equaled 10.6±2.6 U/l. Alanine aminotransferase levels were negatively associated
with whole-body insulin sensitivity as well as with FMD (both P = 0.03, in
multivariate analyses; regression coefficients beta [95%CI]: -0.76 [-1.4 to –0.08] and
–0.31 [-0.58 to –0.03] respectively).
Conclusions In metabolically well-controlled patients with DM2, ALT levels are
related to decreased insulin-sensitivity and an impaired conduit vessel vascular
function.
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115
INTRODUCTION
Non-alcoholic fatty liver disease (NAFLD) includes a wide spectrum of liver
pathology, ranging from fatty liver alone to the more severe non-alcoholic
steatohepatitis [1]. NAFLD develops in people who are not heavy alcohol
consumers, and usually has a benign clinical course [2,3]. Recent studies have
shown associations between NAFLD and obesity, dyslipidemia and insulin
resistance, all of which are features of the so-called metabolic syndrome [3,4].
People with the metabolic syndrome are at increased risk of type 2 diabetes (DM2)
and cardiovascular and all-cause mortality [5]. In the majority of cases, NAFLD
causes asymptomatic elevation of liver enzyme levels [6], including alanine
aminotransferase (ALT) and aspartate aminotransferase (AST), which are sensitive
indicators of hepatocellular injury [7]. The former is primarily found in the liver, the
latter is also found in other tissues and is a less specific marker of liver integrity
[7,8]. Recent studies have demonstrated that elevated levels of liver enzymes, and
in particular ALT, are associated with obesity [9], insulin resistance and DM2 [10].
Recent prospective studies found that levels of ALT predicted incident DM2,
independently of the classical risk factors [11] or changes in obesity [10].
Endothelial dysfunctions are an early event in the development of atherosclerosis
[12,13]. One of these functions, i.e. the nitric oxide or endothelium-dependent
regulation of vascular tone can be assessed by measuring vasodilatation of the
brachial artery in response to ischemia-induced increases in blood flow (flow-
mediated dilation; FMD) [14]. The brachial FMD is closely correlated to coronary
endothelial function [15], and endothelial dysfunction predicts future
cardiovascular events [16].
To date, there are no studies in subjects with DM2, assessing the association
between liver enzyme plasma levels and the severity of insulin resistance, and
vascular endothelial dysfunction. The aim of the present study was to assess the
association between alanine aminotransferase (ALT) and aspartate
aminotransferase (AST) levels with whole-body insulin sensitivity and brachial-artery
FMD in normotriglyceridemic subjects with DM2.
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MATERIALS AND METHODS
Subjects
Patients with DM2 were recruited from shared care projects in Amsterdam and
Hoorn, and via local newspaper advertisements. The following inclusion criteria
were used: age between 40 and 70 years, DM2 as diagnosed by a physician
according to the WHO-criteria [17], HbA1c <8.5% and fasting triglyceride
concentrations as defined as a mean value from three measurements of <2.2
mmol/l, current smoking was allowed. Pregnancy, the premenopausal state
(defined as the presence of menstrual bleeds), the use of lipid lowering drugs or
insulin, alcohol abuse (see below), and liver impairment (serum ALT>70 U/l, serum
AST>50 U/l) or renal impairment (serum creatinine>130 µmol/l or creatinine
clearance<50 ml/min) were exclusion criteria. Written informed consent was
obtained from all subjects and the Ethics Committee of VU University Medical
Center approved the study-protocol. In total, 293 subjects were invited for
screening, of whom 93 fulfilled the inclusion criteria.
Study Design
The study was performed between 1997 and 1999 and the values for the above
mentioned inclusion and exclusion criteria are those that were in accordance with
the prevailing guidelines and generally accepted at that time. Patients were seen on
6 occasions during a period of 14 weeks. The study consisted of one screening visit
(at week –1) to assess eligibility, 3 follow-up visits (at week 0, 6 and 12), during
which dietary intake was assessed and anthropometry and blood collections were
performed (see below). Finally, patients underwent a hyperinsulinemic euglycemic
clamp and a vascular ultrasound examination during 2 separate visits (between
week 12 and 14).
Alcohol intake
Subjects were asked to record their dietary intake from one day in a week
(morning, afternoon and evening), during a total of 5 occasions, with an interval of
2 weeks between the recorded days. From these recordings, the mean alcohol
intake was calculated as an average of the five recorded days. Subjects with a mean
alcohol intake of more than 40 gram per day were excluded from further analyses.
ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION
117
Hyperinsulinemic-euglycemic clamps
Insulin sensitivity was measured using a hyperinsulinemic euglycemic clamp
technique as previously described [18]. In short, following an overnight fast,
subjects were placed in the semi-supine position and two polytetrafluoroethylene
cannulas (Venflon, Viggo Helsiborg, Sweden) were inserted into the antecubital or
antebrachial veins of each arm, one for intermittent blood glucose sampling and
the other for the infusion of the glucose and insulin solutions. A volume of 0.5 mL
insulin (100 U mL-1; Velusolin, Novo Nordisk, Bagsvaerd, Denmark) was diluted to
50 mL with 45 mL 0.9% sodium chlorine solution and 4.5 mL human albumin (20%;
CLB, Amsterdam, The Netherlands). Arterialized blood glucose levels were
measured every 5 minutes with an automated glucose oxidase method (Yellow
Springs Instruments, Yellow Springs, OH, USA) after the start of the insulin infusion.
The initial insulin infusion rate (120 mU/m2/min) was decreased to 40 mU/m2/min
once the glucose level was 5.0 mmol/l, and thereafter the glucose 20% infusion
rate was adjusted to maintain this blood glucose level for 2 hours. The M-value,
expressed as glucose uptake in mg/m2/min, was calculated from the average
glucose infusion rate during the last 60 minutes and expressed per unit of plasma
insulin concentration (M/I-value).
Vascular Ultrasound
A non-invasive ultrasound method for measuring FMD of the brachial artery as
described previously was used [19]. In short, vessel diameter of the right brachial
artery was measured with an ultrasound system (Ultramark IV, ATL, Bothell, WA,
USA) in combination with a vessel movement detector system (Wall Track System,
Neurodata, Bilthoven, The Netherlands). Measurements were carried out by a single
experienced ultrasound operator (RJAMvD) in a quiet room, in fasted subjects after
a 15-minute rest in the supine position. An ischemia-induced increase in blood flow
by releasing a blood pressure tourniquet that had been inflated for 4 minutes at
the fore-arm to 100 mmHg above systolic blood pressure, which results in an
increase in shear stress, was used as the stimulus for FMD. Nitroglycerin (0.4 mg
sublingually) was used to obtain nitroglycerin mediated dilation (NMD). FMD was
calculated as the percent change in diameter post-occlusion of the brachial artery
at 60 seconds relative to the measurement at baseline before blood pressure
tourniquet inflation {[(response x baseline)/baseline]e100}. NMD was calculated as
the percent change in diameter before and 5 minutes after administration of the
nitroglycerin.
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Anthropometric measures and laboratory analysis
A detailed history, in particular regarding alcohol intake and smoking status, was
obtained from each participant. Furthermore height, weight, and waist and hip
circumference were measured with the subjects wearing only light clothes. Waist
circumference was measured at the level midway between the lowest rib margin
and the iliac crest, and hip circumference was measured at the widest level over the
greater trochanters. Body mass index (BMI; weight divided by height squared; kg m-
2) was calculated. Serum cholesterol and HDL-cholesterol were measured with the
CHOD-PAP-method (Roche, Mannheim, Germany). Serum triglycerides were
measured with the GPO-PAP-method (Roche, Mannheim, Germany). LDL-cholesterol
was calculated according to the Friedewald-formula [20]. ALT and AST levels were
determined by enzymatic assays (Roche, Mannheim, Germany). Plasma glucose was
determined with a glucose oxidase method (Granutest, Merck, Darmstadt,
Germany). Glycated hemoglobin (HbA1c) was measured with ion exchange high
performance liquid chromatography (Modular Diabetes Monitoring System, BioRad
Lab, Veenendaal, The Netherlands). Insulin was measured with a double antibody
radio-immunoassay (SP21, Linco Research, St. Louis, USA). Pro-insulin was
measured with an immunometric assay (Dako Diagnostics Ltd., Cambridgeshire,
UK). C-reactive protein (CRP) was measured by an immunoturbidimetric assay (Tina-
quant CRP, Boehringer Mannheim, Mannheim Germany)(reference range: 0-8 mg/l).
Statistical analysis
Analyses were performed by SPSS for Windows 11.0.1 (SPSS Inc. Chicago, IL). Data
are mean (standard deviation) or percentage. Reported values for serum lipids,
glucose, and HbA1c are the mean value of three assessments, which were
performed on the morning of inclusion, on the morning of week 6 and on the
morning of week 12 after an overnight fast; all other laboratory assessments were
determined once. Pearson’s correlation coefficients were calculated to evaluate
relationships between variables. Multiple linear regression analyses were performed
to investigate the associations between ALT as determinant and insulin sensitivity
(M/I-value) and FMD as outcome variables. In the respective regression models for
M/I-value and FMD, adjustments were made for age and sex (Model 1 and Model 4,
respectively). Subsequently, these models were adjusted for AST levels, yielding
Model 2 and Model 5, respectively. Model 3, which had insulin sensitivity as
dependent variable, was Model 2, with additional correction for BMI, since former
studies have shown that obesity was a confounder of the association between ALT
ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION
119
and insulin resistance [9,10]. In Model 6, which had FMD as outcome variable,
Model 5 was adjusted for insulin sensitivity since previous investigations found
insulin sensitivity to be an important determinant of FMD [21]. A two-sided P-value
<0.05 was considered to indicate statistical significance.
RESULTS
Patient characteristics
Subjects with missing data on insulin sensitivity and/or patients with missing data
on FMD were excluded (n=26). Three subjects were excluded because of an alcohol
intake of more than 40 grams per day or missing data on alcohol intake.
Table 7.1. Clinical characteristics
N 64
Men / women 35 / 29
Age (years) 61.8 ± 7.2 (44 – 70)
Duration of DM2 (years) 4.7 ± 5.6 (0.3 – 33.6)
Body mass index (kg/m2) 28.5 ± 5.3
Waist (cm) male / female 101 ± 12.9 / 97.7 ± 13.7
Hypertension (%)* 42.2
Systolic blood pressure (mmHg) 144 ± 17
Diastolic blood pressure (mmHg) 83 ± 8
Use of alcohol (%) 65.6
Alcohol intake (g/d) 10.0 ± 11.2
Cigarette smoking (%) 15.5
C-reactive protein (mg/l) 2.6 ± 2.6 (0.12-11.9)
Values are mean ± SD, percentage or (range)
*Defined as RR > 160/90 mmHg and/or use of anti-hypertensive medication.
Finally, 64 subjects were included in the present analyses and their clinical and
biochemical characteristics are listed in Tables 7.1 and 7.2. On average, patients
were normotriglyceridemic and had good glycemic control. Forty-two subjects were
treated by sulfonylurea drugs, 18 by metformin and 2 used α-glucosidase
inhibitors (or combinations thereof).
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Table 7.2. Metabolic and vascular characteristics
Glucose and insulin metabolism
Fasting glucose concentration (mmol/l) 8.0 ± 1.7
Fasting insulin concentration (pmol/l)* 75.8 ± 43.0
Fasting pro-insulin concentration (pmol/l) 17.8 ± 10.6
M/I-value (mg/m2/min/pmol/l) 0.33 ± 0.17
HbA1c (%) 6.2 ± 0.8
Lipid metabolism
Triglycerides (mmol/l) 1.3 ± 0.4
Total cholesterol (mmol/l) 5.6 ± 0.9
HDL-cholesterol (mmol/l) 1.29 ± 0.3
LDL-cholesterol (mmol/l) 3.7 ± 0.9
Vascular function of the brachial artery
Flow-mediated dilation (%) 6.3 ± 6.0
Nitroglycerin-mediated dilation (%)† 12.4 ± 8.1
Liver enzyme levels‡
Alanine aminotransferase (U/l) 15.0 ± 7.5 (4 - 48)
Aspartate aminotransferase (U/l) 10.6 ± 2.6 (5 - 17)
Values are mean±SD or (range)
*n=62 †n=63 ‡ Reference ranges: ALT<35 U/l; AST<25 U/l
Liver enzyme correlations
Neither ALT nor AST were correlated with the reported amount of alcohol intake
(r=-0.05; P=0.7 and r=-0.11; P=0.4, respectively) or smoking status (r=0.18; P=0.12
and r=0.07; P=0.5, respectively) and additional corrections for alcohol intake or
smoking status did not change the presented correlations. Table 7.3 lists the
correlations between ALT and AST and anthropometric, metabolic and vascular
parameters. ALT, but not AST, was weakly but significantly correlated with waist,
hip, fasting insulin, fasting pro-insulin, M/I-value and NMD. The association
between ALT and FMD tended to be significant (P=0.08). AST was weakly but
significantly correlated with fasting blood glucose levels only.
ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION
121
Table 7.3. Correlations among hepatic enzymes and selected anthropometric,
metabolic and vascular parameters
ALT AST
r P r P
Anthropometry
BMI 0.13 0.31 -0.06 0.63
Waist 0.27 0.03 -0.003 0.98
Hip 0.26 0.03 0.08 0.51
Insulin and glucose metabolism
Fasting glucose 0.03 0.83 -0.27 0.03
Fasting insulin* 0.43 <0.001 0.20 0.12
Fasting pro-insulin 0.39 0.006 0.09 0.49
M/I-value -0.29_ 0.02 -0.08 0.53
Vascular function
FMD -0.22 0.08 -0.06 0.63
NMD -0.27 0.04 -0.16 0.22
r, correlations by Pearson; FMD, flow mediated dilation; NMD, nitroglcyerin mediated dilation. *n=62
Associations of liver enzymes with insulin resistance and endothelial function
Multiple linear regression models were used to study the association of ALT with
M/I-values and FMD. In multiple linear regression analyses, a significant association
of insulin sensitivity with ALT after adjustment for AST (Model 2, Table 7.4) and
adjustment for BMI (Model 3, Table 7.4) was found. The age- and gender adjusted
association of FMD and ALT tended to be significant (P=0.06; Model 4, Table 7.4),
was significant after adjustment for AST levels (P=0.045; Model 5, Table 7.4), and
remained significant after adjustment for insulin sensitivity (P=0.03; Model 6, Table
7.4).
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122
Table 7.3. Correlations among hepatic enzymes and selected anthropometric,
metabolic and vascular parameters
ALT AST
r P r P
Anthropometry
BMI 0.13 0.31 -0.06 0.63
Waist 0.27 0.03 -0.003 0.98
Hip 0.26 0.03 0.08 0.51
Insulin and glucose metabolism
Fasting glucose 0.03 0.83 -0.27 0.03
Fasting insulin* 0.43 <0.001 0.20 0.12
Fasting pro-insulin 0.39 0.006 0.09 0.49
M/I-value -0.29_ 0.02 -0.08 0.53
Vascular function
FMD -0.22 0.08 -0.06 0.63
NMD -0.27 0.04 -0.16 0.22
r, correlations by Pearson; FMD, flow mediated dilation; NMD, nitroglcyerin mediated dilation. *n=62
Table 7.4. Regression models of the associations of ALT with insulin-sensitivity
(M/I-value) and vascular dysfunction (FMD)
Model* b 95% CI P
M/I-value (*100)
Model 1 -0.73 -1.3 to -0.18 0.010
Model 2 -0.99 -1.7 to -0.30 0.006
Model 3 -0.76 -1.4 to -0.08 0.029
FMD
Model 4 -0.20 -0.40 to +0.08 0.060
Model 5 -0.26 -0.52 to -0.006 0.045
Model 6 -0.31 -0.58 to -0.03 0.030
b, unstandardized regression coefficient; CI, confidence interval; FMD, flow-mediated dilation *Model 1: adjusted for age and gender; Model 2: as Model 1 with additional adjustment for AST; Model 3: as
Model 2 with additional adjustment for BMI; Model 4: adjusted for age and gender; Model 5: as Model 4 with
additional adjustment for AST; Model 6: as Model 5 with additional adjustment for insulin sensitivity (M/I-
value)
ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION
123
DISCUSSION We observed that circulating ALT was inversely associated with whole-body insulin
sensitivity, measured with the hyperinsulinemic euglycemic clamp, in
normotriglyceridemic subjects with DM2. Moreover, we demonstrated a negative
association between plasma ALT levels and vascular endothelial function, which
was measured as FMD.
High ALT levels are associated with fatty liver disease and might therefore be
considered as a feature of the metabolic syndrome. Furthermore, elevated ALT, but
not AST levels, were related to hepatic insulin resistance in normoglycemic subjects
at high risk of DM2, and in these subjects, elevated ALT predicted the development
of DM2 [10]. In some studies, the prospective relationship between ALT and DM2
reflected the cross-sectional association with obesity and insulin resistance [9,10],
whereas others found the predictive effect of ALT to be independent of obesity and
body fat distribution [22]. In the present study, the association between ALT and
whole-body insulin sensitivity was independent of BMI.
The pathogenesis of NAFLD is not yet fully understood but oxidative stress and
lipid peroxidation resulting from excessive lipid accumulation (lipotoxicity) are
regarded as important pathophysiological factors [23-25]. Central obesity and the
inappropriate suppression of lipolysis arising from insulin resistance, result in an
increased flux of non-esterified or free fatty acids (FFA) and an increased output of
triglyceride-rich VLDL particles from the liver. Due to the increased triglyceride/FFA
load, in the presence of impaired glucose utilization, non-adipose tissues, including
liver, muscle and the pancreatic β cells, accumulate triglycerides [26]. In these
tissues, intracellular triglyceride overload increases the pool of long-chain acyl Co-
A, thus providing abundant substrate for nonoxidative metabolic pathways and the
formation of toxic metabolites and also causing mitochondrial dysfunction and
subsequent production of reactive oxygen species. These lipid-induced changes
ultimately lead to organ dysfunction and cell apoptosis.
At present there are no studies addressing the value of ALT as a risk marker once
DM2 has developed. In addition, since hypertriglyceridemia is one of the hall-marks
of the metabolic syndrome [27], demonstration of high plasma ALT levels in
normotriglyceridemic DM2 subjects may be of additional value in predicting high
risk of microvascular and/or macrovascular complications. Abnormal liver function
tests are more common in DM than in non-DM populations as well as in subjects
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124
with DM2 than in those with DM1 [28]. In DM1, elevated liver enzymes were
associated with diabetic complications such as retinopathy and neuropathy,
independently of alcohol consumption, BMI and glycemic control [29]. To our
knowledge, at present no such data have been reported for DM2 subjects. Here, we
show an association between ALT levels and impaired endothelial function in
normotriglyceridemic subjects with DM2. Previously, the presence of generalized
endothelial dysfunction in insulin-resistant subjects and patients with DM2 was
proposed to explain the excess cardiovascular morbidity and mortality in these
populations [21]. The mechanisms underlying this endothelial dysfunction include
insulin resistance at the level of the vascular wall, in part due to elevated circulating
FFA levels, causing a reduction in insulin-mediated nitric oxide production, as well
as insulin- resistance associated induction of inflammatory pathways and oxidative
stress [30,31]. Collectively, these mechanisms decrease the bioavailability of NO
leading to loss of the anti-atherogenic properties of the vascular endothelium
including impairment of NO-mediated vasodilatation. Indeed, the significant
association between ALT and FMD did not change after adjustment for insulin
resistance, indicating that the association of ALT with FMD is not only mediated by
insulin resistance but that other factors, including oxidative stress and
inflammation, may also play a role. This latter suggestion is in accordance with a
recent study that demonstrated that ALT is associated with CRP-levels, indicating
that a low-grade inflammatory process is involved in the pathogenesis of NAFLD
[32].
Our study has some limitations. First, the study population consisted of patients
with relatively well-controlled DM2 with regard to glycaemia and triglyceridemia
and therefore, the observed associations may be an underestimation of the
relationships in diabetic subjects with poor metabolic control. Second, we used
elevation of liver enzyme levels as an indicator of NAFLD instead of biopsies, which
are regarded as the gold standard. Third, although we addressed alcohol as major
potential confounder in this study, we could not account for other factors that may
cause liver enzyme elevations, including iron-status [33], infections and/or
hepatotoxic medication [34]. However, we acknowledge that the cut-off value of 40
grams of alcohol per day is slightly higher that the most recently suggested cut-off
value of 20 grams per day for women and 40 grams per day for men [35]. Fourth,
the study had a cross-sectional design and the observed associations cannot be
interpreted as a reflection of causal relationships. Finally, although we assessed
whole-body insulin resistance and not hepatic insulin resistance, the positive
ALANINE AMINOTRANSFERASE, INSULIN RESISTANCE AND ENDOTHELIAL DYSFUNCTION
125
association of ALT with fasting insulin values might indicate that clearance of
insulin in the liver is less effective [36,37].
In summary, in relatively well-controlled patients with DM2, plasma ALT levels are
related to a decline in insulin-sensitivity and an impairment of conduit vessel
endothelial function. The findings might suggest that elevated ALT is a marker of
liver fat also in normotriglyceridemic DM2. Additional prospective studies are
needed to confirm these results in other study populations and further clarify the
underlying mechanisms.
CHAPTER 7
126
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129
Chapter 8
ALANINE AMINOTRANSFERASE PREDICTS CORONARY HEART DISEASE EVENTS: A TEN YEAR FOLLOW-UP OF THE HOORN STUDY
Roger K. Schindhelm1
Jacqueline M. Dekker2
Giel Nijpels2,3
Lex M. Bouter2
Coen D. A. Stehouwer4
Robert J. Heine1,2
Michaela Diamant1
From the Departments of 1Endocrinology/Diabetes Center, 3General Practice, the
2EMGO Institute, VU University Medical Center, Amsterdam, The Netherlands, and
the 4Department of Internal Medicine, Academic Hospital Maastricht, Maastricht,
The Netherlands.
Atherosclerosis 2007;191:391-6
Copyright © 2006 Elsevier Ireland Ltd.
Reprinted with permission from Elsevier.
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130
ABSTRACT
Alanine aminotransferase (ALT) is a marker of nonalcoholic fatty liver disease
(NAFLD) and predicts incident type 2 diabetes mellitus (DM2). Recently, ALT was
shown to be also associated with endothelial dysfunction and carotid
atherosclerosis. We studied the predictive value of ALT for all cause mortality,
incident cardiovascular disease (CVD) and coronary heart disease (CHD) events in a
population-based cohort of Caucasian men and women aged 50 to 75 years, at
baseline. The ten-year risk of all-cause mortality, fatal and non-fatal CVD and CHD
events in relation to ALT was assessed in 1439 subjects participating in the Hoorn
study, using Cox survival analysis. Subjects with prevalent CVD/CHD and missing
data were excluded. As compared with the first tertile, the age- and sex-adjusted
hazard ratios (95 percent confidence intervals) for all-cause mortality, CVD events
and CHD events were 1.30 (0.92-1.83), 1.40 (1.09-1.81) and 2.04 (1.35-3.10),
respectively, for subjects in the upper tertile of ALT. After adjustment for
components of the metabolic syndrome and traditional risk factors, the association
of ALT and CHD events remained significant for subjects in the third relative to
those in the first tertile, with a hazard ratio of 1.88 (1.21-2.92) and 1.75 (1.12-
2.73), respectively. In conclusion, the predictive value of ALT for coronary events,
seems independent of traditional risk factors and the features of the metabolic
syndrome in a population based cohort. Further studies should confirm these
findings and elucidate the pathophysiological mechanisms.
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
131
INTRODUCTION
Recently, the role of non-alcoholic fatty liver disease (NAFLD) in the pathogenesis of
type 2 diabetes mellitus (DM2) has gained much interest. Several studies have
demonstrated that NAFLD is associated with the components of the metabolic
syndrome (MetS) and DM2 [1,2], and fatty liver is considered to be the hepatic
component of the MetS [3,4]. Circulating concentrations of the liver transaminases,
alanine aminotransferase (ALT) and aspartate aminotransferase (AST) have been
used as markers of NAFLD. Of these two liver enzymes, ALT appears to be the best
marker of liver fat accumulation and is positively correlated with liver fat measured
by magnetic resonance (MR) proton spectroscopy [5]. In cross-sectional and
prospective studies ALT is associated with DM2 [6,7]. Collectively, these data
suggest that hepatic steatosis might play a role in the pathogenesis of DM2, most
probably by contributing to the development of hepatic insulin resistance resulting,
among others, in increased hepatic gluconeogenesis and overproduction of
triglyceride-rich lipoproteins. Subjects with features of the MetS are not only at
increased risk of DM2, but also at risk of developing cardiovascular (CVD) and
coronary heart disease (CHD) [8].
In view of these observations, and the recently described associations between
both NALFD or its marker ALT and vascular structural and functional properties
[9,10], we studied the predictive value of ALT for all-cause mortality, CVD and CHD
events in a population-based study of diabetes and related complications in
Caucasian men and women aged 50 to 75 years.
METHODS
Study population
Subjects were participants in the Hoorn Study, a prospective population-based
cohort study of glucose metabolism and diabetes complications. The study
population and research design have been described in detail previously [11]. In
short, in 1989, a random sample of all men and women aged 50 to 75 was taken
from the municipal registry of the medium-sized town of Hoorn in The Netherlands.
Of the 3552 individuals, who where invited to take part in the study, 2540 agreed
to participate (71.5%). Baseline data, including a 75-g oral glucose tolerance test
(OGTT), were collected from October 1989 through February 1992. After excluding
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132
56 non-Caucasians the cohort consisted of 2484 men and women. For the present
study we excluded 612 subjects with missing information on morbidity during
follow-up, because they did not provide written permission to accesses their
medical records or they had moved out of Hoorn. Subjects with prevalent CVD/CHD
and missing data on variables at baseline were excluded for the analysis, resulting
in 1439 subjects. The Ethical Review Committee of VU University Medical Centre
approved the Hoorn Study and written informed consent was obtained from all
participants.
Baseline Examination
Serum ALT enzyme activity was measured according to the method of the
International Federation of Clinical Chemistry from 1985, and expressed as U/L
[12]. Since the reference range and cut-off values for ALT are controversial [13,14],
we divided ALT into tertiles, instead of using a cut-off value to define abnormality.
The OGTT was performed between 08.00 and 10.00h and subjects were asked not
to drink alcohol from 17.00h and to fast (except for drinking water) from 22.00h
the evening before the measurements. Blood samples were collected before and 2h
after ingestion of the glucose load. Plasma glucose was measured with the glucose
dehydrogenase method (Merck, Darmstadt, Germany). The glucose tolerance
categories were defined according to the 1999 criteria of the World Health
Organization. Immuno-specific insulin was measured in serum with a double-
antibody RIA (antibody SP21, Linco Research, St. Louis, USA). Insulin resistance was
estimated by the homeostasis model assessment for insulin resistance (HOMA-IR),
calculated as fasting insulin (in IU/l ) x fasting glucose (mmol/l)/22.5 [15]. Glycated
haemoglobin (HbA1c) was measured with ion exchange high performance liquid
chromatography (Modular Diabetes Monitoring System, BioRad Lab, Veenendaal,
The Netherlands; reference range 4.3-6.1%). Fasting triglycerides, total and HDL-
cholesterol were determined in serum by enzymatic techniques (Boehringer
Mannheim, Germany). LDL-cholesterol was calculated according to the Friedewald-
formula, except in subjects with triglycerides>4.5 mmol/l. Seated systolic and
diastolic blood pressure was measured at the right arm after a 5-minute rest with a
random-zero mercury sphygmomanometer (Hawksley-Gelman, Lancing, UK). The
average of two measurements was used for the analyses. Weight and height were
measured in subjects wearing light clothes only, and the body mass index (BMI)
was calculated as weight divided by height squared (kg/m2). Waist circumference
was measured at the midpoint between the lowest rib and the iliac crest, hip
circumference was measured at the widest level over the greater trochanters, and
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
133
the mean value of two measurements was used in the analysis. Information on
alcohol intake was assessed by a validated semi-quantitative food frequency
questionnaire [16]. Subjects were asked about their drinking habits and the weekly
number of glasses of alcoholic drinks consumed. This information was converted to
alcohol intake (g/day) using a computerized version of the Dutch Food
Composition Table. Physical activity was assessed by questionnaire as described
before [11]. Information on history of cardiovascular disease was assessed by the
Rose questionnaire. Smoking status was assessed by questionnaire and categorized
as never, former and current smoker.
Follow-up
The registration of follow-up data of the Hoorn Study participants is regularly
updated using the municipal register of the city of Hoorn providing information
about the vital status of the participants. Information of causes of death and non-
fatal events were obtained from medical records of general practitioners and from
the local hospital. Causes of death were coded according to the ICD-9. CVD was
defined as documented angina pectoris (chest pain followed by coronary artery
bypass surgery or angioplasty, or in the presence of >50 % stenosis or ECG
changes, i.e. ST-segment elevation ≥ 1 mm or ST-segment depression ≥0.5 mm, or
positive exercise test), myocardial infarction (in the presence of at least 2 of the
following: typical pain, elevated enzymes and/or ECG changes), congestive heart
failure (in the presence of at least 2 of the following: shortness of breath,
cardiomegaly, dilated neck veins or 1 of the former in the presence of edema or
tachycardia), stroke or transient ischaemic attack (sudden onset of symptoms,
neurological symptoms or change of consciousness), peripheral disease (both
symptomatic and a-symptomatic: by procedure or typical pain accompanied by
stenosis or ankle arm blood pressure ratio <0.9 or positive vascular stress test). In
fatal cases, CVD was defined with ICD codes 390-459 (Diseases of the circulatory
system) or 798 (sudden death, cause unknown), because sudden death in general
is of CVD origin [17]. CHD was defined as documented myocardial infarction and
angina pectoris, with the aforementioned criteria. Fatal CHD cases were defined
with ICD coded 410-414 (ischaemic heart disease). Follow-up time was calculated
as the time between the date of the baseline examination and the date of the first
event or January 1, 2000.
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134
Statistical analysis
Analyses were performed with SPSS 11.0.5 software. Subjects were categorized
according to ALT tertiles. Differences between tertiles were tested with analysis of
variance (ANOVA) for continuous variables and by the χ-square test for proportions.
Hazard ratios and 95% confidence intervals of baseline ALT for all-cause mortality,
incident CVD and CHD events were estimated from multivariable Cox proportional
hazard analysis. The first models were adjusted for age and sex only. In the
subsequent models we added the possible mediating or confounding factors one
by one. In the final models, beside adjustments for age, sex, alcohol-intake,
smoking and physical activity, we corrected for the traditional CVD risk factors, i.e.
glucose tolerance status, systolic blood pressure, HbA1c, LDL-cholesterol, BMI
(Table 2, Model 2) as well as for the components of the metabolic syndrome, as
defined by the Adult Treatment Panel III (NCEP) (Table 2, Model 3), respectively.
Two-sided P values < 0.05 were considered to indicate statistical significance.
RESULTS
Baseline characteristics of the participants
The mean age of the 1439 participants (651 men and 788 women) was 60.9 (7.2)
years. Table 8.1 shows the baseline characteristics of the participants divided into
ALT tertiles. Subjects in the highest ALT tertile had a higher BMI, as well as waist
and hip circumference. Also, they had higher fasting and 2 hour post-load plasma
glucose levels, fasting insulin levels, total cholesterol concentrations, and higher
blood pressure. LDL-cholesterol tended to be different across the tertiles, whereas
HbA1c was similar in all tertiles. The highest ALT tertile consisted of a higher
number of subjects with impaired glucose metabolism and DM2.
Association of ALT and all-cause mortality, CVD and CHD events
During the 10-year follow-up, 174 of the total of 1439 subjects died, 355 CVD
events occurred of which 75 where fatal and 129 CHD events occurred of which 16
were fatal. Table 8.2 shows the hazard ratios of the third relative to the first ALT
tertile for all-cause mortality, CVD and CHD events. The age and sex adjusted risk
for all-cause mortality, CVD events and CHD events were 1.30 (0.92-1.83), 1.40
(1.09-1.81) and 2.04 (1.35-3.10), respectively, for the subjects in the upper ALT
tertile compared to those in the first tertile. The association between ALT and all-
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
135
cause mortality was not statistically significant and the association could be
explained by the traditional CVD risk factors and/or the components of the
metabolic syndrome (Table 8.2, Model 2 and Model 3). The significant association
of ALT with CVD events disappeared after adjustment for the classical CVD risk
factors and/or the components of the metabolic syndrome (Table 8.2, Model 2 and
3). Only the association of ALT and CHD events remained statistically significant
after adjustment for CVD risk factors as well as for the features of the metabolic
syndrome. The association of ALT and CHD events was explained by BMI, waist and
to a lesser extent by total cholesterol and triglycerides. Fasting insulin and HOMA-
IR did not substantially alter any of the associations presented (Table 8.2).
Additional adjustment for baseline HDL in model 2 and additional adjustment for
baseline LDL in model 3 (both HDL and LDL in one model) did not change the
presented associations of ALT with CHD (data not shown).
ALT and CHD events: the effect of gender, alcohol intake and diabetes
In order to establish the robustness of the observed associations, we performed
additional analyses, focusing on the role of gender, alcohol intake and DM2.
Stratified analysis for men and women separately yielded similar results (data not
shown). Since DM2 carries a greater risk of CHD and CVD, and its association with
elevated ALT, we subsequently excluded the 119 DM2 subjects from the analysis,
which resulted in an attenuation of the association of ALT with CHD events. After
adjustment for traditional CVD risk factors, the association became borderline
significant (HR 1.65 [0.97-2.79]), whereas it remained significant after adjustment
for the components of the metabolic syndrome (HR 1.78 [1.05-3.04]).
In order to detail the effect of alcohol on the association of ALT with CHD-events,
we excluded 232 subjects (77 women and 155 men) who had an average daily
alcohol intake of more than 20 g. Exclusion of these subjects attenuated the
association, however, ALT remained a predictor for CHD after adjustment for
classical CVD risk factors (HR 1.72 [1.02-2.90]) and for the components of the
metabolic syndrome (HR 1.67 [0.99-2.80]).
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136
Table 8.1. Baseline characteristics of the participants (N=1439) stratified by ALT
tertiles*
Variable Alanine aminotransferase activity (U/L)
Mean (SD), median (interquartile
range), or percentage
1
N=551
2
N=420
3
N=468
P
ALT (U/L)† 12 (1-14) 17 (15-20) 26 (21-143)
Age (years) 62.9 (7.4) 60.7 (7.1) 59.7 (6.8) <0.001
Male (%) 30.9 48.6 59.2 <0.001
Body mass index (kg/m2) 25.7 (3.4) 26.1 (3.2) 27.4 (3.3) <0.001
Waist (cm) 87.0 (10.1) 89.6 (10.3) 94.2 (10.1) <0.001
Hip (cm) 101.3 (6.7) 100.9 (6.2) 102.9 (6.4) <0.001
Fasting glucose (mmol/l) 5.5 (1.1) 5.7 (1.4) 5.9 (1.5) <0.001
2h post-load glucose (mmol/l) 5.6 (2.3) 5.8 (3.0) 6.5 (3.4) <0.001
Fasting insulin (pmol/l) 79.8 (48.2) 82.6 (49.9) 96.1 (58.0) <0.001
HbA1c (%) 5.4 (0.7) 5.5 (0.8) 5.5 (0.9) 0.15
Impaired glucose metabolism (%) 12.0 15.0 21.6 <0.001
Type 2 diabetes mellitus (%) 6.0 7.6 8.2 0.03
Plasma lipids (mmol/l)
Total-cholesterol 6.6 (1.1) 6.5 (1.1) 6.7 (1.2) <0.001
LDL-cholesterol 4.6 (1.0) 4.5 (1.0) 4.7 (1.1) 0.051
HDL-cholesterol 1.38 (0.36) 1.38 (0.37) 1.30 (0.36) <0.001
Triglycerides 1.4 (0.9) 1.4 (0.7) 1.8 (1.1) <0.001
Systolic BP (mmHg) 133 (20) 134 (20) 137 (20) <0.001
Diastolic BP (mmHg) 80 (11) 82 (10) 84 (10) <0.01
Smoking, current (%) 35.2 27.9 26.3 <0.001
Alcohol intake (g/day) 2.2 (0-10) 5.0 (0-15) 10 (2-17) <0.01
*Abbreviations: ALT, alanine aminotransferase; SD, standard deviation; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein
†median (minimum-maximum value)
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
137
Table 8.2. Hazard ratios of the second and third compared to the first tertile of ALT
in relation to all-cause mortality, CVD and CHD events*
Model All-cause
mortality
CVD-events CHD-events
HR (95%CI) HR (95%CI) HR (95%CI)
Events Tertile 174 355 129
Model 1† 2nd 0.74 (0.49-1.10) 1.02 (0.78-1.33) 0.94 (0.57-1.53)
3rd 1.30 (0.92-1.83) 1.40 (1.09-1.81) 2.04 (1.35-3.10)
Model 1 + alcohol-intake 2nd 0.74 (0.50-1.10) 1.02 (0.78-1.33) 0.93 (0.57-1.52)
3rd 1.30 (0.92-1.84) 1.39 (1.08-1.80) 2.03 (1.38-3.09)
Model 1 + BMI 2nd 0.72 (0.48-1.08) 0.98 (0.75-1.28) 0.90 (0.55-1.47)
3rd 1.23 (0.85-1.76) 1.27 (0.98-1.65) 1.82 (1.18-2.81)
Model 1 + waist 2nd 0.70 (0.47-1.05) 0.96 (0.73-1.26) 0.89 (0.55-1.46)
3rd 1.15 (0.80-1.65) 1.24 (0.96-1.62) 1.80 (1.17-2.77)
Model 1 + triglycerides 2nd 0.73 (0.49-1.09) 1.02 (0.78-1.33) 0.97 (0.57-1.53)
3rd 1.24 (0.87-1.75) 1.34 (1.04-1.74) 1.97 (1.25-2.99)
Model 1 + total cholesterol 2nd 0.74 (0.49-1.09) 1.02 (0.78-1.32) 0.92 (0.57-1.51)
3rd 1.27 (0.89-1.80) 1.36 (1.05-1.75) 1.89 (1.25-2.88)
Model 1 + fasting glucose 2nd 0.67 (0.45-1.01) 0.96 (0.74-1.26) 0.92 (0.56-1.51)
3rd 1.21 (0.79-1.60) 1.25 (0.97-1.62) 1.98 (1.30-3.02)
Model 1 + fasting insulin 2nd 0.72 (0.48-1.07) 1.03 (0.79-1.34) 0.95 (0.58-1.56)
3rd 1.29 (0.91-1.83) 1.41 (1.09-1.82) 2.06 (1.35-3.14)
Model 1 + HOMA-IR 2nd 0.71 (0.47-1.05) 1.01 (0.78-1.32) 0.95 (0.58-1.56)
3rd 1.20 (0.85-1.72) 1.34 (1.03-1.74) 2.02 (1.32-3.10)
Model 2‡ 2nd 0.75 (0.50-1.12) 0.99 (0.76-1.30) 0.89 (0.54-1.47)
3rd 1.21 (0.83-1.76) 1.25 (0.96-1.64) 1.75 (1.12-2.73)
Model 3§ 2nd 0.71 (0.48-1.06) 0.99 (0.76-1.20) 0.93 (0.57-1.52)
3rd 1.10 (0.77-1.61) 1.22 (0.94-1.60) 1.88 (1.21-2.92)
*Abbreviations: ALT, alanine aminotransferase; CVD, cardiovascular disease; CHD, coronary heart disease; HR,
hazard ratio; CI, confidence interval; BMI, body mass index; HOMA-IR, homeostasis model assessment for
insulin resistance
†Model 1: adjusted for age and sex
‡Model 2: adjusted for age, sex, alcohol-intake, smoking, physical activity, glucose tolerance status, systolic
blood pressure, HbA1c, LDL-cholesterol, BMI
§Model 3: adjusted for age, sex, alcohol-intake, smoking, physical activity, waist, triglycerides, systolic blood
pressure, fasting glucose, HDL-cholesterol
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DISCUSSION
The main novel finding of this population-based study with well-documented
characterization and follow-up of the participants, was a significant prospective
association of ALT with CHD events, independent of the traditional CVD risk
factors, such as systolic blood pressure, HbA1c, LDL-cholesterol and BMI, and
independent of the components of the NCEP-defined metabolic syndrome. In
addition we confirmed the cross-sectional association of ALT, a marker of NAFLD,
with components of the MetS, including measures of obesity, fasting glucose and
lipids. No significant associations were found between ALT and CVD events or all-
cause mortality, the latter observation is in accordance with a previous study that
found no significant association of ALT with all-cause mortality [18]. The reason
that we did not demonstrate a significant association of ALT and CVD events may
be explained by that fact that the assessment of non-fatal incident CVD may be
more subject to non-differential misclassification than non-fatal incident CHD
events, thus leading to an underestimation of the true associations. Our study is, to
our best knowledge, the first to demonstrate a prospective relation of ALT with
CHD events and extends the results from cross-sectional studies demonstrating
correlations of ALT with CHD risk factors, including the components of the MetS
[9,19]. Bruckert et al found that elevated ALT was associated with CHD risk factors,
including elevated blood pressure, total cholesterol and triglyceride concentrations
in 8,501 hyperlipidemic subjects [19]. We recently reported a positive association
between ALT and endothelial dysfunction measured as brachial flow mediated
vasodilatation. This association was independent of whole-body insulin sensitivity,
indicating that other mechanisms besides insulin resistance, such as inflammation
and/or oxidative stress might contribute to this association [9]. This is in
accordance with the data from our present study, demonstrating that adjustment
with HOMA-IR did not change the age and sex adjusted models to a significant
extent. It should be noted that, although HOMA-IR is a validated surrogate measure
for whole-body insulin sensitivity, it may not be a specific marker of hepatic insulin
resistance. Targher et al showed that healthy male volunteers with ultrasound
documented NAFLD had an increased cIMT relative to those without NAFLD [20].
Since this relation was largely explained by the amount of visceral abdominal fat,
adipose-tissue derived adipocytokines were implicated as a link between fatty liver
and atherosclerosis.
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
139
Taken together, three mechanisms, which all seem interrelated, may explain the
association between ALT and the risk of coronary atherosclerosis. First, NALFD,
often represented by its marker ALT, are linked with both hepatic and systemic
insulin resistance and various components of the MetS [7,9]. As shown in recent
reports form different populations, adults with the MetS are at increased risk of
CHD, CVD and all-cause mortality [8,21]. Interestingly, however, adjustment for the
components of the MetS did not blunt the association of ALT and CHD, indicating
other contributing mechanisms. In addition, NAFLD is often referred to as the
hepatic component of the MetS, and adjustment for the (other) components of the
MetS may lead to an over adjustment in the association of ALT and CHD, since it is
not yet fully clear whether the other components are cause, consequence or
intermediate factors in the pathophysiology of NAFLD and CHD. Second, the low-
grade systemic and hepatic inflammatory state may link elevated ALT to a high risk
of CHD. In the normal liver, the production of cytokines is absent or minimal
(insignificant), however, various stimuli such as reactive oxygen species, may
induce the production of cytokines, such as tumour-necrosis factor-alpha and
interleukin-6 [22], leading to inflammation and fibrosis. In addition to hepatic low-
grade inflammation, adipose tissue compartments, both in healthy and in obese
subjects are a major source of these cytokines. These cytokines may further
amplify the inflammatory cascade by stimulating hepatic CRP production. A recent
study demonstrated higher CRP levels in subjects with elevated ALT compared to
subjects with normal ALT, even after adjustment for major confounding factors
[23]. Indeed, CRP may be important in linking NAFLD to atherosclerosis since an
elevated plasma CRP level is by now established as an independent risk factor of
CHD and CVD events [24,25]. Third, elevation of ALT in response to fatty
infiltration of the liver may similarly reflect excessive fat deposition in other organs
such as the myocardium and the skeletal muscle. In these non-adipose tissues,
intracellular triglyceride overload increases the pool of long-chain acyl Co-A, thus
providing abundant substrate for non-oxidative metabolic pathways and the
formation of toxic metabolites and also causing mitochondrial dysfunction and
subsequent production of reactive oxygen species and ultimately leading to organ
dysfunction and cell apoptosis [26].
A number of potential limitations of this study should be considered. The
population consisted of Caucasian individuals aged 50 to 75, and caution should
be exercised to generalize our findings to other populations. We excluded subjects
with missing information on nonfatal disease because they did not give permission
to access their hospital records. To study the possibility of selection bias, we
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140
repeated the analyses for all-cause mortality using the data from the entire original
Hoorn Study cohort, without prevalent CVD/CHD (n=2007), and the estimates were
almost identical (data not shown). We used, similar to others, ALT levels as a
marker of NAFLD instead of imaging or biopsies, the latter being regarded as the
gold standard. Since serum ALT shows a strong correlation with biopsy proven fatty
liver disease in patients and with liver triglyceride content, measured by MRI/MRS
[27,28], its use as a marker of NAFLD in epidemiological studies seems
appropriate. Although we addressed alcohol as an important confounder in our
study, we could not account for other factors that might have caused a rise in ALT,
such as viral hepatitis infections or hepatotoxic drugs. We did not assess the
association of other liver enzymes such as AST and gamma-glutamyl transferase
with outcome, as data on these markers were not available in the Hoorn Study
cohort. Finally, we did not measure CRP at baseline in the Hoorn Study, since low
grade inflammation may be a contributing mechanism in the association of ALT
and CHD, future studies should address this issue.
The results of the present study show that ALT predicts CHD events, independent
of traditional CVD risk factors and the components of the metabolic syndrome.
Further studies are warranted to confirm these findings and to elucidate the
pathophysiological mechanisms.
ALANINE AMINOTRANSFERASE AND CORONARY HEART DISEASE EVENTS
141
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143
Chapter 9.1
ALANINE AMINOTRANSFERASE AND THE 6-YEAR RISK OF THE METABOLIC SYNDROME IN CAUCASIAN MEN AND WOMEN:
THE HOORN STUDY
Roger K. Schindhelm1
Jacqueline M. Dekker2
Giel Nijpels2, 3
Lex M. Bouter2
Coen D. A. Stehouwer4
Robert J. Heine1,2
Michaela Diamant1
From the Departments of 1Endocrinology/Diabetes Center, 3General Practice, the
2EMGO Institute, VU University Medical Center, Amsterdam, The Netherlands, and
the 4Department of Internal Medicine, Academic Hospital Maastricht, Maastricht,
The Netherlands.
Diabetic Medicine 2007;24:430-5.
Reprinted with permission from Blackwell Publishing Ldt.
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ABSTRACT
Aims To study the association between alanine aminotransferase (ALT) and the 6-
year risk of the metabolic syndrome in a population-based study in Caucasian men
and women.
Methods The association of ALT with the 6-year risk of the metabolic syndrome in
1,097 subjects, aged 50-75 years, was assessed in the Hoorn Study with logistic
regression analysis. Subjects with the metabolic syndrome at baseline, defined
according to the Adult Treatment Panel III of the National Cholesterol Education
Program, were excluded.
Results After 6.4 (range: 4.4-8.1) years of follow-up, 226 subjects (20.6%) had
developed the metabolic syndrome. The odds ratio (95% confidence interval) for
developing the metabolic syndrome, adjusted for age, sex, alcohol-intake and
follow-up duration was 2.25 (1.50-3.37) for subjects in the upper tertile compared
to those in the lower tertile of ALT. This association persisted after additional
adjustment for all the baseline metabolic syndrome features [1.62 (1.02-2.58)].
Among the individual components of the metabolic syndrome, ALT was significantly
associated only with fasting plasma glucose at follow-up.
Conclusions These data suggest that ALT is associated with risk of the metabolic
syndrome in a general population of middle-aged Caucasian men and women,
further strengthening the role of ALT as an indicator for future metabolic
derangement. These findings warrant further studies to elucidate the role of non-
adipose tissue fat accumulation in the pathogenesis of metabolic syndrome related
complications.
ALANINE AMINOTRANSFERASE AND THE METABOLIC SYNDROME
145
INTRODUCTION
The metabolic syndrome, characterised by abdominal obesity, hyperglycaemia,
hypertension and dyslipidaemia, confers a high risk for the development of type 2
diabetes mellitus (DM2) [1,2] and cardiovascular disease (CVD) [3]. Individuals with
DM2 or the metabolic syndrome have a higher prevalence of elevated liver
enzymes, including alanine aminotransferase (ALT) [4,5]. Several cross-sectional
studies have demonstrated associations of ALT with individual components of the
metabolic syndrome, including obesity and dyslipidaemia [6,7].
To date, a limited number of studies have addressed the prospective relation of
liver enzymes with the future risk of the metabolic syndrome. Nakanishi et al found
that of the liver enzymes studied, ALT was associated with risk of metabolic
syndrome, but this study was limited to middle-aged Japanese men and used body
mass index (BMI) instead of waist circumference to define the metabolic syndrome
[8]. Hanley et al studied the relation of four different liver enzymes (including ALT)
with the development of the metabolic syndrome in a multi-ethnic cohort and
demonstrated that ALT was positively associated with risk of the metabolic
syndrome [9]. In a Japanese cohort of male and female health care employees,
Suzuki et al studies the sequential order of elevated transaminases (ALT and
aspartate aminotransferase) with feature of the metabolic syndrome, and found
that elevated transaminases are preceded by weight gain, while the other feature
are followed by elevated transaminases [10]. To date, no study has assessed the
prospective relation of ALT and the metabolic syndrome in a population-based
study in Caucasian men and women.
In the present study, we addressed the prospective association of ALT with the 6-
year risk of the metabolic syndrome, defined according to the Adult Treatment
Panel III of the National Cholesterol Education Program, and with the individual
components of the metabolic syndrome in a population-based study of glucose
tolerance and related complications in Caucasian men and women aged 50-75
years at baseline.
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MATERIALS AND METHODS
Study population and follow-up
The subjects were participants in the Hoorn Study, a prospective population-based
cohort study of glucose metabolism and diabetes complications [11]. In brief, in
1989, a random sample of all man and women aged 50 to75 was taken from the
municipal registry of the town of Hoorn in The Netherlands. Of the 3,552
individuals who where invited to take part in the study, 2,540 agreed to participate
(71%). Baseline data were collected from October 1989 through February 1992.
After excluding non-Caucasians (n=56) the study cohort consisted of 2,484 men
and women. Between January 1996 and December 1998, 2,086 participants were
invited to take part in a follow-up examination, the other members of the original
study population were not invited because of logistical reasons (n=140), or because
they died (n=150) or had moved out of Hoorn (n=108). A total of 1,513 subjects
(72.5%) took part in the follow-up measurements [12]. Metabolic syndrome at
baseline and at follow-up was defined according to the Adult Treatment Panel III of
the National Cholesterol Education Program, i.e. three or more of the following:
fasting glucose ≥ 6.1 mmol/l, HDL cholesterol <1.0 mmol/l (men) or <1.3 mmol/l
(women), triglycerides ≥ 1.7 mmol/l, waist-circumference ≥ 102 cm (men) or ≥ 88
cm (women) and hypertension ≥130/85 mmHg [13]. For the present study, subjects
with the metabolic syndrome and/or DM2 at baseline were excluded (n=329).
Furthermore subjects with missing data on alcohol intake, missing data on ALT
enzyme activity, insulin, smoking status and physical activity at baseline were also
excluded (n=22). Finally, subjects with missing data to define the metabolic
syndrome at follow-up (n=63) and subjects with ALT levels > 2.5 times of the upper
level of the reference value (n=2) were also excluded, the latter to reduce
confounding due to subjects with ALT elevation caused by viral or toxic agents,
resulting in 1,097 evaluable subjects. The Ethical Review Committee of VU
University Medical Centre approved the Hoorn Study and written informed consent
was obtained from all participants.
Measurements
Fasting blood samples were collected after an overnight fast from 20.00 hours the
evening before. Serum ALT enzyme activity was measured according to the 1985
method of the International Federation of Clinical Chemistry [14]. Glucose was
measured in plasma with the glucose dehydrogenase method (Merck, Darmstadt,
ALANINE AMINOTRANSFERASE AND THE METABOLIC SYNDROME
147
Germany) at the baseline measurement and with the hexokinase method
(Boehringer Mannheim, Germany) at the follow-up measurement. Immuno-specific
insulin was measured in serum with a double-antibody RIA (antibody SP21, Linco
Research, St. Louis, USA). Triglycerides, total and HDL-cholesterol were determined
in serum by enzymatic techniques (Boehringer Mannheim, Germany). Systolic and
diastolic blood pressure was measured twice on the right arm with a random-zero
sphygmomanometer (Hawksley-Gelman, Lancing, UK) and the average of both
measurements was used for the analyses. Information on alcohol intake was
assessed by a validated semi-quantitative food frequency questionnaire [15].
Subjects were asked whether they drink alcohol and if so, how many glasses of
alcoholic drinks they consumed in a week. This information was converted to
alcohol intake in grams/day with the computerised version of the Dutch Food
Composition Table. Physical activity was assessed as described previously [11].
Weight and height were measured in subjects wearing light clothes only, and the
BMI was calculated as weight divided by height squared (kg/m2). Waist and hip
circumference were measured according to a standardised method [16]. Smoking
status was assessed by questionnaire.
Statistical analysis
Data are presented as mean (SD) or as median (interquartile range) for variables
with a skewed distribution according to tertiles of ALT. For the prospective data,
logistic regression analyses were performed to calculate the odds ratios (ORs) and
the 95% confidence intervals (95% CIs) for the metabolic syndrome. The upper
tertile of ALT was compared with the lower tertiles of ALT. The first model was
adjusted for age, sex, alcohol-intake and follow-up duration, because follow-up
differed between subjects. In the final model, additional adjustments were made
for the individual components of the metabolic syndrome criteria entered as
continuous variables. To assess the prospective relation of ALT with the individual
components of the metabolic syndrome, we used multivariate linear regression
models. In these models the individual metabolic syndrome component at follow-
up was entered as the outcome variable and ALT as the determinant. These models
were adjusted for age, sex and follow-up-duration and subsequently adjusted for
the metabolic syndrome component at baseline. These associations were expressed
as standardised betas with the 95% CI. A standardised beta of 0.5 means that if the
independent variable increases by 1 SD, the dependent variable increases by 0.5
SD. Variables with a skewed distribution were entered in the models after
logarithmic transformation. The statistical analyses were performed with SPSS for
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148
Windows version 11.0.5. A two-sided P-value<0.05 was considered to indicate
statistical significance. Effect-modification by age and sex were tested by entering
interaction terms (age times ALT-tertile or sex times ALT-tertile) into the models.
We choose a two-sided P-value<0.1 to indicate effect-modification.
RESULTS
Baseline characteristics
The mean age of the 1,097 participants (465 men and 632 women) was 60.0 (SD
6.7) years. Table 9.1 shows the baseline characteristics of the participants stratified
by ALT divided into tertiles. Subjects in the upper tertile were younger, more likely
to be male, had higher fasting glucose and cholesterol levels, lower HDL-
cholesterol levels and a higher blood pressure, compared to those in the lower
tertile. A borderline significant trend was observed for fasting triglyceride levels.
Risk for development of the metabolic syndrome
Table 9.2 shows the odds ratio of development of the metabolic syndrome among
1,097 men and women who were free of the metabolic syndrome at baseline after a
mean of 6.4 (range: 4.4-8.1) years of follow-up with ALT divided into tertiles. Of
these, 226 (20.6%) subjects developed metabolic syndrome at follow-up. In the
model adjusted for age, sex and follow-up duration and alcohol-intake, subjects in
the upper tertile had an odds ratio of 2.25 (95%CI: 1.50-3.37) compared to those in
the lower tertile. The subsequent models in which components of the metabolic
syndrome were entered as continuous variables showed that this relation was most
strongly attenuated by waist, followed by glucose and to a lesser extent by blood
pressure, but the associations remained statistically significant (OR: 1.62 [1.02-
2.58]). Lifestyle factors, including physical activity and smoking, nor fasting insulin
affected the relation to a significant extent. No effect modification for sex or age
was observed (both P > 0.1). To further address the effect of alcohol-intake as a
major potential confounder, we excluded subjects (n=152) with an alcohol-intake of
more than 20 grams per day. However, this only lead to a small attenuation of
model 1 [2.10 (1.36-2.54)]. The models in which the components of the metabolic
syndrome were entered as dichotomised variables (according to the Adult
Treatment Panel III of the National Cholesterol Education Program) did not
materially change the associations presented in Model 1 (data not shown), the OR
of Model 2 was slightly higher (OR: 2.02 [1.31-3.12])
ALANINE AMINOTRANSFERASE AND THE METABOLIC SYNDROME
149
The analyses assessing the relation of ALT with the individual components of the
metabolic syndrome showed that ALT was associated with all the components at
follow-up except for HDL-cholesterol. After adjustment for the baseline values of
the individual components, ALT was significantly associated with glucose only
(Table 9.3).
Table 9.1. Baseline characteristics of the participants (n=1,097) stratified by ALT
tertiles
Variable Alanine aminotransferase activity (U/L)
Tertile 1 Tertile 2 Tertile 3 Ptrend*
N 329 418 350
ALT (U/L) 10.4 (2.1) 15.6 (1.54) 27.3 (10.1)
Age (years) 60.9 (6.9) 60.4 (6.8) 58.6 (6.3) <0.001
Male (%) 27.1 41.9 57.4 <0.001
Body mass index (kg/m2) 25.2 (2.9) 25.5 (2.7) 26.6 (3.0) <0.001
Waist (cm) 84.5 (9.5) 87.4 (8.7) 91.0 (8.8) <0.001
Fasting glucose (mmol/l) 5.2 (0.5) 5.3 (0.5) 5.4 (0.5) <0.001
Plasma lipids (mmol/l)
Total-cholesterol 6.5 (1.1) 6.5 (1.0) 6.6 (1.1) 0.016
HDL-cholesterol 1.42 (0.35) 1.41 (0.36) 1.38 (0.39) 0.020
Triglycerides 1.2 (0.9-1.5) 1.2 (0.9-1.5) 1.3 (1.0-1.6) 0.054
Systolic BP (mmHg) 127 (18) 130 (17) 131 (18) <0.001
Diastolic BP (mmHg) 78 (10) 79 (9) 82 (9) <0.001
Abbreviations: ALT, alanine aminotransferase; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein Data are means (SD) or median (interquartile range) for variables with a skewed distribution; *Tested for trend with regression analyses adjusted for age and sex
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Table 9.2 The associations of ALT with development of the metabolic syndrome
(N=1,097)
ALT enzyme activity
Model Tertile 1 Tertile 2 Tertile 3
N 329 418 350
Model 1 1 1.43 (0.96-2.13) 2.25 (1.50-3.37)
Model 1 + waist 1 1.30 (0.89-1.95) 1.70 (1.11-2.58)
Model 1 + glucose 1 1.34 (0.89-2.01) 2.01 (1.33-3.03)
Model 1 + HDL-cholesterol 1 1.55 (1.03-2.32) 2.60 (1.72-3.95)
Model 1 + triglycerides 1 1.54 (1.03-2.35) 2.18 (1.43-3.33)
Model 1 + systolic BP 1 1.39 (0.93-2.08) 2.08 (1.38-3.14)
Model 1 + diastolic BP 1 1.41 (0.95-2.10) 2.11 (1.40-3.18)
Model 1 + life style factors 1 1.45 (0.97-2.18) 2.29 (1.51-3.48)
Model 1 + insulin 1 1.46 (0.97-2.20) 2.19 (1.44-3.35)
Model 2 1 1.40 (0.90-2.18) 1.62 (1.02-2.58)
*Model 1 adjusted for age, sex, alcohol-intake and follow-up duration; Model 2: Model 1 + adjusted for waist,
glucose, HDL-cholesterol, triglycerides and systolic and diastolic blood pressure
Life style factors: smoking and physical activity
Table 9.3. Multivariate linear regression analysis of ALT with the
individual metabolic syndrome components at follow-up*
Standardized betas (95%CI)
Metabolic syndrome
component at follow-up
Model 1 Model 2
Fasting glucose 0.19 (0.13-0.25) 0.13 (0.07;0.18)
Waist circumference 0.17 (0.11-0.22) 0.004 (-0.03;0.04)
Triglycerides 0.10 (0.03;0.16) 0.05 (-0.001;0.10)
Diastolic blood pressure 0.10 (0.04;0.17) 0.03 (-0.02;0.09)
Systolic blood pressure 0.09 (0.03;0;15) 0.03 (-0.02;0.08)
HDL-cholesterol 0.03 (-0.03;0.09) -0.02 (-0.05;0.02)
Models 1 and 2 adjusted for age, sex, alcohol-intake and follow-up duration,
Model 2 additionally adjusted for the baseline value of the individual component.
*Standardized betas indicate SD difference per 1 SD ALT at baseline
ALANINE AMINOTRANSFERASE AND THE METABOLIC SYNDROME
151
DISCUSSION
The main finding of this study is that in an elderly population of non-diabetic
Caucasian men and women free of the metabolic syndrome at baseline, ALT was
associated with an increased risk of conversion to the metabolic syndrome. This
association was independent of age, sex, alcohol-intake, follow-up duration and the
baseline metabolic syndrome components. In the prospective analyses of the
individual components of the metabolic syndrome, ALT was significantly associated
with fasting glucose levels and to a lesser extent with HDL-cholesterol, triglyceride
levels and elevated blood pressure and waist circumference.
The findings of the present study are in accordance with the two previous studies,
predominantly performed in Asian [8] and African American and Hispanic [9]
populations, that addressed the relation of liver enzymes including ALT with future
risk of the metabolic syndrome. Correction of all the baseline metabolic syndrome
criteria may result in over-correction, implying that the models in the present study
may underestimate the true associations. Other risk factors for the development of
the metabolic syndrome have been studied. These factors included C-reactive
protein (CRP) [17], physical inactivity [18], pro-insulin [19] and the individual
components themselves [19]. In the present study, physical activity did not affect
the associations. Unfortunately, pro-insulin and CRP levels were not available at
baseline in the Hoorn study cohort and consequently the effect of these mediating
or confounding variables could not be studied. The chronological ordering of
elevated transaminases (ALT and AST) in relation to components of the metabolic
syndrome was studied by Suzuki and et al, they reported, that weight gain, low
HDL-cholesterol and hypertrglyceridemia preceded transaminase elevation, follow
by hypertension and glucose intolerance [10]. Our study was not designed to study
the chronological ordering of ALT with the individual components of the metabolic
syndrome, however, our results do support some of the findings by Suzuki et al
that elevated ALT precedes components of the metabolic syndrome. Indeed,
glucose at follow-up adjusted for baseline glucose values was the strongest
component associated with elevated ALT. The mechanisms underlying the observed
association between ALT (and other liver enzymes) and the metabolic syndrome are
not yet fully understood. ALT is most strongly associated with liver fat
accumulation measured by proton-magnetic-resonance spectroscopy [20],
reflecting fat deposition in the liver, which is regarded as a feature of the metabolic
syndrome [21,22]. This hepatic fat deposition, as reflected by slightly elevated ALT
levels, may lead to early hepatic and systemic insulin resistance [23,24]. Indeed, an
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152
earlier study demonstrated that hepatic fat content measured by proton
spectroscopy is associated with insulin resistance, independent of obesity [25]. In
addition, hepatic fat content was related to a decreased suppression of insulin-
mediated hepatic glucose output, which is in line with the observation of the
present study that ALT is positively associated to fasting glucose levels in the
prospective analysis. High ALT levels might also reflect hepatic and systemic
inflammation, and the interplay of visceral adipose tissue, stimulating hepatic CRP
production which may contribute to further metabolic derangement. The results of
the present study show that subjects with slightly elevated ALT have a higher risk
of developing the metabolic syndrome. A number of potential limitations of this
study should be considered. The population consisted of Caucasian individuals
aged 50 to 75, and caution should be exercised to generalise our findings to other
populations. Although, we addressed alcohol as a confounder in our study, we
could not account for other factors that might have caused a rise in ALT, such as
viral hepatitis infections or hepatotoxic drugs. To overcome this limitation to some
extend, subjects with an ALT level of 2.5 times the upper level of the reference
value were excluded. In about on third of hepatitis C infections, a condition
associated with an increased risk for development of diabetes, subjects may have
normal ALT levels [26,27]. However, the effect of hepatitis C with regard to our
results may be very limited, as the prevalence of hepatitis C infection in the Dutch
general population is very low [28]. In addition, the association of other liver
enzymes such as asparatate aminotransferase and gamma-glutamyltransferase with
risk of developing the metabolic syndrome were not assessed. A previous study,
however, found ALT most strongly correlated to hepatic fat accumulation [20]. In
the Hoorn study, no diagnostic work-up was performed to assess the cause of
elevated ALT, however all relevant baseline data was send to the individual’s
general practitioner with an advice to perform a further diagnostic work-up
according to prevailing guidelines.
Despite the above mentioned limitations, the findings obtained in a population-
based study of elderly Caucasian men and women support the conclusion that ALT
is positively associated with the metabolic syndrome and further studies are
needed to elucidate and understand the pathophysiological mechanisms.
ALANINE AMINOTRANSFERASE AND THE METABOLIC SYNDROME
153
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1. Sattar N, Gaw A, Scherbakova O, et al. Metabolic syndrome with and without C-
reactive protein as a predictor of coronary heart disease and diabetes in the West of
Scotland Coronary Prevention Study. Circulation 2003;108:414-9.
2. Lorenzo C, Okoloise M, Williams K, Stern MP, Haffner SM. The metabolic syndrome as
predictor of type 2 diabetes: the San Antonio heart study. Diabetes Care 2003;
26:3153-9.
3. Dekker JM, Girman C, Rhodes T, et al. Metabolic syndrome and 10-year cardiovascular
disease risk in the Hoorn Study. Circulation 2005;112):666-73.
4. Meltzer AA, Everhart JE. Association between diabetes and elevated serum alanine
aminotransferase activity among Mexican Americans. Am J Epidemiol 1997;146:565-
71.
5. Nannipieri M, Gonzales C, Baldi S, et al. Liver enzymes, the metabolic syndrome, and
incident diabetes: the Mexico City diabetes study. Diabetes Care 2005;28:1757-62.
6. Schindhelm RK, Diamant M, Bakker SJL, et al. Liver alanine aminotransferase, insulin
resistance and endothelial dysfunction in normotriglyceridaemic subjects with type 2
diabetes mellitus. Eur J Clin Invest 2005;35:369-74.
7. Salvaggio A, Periti M, Miano L, Tavanelli M, Marzorati D. Body mass index and liver
enzyme activity in serum. Clin Chem 1991;37:720-3.
8. Nakanishi N, Suzuki K, Tatara K. Serum gamma-glutamyltransferase and risk of
metabolic syndrome and type 2 diabetes in middle-aged Japanese men. Diabetes Care
2004;27:1427-32.
9. Hanley AJ, Williams K, Festa A, et al. Liver markers and development of the metabolic
syndrome: the insulin resistance atherosclerosis study. Diabetes 2005;54:3140-7.
10. Suzuki A, Angulo P, Lymp J, et al. Chronological development of elevated
aminotransferases in a nonalcoholic population. Hepatology 2005;41:64-71.
11. Mooy JM, Grootenhuis PA, de Vries H, et al. Prevalence and determinants of glucose
intolerance in a Dutch caucasian population. The Hoorn Study. Diabetes Care
1995;18:1270-3.
12. de Vegt F, Dekker JM, Jager A, et al. Relation of impaired fasting and postload glucose
with incident type 2 diabetes in a Dutch population: The Hoorn Study. JAMA
2001;285:2109-13.
13. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in
Adults. Executive Summary of the Third Report of the National Cholesterol Education
Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood
Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486-97.
14. Bergmeyer HU, Horder M, Rej R. International Federation of Clinical Chemistry (IFCC)
Scientific Committee, Analytical Section: approved recommendation (1985) on IFCC
methods for the measurement of catalytic concentration of enzymes. Part 3. IFCC
method for alanine aminotransferase (L-alanine: 2-oxoglutarate aminotransferase, EC
2.6.1.2). J Clin Chem Clin Biochem 1986;24:481-95.
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154
15. Grootenhuis PA, Westenbrink S, Sie CM, et al. A semiquantitative food frequency
questionnaire for use in epidemiologic research among the elderly: validation by
comparison with dietary history. J Clin Epidemiol 1995;48:859-68.
16. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose,
specific insulin and proinsulin concentrations measured by two oral glucose tolerance
tests in a general Caucasian population: the Hoorn Study. Diabetologia 1996;39:298-
305.
17. Han TS, Sattar N, Williams K, et al. Prospective study of C-reactive protein in relation
to the development of diabetes and metabolic syndrome in the Mexico City Diabetes
Study. Diabetes Care 2002;25:2016-21.
18. Laaksonen DE, Lakka HM, Salonen JT, et al. Low levels of leisure-time physical activity
and cardiorespiratory fitness predict development of the metabolic syndrome.
Diabetes Care 2002;25:1612-8.
19. Palaniappan L, Carnethon MR, Wang Y, et al. Predictors of the incident metabolic
syndrome in adults: the Insulin Resistance Atherosclerosis Study. Diabetes Care
2004;27:788-93.
20. Westerbacka J, Corner A, Tiikkainen M, et al. Women and men have similar amounts
of liver and intra-abdominal fat, despite more subcutaneous fat in women:
implications for sex differences in markers of cardiovascular risk. Diabetologia
2004;47:1360-9.
21. Knobler H, Schattner A, Zhornicki T, et al. Fatty liver--an additional and treatable
feature of the insulin resistance syndrome. QJM 1999;92:73-9.
22. Garg A, Misra A. Hepatic steatosis, insulin resistance, and adipose tissue disorders. J
Clin Endocrinol Metab 2002;87:3019-22.
23. Vozarova B, Stefan N, Lindsay RS, et al. High alanine aminotransferase is associated
with decreased hepatic insulin sensitivity and predicts the development of type 2
diabetes. Diabetes 2002;51:1889-95.
24. Bugianesi E, Gastaldelli A, Vanni E, et al. Insulin resistance in non-diabetic patients
with non-alcoholic fatty liver disease: sites and mechanisms. Diabetologia
2005;48:634-42.
25. Seppala-Lindroos A, Vehkavaara S, Hakkinen AM, et al. Fat accumulation in the liver is
associated with defects in insulin suppression of glucose production and serum free
fatty acids independent of obesity in normal men. J Clin Endocrinol Metab
2002;87:3023-8.
26. Noto H, Raskin P. Hepatitis C infection and diabetes. J Diabetes Complications
2006;20:113-20.
27. Bruce MG, Bruden D, McMahon BJ, et al. Hepatitis C infection in Alaska Natives with
persistently normal, persistently elevated or fluctuating alanine aminotransferase
levels. Liver International 2006;26:643-9.
28. Veldhuizen IK, Conyn-van Spaendonck MA, Dorigo-Zetsma JW. Seroprevalentie van
hepatitis B en C in de Nederlandse bevolking [Seroprevalence of hepatitis B and C in
the Dutch population]. Infectieziektenbulletin 1999;10:182-4.
155
Chapter 9.2
NO INDEPENDENT ASSOCIATION OF ALANINE AMINOTRANSFERASE WITH RISK OF FUTURE TYPE 2 DIABETES IN THE HOORN STUDY
Roger K. Schindhelm1
Jacqueline M. Dekker2
Giel Nijpels2,3
Robert J. Heine1
Michaela Diamant1
From the Departments of 1Endocrinology/Diabetes Center and 3General Practice,
and the 2EMGO Institute, VU University Medical Center, Amsterdam, The
Netherlands
Published as letter in: Diabetes Care 2005;28:2812
Copyright © 2005 by the American Diabetes Association, Inc.
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156
We read with great interest the article by Nannipieri et al. [1], which reported no
association between aspartate aminotransferase, alanine aminotransferase (ALT), or
alkaline phosphatase and the 7-year incidence of impaired glucose tolerance and
type 2 diabetes. Interestingly, γ-glutamyltransferase was, but ALT was not, a
significant predictor of impaired glucose tolerance or type 2 diabetes. This is, as
the authors stated, in contrast to previously published papers that found significant
prospective associations of ALT with incident type 2 diabetes after adjustment for
obesity, insulin sensitivity, or inflammation (C-reactive protein) [2–4]. We studied
the baseline ALT enzyme activity in relation to 6-year incident type 2 diabetes in
the Hoorn Study, a population-based cohort study of glucose tolerance among
Caucasian men and women [5]. Glucose tolerance was assessed by oral glucose
tolerance test at baseline and after 6.4 years of follow-up. The study methods were
similar to the Mexico City Diabetes Study described by Nannipieri et al. [1], with the
exception that the participants of the Hoorn Study were 15 years older at baseline
(5). Of the 1,289 subjects free of type 2 diabetes at baseline, 123 developed type 2
diabetes during follow-up. We found that ALT was a significant predictor of type 2
diabetes only in the models adjusted for age, sex, and follow-up duration, with an
odds ratio of 2.18 (95% CI 1.29–3.69) for subjects in the upper tertile versus those
in the lower tertile. However, after additional adjustment for waist circumference,
BMI, alcohol consumption, fasting plasma insulin levels, and 2-h postload glucose
levels, the association attenuated and lost significance (1.18 [0.66–2.11]). The
other liver enzymes were not determined in the Hoorn Study. Thus, in accordance
with the report by Nannipieri et al. [1], but in contrast to the findings by others, we
found that ALT was not an independent predictor of incident type 2 diabetes in the
Hoorn study. We think it is important to report these findings, since we cannot
exclude the possibility of publication bias leading to overrepresentation of studies
showing an independent relationship between liver enzymes and risk of type 2
diabetes. The observed association between ALT and incident type 2 diabetes in
several published reports may be explained by the fact that these studies were
performed in selected populations, i.e., in high-risk populations and may not be
representative for the general population. An alternative explanation is that the
applied models overadjust for potential mediating variables such as insulin and 2-h
glucose, factors that might be directly related to liver fat. To increase the insight
into the role of liver fat in the pathophysiology of the metabolic syndrome and type
2 diabetes, further studies are needed to assess the underlying mechanisms. The
role of oxidative stress, as hypothesized by Nannipieri et al. [1], as well as the
contribution of inflammation should be explored in large-scaled prospective
studies.
ALANINE AMINOTRANSFERASE AND INCIDENT TYPE 2 DIABETES MELLITUS
157
REFERENCES
1. Nannipieri M, Gonzales C, Baldi S, et al. Liver enzymes, the metabolic syndrome, and
incident diabetes: the Mexico City Diabetes Study. Diabetes Care 2005;28:1757–62.
2. Vozarova B, Stefan N, Lindsay RS, et al. High alanine aminotransferase is associated
with decreased hepatic insulin sensitivity and predicts the development of type 2
diabetes. Diabetes 2002;51:1889–95.
3. Hanley AJ, Williams K, Festa A, et al. Elevations in markers of liver injury and risk of
type 2 diabetes: the Insulin Resistance Atherosclerosis Study. Diabetes
2004;53:2623–32.
4. Sattar N, Scherbakova O, Ford I, et al. Elevated alanine aminotransferase predicts
new-onset type 2 diabetes independently of classical risk factors, metabolic
syndrome, and C-reactive protein in the West of Scotland Coronary Prevention Study.
Diabetes 2004;53:2855–60.
5. Mooy JM, Grootenhuis PA, de Vries H, et al. Prevalence and determinants of glucose
intolerance in a Dutch Caucasian population: the Hoorn Study. Diabetes Care
1995;18:1270–3.
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158
159
Chapter 10.1
GENERAL DISCUSSION - METHODOLOGICAL CONSIDERATIONS
CHAPTER 10
160
STUDY DESIGN AND STUDY POPULATIONS
In this thesis both cross-sectional and prospective studies were used to assess the
association of determinants with outcome measures. In cross-sectional studies, the
determinants of interest and one or more outcome variables are measured at the
same time. Thus, causal inference remains hypothetical, but nevertheless, these
studies may contribute to a better insight in pathogenic processes. A causal
relationship implies that the putative cause precedes the effect. In a prospective
study design, determinants are measured before onset of the outcome, allowing
assessment of the temporal relationship between determinant and outcome
measure. However, merely a temporal relationship does not necessarily imply a
causal relationship between the studied variables. More requirements, as proposed
by Hill, need to be fulfilled to establish a true causal relationship between exposure
or determinant and the outcome [1]. Although neither cross-sectional nor
prospective studies can provide formal prove for a causal relation, the prospective
study design is generally considered to be more informative.
In Chapters 2 to 5 (determinants of postprandial metabolic changes and responses
in relation to cardiovascular disease [CVD] risk) and Chapter 7 (ALT in relation to
endothelial dysfunction and insulin sensitivity), cross-sectional study designs were
used. In Chapters 8 and 9, a prospective study design was used to assess the
relation of ALT as the determinant of interest with outcome. As pointed out before,
a significant prospective association does not necessarily imply causality, but
provides a stronger insight in the possible pathogenic mechanisms.
Selection bias may affect the internal validity of the study, such that a distortion of
the estimate of the association between determinant and outcome can occur,
resulting in different estimates for participants and non-participants. In Chapters 2
to 5, data from the Hoorn Prandial study was used [2]. The relative paucity of data
in women and the disproportionately high relative risk of CVD in especially post-
menopausal women [3,4], prompted us to perform the study in women only.
Moreover, postmenopausal women have higher postprandial triglyceride
concentrations compared to pre-menopausal women [5]. The participants of the
Hoorn Prandial study were randomly selected from the municipal registry of the city
of Hoorn and from the registry from the Diabetes Care System in Hoorn. In these
studies selection bias may indeed have occurred, since only 24% of the invited
individuals were willing to participate in the study [2], possibly resulting in a
relatively healthy selection of women with DM2 and controls. On the other hand,
GENERAL DISCUSSION
161
the possibility of participation of relatively unhealthy control subjects (participating
in the study as a health check-up) cannot be ruled out. In Chapter 7, the
participants were recruited from shared care projects in Amsterdam and Hoorn,
and through newspaper advertisements. Those who participated were willing to
undergo the extensive study protocol. This may have resulted in a selection of
participants who were relatively healthier than those who were unwilling to
participate (volunteer bias). For Chapters 8 and 9, data from the Hoorn Study were
used. Participants in this study were randomly selected from the municipal registry
from the city of Hoorn and a relatively large number of the invited persons
participated in the baseline (71.5% of those invited) and follow-up examinations
(72.5% of those invited) [6,7], this may considered to be a representative sample of
the general population. In Chapter 8, we excluded individuals with missing
information on non-fatal disease because they did not give permission to access
their hospital records. We have studied the possibility of selection bias and
repeated the analyses for all-cause mortality using the data from the entire original
Hoorn study cohort, without prevalent CVD and coronary heart disease, which
yielded almost identical estimates.
DETERMINANTS OF INTEREST
In Chapter 2, we studied the associations of clinical and biochemical variables with
fasting and postprandial glucose and triglycerides. The clinical and biochemical
variables included anthropometric measures, laboratory analyses and information
on physical activity and alcohol-intake assessed by questionnaires. Anthropometric
measurements were determined in duplicate according to a predefined protocol.
Medical history, medication, (former) smoking and alcohol use were assessed by
questionnaires [8] and physical activity was assessed by the Short Questionnaire to
Assess Health-enhancing physical activity of which reproducibility and relative
validity were described previously [9]. The inter-assay coefficients of variation of
the laboratory analyses were <7%.
In Chapters 3 to 5, we measured fasting and postprandial glucose and triglyceride
responses following two consecutive fat-rich or carbohydrate-rich meals and
expressed these postprandial changes as incremental and/or total area under the
curve [10]. Most postprandial studies applied a single fat-rich or carbohydrate-rich
meal (e.g. [11-14]). However, glucose excursions following a first meal have been
shown to influence the responses after a second meal [15], and type and amount of
carbohydrate at breakfast have been shown to influence glucose, insulin and free
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162
fatty acid (FFA) responses to a subsequent lunch [16]. We assumed that the test-
meal induced responses reflect “real-life” postprandial metabolic changes that have
caused or are in part responsible for clinical or biochemical changes, i.e. structural
and functional vascular changes in the past. However, it is important to notice that
these postprandial responses may have been affected by life-style and/or
medication interventions. This might be especially true in patients with DM2. In
addition, these interventions may also have affected outcome measures. We applied
two consecutive meals with different meal composition. The carbohydrate-rich meal
consisted of 4 g fat, 162 g carbohydrates and 22 g of proteins and the fat-rich meal
consisted of 50 g fat, 56 g carbohydrates and 28 g proteins. The fat-rich meal
should be considered as a mixed meal because it contained carbohydrates, through
which beta-cell stimulation and insulin secretion will be induced, which may have
resulted in a lower postprandial triglyceride response [17], and possibly an
underestimation of the presented associations.
In Chapters 7 to 9, we used the enzyme activity of the liver enzyme alanine
aminotransferase (ALT) as a marker for non-alcoholic fatty liver disease (NAFLD).
The use of surrogate markers has been widely adopted in clinical and
epidemiological research [18], and a number of them have been validated against
the so-called “gold standard”, a diagnostic test, accepted as the most valid test to
diagnose the disease or condition of interest. In the case of NAFLD, direct
measurement of hepatic fat by means of liver biopsies, is regarded as the “gold
standard” [19,20]. However, in epidemiological and clinical studies the use of this
technique is limited, because of the elaborate and invasive nature and associated
risks. Non-invasive imaging techniques, such as ultrasound, computed tomography
and proton magnetic resonance spectroscopy (1H-MRS), have been used instead of
biopsies [21,22]. Ultrasound examination of the liver can be used to indicate the
presence or absence of fatty infiltration of the liver. However, at present, this
technique at its best can provide only semi-quantitative data with regard to NAFLD. 1H-MRS has been validated against direct determination of triglyceride content in
human liver biopsies [23], and may be used for clinical purposes, but has limited
applicability in larger populations, because of logistics and high costs. In
epidemiological studies, ALT has been used as a surrogate marker for liver fat
accumulation and to indicate the presence of NAFLD [24]. Westerbacka and
colleagues assessed the correlation of ALT with hepatic triglyceride content by 1H-
MRS and found a modest, but significant correlation (r=0.5) [25]. In most studies in
patients with biopsy proven NAFLD, ALT was higher compared to healthy controls,
although one study reported on NAFLD patients with “normal” ALT levels (i.e. below
GENERAL DISCUSSION
163
the laboratory reference range) [26]. Studies assessing the specificity and
sensitivity of ALT as a marker of NAFLD are limited. The sensitivity and specificity
of ALT in the diagnosis of NAFLD are low and depend on the cut-off value used
[27]. Therefore, lower cut-off values than the current upper limit of the laboratory
reference range have been proposed [28], but, since cut-off values depend on the
assay used, this proposal has been questioned by others [29,30]. In the studies
presented in this thesis, we chose not to define a cut-off value to delineate the
presence or absence of NAFLD. Instead we modeled ALT as a continuous variable or
divided into tertiles. In order to avoid confounding by other factors that might
cause ALT elevation, a detailed history on alcohol intake is mandatory to exclude
those patients with ALT elevation caused by alcohol-abuse, and to address alcohol-
intake as a possible confounder or effect-modifier. With these limitations in mind,
we considered ALT to be an acceptable marker for NAFLD in epidemiological
studies. However, we cannot rule out the possibility of non-differential
misclassification, as ALT is not always elevated in patients with NAFLD.
Furthermore, ALT may be elevated due to causes other than NAFLD. Unfortunately,
no information on viral and autoimmune liver disease and hepatotoxic medication
that might have caused ALT elevation in the presented studies was available, and
therefore we cannot rule out the possibility of non-differential misclassification.
Because non-differential misclassification may lead to an underestimation of the
true estimate, our reported estimates may be regarded as conservative. As we [24]
and others [31] have argued, measurement of ALT enzyme activity in fresh samples
(non-frozen) is of utmost importance to avoid loss of enzyme activity after repeated
freezing and thawing, which may further contribute to information bias (non-
differential misclassification). In the studies presented in this thesis in which ALT
enzyme activity was assessed, measurements were performed in previously non-
frozen blood samples. The interassay coefficient of variation of the ALT assay was
2.3%.
OUTCOME VARIABLES
Fasting and postprandial triglyceride and glucose concentrations after two
consecutive fat-rich and carbohydrate-rich meals were used as outcome variables in
Chapter 2 in order to study a number of determinants of these responses.
Triglycerides and glucose were assessed by precise and validated methods, with
interassay coefficients of variation of 2.2% and 1.3%, respectively. In Chapter 3, the
intima-media thickness of the carotid artery (cIMT) was used as outcome variable. It
is a well-established and widely applied marker of sub-clinical atherosclerosis in
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164
epidemiological studies [32]. cIMT was measured by a single observer with an intra-
observer coefficient of variation of 5.2%. Markers of fasting and postprandial
glycoxidative and lipoxidative stress were assessed in Chapter 4. Oxidized LDL was
measured with a validated commercially available competitive ELISA with inter-assay
and intra-assay coefficients of variation of 7.8% and 4.8%, respectively. Unbound
CML and CEL were measured in EDTA-plasma by high-performance liquid
chromatography with stable-isotope-dilution tandem mass spectrometric detection
(LC-MS/MS) using a procedure developed for the analysis of protein-bound CML and
CEL [33], with intra-assay and inter-assay coefficients of variation for CML of 3% and
4%, respectively and for CEL 5% and 9%, respectively. 3DG was measured with a
newly developed LC-MS/MS method after pre-column derivatization according to a
published procedure [34]. Intra-assay and inter-assay coefficients of variation for
3DG were 8% and 12%, respectively. Myeloperoxidase was determined in duplicate
by a commercially available sandwich ELISA in EDTA-plasma. The intra-assay and
inter-assay coefficients of variation were 3.3% and 5.0%, respectively (Chapter 5). In
Chapter 7, brachial artery, endothelium-dependent flow-mediated vasodilatation
(FMD) and whole body insulin sensitivity (M/I-value) as assessed with the
euglycemic hyperinsulinemic clamp technique, were used as outcome variables.
Important limitations for the FMD technique are the need for ultrasonographic
expertise and the significant day-to-day [35,36], intra and inter-observer, and
diurnal variability [37]. The intra-observer coefficient of variation of the FMD
measurements in Chapter 7 was 10.1% [38]. We used the euglycemic
hyperinsulinemic clamp technique, which is regarded as the “gold standard” for
measuring insulin sensitivity. However, this technique provides information on
whole body insulin sensitivity instead of the more relevant hepatic insulin
sensitivity with respect to NAFLD.
CONFOUNDING AND EFFECT MODIFICATION
In epidemiological studies it is common to adjust for putative confounding
variables and to present the estimates both before and after adjustment. A
confounder is related to both exposure and outcome, and should not be involved
as an intermediate variable. However, how to select potential confounders? This is
still debated. This can be illustrated in Chapter 8.2 in which we could not
demonstrate an independent association of ALT with incident DM2. We argued that
the applied models might have over-adjusted for potential mediating variables such
as insulin and 2-hour glucose. However, due to uncertainty of the pathogenic
GENERAL DISCUSSION
165
processes involved, a clear distinction between confounding and mediating
variables is not always possible. In addition, unmeasured confounding variables
may also play an important role. In our studies in which ALT was used as a
determinant, we could not rule out the effect of other causes besides alcohol that
might have resulted in elevated ALT activity. However, for example, the prevalence
of viral infections in the general population is relatively low [39], and therefore
probably did not contribute to bias of the estimates. In the studies presented in
this thesis, the analyses were adjusted for the appropriate confounding variables.
Effect modification is present if the magnitude of an association of a determinant
and an outcome is not uniform across a third variable. The higher relative CVD risk
and the paucity of studies in postmenopausal women, prompted us to perform the
postprandial studies (Chapter 2 to 5) in postmenopausal women only, which limits
the external validity, but yields a relatively homogeneous group. We presented the
analyses separately for women with NGM and DM2 to account for possible effect-
modification by DM2-status. In addition, we considered women with DM2 who used
statin medication as a separate study group, as statin medication is known to
influence lipid metabolism [40] and cIMT [41]. In Chapters 7 to 9 we addressed the
possibility of effect-modification by sex and when applicable by DM2-status.
REFERENCES
1. HILL AB. The environment and disease: association or causation? Proc R Soc Med
1965;58:295-300.
2. Alssema M, Schindhelm RK, Dekker JM, et al. Postprandial glucose and not
triglyceride concentrations are associated with carotid intima media thickness in
women with normal glucose tolerance: the Hoorn prandial study. Atherosclerosis (in
press).
3. Kaseta JR, Skafar DF, Ram JL, et al. Cardiovascular disease in the diabetic woman. J
Clin Endocrinol Metab 1999;84:1835-8.
4. Lewis SJ. Cardiovascular disease in postmenopausal women: myths and reality. Am J
Cardiol 2002;89:5E-10E.
5. van Beek AP, Ruijter-Heijstek FC, Erkelens DW, de Bruin TW. Menopause is associated
with reduced protection from postprandial lipemia. Arterioscler Thromb Vasc Biol
1999;19:2737-41.
6. Mooy JM, Grootenhuis PA, de Vries H, et al. Intra-individual variation of glucose,
specific insulin and proinsulin concentrations measured by two oral glucose tolerance
tests in a general Caucasian population: the Hoorn Study. Diabetologia 1996;39:298-
305.
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166
7. de Vegt F, Dekker JM, Jager A, et al. Relation of impaired fasting and postload glucose
with incident type 2 diabetes in a Dutch population: The Hoorn Study. JAMA
2001;285:2109-13.
8. Mooy JM, Grootenhuis PA, de Vries H, et al. Prevalence and determinants of glucose
intolerance in a Dutch caucasian population. The Hoorn Study. Diabetes Care
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9. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D. Reproducibility and relative validity
of the short questionnaire to assess health-enhancing physical activity. J Clin
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10. Allison DB, Paultre F, Maggio C, et al. The use of areas under curves in diabetes
research. Diabetes Care 1995;18:245-50.
11. Ceriello A, Taboga C, Tonutti L, et al. Evidence for an independent and cumulative
effect of postprandial hypertriglyceridemia and hyperglycemia on endothelial
dysfunction and oxidative stress generation: effects of short- and long-term
simvastatin treatment. Circulation 2002;106:1211-8.
12. Vogel RA, Corretti MC, Plotnick GD. Effect of a single high-fat meal on endothelial
function in healthy subjects. Am J Cardiol 1997;79:350-4.
13. Anderson RA, Evans ML, Ellis GR, et al. The relationships between post-prandial
lipaemia, endothelial function and oxidative stress in healthy individuals and patients
with type 2 diabetes. Atherosclerosis 2001;154:475-83.
14. Ong PJ, Dean TS, Hayward CS, et al. Effect of fat and carbohydrate consumption on
endothelial function. Lancet 1999;354:2134.
15. Mari A, Tura A, Gastaldelli A, Ferrannini E. Assessing insulin secretion by modeling in
multiple-meal tests: role of potentiation. Diabetes 2002;51 Suppl 1:S221-S226.
16. Wolever TM, Bentum-Williams A, Jenkins DJ. Physiological modulation of plasma free
fatty acid concentrations by diet. Metabolic implications in nondiabetic subjects.
Diabetes Care 1995;18:962-70.
17. Kriketos AD, Sam W, Schubert T, et al. Postprandial triglycerides in response to high
fat: role of dietary carbohydrate. Eur J Clin Invest 2003;33:383-9.
18. Kluft C. Principles of use of surrogate markers and endpoints. Maturitas
2004;47:293-8.
19. Brunt EM, Janney CG, Di Bisceglie AM, et al. Nonalcoholic steatohepatitis: a proposal
for grading and staging the histological lesions. Am J Gastroenterol 1999;94:2467-
74.
20. Brunt EM. Nonalcoholic steatohepatitis: definition and pathology. Semin Liver Dis
2001;21:3-16.
21. Saverymuttu SH, Joseph AE, Maxwell JD. Ultrasound scanning in the detection of
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22. Ricci C, Longo R, Gioulis E, et al. Noninvasive in vivo quantitative assessment of fat
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23. Szczepaniak LS, Babcock EE, Schick F, et al. Measurement of intracellular triglyceride
stores by H spectroscopy: validation in vivo. Am J Physiol 1999;276:E977-E989.
24. Schindhelm RK, Diamant M, Dekker JM, et al. Alanine aminotransferase as a marker of
non-alcoholic fatty liver disease in relation to type 2 diabetes mellitus and
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169
Chapter 10.2 – Part 1
GENERAL DISCUSSION - PATHOPHYSIOLOGICAL CONSIDERATIONS
POSTPRANDIAL DYSMETABOLISM, CARDIOVASCULAR DISEASE RISK AND TYPE 2 DIABETES MELLITUS
CHAPTER 10
170
INTRODUCTION
Already in 1979, Zilversmit postulated that atherogenesis might be a postprandial
phenomenon [1]. Indeed, a number of studies suggested that postprandial or post-
load glucose and postprandial triglyceride concentrations are much stronger
associated with some CVD risk factors and mortality than the respective fasting
values [2-8]. There is, however, still debate as to whether postprandial glucose is
causally related to CVD risk factors or merely a risk marker [9,10], i.e. reflecting the
underlying metabolic abnormalities in for example lipid metabolism. To date, most
studies used a single, often artificially composed liquid fat and/or carbohydrate
load to assess postprandial responses. However, in normal daily life, the metabolic
alterations following breakfast will affect the responses after lunch, thus potentially
resulting in a cumulative effect of breakfast on the plasma glucose and
triglycerides following lunch [11], especially in insulin resistant patients with type 2
diabetes mellitus (DM2). The main objective of the first part of this thesis was to
assess the relative contributions and elaborate on the potential mechanisms of
postprandial glucose and postprandial triglyceride levels following two consecutive
(breakfast and lunch) fat-rich or carbohydrate-rich meals to CVD risk in
postmenopausal women with DM2 and in women with normal glucose metabolism
(NGM).
DETERMINANTS OF POSTPRANDIAL GLUCOSE AND TRIGLYCERIDES
In Chapter 2, we have shown that the determinants of postprandial glucose and
triglyceride responses differ. Fasting triglycerides were the main determinant of
postprandial triglycerides, whereas postprandial glucose was determined by other
factors than fasting glucose values. Although women with DM2 had higher total
postprandial triglyceride responses (total area under the curve) as compared to
women with NGM, we found no significant differences in postprandial increase in
triglyceride concentrations (incremental area under the curve) between DM2 and
NGM. We expected a greater increase in postprandial triglyceride concentrations in
women with DM2, especially following the second meal (lunch). The relatively low
triglyceride response in women with DM2 might be the result of the meal-
composition. The applied fat-rich meal should be considered as a mixed-meal,
because of the substantial amount of carbohydrates. The resulting insulin response
suppresses the VLDL-secretion by the liver, which in turn attenuates the
postprandial triglyceride response. Furthermore, the women with DM2 included in
GENERAL DISCUSSION
171
our study were in relatively good glycemic control, possibly contributing to an
ameliorated response of the liver to the meal-induced insulin response. Finally, we
measured up to 8 hours after the first meal was given (4 hours after the second
meal). It may be conceivable that differences in postprandial triglyceride
metabolism become evident only after a longer study period (i.e. 24 hours), due to
differences in clearance between NGM and DM2 [12]. These explanations are
speculative, as we did not measure the triglyceride kinetics in our studies.
POSTPRANDIAL GLUCOSE AND TRIGLYCERIDE RESPONSES IN
RELATION TO CVD RISK
In the Hoorn Prandial study we addressed the question whether postprandial
glucose or postprandial triglyceride concentrations were more closely related to
CVD risk. In Chapter 3, we showed that postprandial glucose, and not the
triglyceride concentrations, are associated with carotid intima-media thickness
(cIMT) in women with NGM. The association between postprandial glucose and cIMT
was found for the meals, irrespective of their composition. We also showed that
measuring glucose 2 hours after the first meal was enough to assess the
association between postprandial glucose and cIMT. In contrast, plasma glucose
measured two hours after a 75-g oral glucose tolerance test (OGTT), was not as
strongly associated with cIMT in women with NGM. This could not be attributed to
limited precision of the single glucose measurement after the OGTT, because we
found that a single 2-hour glucose measurement following a meal, although
borderline significant, also was associated with cIMT. Based on this observation we
concluded that postprandial glucose concentrations following a meal may provide
additional clinical information above and beyond the 2-hour post-load glucose
following an OGTT.
In women with DM2, an association of cIMT was found with fasting glucose but not
with postprandial glucose concentrations. A possible explanation for the lack of an
association of postprandial glucose concentrations with cIMT in DM2, may be the
fact that women with DM2 were treated with blood glucose lowering medication.
These variably affect postprandial glucose, dependent on the mode of action of the
agents. In addition, some blood glucose lowering agents and life-style interventions
have been shown to retard IMT progression [13-15].
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172
Based on previously published papers, we would have expected an association
between postprandial triglycerides and cIMT, not only in patients with DM2 [3,7],
but also in healthy subjects [8,16]. However, no such associations between were
found in the study described in Chapter 3. In the Atherosclerosis Risk in
Communities (ARIC) Study, postprandial triglyceride levels were associated with
early atherosclerosis, but only in non-obese subjects [16]. The ARIC investigators
suggested that this association might be explained by high levels of atherogenic
triglyceride-rich lipoprotein remnants in the postprandial state. These lipoprotein
remnants were indeed related to cIMT, independent of postprandial triglycerides
[17]. Since we did not measure lipoprotein remnant particles in the present study,
the question regarding the presence or absence of an association between
postprandial lipids and cIMT in our study cannot be conclusively answered. It
should furthermore be considered that gender-specific studies on the relation
between postprandial triglycerides and CVD risk are scarce. The above-mentioned
ARIC Study reported an association between postprandial triglycerides and cIMT in
both men and women [16], whereas in another study the association was only
present in men and not in women [18].
MECHANISMS LINKING POSTPRANDIAL DYSMETABOLISM TO CVD
Glycoxidative and lipoxidative stress, AGE-formation and inflammation are
implicated as potential mechanisms that may link postprandial dysmetabolism to
CVD risk [19-22]. In chapter 4, we studied acute changes in markers of
glycoxidative and lipoxidative stress, including oxidized low-density-lipoprotein
(oxLDL), Nε-(carboxyethyl)-lysine (CEL), Nε-(carboxymethyl)-lysine (CML) and 3-
deoxyglucosone (3DG), following two consecutive fat-rich or carbohydrate-rich
meals. We found that oxLDL, 3DG and CML increased in the postprandial state. The
fasting concentrations of 3DG, oxLDL and CML in women with DM2 were
comparable with the postprandial concentrations of these markers in women with
NGM (Chapter 4). These finding extend a previous study from our group,
describing an increase in oxLDL and malondialdehyde in healthy young men
following 2 consecutive fat-rich mixed meals [23]. The increased postprandial
concentrations of oxLDL and 3DG may contribute to functional and structural
vascular changes which may contribute to the increased CVD risk. Indeed, the
postprandial increase in oxidative stress was associated with an impaired
postprandial flow-mediated vasodilatation in healthy young men in the
aforementioned study [23]. Future studies should address the relation of
GENERAL DISCUSSION
173
postprandial oxidative stress markers with vascular (dys)function in patients with
DM2.
Postprandial dysmetabolism may affect leukocyte recruitment and/or activation. In
turn, myeloperoxidase (MPO), which is expressed in leukocytes and released upon
activation, has been associated with CVD [24-26]. In Chapter 5, we hypothesized
that MPO would increase in the postprandial state, due to postprandial leukocyte
recruitment and/or activation, especially in individuals with DM2. However, in
contrast to our hypothesis, MPO decreased in NGM (both meal types) and in DM2
(fat-rich meals). Moreover, despite an increase of leukocytes following the fat-rich
meals in both NGM and DM2, no postprandial MPO increase was observed. The
mechanism by which a postprandial increase in number of leukocytes contributes
to CVD risk remains unclear. One study has reported that the postprandial increase
in number of leukocytes was associated with an impaired flow-mediated
vasodilatation [27].
CONCLUSIONS
The association of postprandial glucose with cIMT in women with NGM was
stronger than that of postprandial triglycerides. The association between
postprandial glucose and cIMT in women with NGM suggests that postprandial
glucose, in the normal range, is a marker or a risk factor of CVD. Possible
mechanisms that may contribute to the association between postprandial glucose
and CVD risk may include generation of postprandial oxidative stress. Future
studies should address the widely debated question whether lowering postprandial
glucose and oxidative stress may lower CVD risk.
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8. Boquist S, Ruotolo G, Tang R, et al. Alimentary lipemia, postprandial triglyceride-rich
lipoproteins, and common carotid intima-media thickness in healthy, middle-aged
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14. Katakami N, Yamasaki Y, Hayaishi-Okano R, et al. Metformin or gliclazide, rather than
glibenclamide, attenuate progression of carotid intima-media thickness in subjects
with type 2 diabetes. Diabetologia 2004;47:1906-13.
15. Kim SH, Lee SJ, Kang ES, et al. Effects of lifestyle modification on metabolic
parameters and carotid intima-media thickness in patients with type 2 diabetes
mellitus. Metabolism 2006;55:1053-9.
16. Sharrett AR, Chambless LE, Heiss G, et al. Association of postprandial triglyceride and
retinyl palmitate responses with asymptomatic carotid artery atherosclerosis in
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GENERAL DISCUSSION
175
17. Karpe F, Boquist S, Tang R, et al. Remnant lipoproteins are related to intima-media
thickness of the carotid artery independently of LDL cholesterol and plasma
triglycerides. J Lipid Res 2001;42:17-21.
18. Tentor J, Harada LM, Nakamura RT, et al. Sex-dependent variables in the modulation
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19. Brownlee M. Glycation products and the pathogenesis of diabetic complications.
Diabetes Care 1992;15:1835-43.
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21. Baynes JW, Thorpe SR. Role of oxidative stress in diabetic complications: a new
perspective on an old paradigm. Diabetes 1999;48:1-9.
22. Bavenholm PN, Efendic S. Postprandial hyperglycaemia and vascular damage--the
benefits of acarbose. Diab Vasc Dis Res 2006;3:72-9.
23. Tushuizen ME, Nieuwland R, Scheffer PG, et al. Two consecutive high-fat meals affect
endothelial-dependent vasodilation, oxidative stress and cellular microparticles in
healthy men. J Thromb Haemost 2006;4:1003-10.
24. Baldus S, Heeschen C, Meinertz T, et al. Myeloperoxidase serum levels predict risk in
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26. Zhang R, Brennan ML, Fu X, et al. Association between myeloperoxidase levels and
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27. Van Oostrom AJ, Sijmonsma TP, Verseyden C, et al. Postprandial recruitment of
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CHAPTER 10
176
177
Chapter 10.2 – Part 2
GENERAL DISCUSSION - PATHOPHYSIOLOGICAL CONSIDERATIONS
LIVER ENZYMES IN RELATION TO METABOLIC SYNDROME, TYPE 2 DIABETES MELLITUS AND CARDIOVASCULAR RISK
This chapter is based on: Schindhelm RK, Diamant M, Heine RJ. Non-alcoholic fatty
liver disease and cardiovascular disease risk. Current Diabetes Reports 2007.
With permission from Current Science Inc., Philadelphia, USA.
CHAPTER 10
178
INTRODUCTION
Non-alcoholic fatty liver disease (NAFLD) is characterized by a wide spectrum of
liver abnormalities ranging from liver steatosis to the more severe non-alcoholic
steatohepatitis (NASH), resembling alcohol-induced liver disease. By definition
NAFLD develops in subjects who are not heavy alcohol consumers and who have
negative tests for viral and autoimmune liver diseases. It usually has a benign
clinical course, but it may progress to NASH, fibrosis, cirrhosis and rarely, to
hepatocellular carcinoma [1,2]. In recent years, NAFLD has gained appreciation
because of its relationship with insulin resistance, metabolic syndrome and type 2
diabetes mellitus (DM2). Currently, NAFLD is considered by some authors to be the
hepatic component of the metabolic syndrome [3,4], and evidence is accumulating
that patients with NAFLD are at increased risk of developing cardiovascular disease
(CVD).
NAFLD IN RELATION TO THE METABOLIC SYNDROME AND DM2
The putative role of the liver in the pathogenesis of DM2 has gained much interest
and several cross-sectional studies have demonstrated that NAFLD is related to
features of the metabolic syndrome and DM2 [5-7]. In the analysis of the third
National Health and Nutrition Examination Survey (NHANES III) up to 31% of the
elevated alanine aminotransferase (ALT) activity could be explained by high alcohol
consumption, hepatitis B or C infection and/or high transferrin saturation, whereas
in the remaining 69%, the elevated ALT activity was significantly associated with
higher body mass index (BMI), waist circumference, triglycerides, fasting insulin
and lower high density lipoprotein (HDL) cholesterol [5]. Several studies have
addressed the prospective relation of ALT and the metabolic syndrome and DM2
[8,9]. In the Hoorn Study, a population-based cohort study among elderly Caucasian
men and women, we found that ALT was associated with the development of the
metabolic syndrome after 6 years of follow-up (Chapter 9). In patients with DM2,
elevated serum ALT enzyme activity is more frequently observed than in the
general population [10,11]. In addition, some [12-16], but not all studies [8,17-19]
(including the study presented in Chapter 9), have demonstrated independent and
significant associations of ALT with future DM2. The observed association between
ALT and incident DM2 in the mentioned studies may be explained by the fact that
they were performed in high-risk populations that may not be representative for
the general population. Overall, these studies show that patients with NAFLD are at
increased risk for developing DM2 and the metabolic syndrome, suggesting that
the increased CVD risk may be mediated via components of the metabolic
GENERAL DISCUSSION
179
syndrome and DM2. Since the pathophysiology linking NAFLD with either the
metabolic syndrome and/or diabetes has not been clarified, it is difficult to make
the distinction between confounding and mediating variables in the
epidemiological analyses.
NAFLD AND INCREASED CVD RISK
Several cross-sectional studies have demonstrated an increase in carotid artery
intima-media thickness (cIMT) in patients with NAFLD [34-36]. However, in these
studies diagnosis of NAFLD was based on evaluation of liver enzymes or on
ultrasound evaluation but not confirmed by liver biopsy, which is regarded as the
“gold standard” in the diagnosis of NAFLD [20]. In a recent study, Targher and
colleagues have demonstrated that patients with biopsy proven NAFLD have a
significantly higher cIMT compared to age-, sex- and BMI matched healthy controls.
Moreover, they demonstrated that the histologically assessed NAFLD predicted
cIMT, independent of classical risk factors, including insulin resistance and
components of the metabolic syndrome [21]. In well-controlled DM2 patients, we
have shown that slightly elevated ALT, as a surrogate marker of NAFLD, is
associated with a decreased brachial artery flow-mediated vasodilatation and an
impaired whole-body insulin sensitivity (Chapter 7). In line with the observation in
patients with DM2, it was shown that non-diabetic patients with NAFLD have a
decreased brachial artery flow-mediated vasodilatation compared to matched
healthy controls, independent of other risk factors, including insulin resistance and
components of the metabolic syndrome [22]. Ioanou and colleagues studied the
association of ALT and the calculated 10-year risk of coronary heart disease as
estimated with the Framingham risk score (FRS). Subjects with elevated ALT values
had a significantly higher FRS compared to those with normal ALT values,
indicating a higher CVD risk in patients with NAFLD [23]. Only a limited number of
studies have addressed the prospective relation of ALT with CVD and mortality. In
the Hoorn Study, a population-based cohort of Caucasian men and women aged 50
to 75 years, we studied the association of ALT at baseline with all-cause mortality,
and incident cardiovascular and coronary heart disease events (CVD and CHD,
respectively) and found a significant association of ALT with incident CHD after
adjustment for components of the metabolic syndrome and traditional CVD risk
factors (Chapter 8). We found no independent associations of ALT with CVD events
and all-cause mortality. The latter result was in line with a previous study by Arndt
and colleagues who studied the association of ALT with all-cause mortality in 8,043
male construction workers and found no significant association [24]. In contrast, a
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180
recent study by Nakamura and co-workers found a positive association of ALT with
all-cause mortality in Japanese men and women, but only for those with a body
mass index below the median (22.7 kg/m2) [25].
MECHANISMS LINKING NAFLD TO INCREASED CVD RISK
The increased CVD risk associated with NAFLD might be explained by the close
relation of NAFLD with components of the metabolic syndrome and DM2, although
recent studies support the notion that NAFLD in itself might contribute to the
increased CVD risk [21,22]. However, the mechanisms for this putative relationship
are not clear. Several of the following, probably highly interrelated, factors
contribute to the enhanced risk of DM2 and metabolic syndrome in persons with
NAFLD.
Oxidative stress and inflammation
Hepatic steatosis, promoted by an insulin resistance mediated elevated flux of free
fatty acids (FFA), may worsen to further liver injury as a consequence of oxidative
stress [26] (Figure 10.1). However, the underlying factors that promote disease
progression to cirrhosis are not understood. Enhanced FFA oxidation increases the
formation of oxygen radicals, in turn leading to lipid peroxidation, mitochondrial
dysfunction and cell damage with subsequent release of cytokines, including tumor
necrosis factor-α (TNF-α), interleukin-6 (IL-6) and C-reactive protein (CRP). Indeed,
Kerner and colleagues found an association of ALT with CRP [27]. In contrast,
Haukeland and coworkers studied the role of systemic inflammation in individuals
with NAFLD and NASH compared to healthy controls and demonstrated that IL-6,
but not CRP, after adjustment for BMI, age and sex, was elevated in NAFLD
compared to controls and TNF-α was higher in NASH as compared to NAFLD [28].
Of interest, a study by Targher and coworkers demonstrated that CRP was elevated
in individuals with NAFLD, but this association was largely explained by the amount
of visceral fat [29].
Adiponectin, leptin and resistin
Decreased adiponectin levels, may represent another mechanism linking NAFLD to
CVD. Patients with NAFLD have lower levels of adiponectin compared to healthy
controls, independent of components of the metabolic syndrome [28,30,31]. This
observation may be relevant, as some studies have shown that lower levels of
GENERAL DISCUSSION
181
adiponectin are associated with CVD [32]. Leptin is a cytokine hormone mainly
produced by adipocytes, that regulates food intake and fat metabolism through
actions on the central nervous system. It has been suggested that leptin may act as
one of the proinflammatory regulators in the progression of NAFLD to NASH by up-
regulation of transforming growth factor β [33]. However, a recent study found no
association of leptin with liver disease severity [34]. Resistin is a protein expressed
in adipose tissue and related to insulin resistance in mice [35]. Recently, a
relationship between resistin and NAFLD and NASH was demonstrated in humans,
with higher plasma resistin levels in individuals with NASH [34].
insulin resistance
↑↑↑↑ lipolysis
obesity
↑↑↑↑ portal FFA↑↑↑↑ FFA
mitochondrial
dysfunction
↑↑↑↑ TGVLDL
assembly/secretion
oxidative
stress
impaired
hepatic
Insulin
signaling
↑↑↑↑ hepatic glucose
↑↑↑↑ de novo
lipogenesis
TG-rich VLDL
glucose
hyperglycemia
[↑↑↑↑ALT]
ALT
hepatocyte
damage
cytokines
adipokines
inflammatory
cytokines
(e.g. CRP)
insulin resistance
↑↑↑↑ lipolysis
obesity
↑↑↑↑ portal FFA↑↑↑↑ FFA
mitochondrial
dysfunction
↑↑↑↑ TGVLDL
assembly/secretion
oxidative
stress
impaired
hepatic
Insulin
signaling
↑↑↑↑ hepatic glucose
↑↑↑↑ de novo
lipogenesis
TG-rich VLDL
glucose
hyperglycemia
[↑↑↑↑ALT]
ALT
hepatocyte
damage
cytokines
adipokines
inflammatory
cytokines
(e.g. CRP)
Figure 10.1. Overview of mechanisms involved in the development of non-alcoholic fatty liver disease (NAFLD)
and the contribution of NAFLD to cardiometabolic risk. NAFLD is characterized by an increased uptake of free
fatty acids (FFA) by the liver, an increased de novo lipogenesis, impaired β-oxidation caused by mitochondrial
dysfunction and an insufficient assembly and secretion of very-low-density lipoproteins (VLDL). The impaired
mitochondrial β-oxidation enhances formation of reactive oxygen species causing hepatocyte damage and up-
regulation of pro-inflammatory cytokines. Oversupply of FFA may lead to excess TG storage and hepatic insulin
resistance due to ser-phosphorylation of components of the insulin-signaling cascade. Hepatic insulin
resistance and increased FFA supply will increase gluconeogenesis, collectively leading to an increased hepatic
glucose output. Alanine aminotransferase (ALT) is released upon hepatocyte damage and possibly up-regulated
by the hepatic insulin resistant state.
CHAPTER 10
182
Postprandial dysmetabolism
Studies comparing the postprandial response of triglycerides and FFAs to a fat-rich
meal in non-diabetic subjects with biopsy proven NASH to controls, showed that
patients with NASH had significantly higher postprandial triglyceride levels than
healthy controls [36,37]. Another study demonstrated that patients with ultrasound
diagnosed NAFLD with an abnormal ALT and or AST had higher glucose levels after
a 75-g oral glucose tolerance test than those with normal ALT and AST [38]. Toledo
and colleagues observed that in obese patients with DM2, increased hepatic
steatosis, as quantified by computed tomography scanning, was positively
correlated with serum triglycerides and inversely with HDL-cholesterol. No
difference in LDL-cholesterol and apolipoprotein B100 was observed relative to the
degree of hepatic steatosis. However, the LDL particle size was smaller in the group
with severe hepatic steatosis. These observations indicate that hepatic steatosis
may contribute to the increased CVD risk in these patients by increasing
triglyceride enrichment of VLDL particles, lowering HDL-cholesterol and by
increasing the number of small, dense LDL-particles. The relationship of hepatic
steatosis with serum triglycerides was stronger in subjects with a minor degree of
hepatic steatosis and weaker in those with a more severe degree of hepatic
steatosis, which may, as the authors suggested, indicate that the incorporation of
triglycerides into VLDL has a limited capacity [39]. In the Hoorn Prandial study, we
found no significant association between ALT and postprandial glucose and
triglycerides (Chapter 2), in contracts to a recent study form our group, in which
we found a significant association between liver fat (quantified by proton-magnetic-
resonance-spectroscopy) and postprandial triglycerides (Tushuizen et al,
unpublished data). These observations suggest that the latter method may be a
better indicator of liver fat in the associations of liver fat with postprandial
dysmetabolic changes than ALT.
CONCLUSIONS
Evidence is accumulating that NAFLD is associated with cardiovascular risk factors
and markers of sub-clinical atherosclerosis, but also to overt CVD events. This
increased risk for CVD necessitates the evaluation and treatment of these patients.
Long-term outcome studies need to establish the benefit of treatment of NAFLD on
the reduction and prevention of diabetes and CVD risk.
GENERAL DISCUSSION
183
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CHAPTER 10
186
187
Appendix
SUMMARY & SAMENVATTING
188
SUMMARY
Chapter 1 starts with a brief outline about diabetes and the metabolic syndrome in
relation to cardiovascular disease (CVD) risk in patients with type 2 diabetes
mellitus (DM2) and introduces some new putative CVD risk determinants that may
explain the excess CVD risk in patients with DM2.
In Chapter 2, we describe the determinants of postprandial triglyceride and
glucose concentrations following two consecutive (breakfast and lunch) fat-rich and
carbohydrate-rich meals in post-menopausal women with normal glucose
metabolism (NGM) and in women with DM2 who were part of the Hoorn Prandial
Study. The postprandial increment in triglycerides did not differ between women
with NGM and DM2, whereas fasting triglycerides and the total triglyceride
response was higher in DM2. Fasting triglyceride concentration, HbA1c, total
cholesterol, and, inversely, high density lipoprotein (HDL) cholesterol were
independently associated with the postprandial increment in triglycerides in women
with NGM. In women with DM2, the fasting triglyceride concentration was the only
determinant of the postprandial increment in triglycerides. In women with NGM,
age and fasting triglycerides were independently associated with postprandial
increment in glucose. In women with DM2, HbA1c was independently associated
with the postprandial increment in glucose, and in women on statin therapy
physical activity was an additional determinant. We concluded that the postprandial
increment in triglycerides did not differ between post-menopausal women with
NGM and well-controlled DM2. Furthermore, fasting triglyceride levels were a
strong predictor of the postprandial increment in triglycerides, whereas the
postprandial increment in glucose was especially determined by variables other
than fasting glucose.
The study in Chapter 3 compared the associations of postprandial glucose and
postprandial triglycerides with carotid intima media thickness (cIMT) after two
consecutive fat-rich or carbohydrate rich meals in women with NGM and DM2. In
women with NGM, an increase of 1.0 mmol/l in plasma glucose following the fat-
rich meals and an increase of 1.8 mmol/l in plasma glucose concentration
following the carbohydrate meals were associated with a 50 µm larger cIMT. No
association between postprandial glucose and cIMT was found in women with DM2.
Postprandial triglycerides were not associated with cIMT. The association between
postprandial glucose and cIMT in women with NGM suggests that postprandial
glucose in the normal range is a marker or a risk factor for atherosclerosis.
SUMMARY & SAMENVATTING
189
In the next chapter (Chapter 4), we addressed possible mechanisms that link
postprandial dysmetabolism to CVD risk. The influence of two consecutive meals
(breakfast and lunch) with different composition (both fat-rich or carbohydrate-rich)
on acute changes in various markers of glycoxidative and lipoxidative stress,
including oxidized low-density-lipoprotein (oxLDL), Nε-(carboxyethyl)-lysine (CEL),
Nε-(carboxymethyl)-lysine (CML) and 3-deoxyglucosone (3DG) were studied in a
sub-sample of the Hoorn Prandial Study (27 with NGM and 26 with DM2). We found
significant elevations of oxLDL, 3DG and CML during the postprandial period in
post-menopausal women with DM2 and NGM. The fasting values of oxLDL, 3DG
and CML in women with DM2 were of similar magnitude as the postprandial values
in women with NGM. The elevations of oxLDL were closely correlated with changes
in postprandial triglycerides, whereas 3DG alterations were correlated with changes
in postprandial glucose following the fat-rich meals. CML and CEL correlated to with
neither postprandial glucose nor triglyceride changes. These results suggest that
exaggerated glycoxidative and lipoxidative stress, due to postprandial
dysmetabolism, may contribute to the increased CVD risk in DM2.
In Chapter 5 we studied the 8-hour time course of myeloperoxidase (MPO)
following two consecutive (breakfast and lunch) fat-rich and carbohydrate rich-
meals in postmenopausal women with NGM and in women with DM2. MPO is
abundantly expressed in leukocytes, and released upon activation in the
extracellular space. MPO has been associated with CVD and endothelial
dysfunction. Postprandial leukocyte recruitment and activation with subsequent
MPO release may contribute to atherosclerosis and CVD. We hypothesized that
plasma MPO would increase in the postprandial state, due to leukocyte recruitment
and/or activation, especially in individuals with DM2. Baseline MPO was not
significantly different between NGM and DM2. Baseline MPO was weakly associated
with leukocytes and inversely associated with HDL-cholesterol. In the postprandial
phase, in contrast to our hypothesized increase, MPO decreased in NGM (both meal
types) and in DM2 (fat-rich meals). Based on these results we concluded that our
findings provide no support to our initial hypothesis that meal induced release of
MPO might be a mechanism that contributes to CVD risk in DM2.
Chapter 6 reviews the applicability of alanine aminotransferase (ALT) as a marker
of non-alcoholic fatty liver disease (NAFLD) and provides an overview of the
epidemiological studies that assessed the associations between ALT and the
metabolic syndrome, DM2 and CVD. We concluded that in epidemiological studies,
ALT is an adequate marker of NAFLD, provided that alcohol-intake as a
SUMMARY & SAMENVATTING
190
confounding or effect-modifying variable is considered, and provided that other
more rare causes of ALT elevation are excluded.
In a cross-sectional study in 64 normotriglyceridemic patients with DM2, we
studied the relation of ALT with endothelial function, measured as flow-mediated
vasodilatation (FMD), and with insulin sensitivity (M/I-value) (Chapter 7). We found
that ALT was significantly, albeit weakly, associated with FMD and M/I-value,
independent of obesity. Moreover, the relation of ALT with FMD was independent of
insulin sensitivity. These findings suggest that in DM2, elevated ALT is associated
with a more unfavorable CVD risk profile.
In the study described in Chapter 8 we used data from the Hoorn Study to assess
the prospective relation of ALT with all-cause mortality, CVD and coronary heart
disease (CHD) events in a population-based cohort of elderly Caucasian men and
women. We demonstrated that individuals with ALT levels in the upper tertile, had a
significantly higher risk for CHD events that those in the lower tertile, after
adjustment for components of the metabolic syndrome and traditional risk factors.
No significant associations were found between ALT and all-cause mortality and
CVD events, after applying the same adjustment. This study shows that individuals
with even slightly elevated ALT levels are at an increased risk for CHD events. The
pathogenic mechanisms underlying this association are not incompletely
understood and require further study.
In Chapter 9, we studied the prospective relation of ALT with incident metabolic
syndrome and DM2. We found that higher ALT levels were related to conversion to
the metabolic syndrome, after adjustment for confounding factors including
alcohol-intake, age and the individual components of the metabolic syndrome.
However, in contrast to previous reports, we could not demonstrate an
independent prospective relation between ALT and the risk of DM2. These results
suggest that the increased CVD risk might be mediated by metabolic derangements
that differ from those that result in hyperglycemia (Chapter 9).
The last chapter (Chapter 10) discusses the methodological and pathophysiological
aspects of the studies presented in this thesis.
SUMMARY & SAMENVATTING
191
SAMENVATTING VOOR NIET INGEWIJDEN
Het aantal gevallen van “ouderdomssuikerziekte” (type 2 diabetes mellitus) neemt
in de Westerse wereld snel toe als gevolg van veranderingen in leefstijl (verhoogde
consumptie van vet- en energierijke voeding en verminderde lichaamsbeweging),
en als gevolg van de vergrijzing van de bevolking. Mensen met type 2 diabetes
mellitus hebben een verhoogd risico op het krijgen van hart- en vaatziekten. Dit
verhoogde risico kan voor een deel verklaard worden door reeds bekende
risicofactoren voor hart- en vaatziekten, zoals hoge bloeddruk (hypertensie), roken,
afwijkende vetspiegels in het bloed (een verhoogd LDL-cholesterol (het “slechte”
cholesterol), laag HDL cholesterol (goede cholesterol) en verhoogde hoeveelheden
triglyceriden), alsook hoge glucose (suiker) concentraties in het bloed. Met name
bij vrouwen na de menopauze (overgang) is het risico op hart- en vaatziekten hoger
dan bij vrouwen vóór de menopauze. Na de overgang stijgt de hoeveelheid
triglyceriden en daalt de hoeveelheid HDL-cholesterol in het bloed. Dit heeft ons
aangespoord de effecten van maaltijden te bestuderen bij vrouwen na menopauze.
Het is al langer bekend dat glucose en triglyceriden, en verbindingen die hieruit
gevormd kunnen worden, schadelijk kunnen zijn voor de bloedvaten en daardoor
kunnen bijdragen aan het ontwikkelen van hart- en vaatziekten. Ruim een kwart
eeuw geleden is geopperd dat verstoringen in de vet- en glucosestofwisseling die
optreden na een maaltijd mogelijk kunnen bijdragen aan het verhoogde risico op
hart- en vaatziekten. Na het eten van een maaltijd stijgen de triglyceriden- en
glucoseconcentraties in het bloed. De triglyceriden en de glucose worden gebruikt
als bouwstoffen of als brandstoffen voor het lichaam. De mate waarin deze stoffen
worden verwerkt en opgeslagen verschilt tussen gezonde mensen en mensen met
type diabetes mellitus. De lever speelt hierbij een belangrijke rol. Een verhoogde
hoeveelheid vet in de lever (steatose), een aandoening die vaker voorkomt bij
mensen met diabetes en overgewicht, kan deze processen verstoren, alsook het
risico op hart- en vaatziekten verhogen. Het hormoon insuline speelt een
belangrijke rol bij het verwerken van de triglyceriden en de glucose. Bij mensen
met type 2 diabetes mellitus werkt het insuline minder goed waardoor de
triglyceriden en de glucose trager worden opgenomen en verwerkt. De vraag is
welke stof schadelijker is voor de vaatwand: de glucose of de triglyceriden?
De onderzoeken die in dit proefschrift beschreven zijn, proberen antwoord te
geven op de volgende vragen: 1a) hoe is het beloop van glucose en triglyceriden in
het bloed na twee vetrijke en twee koolhydraatrijke maaltijden, 1b) wat is de
relatieve bijdrage van glucose en triglyceriden in relatie tot hart- en vaatziekten en
SUMMARY & SAMENVATTING
192
1c) welke mechanismen kunnen het verhoogde risico op hart- en vaatzieken
verklaren bij patiënten met type 2 diabetes mellitus; 2a) hebben mensen met
leververvetting een verhoogde kans op het risico van hart- en vaatzieken en 2b) wat
zijn de mogelijke mechanismen waardoor dit verklaard kan worden?
In Hoofdstuk 1 geven we een korte beschrijving van type 2 diabetes mellitus en
het metabool syndroom (cluster van risicofactoren samenhangend met
overgewicht). Daarnaast geven we een kort overzicht van bekende en nieuwe
determinanten voor hart- en vaatziekten bij mensen met type 2 diabetes mellitus.
Tenslotte beschrijft dit hoofdstuk de vraagstellingen en de opbouw van dit
proefschrift.
Hoofdstuk 2 beschrijft het beloop van glucose en triglyceriden in het bloed na
twee vetrijke dan wel koolhydraatrijke maaltijden in post-menopauzale vrouwen
met en zonder type 2 diabetes mellitus. Ongeveer de helft van de vrouwen met
type 2 diabetes mellitus werd behandeld met cholesterolverlagende medicatie
(statines) en gezien het mogelijke effect op de vetstofwisseling werden de
resultaten voor deze vrouwen afzonderlijk gepresenteerd. Bij vrouwen met diabetes
(met en zonder statines) waren de hoeveelheid triglyceriden en glucose hoger dan
bij de gezonde vrouwen. We vonden echter geen verschil in de toename van
triglyceriden na de vetrijke maaltijden tussen de vrouwen met diabetes en de
gezonde vrouwen. Bij de gezonde vrouwen werd een verband gezien tussen de
stijging van de triglyceriden concentratie na de vetrijke maaltijden en de nuchtere
triglyceriden waarde, het totaal cholesterol, het HbA1c (afspiegeling van de
gemiddelde hoeveelheid glucose in het bloed gedurende 6-8 weken) en het HDL-
cholesterol. Bij vrouwen met diabetes was de nuchtere triglyceride waarde de
belangrijkste bepalende factor voor de triglyceride waarden na beide vetrijke
maaltijden. De glucosewaarde na de koolhydraatrijke maaltijden werd bij de
gezonde vrouwen voornamelijk bepaald door leeftijd en de nuchtere
triglyceridenwaarde en in de vrouwen met diabetes door HbA1c.
Het onderzoek naar het mogelijke verband tussen de bijdrage van de nuchtere en
post-prandiale (na de maaltijd) glucose en triglyceridenconcentraties en
manifestaties van hart- en vaatziekten is beschreven in Hoofdstuk 3. De dikte van
de vaatwand (intima-media dikte) van de halsslagader gemeten met een
echotechniek werd gebruikt als maat voor het risico op hart- en vaatziekten. We
vonden dat postprandiale glucosewaarden gerelateerd was aan de intima-media
dikte bij de gezonde vrouwen en niet bij de vouwen met type 2 diabetes mellitus.
SUMMARY & SAMENVATTING
193
De postprandiale triglyceridenconcentraties waren niet gerelateerd aan de intima-
media dikte. We concludeerden dat de glucosespiegel na de maaltijd een indicator
kan zijn voor hart- en vaatziekten bij vrouwen zonder type 2 diabetes mellitus.
In Hoofdstuk 4 bestudeerden we het effect van twee vetrijke en van twee
koolhydraatrijke maaltijden op de vorming van geoxideerde LDL-cholesterol
deeltjes (oxLDL), de reactieve suikerverbinding 3-deoxyglucoson (3DG) en twee
verschillende versuikerde eiwitverbindingen (de zogenaamde ‘advanced glycation
end-products’ CML en CEL). Van deze verschillende verbindingen wordt
verondersteld dat ze mogelijk een relatie hebben met hart- en vaatziekten. Over de
vorming van deze verbindingen na de maaltijd is weinig bekend. We vonden dat de
nuchtere waarden van oxLDL, 3DG en CML hoger waren bij de vrouwen met type 2
diabetes mellitus dan bij de gezonde vrouwen. De nuchter waarden van deze
verbindingen bij vrouwen met type 2 diabetes waren vergelijkbaar met de
postprandiale waarden bij de gezonde vrouwen. Het oxLDL correleerde met de
hoeveelheid postprandiale triglyceriden na de vetrijke maaltijden en het 3DG
correleerde met hoeveel postprandiale glucose na de vetrijke maaltijd. We
concludeerden dat oxLDL, 3DG en CML mogelijk kunnen bijdragen aan het
voorhoogde risico op hart- en vaatziekten.
In Hoofdstuk 5 besschrijven we het effect van twee koolhydraatrijke en twee
vetrijke maaltijden op de vorming van myeloperoxidase. Dit is een enzym dat in
witte bloed cellen (leukocyten) wordt gemaakt en dat betrokken is bij de afweer van
infecties. Een aantal studies hebben een relatie laten zien met hart- en vaatzieken.
We onderzochten of dit enzym toe neemt na de maaltijden en in het bijzonder bij
de vrouwen met type 2 diabetes mellitus. Tot onze verbazing zagen we, ondanks
de stijging van de leukocyten, geen toename, maar juist een afname van het enzym
na beide maaltijdtypen. Dit gebeurde zowel bij de gezonde vrouwen als bij de
vrouwen met type 2 diabetes mellitus. We concludeerden dat myeloperoxidase na
de maaltijd geen belangrijk factor is in relatie tot hart- en vaatziekten.
Hoofdstuk 6 introduceert de niet-alcoholische leververvetting, een vorm van
leververvetting die optreedt bij mensen die niet of matig alcohol nuttigen. Deze
vorm wordt vaak gezien bij mensen met overgewicht en type 2 diabetes mellitus. In
de onderzoeken die in dit proefschrift beschreven zijn, wordt het leverenzym
alanine aminotransferase (ALAT) gebruikt als indicator voor leververvetting. Dit
hoofdstuk beschrijft de toepasbaarheid van ALAT in relatie tot leververvetting.
SUMMARY & SAMENVATTING
194
In Hoofdstuk 7 vonden we dat bij goed ingestelde patiënten met type 2 diabetes
mellitus, een hoge ALAT waarde in het bloed, als marker voor leververvetting,
samenhangt met een verminderde endotheel-afhankelijke vaatverwijding (geeft een
indruk over de werking van het endotheel, de “binnenbekleding” van de bloedvaten)
en een verminderde insulinegevoeligheid. Deze bevinding suggereert dat mensen
met leververvetting een verhoogde kans hebben op hart- en vaatziekten. Alanine
aminotransferase, endotheel-afhankelijke vaatverwijding en de insuline
gevoeligheid zijn echter op één moment gemeten, waardoor er geen uitspraak
gedaan kan worden over oorzaak en gevolg.
De vraag of een verhoogde ALAT waarde gepaard gaat met het optreden van hart-
en vaatzieken wordt beantwoord in Hoofdstuk 8. In de Hoorn Studie (een
bevolkingsonderzoek van mannen en vrouwen tussen 50 en 75 jaar) vonden wij dat
een hoge ALAT waarde, als marker voor leververvetting, samenhangt met het
optreden van kransslagaderaandoeningen (coronaire hartziekten) gedurende een
vervolgperiode van 11 jaar. Deze relatie kon niet verklaard worden door reeds
bekende risicofactoren. Onze conclusie was dan ook dat mensen met
leververvetting een verhoogd risico hebben op coronaire hartziekten.
Hoofdstuk 9 beschrijft de rol van ALAT als voorspeller (na 6 jaar) voor het
optreden van type 2 diabetes mellitus en het metabool syndroom in de Hoorn
Studie. We vonden dat ALAT type 2 diabetes voorspelt, maar deze relatie kon deels
verklaard worden door reeds bekende risicofactoren voor diabetes mellitus, zoals
een licht verhoogde hoeveelheid glucose in het bloed en het hebben van
overgewicht. ALAT voorspelde wel het optreden van het metabool syndroom.
In het laatste hoofdstuk (Hoofdstuk 10) worden de bevindingen van dit
proefschrift bediscussieerd, de relevantie voor de klinische praktijk beschreven en
worden suggesties gedaan voor verder onderzoek op dit gebied.
SUMMARY & SAMENVATTING
195
DANKWOORD
196
DANKWOORD
Velen hebben bijgedragen aan de totstandkoming van dit proefschrift en graag wil ik hen
danken voor hun bijdrage.
De deelnemers van de Hoorn VeT studie, de Hoorn studie en de “triglyceriden-thuismeet”-
studie, wil ik bedanken voor hun deelname: zonder uw medewerking geen
wetenschappelijk onderzoek!
Mijn promotor, prof. dr. R. J. Heine, beste Rob, dank voor de mogelijkheid en de ruimte om
dit onderzoek te doen, en je aanstekelijk enthousiasme over alles wat met
diabetes(onderzoek) te maken heeft.
Mijn copromotor, dr. M. Diamant, beste Michaela, wat een energie en enthousiasme heb jij,
jouw kennis is ongeëvenaard en ik ben zeer vereerd om één van jouw eerste promovendi te
mogen zijn. Dank voor je begeleiding en de nodige pep-talks, ik heb heel veel van je
geleerd!
Mijn copromotor, dr. T. Teerlink, beste Tom, jouw commentaren op mijn manuscripten
lagen meestal al eerste in mijn email-box, dank voor al je ondersteuning en meedenken bij
de Hoorn VeT studie en de (vele) pilotstudies.
Dr. M. Alssema, beste Marjan, samen hebben we deze onderzoeks-“klus” geklaard, dank
voor de geweldige samenwerking en de gezelligheid. Ik vind het een enorme eer dat je
mijn paranimf wilt zijn. Heel veel succes en inspiratie in je verdere carrière!
De Hoorn VeT studie was zeker niet mogelijk geweest zonder de enorme inzet van Jannet
Entius, Tanja Nansink, Lida Ooteman and Marianne Veeken, die zorg droegen voor het
versturen van de brieven, bellen van de deelnemers, invriezen van de enorme hoeveelheid
bloedmonsters,… en de vele gezellige momenten tijdens het “veld-werk” in het Diabetes
Onderzoek Centrum in Hoorn. Jannet, Tanja, Lida en Marianne, heel veel dank voor jullie
inzet! Marianne, super dat je mijn paranimf wilt zijn!
DANKWOORD
197
Jolanda Bosman, dank voor je hulp en meedenken in het opzetten en plannen van de
Hoorn VeT studie binnen het Diabetes Onderzoek Centrum in Hoorn.
De overige begeleiders van de Hoorn VeT studie: prof. dr. ir. J. M. Dekker, beste Jacqueline,
dank voor al je input bij de Hoorn VeT studie, en je hulp bij het analyseren van de Hoorn
Studie data. Prof. dr. G. Nijpels, beste Giel, dank voor je input en je commentaren op mijn
manuscripten. Dr P. J. Kostense, beste Piet, dank voor het meedenken bij de opzet van de
Hoorn VeT studie en je veelvuldige uitleg over de meest (in mijn ogen dan) ingewikkelde
statistische technieken.
Dr. P. G. Scheffer, beste Peter, dank voor al je (lab-technische) input, en je begeleiding bij
het MPO-stuk. Ik ben vereerd dat je plaats neemt in de leescommissie en zeer benieuwd
naar je opmerkingen en vragen tijdens de verdediging.
De overige leden van de leescommissie: prof. dr. J. C. Seidell, prof. dr. Y. M. Smulders,
prof. dr. U. Smith en dr. E. S. G. Stroes. Ik dank u voor het beoordelen van het manuscript
van mijn proefschrift en de tijd die u hiervoor heeft vrijgemaakt.
Prof. dr. L. M. Bouter en prof. dr. C. D. A. Stehouwer, dank voor uw snelle en vaak
kleurrijke commentaren op mijn Hoorn Studie manuscripten. Ik ben er veel van mogen
leren.
De overige co-auteurs, dr. S. J. L. Bakker, dr. R.A. van Dijk, dr. C. G. Schalkwijk en drs. M.E.
Tushuizen, beste Stephan, Rob, Casper en Maarten, dank voor jullie bijdragen!
Danielle van Assema, dank voor je bijdrage aan de 3DG-pilot studie.
De medewerkers van de afdeling Klinische Chemie, in het bijzonder Rob Barto, Sigrid de
Jong, Jeany Huijser, Ina Kamphuis, Astrid Kok, Harry Twaalfhoven, Rick Vermue: dank voor
jullie ondersteuning en/of het uitvoeren van de vele laboratoriumbepalingen.
Alle oud-collega’s van de afdeling Endocrinologie, het EMGO instituut en het Diabetes
Onderzoek Centrum wil ik danken voor de prettige samenwerking en de gezellige
momenten. Annemarie, Carolien, Daniël, Esther, Josina, Hata, Karlijn, Katja, Laura, Luuk,
DANKWOORD
198
Maarten, Marieke, Mathijs, Natalia, Nynke, Renate, Sigridur en Wiebe, heel veel succes met
(het afronden van) jullie (promotie)onderzoeken!
Mijn lieve familie, schoonfamilie en vrienden wil ik bedanken voor hun belangstelling en
alle gezeligheid naast het onderzoek.
Pap en mam, heel veel dank voor jullie onvoorwaardelijk liefde en steun om te worden wie
ik nu ben.
Lieve Maurice, wat zou ik zijn zonder jou. Dank voor al je steun en liefde. Op naar ons
volgende avontuur… Ti amo da morire!
199
LIST OF PUBLICATIONS
200
LIST OF PUBLICATIONS
1. Schindhelm RK, Hospers HJ. Sex with men before coming-out: relation to sexual activity
and sexual risk-taking behavior. Arch Sex Behav 2004;33:585-91.
2. Schindhelm RK, Diamant M, Bakker SJ, Van Dijk RA, Scheffer PG, Teerlink T, Kostense PJ,
Heine RJ. Liver alanine aminotransferase, insulin resistance and endothelial dysfunction in
normotriglyceridaemic subjects with type 2 diabetes mellitus. Eur J Clin Invest
2005;35:369-74.
3. Schindhelm RK, Dekker JM, Nijpels G, Heine RJ, Diamant M. No independent association
of alanine aminotransferase with risk of future type 2 diabetes in the Hoorn Study (letter).
Diabetes Care 2005;28:2812.
4. Schindhelm RK, Hospers HJ. Mannen die seks hebben met mannen vóór de coming-out:
relatie met seksuele activiteit en seksueel risicogedrag. Tijdschrift voor Seksuologie
2006;30:10-5.
5. Schindhelm RK, Diamant M, Dekker JM, Tushuizen ME, Teerlink T, Heine RJ. Alanine
aminotransferase as a marker of non-alcoholic fatty liver disease in relation to type 2
diabetes mellitus and cardiovascular disease (review). Diabetes Metab Res Rev
2006;22:437-43.
6. Schindhelm RK, Diamant M, Heine RJ. Non-alcoholic fatty liver disease as a determinant of
cardiovascular disease: response to Targher (letter). Atherosclerosis 2007;190:20-21.
7. Schindhelm RK, Dekker JM, Nijpels G, Bouter LM, Stehouwer CD, Heine RJ, Diamant M,
Alanine aminotransferase predicts coronary heart disease events: a 10-year follow-up of
the Hoorn Study. Atherosclerosis 2007;191:391-396.
8. Schindhelm RK, Dekker JM, Nijpels G, Stehouwer CD, Bouter LM, Heine RJ, Diamant M.
Alanine aminotransferase and the 6-year risk of the metabolic syndrome in Caucasian men
and women: The Hoorn Study. Diabet Med 2007;24;430-435.
9. Schindhelm RK, Diamant M, Heine RJ. Nonalcoholic fatty liver disease and cardiovascular
disease risk. Curr Diab Rep (in press).
LIST OF PUBLICATIONS
201
10. Alssema M, Schindhelm RK, Dekker JM, Diamant M, Kostense PJ, Teerlink T, Scheffer PG,
Nijpels, G, Heine RJ. Postprandial glucose, and not triglyceride concentrations are
associated with carotid intima media thickness in women with normal glucose
metabolism: the Hoorn Prandial Study. Atherosclerosis (in press).
11. Schindhelm RK, Alssema M, Scheffer PG, Diamant M, Dekker JM, Bart R, Nijpels G,
Kostense PJ, Heine RJ, Schalkwijk CG, Teerlink T. Fasting and postprandial glycoxidative
and lipoxidative stress are increased in women with type 2 diabetes mellitus. (submitted).
12. Schindhelm RK, Alssema M, Diamant M, Teerlink T, Dekker JM, Kok A, Kostense PJ,
Nijpels G, Heine RJ, Scheffer PG. Comparison of the effect of two consecutive fat-rich and
carbohydrate-rich meals on levels of postprandial plasma myeloperoxidase in women with
and without type 2 diabetes mellitus. (submitted).
13. Alssema M*, Schindhelm RK*, Dekker JM, Diamant M, Nijpels G, Teerlink T, Scheffer PG,
Kostense PJ, Heine RJ. Determinants of postprandial triglyceride and glucose responses
following two consecutive fat-rich or carbohydrate-rich meals in normoglycemic women
and in women with type 2 diabetes: the Hoorn Prandial study. (submitted). (*both authors
contributed equally).
14. Alssema M, Schindhelm RK, Rijkelijkhuizen JM, Kostense PJ, Teerlink T. Nijpels G, Heine
RJ, Dekker JM. Meal-composition affects insulin secretion in women with type 2 diabetes: a
comparison with healthy controls. The Hoorn Prandial Study. (submitted).
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About the author
Roger Schindhelm was born on February 1st, 1974 in Kerkrade, The Netherlands.
After graduation from secondary school (pre-university education, Sancta Maria
College, Kerkrade) in 1992, he was trained as a biomedical laboratory technician
in clinical chemistry at the university of professional education Limburg and
received his bachelor’s degree in 1996. Subsequently, he studied medicine at
Maastricht University and graduated as a medical doctor (MD) in August 2002.
From October 2002 until December 2006, he was a PhD-student at the Diabetes
Center at the Department of Endocrinology of the VU University Medical Center
in Amsterdam, under supervision of Prof. Robert J. Heine and Drs. Michaela
Diamant and Tom Teerlink. During that period he completed the Postgraduate
Epidemiology Program from the EMGO Institute and received a Master of
Epidemiology degree in 2005.
Over de auteur
Roger Schindhelm is geboren op 1 februari 1974 in het Zuid-Limburge
Kerkrade. Na het afronden van de middelbare school in 1992 (VWO, Sancta
Maria College in Kerkrade), volgde hij de opleiding tot klinisch-chemisch analist
aan de Hogeschool Limburg en behaalde hij zijn HBO-getuigschrift in 1996.
Vervolgens studeerde hij geneeskunde aan de Universiteit Maastricht en slaagde
hij in 2000 voor zijn doctoraalexamen en in 2002 voor zijn artsexamen. Vanaf
oktober 2002 tot en met december 2006 was hij werkzaam als artsonderzoeker
bij het Diabetes Centrum van de afdeling Endocrinologie van het VU medisch
centrum in Amsterdam onder begeleiding van prof. dr. Rob Heine en dr.
Michaela Diamant (afdeling Endocrinologie), en dr. Tom Teerlink (afdeling
Klinische Chemie). Tijdens deze periode volgende hij de postdoctorale opleiding
epidemiologie van het EMGO Instituut welke in 2005 werd afgerond met
registratie tot Epidemioloog A bij de Vereniging voor Epidemiologie.