serum hepcidin levels in diabetes mellitus...
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SERUM HEPCIDIN LEVELS IN DIABETES MELLITUS
DISSERTATION
Submitted to
THE TAMILNADU DR.MGR MEDICAL UNIVERSITY
In partial fulfilment for the degree
DEGREE OF MEDICINE
IN
BIOCHEMISTRY - BRANCH XIII
APRIL 2017
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SERUM HEPCIDIN LEVELS IN DIABETES MELLITUS
DISSERTATION
Submitted to
THE TAMILNADU DR.MGR MEDICAL UNIVERSITY
In partial fulfilment for the degree
DEGREE OF MEDICINE
IN
BIOCHEMISTRY - BRANCH XIII
APRIL 2017
DEPARTMENT OF BIOCHEMISTRY
CHRISTIAN MEDICAL COLLEGE
VELLORE-632002, INDIA
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CERTIFICATE
This is to certify that the study titled "SERUM HEPCIDIN LEVELS IN
DIABETES MELLITUS" is the bona fide work of Dr. Padmanaban V, who
conducted it under the guidance and supervision of Dr. Molly Jacob, Professor
of Biochemistry, Christian Medical College, Vellore. The work in this
dissertation has not been submitted to any other university for the award of a
degree.
Dr. Molly Jacob,
Professor and Head of the Department,
Department of Biochemistry,
Christian Medical College
Vellore.
Dr. Anna B. Pulimood,
Principal,
Christian Medical College,
Vellore.
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DECLARATION
I hereby declare that the investigations, which form the subject matter of this
study, were conducted by me under the supervision of Dr. Molly Jacob,
Professor of Biochemistry, Christian Medical College, Vellore.
Dr. Padmanaban V,
PG Registrar,
Department of Biochemistry,
Christian Medical College,
Vellore.
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PLAGIARISM CHECK
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ACKNOWLEDGEMENTS
I take this opportunity to express my thanks and profound gratitude to the following
people for their support and encouragement which made this work possible.
Dr. Molly Jacob, my guide and mentor. I am grateful for her patience, valuable time,
guidance, encouragement, care and support. I am indebted to her for what she has
been to me.
Dr. Jasmine Prasad, Professor, Department of Community Health And Development
(CHAD), CMC, Vellore, for guidance, support and help in recruitment of patients.
Dr. Joe Varghese, my co-guide for his guidance, encouragement, technical support
and valuable opinions.
Dr.Manjunath, Assistant Professor, Department of Community Health And
Development (CHAD), CMC, Vellore, for help in recruitment of patients.
Dr. Minnie Faith, Dr. Dhayakani Selvakumar, Dr. Premila Abraham for their constant
encouragement and support.
Dr. Anand R, Dr. Prakash SS, Dr Asmita Hazra, Dr.Muthuraman for their
encouragement and support.
Dr. Victoria Job and Mr. Joseph Dian Bondu, Department of Clinical Biochemistry
for their help and support.
Dr. Arthi TS for her assistance in sample collection and support.
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Dr.Jagadish, Dr. Mathuravalli, Dr. Rosa Mariam Mathew, Mr. Jithu James,
Dr.Gopinath for their cheerful presence and support.
Mr. Sridhar, Mr. Issac, Mr. Lalu, Mr. Kumerasan for their assistance and support
Mrs. Punitha Martin for secretarial help.
I thank my parents, my sister and my friends for always being there for me.
I gratefully acknowledge CMC’s Fluid Research Funds for financial support for this
study (IRB Min No. 9235, Dated 12.1.2015).
I gratefully acknowledge the Indian Council of Medical Research, New Delhi, for an
MD thesis grant for financial support for this study (No: 3/2/May-2015/PG-Thesis-
HRD-18).
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Table of contents
Chapter No. Title Page number
1 Abstract 9
2 Review of literature 11
3 The study 49
4 Materials 50
5 Methods 52
6 Results 67
7 Discussion 87
8 Conclusions 96
9 Limitations of the study 96
10 Bibliography 97
11 Appendices
Appendix 1-Letter of
approval from Institutional
Review Board (IRB)
121
Appendix 2 -Information
sheet and consent form
125
Appendix 3- Proforma for
study
131
Appendix 4
(Master data sheet)
133
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ABSTRACT
Serum hepcidin levels in diabetes mellitus
Introduction
Diabetes mellitus is known to be associated with increased levels of body iron stores.
The mechanisms that link these conditions are unclear. Hepcidin is known to play a
key role in iron homeostasis. It is possible that changes in hepcidin may underlie the
association between these two conditions. What little data are available in this area are
inconclusive.
Aim
To estimate serum levels of hepcidin and iron-related parameters in adults diagnosed
to have diabetes mellitus, and compare these with levels of these parameters in those
without diabetes mellitus.
Methods
Informed consent was obtained to collect fasting blood samples from adult males who
were diagnosed, for the first time, to have diabetes mellitus (DM). Age-matched males
with fasting glucose values within the reference range served as control subjects. Each
sample obtained was used to estimate haemoglobin, glucose, C-reactive protein
(CRP), ferritin, total iron-binding capacity (TIBC), iron and hepcidin. Anthropometric
measurements were also made on each subject.
Results
Twenty one subjects were studied in each group. Both groups were similar in age and
anthropometric measurements. There were no differences in values of haemoglobin,
TIBC, transferrin saturation and serum iron between controls and diabetics. Serum
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ferritin levels were significantly higher in those with DM (median [interquartile
range]: 147 [114-251.5] ng/ml) when compared with controls (79 [46.65-155.5]
ng/ml). Serum hepcidin levels were similar in the 2 groups. Ratio of hepcidin to
ferritin was significantly lower in those with DM when compared with control
subjects. Serum ferritin showed significant positive correlation with fasting plasma
glucose, serum iron, serum hepcidin and transferrin saturation. It also showed
significant negative correlations with total iron-binding capacity, hepcidin-ferritin
ratio, BMI, waist and hip circumference. Hepcidin-ferritin ratio was negatively
correlated with fasting plasma glucose levels. When adjusted for fasting plasma
glucose levels, in multiple regression analysis, it was found that serum ferritin had
significant positive correlation with serum hepcidin, hepcidin-ferritin ratio, serum
iron and transferrin saturation.
Conclusions
Serum ferritin, a marker of body iron stores, was significantly increased in subjects
with diabetes mellitus, but hepcidin levels were not. However, these patients had
lower hepcidin-ferritin ratio, showing that serum hepcidin levels were inappropriately
low for the increased serum ferritin levels seen in these subjects. This suggests that the
biological response of the body to increase hepcidin levels in response to increased
body iron levels seems to be blunted in diabetic subjects. This finding requires
confirmation in larger samples and its significance requires exploration.
Keywords : Hepcidin, diabetes mellitus, ferritin, iron
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REVIEW OF LITERATURE
Introduction
As a developing country, India faces the rising threat of non-communicable diseases,
while still struggling to control communicable diseases (Nongkynrih et al., 2004).
Increases in life expectancy and improved standards of living coupled with a
sedentary lifestyle have led to an explosion of lifestyle diseases in the past few
decades (Lee et al., 2012; Upadhyay, 2012). Diabetes mellitus takes centre stage in
this scenario, due to its high prevalence and its impact on society, not just in terms of
mortality and morbidity but also due to its impact on the economy (Bloom et al.,
2014). Diabetes mellitus is no new disease, recorded as ‘madumeha’ in ancient Indian
texts, and has been on the rise in recent times (Dwivedi and Dwivedi, 2007). A
glimpse at numbers will shed some light on the impact of the disease. Currently, there
are 415 million people suffering from diabetes worldwide, of which India’s share is a
little under 70 million (IDF Diabetes, 2015). The prevalence of diabetes among the
adult population is an alarming 9.3% in India. The International Diabetes Federation
(IDF) has projected that by the year 2040, over 123 million people will suffer from
diabetes in India, which amounts to an adult prevalence rate of 10.1% (IDF Diabetes,
2015). An exponential increase in urbanisation, decrease in physically active labour
andchanges in diet have contributed to the rise in prevalence of diabetes and other
non-communicable diseases (Mohan et al., 2007). The economic impact of diabetes
in the country is significant too; it is expected to cost $150 billion in the period from
2012 to 2030 (Bloom et al., 2014).
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Definition and classification of diabetes mellitus
Diabetes mellitus is a disease of energy metabolism associated with chronic
hyperglycaemia (American Diabetes Association, 2010). This generally results from
inappropriate secretion or action of insulin or both (Pratley and Weyer, 2001; Weyer
et al., 1999). Chronic hyperglycaemia leads to multi-organ system dysfunction and
even failure, resulting in significant morbidity and mortality in people with diabetes
(Fowler, 2008).
Diabetes mellitus is classified into several types based on etiology (American
Diabetes Association, 2010):
1. Type 1 diabetes mellitus: Due to insulin deficiency. Occurs due to autoimmune
or idiopathic destruction of pancreatic β-cells
2. Type 2 diabetes mellitus: Characterised by reduced insulin sensitivity and
defect in insulin secretion
3. Gestational diabetes mellitus occurring in pregnant women
4. Other specific forms such as:
o β-cell genetic defects: Mitochondrial disorders, Maturity-onset diabetes
of the young (MODY),
o Defects in insulin action due to genetic causes: Type A insulin
resistance, lipoatrophic diabetes, Rabson-Mendenhall syndrome
o Exocrine pancreas disorders: Pancreatitis, fibrocalculous pancreatopathy
o Endocrinopathies: Acromegaly, Cushing syndrome
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Pathogenesis of type 2 diabetes mellitus
Most cases of diabetes mellitus (90-95%) come under the category of Type 2 diabetes
mellitus (American Diabetes Association, 2010). The etio-pathogenesis of this
condition remains elusive even after extensive research for more than a century.
Even during ancient times, it was known to Indian physicians that subjects with
features of diabetes were obese and led a sedentary lifestyle (Dwivedi and Dwivedi,
2007). Later, during the 20th
century, researchers have investigated the etiology of
what they called non-insulin dependent diabetes mellitus (Ahmed, 2002). Since then,
it was generally known that obesity was associated with diabetes mellitus, which was
later confirmed by multiple epidemiological studies (Adams, 1929; Medley and
others, 1965; Munro et al., 1949; Preble, 1923). Extensive studies into the etio-
pathogenesis of diabetes mellitus, over the century that followed, unravelled a
complex disease process, which is still not fully understood. The following section
reviews what is known so far about the pathogenesis of diabetes mellitus.
Insulin resistance
Insulin is a major hormone that regulates energy metabolism. It is the principal
hormone which promotes anabolism during periods of food intake. Its actions, such as
increasing glucose uptake by cells and inhibiting glycogenolysis and gluconeogenesis,
result in a decrease in blood glucose levels (Saltiel and Kahn 2001). Insulin resistance
is clinically defined as the inability of insulin to elicit appropriate glucose-lowering
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effects, i.e. glucose uptake and use, when compared to a normal population (Lebovitz,
2001).
Insulin is secreted from the β cells of the pancreas in response to a rise in blood
glucose levels. It acts by binding with its receptor, which is present in most tissues
(Freychet et al., 1971). A deficiency in the action of insulin is termed insulin
resistance. A failure of compensatory insulin secretion in response to hyperglycaemia
results in type 2 diabetes mellitus in most cases (Weyer et al., 1999).
Pathogenesis of insulin resistance
Insulin resistance is usually regarded as an early event in the development of diabetes
mellitus (Martin et al., 1992). It is generally known that a majority of cases with
insulin resistance are associated with obesity(Greenfield and Campbell, 2004).
Mechanisms linking obesity and insulin resistance have been extensively studied and
though much is known about it, knowledge in this area is still incomplete.
Obesity is a dysmetabolic state where there is an energy surplus which ultimately
leads to storage of excess energy as fat in adipose tissue. There are several known
mechanisms by which adipose tissue can contribute to the development of insulin
resistance.
The Function of adipose tissue is to store energy. It is also a dynamic organ with
multiple roles to play in regulating metabolism. It is now considered an endocrine
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organ, as it secretes hormone-like substances (Galic et al., 2010). It also secretes
several inflammatory signals and hormones that are involved in energy homeostasis.
These secreted substances are termed as adipokines or adipocytokines (Fantuzzi,
2005). Examples include leptin, adiponectin, resistin, adipsin and visfatin (Bełtowski,
2003; Fantuzzi, 2005; Maffei et al., 1995).
Leptin is a peptide hormone; it is called the satiety hormone as it suppresses hunger by
acting on the hypothalamus (Pelleymounter et al., 1995). Its levels are increased in
obesity due to increase in adipose tissue mass (Zimmet et al., 1996). Genetic defects
in leptin or the leptin receptor leads to obesity, but are uncommon (Clément et al.,
1998; Montague et al., 1997). Leptin resistance, where leptin is not as effective as in
normal subjects, is suggested to play a role in development of obesity (Münzberg and
Myers, 2005).
Adiponectin is known to play a role in glucose and fatty acid metabolism (Karbowska
and Kochan, 2006). Its levels in blood are negatively associated with insulin
sensitivity (Tschritter et al., 2003).
Obesity is a pro-inflammatory state. Macrophage infiltration is seen in obesity, with
an inflammatory process ensuing subsequently (Zeyda and Stulnig, 2009). This leads
to the production of inflammatory cytokines which may play an important role in
developmentof insulin resistance (Visser et al., 1999; Yudkin et al., 1999). Tumour
necrosis factor-α (TNF- α), secreted by adipocytes, and IL-6, secreted by macrophages
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residing in adipose tissue, have been associated with reduced insulin sensitivity
(Hotamisligil and Spiegelman, 1994; Yudkin et al., 2000). Retinol-binding protein-4,
secreted from adipocytes, has also been implicated in the pathogenesis of insulin
resistance (Graham et al., 2005).
Obesity-associated insulin resistance is characterised by increased release of non-
esterified fatty acids (NEFA) from adipocytes (Kahn et al., 2006). NEFA are released
during lipolysis of triglycerides. Its concentration in the blood is increased in obesity
and bears a positive correlation with insulin resistance (Boden, 1997). Even an acute
administration of NEFA has been shown to reduce insulin sensitivity (Bevilacqua et
al., 1987). There are several ways in which this may occur. NEFA can compete with
glucose for oxidation; it can inhibit several enzymes of glycolysis (Randle et al., 1963,
1994). Mostly, NEFA produces indirect effects via its derivatives such as
diacylglycerol, fatty acyl-CoA and ceramide. These lipids can act as signalling
molecules and bring about multiple changes in the cell, contributing to impaired
insulin signalling (Boden, 1997). For example, they can phosphorylate insulin
receptor 1 and 2 (IRS-1 and IRS-2), which are proteins involved in insulin signalling;
this reduces their ability to activate phosphatidylinositol 3 (PI-3) kinase, there by
reducing insulin sensitivity (Boden, 1997).
There is evidence to suggest that ectopic accumulation of fat in the liver and muscle
may also contribute to the development of insulin resistance (Yki-Järvinen, 2002). In
this regard, concentrations of intramyocellular lipid and intrahepatic lipid are also
17
associated with impaired peripheral and hepatic insulin sensitivity respectively (Snel
et al., 2012).
Pancreatic β-cell dysfunction
Insulin resistance alone can not result in diabetes mellitus; most people who develop
diabetes mellitus usually have a preceding period when they are insulin-resistant, but
remain euglycaemic. This is on account of the remarkable adaptability of β-cells of
the pancreas to secrete increased amounts of insulin to meet the demand. There is a
feedback loop between β-cell function and and insulin sensitivity in normal people.
Insulin resistance will lead to increase in insulin secretion (Kahn et al., 1993).
Diabetes mellitus develops only when this compensation by β-cells fails (Meier and
Bonadonna, 2013; Ward et al., 1984). It is now generally agreed that a certain degree
of β-cell dysfunction is necessary for the development of type 2 diabetes mellitus
(Meier and Bonadonna, 2013).
Pathogenesis of pancreatic β-cell dysfunction
Compensation by β-cells is usually the initial event in type 2 diabetes mellitus (Prentki
and Nolan, 2006). Hyperglycaemia causes insulin release by producing more ATP. It
does this by increasing expression of glycolytic enzymes, which increases the
production of pyruvate and subsequently ATP via the citric acid cycle (Farfari et al.,
2000). Glucose also increases the synthesis of insulin by stimulation of several
transcription factors (Macfarlane et al., 1999). Apart from glucose, NEFA also
stimulate β-cells to secrete increased amount of insulin. NEFA stimulate calcium
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influx and insulin secretion by binding with their receptor, free fatty acid
receptor1 (FFA1) (also known as GPR40), which is a G-protein coupled receptor
(Schnell et al., 2007).These mechanisms account for the increase in β-cell function in
response to insulin resistance. An increase in β-cell mass is also seen as a response to
insulin resistance. This occurs by hypertrophy, hyperplasia and even neogenesis of β-
cells from stem cells (Cerf, 2013). These changes are brought about by various signals
that are activated mainly by glucose and NEFA (Nolan et al., 2006; Weir and Bonner-
Weir, 2007). Insulin has been shown to act via IRS1 and IRS2 to stimulate the growth
of β-cells (Burks and White, 2001). Several transcription factors, such as PDX1, are
involved in β-cell proliferation in this context (Prentki, 2006). Incretins such as GLP-1
also promotes β-cell proliferation (Egan et al., 2003).
Apoptosis of β-cells is inhibited by overexpression of anti-apoptotic factors (Buteau et
al., 2004). Stems cells undergoes differentiation to form β-cells (Bonner-Weir,
2000).The net result is an increase in the mass of β-cells, which secrete increased
amounts of insulin to compensate for the reduced insulin sensitivity. If the overall
increase in mass and function of β-cells are sufficient to compensate for the prevailing
insulin resistance, the patient will remain euglycaemic. Many obese subjects with
insulin resistance will never develop diabetes mellitus as their β-cells adapt
adequately. Thus, β-cell adaptability is a critical factor in determining progression of
insulin-resistant subjects to diabetes mellitus (Kahn, 2003). What causes dysfunction
and failure of β-cells and how this happens are frequently asked questions in research
on diabetes mellitus. Ironically, glucose and NEFA (which caused increased insulin
19
secretion in the short term) are considered to be the most important factors involved in
producing β-cell dysfunction and failure (Poitout et al., 2010). Increased supply of
nutrients and accelerated production of ATP result in excess generation of reactive
oxygen species (ROS). ROS formation is increased in glucolipotoxicity (El-Assaad et
al., 2010). Mitochondrial dysfunction and alterations in uncoupling protein (UCP) are
also known to be involved in increased ROS production. ROS has multiple effects on
β-cells, such as inducing inflammation, promoting apoptosis and possibly affecting
insulin synthesis (Poitout and Robertson, 2008). One of the features in β-cell
dysfunction is the process of β-cell de-differentiation, where the cells lack expression
of adequate levels of several important proteins required for maintaining β-cell
function (Talchai et al., 2012).
Another mechanism proposed for β-cell dysfunction and failure is endoplasmic
reticulum (ER) stress. This happens because of highly increased rates of insulin
synthesis; this results in ER stress due to the limited capability of endoplasmic
reticulum in protein folding. ER stress then contributes to impaired function and even
death of β- cells (Laybutt et al., 2007). Studies in humans and animal models have
shown that in diabetes, the mass of β-cells is significantly lesser; this is primarily
because of increased apoptosis, reduced β-cell proliferation, as described above
(Donath and Halban, 2004).
Apart from the reduced mass of β-cells, the ability of individual β-cells to secrete
insulin is also diminished. This results in impaired insulin response to glucose, seen in
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intravenous glucose tolerance tests (Davies et al., 1994). It has also been shown that
the reduction in insulin synthesis is out of proportion to the reduced β-cell mass (Ward
et al., 1984; Weir and Bonner-Weir, 2004).
Genetic susceptibility also determines the onset and progression of β-cell dysfunction
and failure (Bell and Polonsky, 2001). This is supported by the fact that most genetic
variants identified to be associated with type 2 DM are related to β-cells (Halban et
al., 2014; Rosengren et al., 2012; Voight et al., 2010). In addition, the intrauterine
environment has been reported to play a role in determining the susceptibility of β-cell
to dysfunction and failure during adult life (Prentki and Nolan, 2006).
Pathogenesis of diabetes mellitus in Indians
Studies have shown that Indians have a high predisposition to develop diabetes
mellitus (Mohan, 2004; Mohan et al., 2006, 2007). Indians tend to have a higher risk
of developing diabetes even at lower body mass index (BMI), when compared with
other populations (Chandalia et al., 1999; Joshi, 2003; Mohan, 2004). A consensus
statement was published by Misraet.al (2009)., to revise the definiton of overweight
and obesity in Indians. According to this classification, persons with BMI values
between23 -24.9 kg/m2 are considered to be overweight and those with values >25
kg/m2(Misra et al, 2009) are considered obese. However, international classifications
do not specify BMI cut-offs for Indian population and the above mentioned cut-offs
have not been included in any of the international classifications.
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The prevalence of insulin resistance has been reported to be higher in Indians than in
Caucasian populations (Chandalia et al., 1999; Misra and Vikram, 2002; Sharp et al.,
1987). The body composition of Indian subjects is also quite unique. Indians tend to
have higher amounts of abdominal fat and greater truncal adiposity for similar values
of BMI than Caucasians. This is an indicator of increased visceral adipose tissue,
which is associated with insulin resistance (Chandalia et al., 1999; Deepa et al., 2006;
Joshi, 2003; Mohan et al., 2007). Apart from insulin resistance, β-cell function is also
shown to be impaired in Indian patients with mild dysglycemia (Staimez et al., 2013).
Another hypothesis that has been frequently proposed to explain the high incidence of
diabetes mellitus in Indians is that intrauterine malnutrition and low birth weight may
predispose to development of diabetes mellitus later in life (Yajnik, 2004). Finally,
heritability of diabetes mellitus in Indians is higher than in other populations, which
points to the possibility that Indians are genetically more susceptible than other
populations to develop diabetes mellitus (Mohan et al., 1986).
Despite the availability of the vast amount of information regarding the pathogenesis
of diabetes mellitus, the story is far from complete. Several pertinent questions remain
unanswered. One such question pertains to the role of iron homeostasis in the
pathogenesis of diabetes mellitus.
Iron homeostasis
All life forms on earth require iron for survival. This is due to some remarkable
physiochemical properties of iron. As a transition metal, it can exist in different
22
oxidation states, ranging from -2 to +6; this means that it can be used as a carrier of
oxygen in life forms, as it can bind and release oxygen according to need. It is also an
important component of several enzymes involved in redox reactions. This property of
iron is a double-edged sword because iron can participate in reactions generating free
radicals (for example in the Fenton reaction) (Winterbourn, 1995). This makes iron
toxic to tissues. To prevent this, elaborate mechanisms have evolved to regulate and
handle iron within the body.
Absorption of iron
Dietary iron that enters the gut is commonly classified into two groups: heme and non-
heme iron. Hemoproteins such as myoglobin and hemoglobin found in foods of
animal origin contributes to the heme iron. Non-heme iron includes inorganic iron and
iron bound to ferritin. Acid in the stomach releases heme from hemoproteins; it also
reduces some amount of free ferric iron into ferrous iron (Fuqua et al., 2012). In the
presence of an acidic medium, iron exists in the ferrous form. Evidence suggests that
different forms of dietary iron are absorbed by different mechanisms, though not all
mechanisms are clearly understood.
Absorption of iron takes place via the enterocytes of the duodenum and jejunum
(Wheby, 1970). Enterocytes express a protein, divalent metal transporter 1(DMT1) on
the apical side. It transports ferrous iron into the cells from the lumen (Andrews,
1999). DMT1 is a highly conserved transmembrane protein that transports divalent
cations such as Mg2+
, Cu2+
, Zn2+
etc., apart from Fe2+
. DMT1 requires symport of
23
protons for transport of the cations (Fleming et al., 1997; Gunshin et al., 1997). The
ferric form of iron has to be converted to its ferrous form so that it can enter via
DMT1. This is aided by several ferric reductases. One of them is duodenal
cytochrome B (DcytB) (McKie et al., 2001). Another enzyme, Steap2, is also
involved in this process (Ohgami et al., 2006).
How exactly heme iron and ferritin iron are absorbed is not clear, but are thought to be
absorbed by receptor-mediated endocytosis (Kalgaonkar and Lönnerdal, 2009; West
and Oates, 2008). Once inside the cell, all forms of iron enter the common pool as
ferrous iron (Fuqua et al., 2012). Heme oxygenase releases Fe2+
from heme and it
joins this common pool (Raffin et al., 1974). The mechanisms by which iron is
handled inside the enterocytes are not clearly known.
Once inside the enterocytes, the iron may have several possible fates; it may get
utilized for intracellular needs; it may bind with ferritin and be stored (these stores are
lost when the enterocytes are sloughed off); it may enter the circulation (Wheby et al.,
1964). Export from enterocytes to reach the circulation is facilitated by a specialized
protein, ferroportin, that is situated on the basolateral side of enterocytes. Ferroportin
is a transmembrane protein and is the only known mammalian iron exporter (Donovan
et al., 2005).
Once in the circulation, iron binds with transferrin. Transferrin can only bind iron in
its ferric form; hence,the ferrous iron exported by ferroportin must be oxidized to its
24
ferric form before binding with transferrin (Huebers and Finch, 1987). This is
achieved by a copper-containing ceruloplasmin-like protein called hephaestin, which
displays ferroxidase activity (Petrak and Vyoral, 2005). Iron is then transported in
circulation to target tissues.
Apo-transferrin, the protein component of transferrin is a glycoprotein, with two high-
affinity binding sites for Fe3+
. Affinity of these binding sites for iron is greatly
reduced in acidic medium (Aisen et al., 1978). Transferrin levels in circulation reflect
the iron needs of the body and it is eleveated in iron-deficiency states (Rajamäki et al.,
1979). Clinically, total iron binding capacity (TIBC) of blood is an indirect measure of
the transferrin concentration and is helpful in assessing the body iron status, as the
TIBC is increased in iron-deficiency states(Kasvosve and Delanghe, 2002).
Transferrin levels are decreased in inflammation as it is a negative acute phase
reactant (Ritchie et al., 1999). Figure 2.1 illustrates the process of intestinal iron
absorption.
Cellular iron uptake
Transferrin, on reaching its target tissue such as erythroblasts, binds with transferrin
receptor 1(Tfr1). This complex undergoes clathrin-mediated endocytosis. Once inside
the cell, the pH of the endosome becomes acidic, which releases iron from transferrin
(Richardson and Ponka, 1997). The six-transmembrane epithelial antigen of the
prostate (STEAP) family of metalloreductases present in the endosomes reduces the
25
released iron to ferrous form and exports iron out of the endosome, via DMT1 present
in the endosome membrane (Ohgami et al., 2005; Tabuchi et al., 2000).
Figure 2.1: Absorption of iron in the intestine
Source: Rodwell, V., Bender, D., Botham, K.M., Kennelly, P.J., and Weil, P.A.
(2012). Harpers Illustrated Biochemistry 29th
Edition (McGraw Hill Professional).
Transferrin and Tfr1 are then recycled. This process, referred to as the transferrin
cycle and seen in all cells, is most important for iron-hungry erythroid precursors
(Tabuchi et al., 2000). Several studies have suggested that the transferrin cycle is not
the only way by which cells acquire iron, but details of alternate mechanisms are not
clear. An L-type calcium channel is known to transport iron, especially in iron-
overloaded conditions (Tsushima et al., 1999). Several ferritin receptors such as
26
Scara5 and TIM2 have also been reported to be involved in uptake of iron by cells
(Han et al., 2011; Li et al., 2009).
During the destruction of senescent RBCs in the macrophages of the
reticuloendothelial system, iron is released from the heme and is recycled. Inside the
phagosome, which is formed after the macrophage phagocytoses the RBC, the action
of hemeoxygenase releases iron from the heme (Kikuchi et al., 2005). Iron is released
from phagosome into the cytosol by the natural resistance-associated macrophage
protein (NRAMP) 1 and DMT1 present in the phagosome membrane (Soe-Lin et al.,
2010). Iron is exported out of the macrophage via ferroportin (Ganz, 2005). Iron
recycled by macrophages in this way contributes a major portion of iron incirculation,
which meets the demands of erythropoiesis and other requirements foriron inthe body
(Hentze et al., 2010).
Fate of cellular iron
Iron utilization
Iron inside the cell exists in a labile pool, from which it is utilized for various
purposes such as synthesis of heme, Fe-S cluster proteins and iron-containing
enzymes in cells (Crichton, 1984). It is transported from the cytoplasm into
mitochondria, a major site that requires iron (for the above purposes) by carrier-
mediated transport and also by direct acquisition from endosomes (Sheftel et al.,
2007). The proteins involved in mitochondrial iron import are mitoferrin1 and 2
(Shaw et al., 2006). These are present in the inner mitochondrial membrane and it is
27
aided by the ATP-binding cassette protein Abcb10, which increases the stability of
mitoferrin 1(Chen et al., 2009).
Cellular iron storage
Free iron inside cells can be toxic and can potentially cause intracellular damage
through redox reactions. To prevent this, iron that is not immediately utilized is stored
as ferritin. This is a 24-subunit spherical protein that stores up to 4000 iron atoms in
its core (Arosio and Levi, 2010; Harrison et al., 1974). Cytoplasmic ferritin is made
from different combinations of H (FtH) and L chains (FtL). The proportion of each
chain present varies according to the tissue in which it is expressed (Arosio and Levi,
2010). The H chain has catalytic sites with ferroxidase activity which oxidizes Fe2+
to
Fe3+
before storing the iron in the core of ferritin (Cozzi et al., 2000). The ferroxidase
site of the H chain is known to confer anti-oxidant properties upon ferritn, as it is
involved in significantly reducing the hydroxyl radical produced during the Fenton
reaction (Zhao et al., 2006). The L chain accelerates the transfer of iron from the
ferroxidase site to the core and contributes to stability of ferritin (Levi et al., 1994).
Ferritin in the heart and brain are rich in H chains,while L chains predominate in
ferritin expressed in iron storage tissues such as the liver and spleen (Andrews, 2008).
The liver is the major organ for storage of iron (Anderson, 2007). In the
mitochondria, ferritin is a 24-subunit homopolymer of mitochondrial ferritin; it stores
the iron in the mitochondrial matrix (Arosio and Levi, 2010).
28
Ferritin safely sequesters iron and prevents free radical damage under physiological
conditions. In pathological states, such as in iron overload, when ferritin is saturated,
it can release reactive iron radicals and cause oxidative damage in cells. Thus, ferritin
can act as a pro-oxidant in pathological states (Watt, 2011).
Regulation of iron metabolism
The potentially toxic nature of iron dictates that life forms must evolve efficient
mechanisms to regulate iron homeostasis. In humans, this involves regulation of
intestinal absorption of iron and its distribution in the body. Both these processes are
regulated by local cellular and systemic mechanisms.
Cellular regulation
Cellular regulation of iron is one of the most interesting and well-studied examples of
post-transcriptional regulation. Untranslated regions of mRNA coding for proteins
involved in iron metabolism have unique conserved sequences, forming a stem-loop
structure called an iron-responsive element (IRE) (Casey et al., 1988). IREs are
present in 5’ untranslated regions (UTR) of mRNAs of H-ferritin, L-ferritin,
ferroportin, mitochondrial aconitase, hypoxia-inducible factor 2α (HIF-2 α),
aminolevulinic acid (ALA) synthase and in 3’ UTRsof TFR1 and DMT1
(Muckenthaler et al., 2008). IRE can bind with iron-regulatory proteins (IRP) and
result in an increase or decrease in protein translation, depending on whether the IRE
is in 5’ or 3’ UTRs (Muckenthaler et al., 2008).
29
IRP1 and IRP2 are iron regulatory proteins. The first is a bifunctional protein, as it can
function either as cytoplasmic aconitase or as an IRE-binding protein (depending on
the availability of intracellular iron). When intracellular iron levels are high, it forms
Fe-S clusters on IRP1, conferring on it aconitase activity; it does not bind to IRE
under these conditions. When intracellular iron levels are low, IRP1 will bind with
IREs present in various mRNAs (Walden et al., 2006) and produce subsequent effects.
IRP2 does not have aconitase activity; instead, it has a binding site for iron. When
bound to iron (when its levels are high), IRP2 undergoes ubiquitination and
proteasomal degradation (Wallander et al., 2006). Thus, when iron levels are high, the
concentration of IRP2 will decrease; the opposite occurs when iron is deficient.
Hence, when iron is deficient, IRP1 and IRP2 will bind with IRE of mRNA coding
various iron regulatory proteins. When IRP1 or IRP2 binds with IRE present in the
5’UTR of H and L-ferritin, it prevents assembly of the initiation complex and prevents
protein translation (Kim et al., 1995). The end result is that when iron is deficient,
ferritin expression is reduced as the demand for iron storage is less. Similar events
happen with ferroportin and ALA synthase. Opposite occur in the presence of excess
iron; the translation of these proteins are increased resulting in increased storage (as
ferritin), iron export from cells (via ferroportin) and iron consumption (mediated by
ALAsynthase) (Wallander et al., 2006). Regulation of ferroportin is different in
enterocytes where differential splicing produces a ferroportin mRNA that lacks the
IRE. This helps in the continuous expression of ferroportin even when iron is low in
the enterocytes (Zhang et al., 2009). This ensures iron absorption in case of a systemic
iron deficient conditions.
30
Figure 2.2: Regulation of cellular iron metabolism.
Source: Hentze, M.W., Muckenthaler, M.U., Galy, B., and Camaschella, C. (2010).
Two to Tango: Regulation of Mammalian Iron Metabolism. Cell 142, 24–38.
IRPs bound to IRE present at 3’ UTR of TFR1 mRNA results in stabilisation of the
mRNA, resulting in increased expression of TFR1. In the presence of excess iron,
IRP1 and IRP2 will not bind with IRE; the TFR1 mRNA will then undergo
degradation (Müllner et al., 1989; Wallander et al., 2006). TFR1 is regulated in this
way so as to import iron in times of need and restrict import when iron is not required.
DMT1 has an IRE in its 3’ UTR; its expression increases in case of low iron states in
the enterocytes of the duodenum and jejunum (Gunshin et al., 1997). Figure 2.2
illustrates the regulation of cellular iron metabolism.
31
Systemic regulation
As early as the 1930s, researchers found that iron absorption was higher in subjects
with iron-deficiency anaemia (Moore et al., 1939). Studies on absorption of radio-
labelled iron revealed that iron absorption and export from enterocytes and export
from macrophages and hepatocytes were higher in iron-deficient states (Granick,
1954). Without significant mechanisms to excrete iron, its levels are mainly
maintained by regulation at the level of absorption. Such regulation is responsive to
the demands of erythropoiesis, iron stores and the presence of inflammation (Hahn et
al., 1939; Weintraub et al., 1965; Freireich et al., 1957). A circulating factor was
thought to mediate signalling from erythropoiesis, the liver and inflammatory
processes to the intestine (Beutler and Buttenwieser, 1960). This factor was later
found to be the peptide hormone hepcidin (Pigeon et al., 2001).
Hepcidin
Hepcidin, a 25-amino acid hormone, was initially discovered as a bactericidal
molecule secreted by the liver in response to infection and inflammation (Krause et
al., 2000; Park et al., 2001). Hepcidin is cleaved from a larger protein preprohepcidin,
an 84-amino acid precursor, by the protease enzyme furin (Valore and Ganz, 2008).
Studies in mice initially suggested a link between iron-loading and hepcidin
expression. Mice with defects in the hepcidin gene (HAMP) developed iron overload;
when HAMP was over-expressed, the animals developed severe iron deficiency
(Nicolas et al., 2002; Pigeon et al., 2001). Subsequently, it was confirmed that
hepcidin plays a key role in the regulation of systemic iron homeostasis and that the
32
liver is the centre of iron homeostasis (Viatte and Vaulont, 2009). Though hepcidin is
now found to be expressed in various tissues such as adipose tissue, pancreas and
macrophages, the importance of these tissues in iron homeostasis is not clear (Bekri et
al., 2006; Kulaksiz et al., 2008; Sow et al., 2007).
Hepcidin acts through ferroportin. Hepcidin-bound ferroportin undergoes endocytosis
and proteolytic degradation in lysosomes (Nemeth et al., 2004a). Ferroportin is mostly
present in enterocytes, macrophages and hepatocytes. Thus, hepcidin suppresses iron
export from enterocytes, recycling from macrophages and release from iron storesin
hepatocytes; this reduces circulating iron levels and hence delivery of iron
forerythropoiesis (Donovan et al., 2005; Knutson et al., 2005; Nemeth et al., 2004a).
Details of regulation of hepcidin have been the subject of intense research over the
past 15 years. A great amount of information has been unravelled. It is now known to
be regulated by several factors, such as systemic iron availability, iron stores,
erythropoiesis, hypoxia and inflammation.
Regulation of hepcidin
Systemic iron availability and hepatic iron stores
Diferric transferrin (Tf-Fe) present in circulation acts as a signal to the liver,
indicating systemic iron availability. HFE, one of the proteins mutated in
haemachromatosis, is involved in sensing systemic iron availability (Gao et al., 2009).
HFE belongs to the MHC class I protein family; it is found on the plasma membrane
33
in association with β2-microglobulin (Barton et al., 2015). HFE has domains that bind
with Tfr1.It can also bind with transferrin receptor 2 (Tfr2) (Goswami and Andrews,
2006). Tfr2 is a transmembrane protein found on hepatocytes; similar to Tfr1, it can
also bind and take up transferrin, but with lesser affinity (Trinder and Baker,
2003).When Tf-Fe is high in the circulation, it interacts with the HFE-Tfr1 complex;
its binding toTfr1 displaces HFE from the complex. This results in HFE binding to
Tfr2; this binding results in induction of hepcidin; (Gao et al., 2009; Goswami and
Andrews, 2006; Hentze et al., 2010). This is postulated to occur via activation of the
ERK/MAPK pathway and the BMP/SMAD pathway (Hentze et al., 2010).
The BMP/SMAD pathway
The BMP/SMAD pathway is the most important regulatory pathway for hepcidin
expression (Babitt et al., 2006). Hepatic iron stores and systemic iron availability
increase the production of bone morphogenic protein (BMP). The mechanisms that
underlieregulation of BMP expression are not clear (Rishi et al., 2015). The BMPs are
a TGF-β superfamily of cytokines and are known for their role in cell development,
differentiation and apoptosis (Waite and Eng, 2003). BMPs 1 to 9 are involved in
bone and cartilage development (BMP 1 is a metalloprotease and not a TGF-β
cytokine). BMP 10 is involved in embryogenesis of the heart and BMP 15 is involved
in oocyte development (Xiao et al., 2007). Apart from its osteogenic function, BMP6
is also involved in iron homeostasis. It is secreted, in response to increased iron levels,
from hepatocytes and other cells (such as sinusoidal and stellate cells) in the liver
(Rishi et al., 2015). BMP6 then acts on hepatocytes to induce HAMP expression
34
(Babitt et al., 2006). It has been shown to play a crucial role in iron homeostasis
(Andriopoulos et al., 2009).
BMPs acts through BMP receptor types1 and 2 (BMPR1 and BMPR2). Haemojuvelin
(HJV) is important for the BMP/SMAD pathway of hepcidin regulation. This is a
membrane protein involved in iron sensing; it is mutated in juvenile-onset
haemochromatosis (Niederkofler, 2005; Papanikolaou et al., 2004). HJV acts as a co-
receptor for BMP6 (Xia et al., 2008). Binding of BMP6 to its receptor results in the
phosphorylation of SMAD proteins 1, 5 and 8 (Kautz et al., 2008). Phosphorylated
SMAD proteins interact with co-SMAD factor, SMAD4 (Wang et al., 2005). This
complex induces the transcription of HAMP by binding with the following motifs:
BMP-response element 1 and BMP-response element 2 present in the HAMP gene
promoter region (Casanovas et al., 2009). This pathway is illustrated in figure 2.3.
The transmembrane serine protease enzyme, matriptase 2 (Tmprss6) is involved in
liver matrix remodelling and known to play a role in regulation of hepcidin synthesis
(Folgueras et al., 2008). Tmprss6 is involved in the the process of activating the
BMP/SMAD pathway. It interacts with HJV and causes its degradation, thereby
inhibiting the BMP/SMAD pathway(Silvestri et al., 2008a). Furin, which belongs to
the proprotein convertase family of proteins, proteolytically cleaves HJV, resulting in
the release of soluble HJV (sHJV)(Silvestri et al., 2008b). sHJV is an inhibitor of the
BMP/SMAD pathway(Lin et al., 2005).
35
Figure 2.3: BMP/SMAD pathway of hepcidin regulation.
Source: Pipard. M.J. Iron deficiency anemia, anemia of chronic disorders and iron
overload | Clinical Gate. Chapter 11, Fig 11.6 Acessed from:
http://clinicalgate.com/iron-deficiency-anemia-anemia-of-chronic-disorders-and-iron-
overload/
Inflammation
Inflammation induces the expression of hepcidin (Nemeth et al., 2003). In fact,
hepcidin was first discovered as a component of an innate immune response (Park et
al., 2001). Apart from having direct but mild bactericidal activity, increased levels of
hepcidin are protective against infections, as it reduces systemic availability of iron by
sequestering iron away from invading pathogens. As stated earlier, iron is absolutely
36
essential for all life forms, and pathogens are no exception. Depriving them of iron
results in inhibition of their growth, as has been shown in several studies (Al-Younes
et al., 2001; Drakesmith and Prentice, 2012; Paradkar et al., 2008)
Hepcidin expression increases, not only in response to infection, but also to any kind
of systemic inflammation. Like many other acute phase reactants, its production is
stimulated by the inflammatory cytokine interlekin 6 (IL-6) (Nemeth et al., 2004). IL-
6 binds with its receptors on hepatocytes and activates the JAK/STAT pathway. The
HAMP gene promoter has a STAT-binding site; when STAT binds to this site, it
results in increased expression of the HAMP gene (Wrighting and Andrews, 2006).
Several investigations have suggested that the IL-6/JAK/STAT pathway interacts with
the BMP/HJV/SMAD pathway (Falzacappa et al., 2008; Wang et al., 2005). BMP-
receptor 1 (BMPR1) appears necessary for the action of IL-6; mice with defective
BMPR1showed diminished hepcidin expression in response to IL-6 (Mayeur et al.,
2014).The changes seen as a result of inflammation-induced hepcidin expression
underlie the development of anaemia of chronic disease (Fleming and Sly, 2001).
Figure 2.4 illustrates the pathway of inflammation-mediated regulation of hepcidin.
37
Figure 2.4: Hepcidin regulation by inflammation
Source: Hentze, M.W., Muckenthaler, M.U., Galy, B., and Camaschella, C. (2010).
Two to Tango: Regulation of Mammalian Iron Metabolism. Cell 142, 24–38.
Erythropoiesis
One of the major roles of iron in the body is in formation of haemoglobin, which
transports oxygen in blood. Formation and maturation of RBCs is thus critically
dependant on the availability of iron. Erythropoietic activity inhibits the expression of
hepcidin (Vokurka et al., 2006). Decreased expression of hepcidin leads to increased
intestinal iron absorption and release of iron from hepatocytes and macrophages,
resulting in delivery of adequate amount of iron to the bone marrow for
erythropoiesis. One of the interesting aspects of erythropoiesis-mediated suppression
of hepcidin expression is that it is effective even in states of increased iron stores. This
38
is typically seen in thalassemia, where erythropoiesis results in hepcidin suppression,
despite iron overload (Kattamis et al., 2006).
Though it is clear that erythropoiesis suppresses hepcidin expression, the molecular
mediator of this suppression is not conclusively known. Several molecules have been
suggested to be possible erythroid regulators of hepcidin. Erythropoietin treatment in
human subjects resulted in inhibition of hepcidin; this effectis probably due to
stimulation of erythropoiesis, rather than a direct action on hepcidin expression
(Ashby et al., 2010; Sasaki et al., 2012). Tanno et.al (2007) proposed that the
erythroid regulator is most likely to be secreted from erythroblasts. Microarray
analysis of erythroblastshas revealed two potential candidates: growth differentiation
factor 15 (GDF15) and twisted gastrulation factor 1 (TWSG1) (Tanno et al., 2007,
2009). GDF15 levels are elevated in thalassemia; the addition of GDF15 to human
hepatocytes resulted in inhibition of hepcidin expression (Tanno et al., 2007). The
mechanism of the effect of GDF15 on hepcidin is not known. However, the role of
GDF15 in suppressing hepcidin has been questioned, since hepcidin synthesis was
suppressed after phlebotomies in GDF15 knockout mice (Casanovas et al., 2013).
TWSG1 has also been shown to inhibit the BMP-SMAD signalling pathway. Addition
of TWSG1 to cell culture medium resulted in reduced hepcidin expression in human
hepatocytes (Tanno et al., 2009).
39
Figure 2.5: Regulation of hepcidin by erythropoiesis
Source: Adapted from Hentze, M.W., Muckenthaler, M.U., Galy, B., and
Camaschella, C. (2010). Two to Tango: Regulation of Mammalian Iron Metabolism.
Cell 142, 24–38.
Another potential erythroid regulator of hepcidin is erythroferrone (ERFE). It is
secreted from erythroblasts; mice with defective erythroferrone were found to have
diminished suppression of hepcidin in response to blood loss (Kautz et al., 2014).
An iron-regulated transcription factor, ATOH8, has been reported to promote hepcidin
expression by directly binding to the HAMP promoter, as well as through the
BMP/SMAD pathway. This factor is suppressed during increased erythropoiesis,
indicating its potential role in erythropoiesis-mediated hepcidin suppression (Patel et
al., 2014). Figure 2.5 illustrates the regulation of hepcidin by erythropoiesis.
40
Hypoxia
Hypoxia is known to affect iron metabolism in several ways. Hypoxia-inducible
factors (HIF) are constitutively expressed in cells. Their levels are affected by the
activity of the enzyme, prolyl hydroxylase (PHD). Under normoxic conditions, PHD
modifies HIFs, resulting in their proteasomal degradation. In the hypoxic state , PHD
is inactivated, resulting in stabilization of HIFs. HIFs then bind with HIF-responsive
elements (HRE) present in their target genes(Greer et al., 2012). HIFs are composed
of an α (HIF-α) and a β-subunit (HIF-β) There are three different types of HIF-α in
humans : HIF-1α, HIF-2α and HIF-3α. HIF-α responds to hypoxia, while HIF-β
subunit expression is constant (Greer et al., 2012). HIF-1 α is involved in modulating
multiple responses to hypoxia, such as angiogenesis and erythropoiesis (Semenza,
2012). HIF-2α is involved in embryonic development of the heart and also in iron
homeostasis (Semenza, 2012; Shah et al., 2009). In the intestine, HIF-2α binds to
theHRE present in genes of proteins involved in iron absorption, such as DMT1 and
FPN1, and increases their expression (Shah et al., 2009).
HIF-1α and HIF-2αhave been shown to inhibit hepcidin expression, but the details of
this action is not clear. Possible mechanisms include their binding to HRE of the
hepcidin promoter or through their effect in inducing erythropoiesis (Peyssonnaux et
al., 2008). Hypoxia also stimulates the expression of furin, which cleaves HJV to
produce sHJV, thereby inhibiting the BMP/SMAD pathway of hepcidin expression
(McMahon et al., 2005). Hypoxia-associated suppression of hepcidin was also
correlated with decreased levels of the growth factor, platelet derived growth factor
41
(PDGF)-BB. PDGF-BB was found to regulate hepcidin through the transcription
factor, cAMP response-element binding protein (CREBP) and CREBP-H.
Other regulators
Research in the field of regulation of hepcidin continues to reveal newer regulators.
Growth factors (such as hepatocyte and epidermal growth factors) and hormones
(such as oestrogen and testosterone) have been found to inhibit hepcidin expression
(Bachman et al., 2010; Goodnough et al., 2012; Ikeda et al., 2012). Figure 2. 6
illustrates the various pathways that regulate hepcidin.
Iron and diabetes mellitus
Iron is one of the multiple factors known to play a role in the development of type 2
diabetes mellitus and diabetes-related complications. Indications of the link between
iron and diabetes mellitus initially came from the observation that patients with
hemochromatosis had a high incidence of diabetes mellitus (Dymock et al., 1972;
Futcher, 1907). Such a high incidence is seen in other iron-overloaded conditions as
well. Patients with thalassemia who receive repeated blood transfusions were found to
be at a higher risk for developing diabetes mellitus, when compared with a normal
population (Bannerman et al., 1967; Merkel et al., 1988). Similar observations have
been noted in ‘African iron overload’ syndrome, where cooking in iron utensils leads
to iron overload and an increase in the incidence of diabetes mellitus (Walker and
Segal, 1999).
42
Figure 2.6: Regulation of hepcidin
TFR 1 and 2, transferrin receptors 1 and 2; BMP6, bone morphogenetic protein 6;
BMPR-I and II, bone morphogenetic protein receptors-I and II; SMAD4, sma and
mothers against decapentaplegic homologue 4; SMAD1/5/8, sma and mothers against
decapentaplegic homologue 1/5/8 complex; IL6, interleukin 6; IL-6R, interleukin 6
receptor; TWSG1, twisted gastrulation BMP signaling modulator 1; STAT3, signal
transducer and activator of transcription 3; JAK, Janus kinase; CREB/H, cAMP
response-element binding protein/H; EGFR, epidermal growth factor receptor; EPO,
erythropoietin; EPOR, erythropoietin receptor; ERFE, erythroferrone; GDF15, growth
differentiation factor 15; HFE, hemochromatosis protein; HIF, hypoxia-inducible
factor; HJV, hemojuvelin; EGF, epidermal growth factor; PDGF-BB, platelet-derived
growth factor-BB; PDGFR, platelet-derived growth factor receptor.
Source: Rishi, Gautam, Daniel F. Wallace, and V. Nathan Subramaniam. “Hepcidin:
Regulationof the Master Iron Regulator.” Bioscience Reports 35, no. 3 (2015):
e00192.
43
Conditions, such as porphyria cutanea tarda and infection with hepatitis C virus,
where there is iron overload have also been reported to be associated with increased
risk of developing diabetes (Mason et al., 1999; Rook and Champion, 1960).
Friedreich ataxia is a condition characterized by mitochondrial iron overload; patients
with this disease also have a higher incidence of diabetes mellitus (Harding, 1981).
This consistent finding of associated between diabetes mellitus and iron-overloaded
conditions of different etiologies makes a strong case for the role of iron overload in
the development of diabetes mellitus.This observation led epidemiologists to study the
association of body iron stores and diabetes mellitus.
Association of markers of iron stores, such as ferritin with increased risk of
developing diabetes mellitus have been shown in multiple studies (Forouhi et al.,
2007; Jiang R et al., 2004; Salonen et al., 1998). One of the striking findings from
these studies is that the odds of developing diabetes mellitus is higher with increasing
level of markers of iron stores, even when the markers are well within reference limits
(Fernández-Real et al., 2002). This has generated much interest among researchers to
study the association of iron homeostasis with risk of developing diabetes mellitus,
even when there is no overt iron overload.
Cross sectional studies have reported that elevated serum ferritin levels are seen in
type 2 diabetes mellitus (Ford and Cogswell, 1999; Jiang R et al., 2004; Lee et al.,
2011; Shi et al., 2006). Furthermore, prospective studies have shown that the increase
44
in ferritin levels is associated with higher risk of developing diabetes mellitus
(Forouhi et al., 2007; Jehn et al., 2007; Salonen et al., 1998). The risk of developing
diabetes increased with higher dietary iron intake, such as high meat consumption
(Snowdon and Phillips, 1985). Later studies have found that chances of developing
mellitus increases with excess intake of red meat, which is rich in heme iron (Fung
TT et al., 2004; Song et al., 2004). Pregnant women who took iron supplements were
found to have higher chance of developing gestational diabetes mellitus (Bo et al.,
2009). Body iron stores, as indicated by serum ferritin, also positively correlated with
insulin resistance, metabolic syndrome and increased insulin and glucose
concentrations (Fernández-Real et al., 1998; Fumeron et al., 2006; Jehn et al., 2004).
Some studies have also shown that increased body iron stores are associated with
dyslipidemia (Choi et al., 2005; Kim et al., 2016). Body iron stores were also found to
positively correlate with impired ability of insulin to suppress lipolysis in adipocytes
(Wlazlo et al., 2013). A meta-analysis of 12 studies has concluded that increases in
serum ferritin is associated diabetes mellitus (Kunutsor et al., 2013). Iron is also found
to be associated with development of diabetes mellitus-related complications, such as
nephropathy and vascular dysfunction (Day et al., 2003; Nankivell et al., 1992).
Studies have reported that people who frequently donate blood have improved insulin
sensitivity and better lipid profiles after donations (Fernández-Real et al., 2005;
Houschyar et al., 2012). This has been suggested to be a result of reduced iron stores
in these individuals. A clinical trial with desferrioxamine, an iron chelator, have
shown improved insulin sensitivity in response to iron chelation, while another did not
45
show conclusive improvements (Cutler, 1989; Redmon et al., 1993). How iron is
associated with diabetes mellitus and related complications has been under much
study over the past decade; the underlying complex mechanisms are now beginning to
be unravelled.
Links between iron metabolism and diabetes mellitus
The existence of a link between iron metabolism and diabetes mellitus seems to be
well established. The association between markers of iron stores and iron intake with
disorders of insulin resistance such as diabetes mellitus, gestational diabetes mellitus,
and metabolic syndrome were consistently found in numerous studies (Fernández-
Real et al., 2002; Rajpathak et al., 2009; Swaminathan et al., 2007). What remains to
be elucidated are the exact mechanisms underlying the link between iron metabolism
and diabetes melitus.
Iron-induced oxidative damage
Iron, when not bound to ferritin or transferrin, can potentially act as a pro-oxidant. It
generates highly reactive free radicals, such as hydroxyl ions and superoxide ions,
through the Fenton reaction. Free radicals induce oxidative damage such as lipid
peroxidation and DNA cross-linking (Gutteridge, 1986; Imlay et al., 1988). Oxidative
damage is implicated in the development and progression of complications of diabetes
mellitus (Rösen et al., 2001). Diabetes mellitus also promotes iron-induced oxidative
damage in several ways. Diabetes mellitus is known to be associated with increased
oxidative stress due to hyperglycemia induced superoxide production in mitochondria
46
(Brownlee, 2001). Increased oxidative stress and free radicals can reduce Fe3+
to Fe2+
and subsequently release iron from ferritin which will further contribute to the
oxidative stress (Reif, 1992). Glycation of holotransferrin is increased in diabetes
mellitus, which leads to decreased binding of iron to transferrin (Fujimoto et al.,
1995).
Inflammation
Inflammation is commonly present in adipose tissue of obese people, with production
of pro-inflammatory cytokines by adipocytes (Tilg and Moschen, 2006; Wellen and
Hotamisligil, 2003). Such inflammation is likely to induce hepcidin synthesis and
cause hypoferremia of obesity (Cepeda-Lopez et al., 2010; Yanoff et al., 2007).
Increased hepcidin levels results in decreased availability of systemic iron by
decreasing absorption from the gut as well as trapping iron inside macrophages and
hepatocytes.Adipocytes can also produce hepcidin but the significance of this is not
clear (Bekri et al., 2006). Inflammatory cytokines also increase the uptake of iron by
hepatocytes (Hirayama et al., 1993).
Influence of insulin on iron regulation
Transferrin receptors co-localize with insulin-dependent glucose transporters (GLUT)
inside intracellular vesicles of adipocytes (Tanner and Lienhard, 1989). This means
that when insulin stimulates GLUT translocation to the cell membrane, it is associated
with similar translocation of transferrin receptors as well. This has been shown
experimentally where treatment of isolated adipocytes with insulin resulted in
47
increased iron uptake (Tanner and Lienhard, 1987). Increased iron levels in adipocytes
are associated with decreased production of adiponectin and insulin resistance
(Gabrielsen et al., 2012). A study by Wlazlo et al (2013) has shown that markers of
iron stores are associated with impaired action of insulin on adipocytes in humans.
The effects of iron on insulin signalling
Incubation of a hepatocyte cell line in culture with iron resulted in increased
intracellular iron, and subsequent impaired insulin signalling (Messner et al., 2013).
Expression of insulin receptors were diminished in HepG2 cells treated with iron
(Fargion et al., 2005). Insulin metabolism by the liver was found to be impaired in
patients with iron overload, leading to hyperinsulinemia and hepatic insulin resistance
(Niederau et al., 1984). Iron and transferrin have been shown to stimulate lipolysis in
isolated adipocytes, resulting in the release of free fatty acids (Rumberger et al.,
2004). As discussed earlier, free fatty acids play a major role in the pathogenesis of
systemic insulin resistance.
Iron and β-cells
Iron overload disorders, such as hemochromatosis is associated with decreased insulin
secretory capacity (McClain et al., 2006). This is possibly due to iron deposition in the
pancreas, which might destroy β-cells (Rahier et al., 1987). β-cells are particularly
susceptible to oxidative damage by iron, as they are already stressed due to high
metabolic rate and diminished anti-oxidant systems (Cooksey et al., 2004; Robertson
48
et al., 2004). Apart from oxidative damage, iron can induce cell death via a
mechanism known as ferroptosis (Dixon et al., 2012).
Hepcidin and diabetes mellitus
After the discovery of hepcidin as the master regulator of iron homeostasis, several
researchers have attempted to determine if hepcidin may be a link between iron and
diabetes mellitus. Apart from the liver, hepcidin is secreted from adipocytes,
macrophages and β-cells of the pancreas. It has been shown that increased glucose
intake is associated with higher hepcidin concentration, possibly by unknown
mechanisms by which glucose stimulated β-cells to secrete hepcidin (Aigner et al.,
2013). Insulin has also been reported to increase hepcidin expression in HepG2 cells
in culture (Wang et al., 2014).
So far, very few studies have attempted to determine if serum hepcidin levels in
diabetics is different from those who are not. Sam et.al (2013) conducted a study in
subjects in the UK and found that serum hepcidin levels were significantly lesser in
patients with diabetes mellitus. Other studies found significant increases in serum
hepcidin levels in patients with diabetes mellitus (Andrews et al., 2015; Jiang et al.,
2011). Guo et al (2013) showed no difference in serum hepcidin levels between
controls and those with diabetes mellitus. Thus, the few studies that have been carried
out have shown inconsistent results. There are no such studies that have been done on
Indian subjects.
49
THE STUDY
Hypothesis
The hypothesis of the study carried out was that serum hepcidin level would be altered
in patients with diabetes mellitus when compared with controls, who did not have
diabetes mellitus; such alterations would cause dysregulation of iron homeostasis in
those with diabetes mellitus.
Aim
The aim of this study was to determine if serum hepcidin levels were altered in
subjects who were diagnosed, for the first time, with diabetes mellitus, when
compared with control subjects, and whether iron-related parameters were
dysregulated as a result.
Objectives
1. To estimate serum levels of hepcidin and iron-related parameters in diabetic
subjects, diagnosed, for the first time, and those without diabetes mellitus
2. To compare serum levels of these parameters in these 2 groups of subjects
3. To correlate the parameters estimated
50
MATERIALS
Equipments used
Elix and Milli-Q ultrapure water systems (Millipore, USA)
Table-top refrigerated centrifuge (MPW R 350, MPW Poland)
Microplate reader, model 680 from Bio-Rad laboratories, Inc. (UK)
Minus 70oC and minus 20
oC freezers (Thermo Scientific, Massachusetts,
USA)
Chemicals and reagents used for estimation of hepcidin
The chemicals and reagents for estimation of hepcidin were bought from Peninsula
Laboratories (San Carlos, USA). These included the following:
1. Standard diluent (peptide-free human serum)
2. Lyophilized standard
3. Anti-serum against hepcidin
4. Biotinylated peptide
5. Enzyme immune-assay buffer
6. Streptavidin- horseradish peroxidase (HRP)
7. Substrate solution (TMB - 3, 3′, 5, 5′-tetramethylbenzidine solution)
8. Stop solution (2N HCl)
9. 96-well polystyrene microplate pre-coated with monoclonal antibody
specific for human hepcidin
51
The standard diluent, lyophilized standard, anti-serum and biotinylated peptide were
stored at -20°C, as per manufacturer’s instructions. The enzyme immunoassay
buffer, streptavidin-HRP, substrate solution and stop solution were stored in a
refrigerator (2-4°C). These reagents were stable for one year, under these conditions,
according to the manufacturer’s claims.
Miscellaneous consumables used
1. Vacutainer tubes for blood collection- red tubes for serum samples (BD
Biosciences, Plymouth, UK).
2. Micro-centrifuge tubes (1.5ml) (Tarsons Products Private Limited, Kolkata,
India).
3. Micro tips (Tarsons Products Private Limited, Kolkata, India).
52
METHODS
The study was a cross sectional observational study and was approved by the
Institutional Review Board (IRB) of Christian Medical College, Vellore (IRB
approval no: 9235 dated 12-1-2015). IRB approval letter is attached as appendix 1
Subjects
Male patients who attended the outpatient clinic of the Community Health and
Development (CHAD) Hospital, CMC, Vellore,and who were referred for
investigation of fasting plasma glucose levels, as part of their clinical investigation,
were included in the study, if they met the following inclusion criteria. Subjects were
recruited during the period from March 2015 to July 2016.
Inclusion criteria
Male
Age 18-60 years
Consented to participate in the study
Exclusion criteria
Previously diagnosed to have diabetes mellitus
History suggestive of complications of diabetes mellitus
Hemoglobin levels < 13 g/dL
Serum C-reactive protein (CRP) level > 10 mg/L
BMI >35 kg/m2
Refusal to participate in the study
53
Subjects with Hb<13 g/dL were considered to be anaemic (World Health Organization
and others, 2015) and were excluded from the study, as anaemia is known to affect
hepcidin levels (Nicolas et al., 2002). The reference interval for CRP is 0 to 10mg/L
(Erlandsen and Randers, 2000; Hutchinson, 2000); subjects with levels more than 10
mg/L are likely to have underlying inflammation and were excluded from the study as
inflammation can affect hepcidin levels (Nemeth et al., 2003).
Cases: Those whose fasting plasma glucose levels were >125 mg/dL (hence diagnosed
to have diabetes mellitus for the first time) were defined as cases.
Controls: Those with fasting glucose levels <100 mg/dL were defined as control
subjects.
These definitions were adopted as per the criteria of the American Diabetes
Association (2016).
Sample size calculation
The required sample size to show adifference of about 150 units in hepcidin levels
between patients with diabetes mellitus and those with normal fasting glucose was
found to be38 subjects in each group, with 80% power and 5% level of significance.
Calculations were based on the publication by Jiang et.al (Jiang et al., 2011).
54
Two Means - Hypothesis testing for two means
Standard deviation in group I 175.22 175.22 175.22
Standard deviation in group II 281.73 281.73 281.73
Mean difference 250 200 150
Effect size 1.09 0.87 0.65
Alpha error (%) 5 5 5
Power (1- beta) % 80 80 80
1 or 2 sided 2 2 2
Required sample size per group 14 22 38
Formula:
s2 =(S1
2 + S2
2)/2
Where,
S12 -Standard deviation in group I
S22 – Standard deviation in group II
is the alpha level of significance
is the power (80%)
d = mean difference between two samples
However, because of financial constraints, it was not possible to study 38patients in
each group. In view of this, we studied only 42 subjects in total: 21 subjects with
diabetes mellitus and 21 with normal fasting glucose levels.
55
Informed consent
Participants who could potentially be included in the study were identified. The details
of the study were explained to them; an information sheet (in Tamil or English) about
the study was provided to them. Written informed consent was obtained from subjects
who were willing to participate in the study. The information sheet and informed
consent form used in the study are attached as appendix 2 .
Demographic and anthropometric data
Demographic data and relevant clinical information were collected for each
participant of the study. The proforma used for collecting such information is shown
in appendix 3.
Anthropometric measurements
The height of each subject was measured by using a tape stuck on the wall.
Participants were positioned according to the Frankfurt plane. The weight of each
participants was measured using an electronic weighing scale available in the CHAD
hospital. Waist circumference of each subject was measured at the midpoint of
inferior costal margin and superior iliac crest. Hip circumference was measured at the
point of widest circumference between the ischial tuberosity and anterior superior iliac
spine. These measurements were done using a non-stretchable measuring tape.
The following was the formula used for calculating body mass index (BMI) of each
participant :
BMI = weight (kg)/(height[m])2.
56
Blood sample collection
Fasting blood samples (no calorie intake for at least 8 hours prior to collection) were
collected from each participant, from a peripheral vein in either arm, using aseptic
precautions. Vacutainer tubes (6ml plain tubes and 4ml heparin tubes) were used for
the collections.
Processing of blood samples
Blood collected in heparin tubes were used for estimating haematological parameters.
Blood collected in the plain tube was allowed to clot, then each tube was centrifuged
for 10 minutes at 3500 rpm to seperate serum. Each serum sample was divided into
multiple aliquots and stored at -70oC, using micro-centrifuge tubes, until further
analyses. These samples were thawed to room temperature and used for analysis of C-
reactive protein, iron hepcidin, ferritin, total iron binding capacity (TIBC) and insulin,
when required.
Estimation of serum hepcidin
Serum hepcidin was measured using a peptide enzyme immune assay kit from
Peninsula Laboratories, LLC ( Member of the Bachem Group), San Carlos, CA
94070, USA.
57
Principle
This kit measured hepcidin by a competitive immunoassay. A 96 well plate was
coated with the antiserum which contained antibodies against human hepcidin. A
fixed concentration of Bt-tracer (biotinylated tracer) and varying concentrations of
unlabeled peptide from standard or sample competed for binding with the antiserum,
upon addition of standard or sample respectively. Bt-tracer was captured and then
bound by the enzyme SA-HRP (streptavidin-conjugated horseradish peroxidase),
which resulted in formation of soluble coloured product after upon addition of
substrate. This is illustrated in figure 5.7
Figure 5.7: Principle of hepcidin estimation
Source of the image: Kit insert from Peninsular Laboratories
58
The standard provided was used to construct a standard graph and unknown
concentrations of samples were calculated using the standard graph.
Preparation of reagents and sample for hepcidin assay
The kit and reagents were equilibrated to room temperature before preparation of
samples and working reagents.
1. Stock standard: To 1μg of lyophilized standard, 1ml of standard diluent was
added and mixed using a vortex mixer.
2. Working standard: From the stock standard, working standards of
concentrations ranging from 0 to 25 ng/ml were prepared, by diluting stock
standard with varying amounts of standard diluent as given below.
Standard Concentration of hepcidin
(ng/ml)
Preparation
Stock standard 1000
S1 25.00 5 μl stock + 195 μl diluent
S2 6.25 40 μl S1 + 120 μl diluent
S3 1.56 40 μl S2 + 120 μl diluent
S4 0.39 40 μl S3 + 120 μl diluent
S5 0.10 40 μl S4+ 120 μl diluent
S6 0.02 40 μl S5+ 120 μl diluent
S0 0.00 120 μl diluent
3. Samples: To bring the hepcidin concentration of the sample within measuring
range of the assay, the 42 samples of the study were diluted 1: 10 with standard
diluent (12 μl of sample + 108 μl of standard diluent).
4. Enzyme immunoassay buffer (EIA buffer):TheEIA buffer (50ml) was diluted to
1000 ml with sterile deionized water (18 MOhm) and mixed well.
59
5. Anti-serum:5ml of EIA buffer was added to the lyophilized anti-serum and
mixed, using a vortex mixer.
6. Biotinylated-tracer (Bt-tracer): 5ml of EIA buffer was added to the lyophilized
Bt- tracer.
7. Streptavidin-HRP: The tube containing streptavidin-HRP was centrifuged and
then diluted 1 in 200 with EIA buffer (60 μl of streptavidin-HRP with 12ml of
EIA buffer) and mixed, using a vortex mixer.
Procedure
Step 1:25 μl of anti serum (in EIA buffer) was added into each well of the
immunoplate. EIA buffer (25 μl) was added to the blank wells.
Step 2:The immunoplate was then incubated at room temperature for 1 hour.
Standard or sample (in diluent) (50 μl) was added into wells. Only the diluent (50 μl)
was added to the wells for blanks.
Step 3:The immunoplate was then incubated at room temperature for 2 hours.
Step 4:Bt-tracer was rehydrated using EIA buffer and 25 μl was added into each well.
Step 5:The immunoplate was then incubated at 4°C in the refrigerator overnight (12
hours) after covering it with an acetate plate cover provided along with the kit.
Step 6:The immunoplate was re-equilibrated to room temperature.
Step 7: 300 μl/well of EIA buffer was used to was the immunoplate for 5 times.
Step 8:Streptavidin-HRP- 100 μl per well was added.
60
Step 9:The plate was incubated for 1 hr at room temperature.
Step 10:The wells were thenwashed 5 times, as described in step 7
Step 11:TMB solution (100 μl/well)was added.
Step 12:The plate was incubated at room temperature (for 30 - 60 minutes).
Step 13: Optical density (OD) readings of each well were taken at 650nm, using a
ELISA micro plate reader (BioRad), at time intervals of 15, 30, 45 and 60 mins.
Step 14:The reaction was terminated by adding 100 μl of 2N HCl into each well.
Step 15:After this, readings were taken at 450 nm, at 3, 6 and 10 mins. All samples
and standards were run in duplicates.
A semi-log scale of standard curve was plotted, using Microsoft Office Excel, 2007.
The concentrations of the standards (ng/mL) and the OD readings were plotted on x
and y axis respectively. The standard curve was constructed by four parameters
logistic regression using the equation:
Y= [(a-d/1) + (x+c)b]+d
The values a (maximum), b(slope), c (IC50, point of inflection), d(minimum) in the
equation were adjusted to get the standard curve to fit the data as closely as possible.
Values for serum hepcidin levels were calculated using the final equation of the
standard curve.
61
Estimation of serum C-reactive protein
C-reactive protein (CRP) was estimated in the diagnostic laboratory of the Department
of Clinical Microbiology, Christian Medical College, Vellore, using the reagents and
auto analyzer from BN Prospec, Siemens GmbH. This was a routine test offered by
this laboratory.
Principle of the assay
The assay was based on the principle of nephelometry. Upon addition of the sample,
the polystyrene particles coated with antibodies specific to human CRP aggregated
with CRP present in the sample. This aggregation led to scattering of light when it
was passed through the reaction chamber. The scattering of light was then measured
by a sensor and was directly proportional to the concentration of CRP in the sample.
Estimation of serum ferritin
Serum ferritin was measured in the diagnostic laboratory of the Department of
Clinical Biochemistry, Christian Medical College, Vellore, using the reagents and
auto analyzer from Siemens, ADVIA Centaur system Xpi, UK.This was a routine test
offered by this laboratory.
Principle
The method was based on a sandwich immunoassay which used direct chemi-
luminescence technology. A polyclonal antibody tagged with acridinium was added to
the reaction chamber. A monoclonal mouse anti-ferritin antibody covalently bound
62
with paramagnetic particles was added to the reaction chamber after addition of the
sample. The paramagnetic particles acted as a solid phase for the immunoassay.
Ferritin present in the sample formed a complex with antibodies and after a wash step
to remove unbound antibodies; a substrate was added to excite acridinium and release
photons. The intensity of photons released was measured and was directly
proportional to the ferritin concentration in the sample.
Estimation of serum iron
Serum iron was measured in the diagnostic laboratory of the Department of Clinical
Biochemistry, Christian Medical College, Vellore,using the auto analyzerRoche
Cobas c702 modular analyzer from Roche Diagnostics GmbH, Mannheim. This was a
routine test offered by this laboratory.
Principle
The method was based on a colorimetric assay using ferrozine. The acidic pH of the
reagent released ferric iron from transferrin. Ferric iron was later reduced to ferrous
iron by the ascorbate present in the reagent. Finally, ferrous iron combined with
ferrozine to form a purple colored complex. Concentration of iron in the sample was
directly proportional to the intensity of the colour. The intensity of the colour was
measured at 560nm in a spectrophotometer in the analyzer.
63
Estimation of total iron binding capacity (TIBC)
TIBC was measured in the diagnostic laboratory of the Department of Clinical
Biochemistry, Christian Medical College, Vellore, using the auto analyzer Roche
Cobas c702 modular analyzer from Roche Diagnostics GmbH, Mannheim. This was a
routine test offered by this laboratory.
Principle
Serum sample was added to a reagent solution containing an alkaline buffer, known
concentration of iron and ferrozine. At alkaline pH, iron in the reagent bound with the
unoccupied sites of transferrin and the remaining free iron formed a colored complex
with ferrozine. The color intensity was measured at 560 nm and was proportional to
the concentration of unbound iron. The difference between added iron and measured
unbound iron was the unsaturated iron binding capacity (UIBC). Sum of serum iron
and UIBC was calculated to give total iron binding capacity .
`Transferrin saturation
Transferrin saturation was estimated as the percentage of serum iron to TIBC ratio, as
shown below:
Transferrin saturation = (Serum iron/ TIBC)*100
64
Estimation of serum insulin
Serum insulin was measured in the diagnostic laboratory of the Department of Clinical
Biochemistry, Christian Medical College, Vellore, using the auto analyzer Immulite
2000 xpi from Siemens, UK. This was a routine test offered by this laboratory.
Principle
The assay was based on a solid-phase enzyme labeled chemiluminescent
immunoassay. Monoclonal murine anti-insulin antibody coated on beads constituted
the solid phase. The liquid phase consisted of two types of antibodies: Polyclonal
sheep anti-insulin antibody and monoclonal murine anti-insulin antibody. Both of
them were conjugated to alkaline phosphatase. Upon addition of sample and reagents
to the reaction chamber, insulin present in the sample formed sandwich complexes
with three anti insulin antibodies: monoclonal murine antibody on the bead,
polyclonal sheep antibody and monoclonal murine antibody in the liquid phase. After
washing unbound sample and reagent, a chemiluminescent substrate that emitted a
light signal, was added. The light emitted was measured by a luminometer in the
instrument and it was proportional to the insulin concentration of the sample.
HOMA-IR and HOMA-β
Homeostatic model assessment- insulin resistance (HOMA-IR), an indirect measure of
insulin resistance, was calculated using the following formula (Matthews et al., 1985):
HOMA-IR = (Fasting glucose (mg/dl) x Fasting insulin (μIU/ml))/405
65
Homeostatic model assessment-beta cell function (HOMA-β %), an index of
pancreatic beta cell function, was estimated using the formula (Matthews et al., 1985):
HOMA- β %= (360 x Fasting insulin (μIU/ml))/ (Fasting glucose (mg/dl) – 63)
Estimation of hematological parameters
Hematological parameters such a hemoglobin, RBC count, reticulocyte count ,
reticulocyte hemoglobin, mean corpuscular hemoglobin, and mean corpuscular
volume were estimated in the diagnostic laboratory of the Department of Transfusion
Medicine, Christian Medical College, Vellore, using the automated hematology
analyzer Sysmex XN 9000. These were routine test offered by this laboratory.
Principles of methods used
Hemoglobin was measured by the sodium lauryl sulphate(SLS) method, where after
lysis of the cells, SLS bound with hemoglobin to form a coloured complex. The
intensity of colour was measured by photometry and was proportional to hemoglobin
concentration in the sample.
RBC count was measured by sheath flow direct current detection method. In this
method, sample was passed through a detector which detected changes in electrical
resistance caused by the cells in the sample. Based on changes in resistance which
depended on the volume of the cell, RBC count was estimated.
66
Reticulocyte count and reticulocyte hemoglobin were measured by fluorescence flow
cytometry technique, where the RNA inside cells were stained with a fluorescence
dye.
Statistical tests
Statistical Package for Social Sciences version 16.0 (SPSS 16.0) was used to do the
statistical analysis. Shapiro-Wilk test was used to tes for normalit of the data. Mean
and standard deviation were used to represent normally distributed data. Median and
interquartile ranges were used to represent data which were not normally distributed.
Normally distributed data in the 2 groups (the controls and those with diabetes
mellitus) were compared by unpaired Student’s t test. The Mann-Whitney U test was
used for comparisons of data that were not normally distributed. Bivariate correlation
analyses were done using Pearson correlation for variables with normal distribution
and Spearman’s rank correlation for variables that are not normally distributed.
Linear regression analyses were also carried out. Statistical significance was assumed
in all cases when P-value is less than 0.05.
67
RESULTS
The study sample comprised 42 males. They were recruited as per the inclusion and
exclusion criteria laid down in the study protocol. Of these, 21 were diagnosed to have
diabetes mellitus (fasting plasma glucose values >126 mg/dL) and 21 matched
controls did not have diabetes mellitus (fasting plasma glucose values <100 mg/dL).
Shapiro-Wilk test was used to determine normality of the data. The parameters of
age, waist and hip circumference, waist-to-hip ratio, BMI, haemoglobin and
reticulocyte count were found to be normally distributed. Those of RBC counts, mean
corpuscular volume, mean corpuscular hemoglobin, reticulocyte hemoglobin, fasting
plasma glucose, fasting serum insulin, HOMA-IR, HOMA-B, Serum iron, ferritin and
hepcidin , total iron-binding capacity, transferrin saturation and hepcidin-ferritin ratio
were found to have skewed distributions.
68
Table 6.1. Age and anthropometric measurements
Controls
(N=21)
Diabetes mellitus
(N=21)
Mean ± SD Mean± SD P value
Age (years) 45.76 ±6.32 45.00 ±6.36 0.699
BMI (kg/m2) 26.01 ±3.04 25.97 ±3.69 0.964
Waist circumference (cm) 92.81 ±8.27 88.95 ±9.86 0.178
Hip circumference (cm) 94.60 ± 7.00 90.55 ±10.86 0.16
Waist-to-hip ratio 0.98 ± 0.03 0.98 ±0.04 0.717
The data in the 2 groups were compared using unpaired student’s t test.
The 2 groups were similar in terms of age and anthropometric measurements.
69
Table 6.2. Haematological parameters
Controls
(N=21)
Diabetes mellitus
(N=21)
Mean±SD/
Median(interquartile
ranges)
Mean±SD/
Median (interquartile
ranges)
P value
Hemoglobin (g/dL) 15.31±1.13 15.76±1.05 0.196
Absolute reticulocyte count
(106/μL)
0.06±0.013 0.077±0.026 0.009*
Reticulocyte count (%) 1.16±0.28 1.44±0.47 0.028*
RBC count (106/μL) 5.4 (5.01 - 5.61) 5.36 (4.99 - 5.685) 0.473
Mean corpuscular volume
(fL) 83.8 (81.05 - 86.85) 82.9(79 - 85.9) 0.406
Mean corpuscular
hemoglobin (pg) 29 (27.9 - 30.15) 28.5 (28.1 - 30.9) 0.92
Reticulocyte hemoglobin
(pg) 33.1 (30.9 - 34.25) 33.2 (32.05 - 34.35) 0.489
The data in the 2 groups were compared using either unpaired student’s t test orMann-
Whitney U test, as appropriate.
* p< 0.05
The absolute reticulocyte count and reticulocyte count (expressed as percent of RBC
count) were significantly higher in subjects with diabetes mellitus.
70
Figure 6.1. Fasting plasma glucose concentrations in control and diabetic
subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
* p< 0.001
Fasting plasma glucose concentrations were significantly higher in patients who had
diabetes mellitus.
71
Figure 6.2. Post-prandial plasma glucose concentrations
in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.Data
on post-prandial glucose concentrations were not available for 3 control subjects (not
measured as part of their clinical investigation); these subjects were excluded from
this analysis.
* p< 0.001
Post-prandial plasma glucose concentrations were significantly higher in patients who
had diabetes mellitus.
72
Figure 6.3. Fasting serum insulin levels in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.One
control subject and 3 diabetic subjects had insulin levels too low to be detected by the
assay used (<2μIU/ml);these subjects were excluded from this analysis.
Fasting serum insulin levels were similar in the two groups.
73
Figure 6.4. HOMA-IR in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.One
control subject and 2 diabetic subjects had insulin levels too low to be detected by the
assay used (<2μIU/ml); these subjects were excluded from this analysis.
* p = 0.002
HOMA-IR, an index of insulin resistance, was significantly higher in subjects with
diabetes mellitus.
74
Figure 6.5. HOMA-B in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.One
control subject and 3 diabetic subjects had insulin levels too low to be detected by the
assay used (<2μIU/ml); these subjects were excluded from this analysis.
* p< 0.001
HOMA-B, an index of β-cell function, was significantly lower in subjects with
diabetes mellitus.
75
Figure 6.6. Serum hepcidin levels in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
Serum hepcidin levels were similar in the two groups.
76
Figure 6.7. Serum ferritin levels in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
* p< 0.007
Serum ferritin levels were significantly higher in subjects with diabetes mellitus than
in control subjects.
77
Figure 6.8. Hepcidin-ferritin ratio in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
* p< 0.004
The hepcidin-ferritin ratio was significantly lower in subjects with diabetes mellitus.
78
Figure 6.9. Serum iron levels in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
Serum iron levels were similar in the two groups.
79
Figure 6.10. TIBC in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
Values for TIBC were similar in the two groups.
80
Figure 6.11. Transferrin saturation in control and diabetic subjects
Box and whisker plot showing medians and interquartile ranges. Outliers are shown as
numbered dots. Mann-Whitney U test was used to compared the data in 2 groups.
Transferrin saturation levels were similar in the two groups.
81
Table 6.3. Results of univariate analysis of iron parameters (N=42)
Serum
ferritin
(ng/mL)
Serum
hepcidin
(ng/mL)
Hepcidin-
ferritin
ratio
Serum
iron
(μg/L)
Total
iron-
binding
capacity
(μg/L)
Transferrin
saturation
(%)
Serum
ferritin
(ng/mL)
Correlation
coefficient - 0.525* -0.498* 0.455* -0.428* 0.548*
P value - <0.001 0.001 0.002 0.005 <0.001
Serum
hepcidin
(ng/mL)
Correlation
coefficient 0.525* - 0.383* 0.187 -0.21 0.188
P value <0.001 - 0.012 0.236 0.183 0.232
Hepcidin-
ferritin
ratio
Correlation
coefficient -0.498* 0.383* - -0.187 0.254 -0.276
P value 0.001 0.012 - 0.236 0.104 0.076
Serum iron
(μg/L)
Correlation
coefficient 0.455* 0.187 -0.187 - -0.145 0.888*
P value 0.002 0.236 0.236 - 0.361 <0.001
Total iron-
binding
capacity
(μg/L)
Correlation
coefficient -0.428* -0.21 0.254 -0.145 - -0.470*
P value 0.005 0.183 0.104 0.361 - 0.002
Transferrin
saturation
(%)
Correlation
coefficient 0.548* 0.188 -0.276 0.888* -0.470* -
P value <0.001 0.232 0.076 <0.001 0.002 .-
* p< 0.05
Correlation analysis was done using Spearman rank analysis.
Serum ferritin showed significant positive correlations with serum hepcidin, serum
iron and transferrin saturation. Italso showed significant negative correlations with
hepcidin-ferritin ratio and total iron-binding capacity. Serum hepcidin also showed
significant positive correlation with the hepcidin-ferritin ratio. In addition, transferrin
saturation showed significant positive correlation with serum iron and negative
correlation with total iron-binding capacity.
82
Table 6.4. Results of univariate analysis of iron and metabolic parameters (N =
38 to 42)#
Fasting
plasma
glucose
(mg/dL)
(N=42)
Fasting
insulin
(μIU/ml)
(N=38)
HOMA-IR
(N=38)
HOMA-B
(%)
(N=38)
Serum ferritin
(ng/mL)
Correlation
coefficient 0.370* 0.032 0.194 -0.293
P value 0.016 0.848 0.243 0.075
Serum hepcidin
(ng/mL)
Correlation
coefficient -0.146 -0.109 -0.072 -0.066
P value 0.357 0.516 0.667 0.694
Hepcidin-
ferritin ratio
Correlation
coefficient -0.433* -0.11 -0.261 0.277
P value 0.004 0.511 0.114 0.092
Serum iron
(μg/L)
Correlation
coefficient 0.056 -0.132 -0.117 -0.156
P value 0.724 0.43 0.483 0.35
Total iron-
binding
capacity (μg/L)
Correlation
coefficient -0.112 0.075 0.074 0.032
P value 0.479 0.652 0.657 0.851
Transferrin
saturation (%)
Correlation
coefficient 0.165 -0.215 -0.181 -0.209
P value 0.296 0.194 0.277 0.209 #One control subject and 3 diabetic subjects had insulin levels too low to be detected
by the assay used (<2μIU/ml); these subjects were excluded from the analysis
involving fasting insulin, HOMA-IR and HOMA-B.
Correlation analysis was done using Spearman rank analysis.
* p< 0.05
Fasting plasma glucose showed significant positive correlation with serum ferritin and
negative correlation with hepcidin-ferritin ratio.
83
Table 6.5. Results of univariate analysis of age and iron and anthropometric
parameters (N=42)
Age
(years)
BMI
(kg/m2)
Waist
circumference
(cm)
Hip
circumference
(cm)
Waist-to-
hip ratio
Serum
ferritin
(ng/mL)
Correlation
coefficient -0.228 -0.314* -0.414* -0.439* 0.057
P value 0.146 0.043 0.006 0.004 0.721
Serum
hepcidin
(ng/mL)
Correlation
coefficient -0.127 0.145 -0.041 -0.067 0.182
P value 0.421 0.359 0.795 0.674 0.25
Hepcidin-
ferritin
ratio
Correlation
coefficient 0.19 0.552* 0.428* 0.422* 0.159
P value 0.228 <0.001 0.005 0.005 0.316
Serum iron
(μg/L)
Correlation
coefficient -0.053 -0.181 -0.233 -0.289 0.212
P value 0.737 0.25 0.137 0.064 0.177
Total iron-
binding
capacity
(μg/L)
Correlation
coefficient -0.002 0.340* 0.337* 0.335* 0.139
P value 0.992 0.028 0.029 0.03 0.381
Transferrin
saturation
(%)
Correlation
Coefficient -0.024 -0.302 -0.307* -0.372* 0.192
P value 0.881 0.052 0.048 0.015 0.224
Correlation analysis was done using Spearman rank analysis.
* p< 0.05
Serum ferritin showed significant negative correlation with BMI and waist and hip
circumferences. Hepcidn-ferritin ratio and total iron-binding capacity showed
significant positive correlation with BMI and waist and hip circumferences.
Transferrin saturation showed significant negative correlation with waist and hip
circumferences.
84
Table 6.6. Results of univariate analysis of iron and haematological parameters
(N=42)
Hemoglobin
(g/dL)
RBC
count
(106/μL)
Mean
corpuscular
volume (fL)
Mean
corpuscular
hemoglobin
(pg)
Reticulocyte
hemoglobin
(pg)
Absolute
reticulocyte
count
(106/μL)
Reticulocyte
count (%)
Serum
ferritin
(ng/mL)
Correlation
coefficient 0.136 -0.004 0.033 0.221 0.209 0.199 0.173
P value 0.389 0.981 0.834 0.16 0.184 0.206 0.273
Serum
hepcidin
(ng/mL)
Correlation
coefficient -0.08 -0.138 0.155 0.226 0.198 0.146 0.144
P value 0.615 0.383 0.327 0.15 0.209 0.358 0.364
Hepcidin-
ferritin ratio
Correlation
coefficient -0.177 -0.133 0.106 0.054 0.042 -0.027 -0.009
P value 0.261 0.4 0.504 0.733 0.794 0.863 0.957
Serum iron
(μg/L)
Correlation
coefficient 0.256 0.033 0.152 0.339* 0.288 0.032 0.04
P value 0.102 0.834 0.338 0.028 0.065 0.842 0.802
Total iron-
binding
capacity
(μg/L)
Correlation
coefficient -0.087 0.016 -0.274 -0.248 -0.196 0.097 0.055
P value 0.584 0.917 0.079 0.113 0.215 0.54 0.727
Transferrin
saturation
(%)
Correlation
coefficient 0.29 -0.007 0.243 0.419* 0.350* -0.041 -0.032
P value 0.062 0.966 0.121 0.006 0.023 0.799 0.843
Correlation analysis was done using Spearman rank analysis.
* p< 0.05
Serum iron showed significant positive correlation with mean corpuscular
hemoglobin. Transferrin saturation showed significant positive correlations with mean
corpuscular hemoglobin and reticulocyte hemoglobin.
85
Parameters that showed significant correlations on univariate analysis, as shown in the
tables above, were subjected to multivariate linear regression analysis. Accordingly,
when plasma glucose levels were adjusted for, serum ferritin was found to be
correlated positively with serum hepcidin (B= 2.606, SE = 0.947, p = 0.009),
hepcidin-ferritin ratio (B= -447.979, SE = 214.313, p = 0.043), serum iron (B= 1.173,
SE = 0.491, p = 0.022) and transferrin saturation (B= 3.888, SE = 1.558, p = 0.017).
When serum ferritin levels were adjusted for in multivariate analysis, using linear
regression, it was found that fasting plasma glucose showed a trend towards a
significant negative correlation with the hepcidin-ferritin ratio (B= -209.33, SE
=113.527, p = 0.073).
86
Summary of results
1. Serum ferritin levels and reticulocyte counts were significantly higher in
subjects with diabetes mellitus, when compared with control subjects.
2. Ratio of hepcidin to ferritin was significantly lower in subjects with diabetes
mellitus, when compared with control subjects.
3. Levels of serum hepcidin, total iron-binding capacity, transferrin saturation and
serum iron were similar in controls and diabetic subjects.
4. On univariate analysis, serum ferritin was found to be positively correlated
with fasting plasma glucose, serum iron, serum hepcidin and transferrin
saturation. It also showed significant negative correlations with total iron
binding capacity, hepcidin-ferritin ratio, BMI and waist and hip
circumferences. The hepcidin-ferritin ratio was negatively correlated with
fasting plasma glucose levels.
5. On multivariate analysis, it was found that serum ferritin was significantly and
positively correlated with serum hepcidin, hepcidin-ferritin ratio, serum iron
and transferrin saturation, independent of fasting plasma glucose levels.
Hepcidin-ferritin ratio showed a trend towards significant negative correlation
with fasting plasma glucose, when adjusted for serum ferritin levels.
87
DISCUSSION
The hypothesis that the present study tested was that serum hepcidin levels would be
altered in patients with diabetes mellitus. The hypothesis was based on the
associations that have been reported between iron status and diabetes mellitus. Iron
metabolism and diabetes mellitus have been shown to influence each other
(Fernández-Real et al., 2002). Serum ferritin levels, commonly used as a marker of
body iron stores, have been reported to be increased in those with diabetes mellitus
(Kunutsor et al., 2013; Podmore et al., 2016). Increased body iron stores, due to high
dietary iron intake or other unknown reasons, have also been postulated to be
associated with the development and/or progression of diabetes mellitus (Bao et al.,
2012; Swaminathan et al., 2007). It is possible that elevated iron stores may represent
metabolic alterations in diabetes mellitus that could potentially play a part in
progression of the disease.
Hepcidin is the central regulator of iron homeostasis (Atanasiu et al., 2007). Hence, it
is conceivable that it may have a role to play in the dysregulation of iron homeostasis,
and is linked to elevated body iron stores often been reported in diabetes mellitus.
Exploring this avenue may contribute to a better understanding of the relationship
between iron and insulin resistance, which is a hall-mark of diabetes mellitus
(Aregbesola et al., 2015; Goldstein, 2002). Hence, it was postulated that studying
alterations in hepcidin levels may shed light on the mechanism of increased iron stores
in patients diagnosed to have diabetes mellitus (Ganz, 2003).
88
In the present study, we recruited men who were diagnosed, for the first time, to have
diabetes mellitus. Hepcidin levels are known to be influenced by gender (Galesloot et
al., 2011). Iron homeostasis is affected by hormonal changes during the menstrual
cycle; it also depends on the phase of the reproductive cycle in women; such
influences would necessitate careful case control selection (Kim et al., 1993; Milman
et al., 1992). To avoid this, only male subjects were studied, since financial constraints
limited the number of subjects that could be studied to 42 (21 in each arm). It was also
decided to study only those with a first-time diagnosis of diabetes mellitus, to reduce
influence of any therapeutic interventions or of complications of the disease on the
parameters of interest.
Serum ferritin levels were significantly increased in the diabetic subjects, when
compared with control subjects of similar age and BMI. Ferritin is an intracellular
protein that stores iron. It is secreted by the liver and macrophages (Cohen et al.,
2010; Ghosh et al., 2004). Its levels in serum are commonly taken to be an indicator of
body iron stores; however, its levels are also elevated in response to inflammation
(Brailsford et al., 1985). Serum C-RP is a marker of inflammation. Patients with C-RP
levels higher than 10 mg/L were excluded from the present study (Erlandsen and
Randers, 2000; Kathleen Hutchinson, 2000). Among the patients included, it was
found that serum CRP levels were not different in the diabetic and control groups
(median [interquartile ranges]: diabetes mellitus: 3.1 [3.1-3.45] mg/L vs controls: 3.2
[3.1-3.98] mg/L). Thus, it is unlikely that the elevations seen in serum ferritin in the
diabetics were a result of the presence of inflammation.
89
Previously published studies have also shown that ferritin levels are higher in patients
with diabetes mellitus (Ford and Cogswell, 1999; Forouhi et al., 2007; Jehn et al.,
2007; Kunutsor et al., 2013; Podmore et al., 2016). Since our study was done in
subjects diagnosed for the first time to have diabetes mellitus, it suggests that
increased serum ferritin levels may be an early event in the development of diabetes
mellitus. This postulate is supported by previous studies that have reported that high
serum ferritin levels predicted future development of diabetes mellitus (Dinneen et al.,
1992; Forouhi et al., 2007; Jehn et al., 2007).
Iron-related parameters, such as TIBC, transferrin saturation and serum iron were
similar in the two groups studied. Transferrin saturation, along with serum ferritin, is
useful as an indicator of iron overload and deficiency, with levels being elevated in
iron overload and decreased in iron-deficiency anaemia (Cook, 2005; Piperno, 1998).
Yeapet.al (2015) have reported that high serum ferritin levels, but not high transferrin
saturation and serum iron, were associated with diabetes mellitus. Based on this, they
have suggested that iron-independent mechanisms may account for the association of
serum ferritin with diabetes mellitus (Yeap et al., 2015). Chen et.al.(2011), studied
individuals with dysmetabolic hyperferritinemia (i.e., hyperferritenemia in those with
metabolic syndrome). These patients showed mild hepatic iron overload,as assessed
by MRI studies, but had normal transferrin saturation, despite elevated serum ferritin
levels. The findings of the present study are consistent with the above reports. The
reason for transferrin saturation not being elevated in hyperferritinemia, both in
90
metabolic syndrome and diabetes mellitus, is not known. Transferrin levels were
found to be higher in diabetics in a previous study, this might prevent increases in
transferrin saturation in diabetic subjects (Podmore et al., 2016). In the present study,
due to financial and logistical constraints, it was not possible to carry out
measurements of iron levels in the liver of the subjects (by MRI) to ascertain if there
was an iron overload in the diabetic subjects. Such information would have been
useful in better interpretation of the results obtained. This is a limitation of the study.
Hepcidin levels are known to be induced in response to increased iron levels in the
body (Ganz, 2011; Pigeon et al., 2001). Despite higher ferritin levels in the diabetic
subjects (indicating increased body iron levels), serum hepcidin levels in these
subjects were not significantly different from those in control subjects. The ratio of
hepcidin to ferritin was found to be significantly lower in subjects with diabetes
mellitus. This ratio is an indicator of the appropriateness of serum hepcidin levels in
response to prevailing iron stores (Piperno et al., 2007). The lowered ratio suggests
that hepcidin levels were lower that what is expected for the presumed iron load in
these subjects. As hepatic iron stores increase (as may be indicated by higher ferritin
levels) in subjects with diabetes mellitus, the expected response would be a
stimulation of the BMP/SMAD pathway, resulting in increased hepcidin secretion
from the liver (Chua et al., 2011). This did not seem to be the case in the diabetic
subjects in this study. The results obtained, thus, suggest that this response may be
blunted in the diabetic group.
91
Previously published studies on serum hepcidin levels in diabetes mellitus have
reported contradictory results. Studies by Jiang et.al.(2011) (in China) and Andrews
et.al. (2015) (in Chile) have reported higher serum hepcidin levels in patients with
type 2 diabetes mellitus than in control subjects. A study by Sam et.al.(2013) (in the
United Kingdom) showed that serum hepcidin levels in patients with type 2 diabetes
mellitus were lower than in control subjects. Another study by Guo et.al.(2013)(in a
Han Chinese population of 704 controls and 555 cases) showed no significant
differences in serum hepcidin levels in controls and patients with type 2 diabetes
mellitus . The reasons for the variable findings reported are not clear.
The present study differs from the above studies in several aspects. The mean age of
the patients in the above studies ranged from 53 to 64 years, while the patients in the
present study were younger (mean age of subjects 45 years, SD 6). Our study recruited
only patients who were diagnosed, for the first time, to have diabetes mellitus, while
the other studies included patients known to have pre-existing diabetes mellitus
(Andrews et.al,2015). Our study included only male subjects, while the other studies
included both male and females. Our study excluded subjects with anaemia while
other studies did not. These differences have to be considered when comparing the
results of our study with previous reports. As our study included only newly
diagnosed subjects as cases, the results obtained may not reflect long-term effect of
diabetes mellitus on iron homeostasis; however, it is likely to be indicative of
alteration in iron homeostasis in the early stages of diabetes mellitus.
92
Anaemia affects iron homeostasis; therefore we excluded anaemic subjects to avoid
potential confounding of results by this factor. Thus, in non-anaemic male subjects
diagnosed for the first time to have diabetes mellitus, serum hepcidin levels were not
found to be different from control subjects. However, the hepcidin-ferritin ratio was
significantly lower in the diabetics than in control subjects. The study by Sam et.al
(2013) is the only other study that has reported on hepcidin-ferritin ratios in subjects
with diabetes mellitus. They have documented significantly lower hepcidin-ferritin
ratios in diabetic subjects. Such a ratio indicates inadequate hepcidin levels for
prevalent ferritin levels. This could lead to inappropriately high levels of expression of
ferroportin and subsequent increases in intestinal iron absorption, which may
exacerbate the initial condition of increased iron stores. It is not clear, however, why
hepcidin levels are inappropriately low in these subjects. This aspect needs further
investigation.
Values for haematological parameters were similar in the two groups, except for the
reticulocyte count. This count was significantly higher in patients with diabetes
mellitus, despite similar hemoglobin concentrations and RBC counts in the two
groups. An increased reticulocyte count is indicative of an increased rate of
erythropoiesis. A similar observation was reported by Giugliano et.al. (1982); in that
study, it was also found that the reticulocyte count correlated positively with levels of
glycated haemoglobin (Giugliano, 1982). Fekete et.al.(1986) have also showed a
positive association between glycated haemoglobin levels and reticulocyte counts
(Fekete and Sopon, 1986). Diabetic subjects have been reported to have higher RBC
93
counts (Simmons, 2010); however, this was not observed in the present study The
cause for erythropoiesis being possibly increased in patients with diabetes mellitus is
not known. A postulate put forward is that hypoxic conditions in diabetes mellitus
may stimulate erythropoiesis (Giugliano, 1982). Another possibility is the fact that
insulin is a stimulant for erythropoiesis. Hyperinsulinemia in diabetes may increase
erythropoiesis (Bersch et al., 1982). However, in our study there was no significant
increase in insulin levels in the diabetic group. Erythropoiesis may also affect iron
homeostasis by suppressing hepcidin expression, resulting in increased iron
absorption (Ganz, 2011; Vokurka et al., 2006). The significance of elevated
reticulocyte counts in diabetes mellitus and its relation to iron homeostasis needs to be
further explored.
Among the metabolic parameters studied, fasting insulin levels were not different
between the two groups, despite significantly higher fasting glucose levels in those
with diabetes mellitus. HOMA-IR, was significantly higher in subjects with diabetes
mellitus, which is as expected. β-cell function, as measured by HOMA-B, was
significantly lower in subjects with diabetes mellitus, once again an expected finding.
The difference in β-cell function was more pronounced than the difference in insulin
resistance in the two groups. As the subjects were relatively younger and newly
diagnosed with diabetes, this is particulary interesting, as β- cell dysfunction seems to
appear early in the course of development of diabetes mellitus. Previous studies have
also suggested that impaired beta-cell function is prevalent in Indian patients with
hyperglycemia (Staimez et al., 2013).
94
Correlation analysis of the data in this study revealed several interesting results. On
univariate analysis, serum ferritin was found to be positively correlated with fasting
plasma glucose, serum hepcidin, serum iron and transferrin saturation. It was also
negatively correlated with the hepcidin-ferritin ratio, total iron-binding capacity, BMI
and waist and hip circumferences. When adjusted for fasting plasma glucose,using
linear regression analysis, serum ferritin correlated positively with serum hepcidin,
serum iron and transferrin saturation and negatively with the hepcidin-ferritin ratio.
These results indicate that serum ferritin levels were significantly associated with
these iron parameters, independent of fasting plasma glucose levels. These findings
are consistent with the biological relationships among these iron-related parameters.
Linear regression analysis also showed that the hepcidin-ferritin ratio showed a trend
towards a significant negative correlation with fasting plasma glucose levels. This is
an interesting finding that suggests that fasting hyperglycemia may be linked to
suppressing or blunting induction of hepcidin. The effect of glucose on hepatic
hepcidin has been reported to be variable. Aigner et al (2013) shown that there was no
detectable hepcidin release when hepatoma cell (HepG2) were incubated with
glucose. They have also shown that serum hepcidin levels increased significantly in
healthy subjects undergoing oral glucose tolerance tests and have suggested that β-
cells could secrete hepcidin upon glucose stimulation (Aigner et al., 2013).
Gluconeogenesis has also been found to be associated with increased expression of
hepcidin in hepatocytes of mice (Vecchi et al., 2014). In this study, mice that had been
95
starved showed increased expression of phosphoenolpyruvate carboxykinase 1(an
enzyme involved in gluconeogenesis) and hepcidin in the liver.
Hepcidin has also been shown to be expressed by the beta-cells of the pancreas; it has
been found to co-localise with insulin in the secretory granules in these cells (Kulaksiz
et al., 2008). Glucose (at 7mM and 11 mM for 180 min) has been shown to stimulate
the release of hepcidin from rat insulinoma cells (INS-1E ) in culture (Aigner et al.,
2013). Insulin has also been reported to up-regulate hepcidin expression in HepG2
cells (Wang et al., 2014). Thus, there seem to be several important links between the
pathways of metabolism of iron and glucose. Despite this, the reason for the negative
correlation between hepcidin-ferritin ratio and fasting plasma glucose is not clear.
This is an area that warrants further exploration in the future.
96
CONCLUSIONS
Serum ferritin levels were significantly higher in subjects with diabetes mellitus, when
compared with controls. Serum hepcidin levels were similar in the two groups. The
ratio of hepcidin to ferritin was significantly lower in subjects with diabetes mellitus,
suggesting that the serum hepcidin levels were possibly inadequate for the prevailing
serum ferritin levels. The reason for this finding is not clear. Further work is required
to elucidate the links between dysregulation of glucose and iron homeostasis.
LIMITATIONS OF THE STUDY
1. The calculated sample size for this study was 38 subjects in each group. Due to
financial constraints, it was possible to study only 21 patients in each group.
This smaller-than-adequate sample size is a major limitation of the study.
2. Although patients with high serum CRP levels were exluded from the study,
we did not measure other inflammatory markers, such as IL-6 (which is known
to induce hepcidin), to definitively rule out any contribution of inflammation
towards the increased serum ferritin levels in the diabetic subjects. This was
also due to financial constraints.
3. Financial and logistical constraints also made it impossible to carry out
measurements of iron levels in the liver of the subjects, by MRI, to ascertain if
there was indeed an iron overload in the diabetic subjects.
97
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121
Appendix 1: Letter of approval from Institutional Review Board
122
123
124
125
Appendix 2: Information sheet and consent from for patients
Information sheet for patients
The Department of Biochemistry at Christian Medical College, Vellore, in association with
the Department of Community Health, is conducting a study to look for changes in blood
levels of hepcidin in people with diabetes mellitus and pre-diabetes. Hepcidin is a substance
in blood that controls iron levels in the body. High levels of iron in the body may be
associated with increased risk of developing diabetes mellitus. This study is being done to
help doctors to understand better the relationship between iron levels in the body and
development of diabetes mellitus. For this study, we require 10 ml of blood to test for certain
substances. We ask if you are willing to provide 10ml of blood for this purpose.
You may not benefit directly from participation in the study. However, if you are willing to
provide the blood sample, it will help us understand how changes in hepcidin levels may
cause diabetes mellitus and may help, in the future, to improve ways of prevention and
treatment for this condition.
The blood sample collected will be used only for research purposes. If there is any sample
left over after this study is completed, we request you for permission to store it and use it for
related research studies in the future. Collection of blood will not cause harm to your health
and will be done at the same time that blood is taken for your routine tests. The medical
information that you give us will be kept confidential.
If you are not willing to participate in the study, you are free to say so. It will not affect the
treatment you will receive in the hospital. Participation in the study does not entitle you to
concession or any other special treatment. If you have further queries, please contact us,
using one of the numbers given below.
Dr. V. Padmanaban, Dr.Jasmine Prasad,
PG Registrar, Professor and Head,
Department of Biochemistry, Department of Community Health,
Christian Medical College, Christian Medical College,
Vellore – 632002 Vellore – 632002
Contact number: 9629740790 Contact number: 0416 - 2284207
Dr. Molly Jacob,
Professor and Head,
Department of Biochemistry,
Christian Medical College,
Vellore – 632002
Contact number: 0416 – 2284267
126
Consent form
Study title:
Serum levels of hepcidin in diabetes mellitus and pre-diabetes
Study number:
Subject’s name and hospital number:
Date of birth /age in years:
I confirm that I have read and understood the information sheet dated ____________ for the
above study and have had the opportunity to ask Dr.Padmanaban any questions I had. [ ]
I understand that my participation in the study is voluntary and that I can decline to
participate, without giving any reason, without my medical care or legal rights being affected.
[ ]
I understand that if I agree to participate in the study, I will be required to provide a blood
sample of 10ml. [ ]
I understand that this will not affect my health in any foreseeable way. [ ]
I understand that the blood sample taken will be used only for research purposes. If there is
any sample remaining after this study is completed, I give permission for the sample to be
stored and used for related studies in the future. [ ]
I understand that my identity will not be revealed in any information released to third
parties or published and that all my medical information will be kept confidential. [ ]
I agree to take part in the above study of my own free will. [ ]
I agree not to restrict the use of any data or results that arise from this study, provided such a
use is only for scientific purpose(s). [ ]
Signature (or thumb impression) of the subject:
Address:
Date: Name and signature of the investigator:
Witness name/ Relation to the subject:
Address:
Witness’s Signature (or thumb impression):
127
Information sheet for patients in Tamil
128
129
Consent form in Tamil
130
131
Appendix 3: Patient proforma
DEPARTMENT OF BIOCHEMISTRY AND DEPARTMENT OF COMMUNITY HEALTH
CHRISTIAN MEDICAL COLLEGE, VELLORE – 632002
Serum levels ofhepcidin indiabetes mellitus and pre-diabetes
PROFORMA FOR DETAILS OF PARTICIPANTS
Date:
Name
Age (years)
Sex
Hospital No DOB
Address
Phone Number
Education/Occupation
1. Medical history:
Reason for current visit/presenting complaint:
2. Final diagnosis:
3. Family history:
Diabetes: Hypertension: Any other:
4. Personal history:
Diet: Veg/non-veg Alcohol use: Smoking:
Any other:
5. Clinical examination:
Acanthosis nigricans: Pallor: BP:
Newly diagnosed diabetic/
impaired fasting glucose/
normalfasting glucose
132
Any other significant finding:
Anthropometric measurements:
Height: Weight: BMI:
Waist circumference: Hip circumference:
6. Laboratory investigations:
Haemoglobin
:
CRP Any other analytes measured
as part of clinical
management
Fasting glucose Post prandial
glucose
Serum ferritin Serum hepcidin
133
Appendix 4: Master data sheet
Controls
Age Fasting
glucose
(mg/dL)
Post prandial
glucose
(mg/dL)
Height
(m)
Weight
(kg)
BMI
(kg/m2)
Waist
circumference
(cm)
Hip
circumference
(cm)
Waist-
hip ratio
50 90 122 1.77 92 29.4 111 107 1.04
52 98 140 1.68 88.5 31.4 104 103 1.01
53 95 115 1.55 66 27.5 97 102 0.95
41 94 84 1.7 67 23.2 87 85 1.02
38 78 NA 1.75 79 25.8 96.5 97 0.99
37 76 97 1.84 95 28.1 104 102 1.02
54 90 95 1.7 79.8 27.6 95 97 0.98
50 90 NA 1.69 70 24.5 92 93 0.99
41 80 116 1.69 75 26.3 92 98 0.94
44 87 NA 1.66 80.8 29.3 98 100 0.98
43 85 70 1.71 89 30.4 98.5 99.5 0.99
40 90 95 1.68 72.5 25.7 90.5 94.5 0.96
52 96 174 1.72 67 22.6 84 93 0.9
57 97 125 1.7 76 26.3 97 98 0.99
49 90 86 1.65 73.5 27 90 93 0.97
45 92 125 1.65 65.4 24 83.5 90.5 0.92
46 98 108 1.79 73.7 23 90 92 0.98
52 94 83 1.55 51 21.2 80 82 0.98
43 94 105 1.6 74.5 29.1 96 94 1.02
38 91 106 1.72 61 20.6 78 81 0.96
36 92 100 1.65 63.5 23.3 85 85 1
134
Hb(%) RBC
count (106
cells/μL)
MCV
(fL)
MCH
(pg)
Reticulocyte
Hb(pg)
Reticulocyte
count
(106/μL)
Reticulocyte
(%)
CRP
(mg/L)
Ferritin
(ng/mL)
Hepcidin
(ng/mL)
15.7 4.88 86.5 32.2 36.3 0.063 1.29 3.7 35.2 12.28
16.9 5.55 87.2 30.5 34.9 0.0477 0.86 <3.17 69 11.38
14.4 5.44 77.8 26.8 30.4 0.0636 1.17 <3.28 38.5 9.37
15.3 5.4 79.8 28.3 31.3 0.0583 1.08 <3.28 111 8.41
15.8 5.37 83.8 29.4 33.1 0.0918 1.71 <3.28 62 14.66
15.5 5.4 89.1 28.7 32.7 0.0497 0.92 4.12 145 25.92
17 5.73 83.2 29.7 34 0.0745 1.30 <3.19 61 13.41
13.3 4.39 91.3 30.3 34.9 0.0457 1.04 <3.19 107 13.54
13.7 5.17 78.3 26.5 30.9 0.0564 1.09 <3.19 58 17.12
14 4.81 84.6 29.1 34.2 0.0635 1.32 5.9 99.8 13.95
15.5 5.49 81.1 28.2 32.4 0.0456 0.83 <3.17 79 11.94
15.5 5.14 83.9 30.2 33.7 0.0637 1.24 3.19 209 16.08
15.4 3.61 112.7 42.7 42.9 0.0614 1.70 <3.27 517 10.86
13 4.63 81 27.6 30.9 0.0764 1.65 8.1 9.2 1.20
16.4 5.69 87.5 28.8 30.9 0.0529 0.93 <3.27 46.7 11.38
15.4 5.19 85 29.7 34.3 0.0452 0.87 <3.17 46.6 10.27
14.4 5.16 83.1 27.9 31.1 0.065 1.26 <3.17 166 9.46
16.6 5.72 86 29 33.1 0.0561 0.98 <3.17 98 12.82
16.3 5.42 82.8 30.1 33.2 0.0672 1.24 5.26 471 140.51
15.7 5.67 82.9 27.7 29.6 0.0397 0.70 <3.17 40 1.12
15.8 5.67 79 27.9 30.9 0.0726 1.28 <3.16 232 17.76
135
Hepcidin-
ferritin ratio
Iron
(μg/dl)
UIBC
(μg/dl)
TIBC
(μg/dl)
Transferrin
saturation(%)
Insulin
(μIU/ml)
HOMA-IR HOMA-B
0.35 97 304 401 24.19 14.7 3.27 196.00
0.16 151 191 342 44.15 6.9 1.67 70.97
0.24 72 306 378 19.05 7.3 1.71 82.13
0.08 101 159 260 38.85 8 1.86 92.90
0.24 63 305 368 17.12 5.6 1.08 134.40
0.18 105 201 306 34.31 6.6 1.24 182.77
0.22 79 269 348 22.70 13.8 3.07 184.00
0.13 106 262 368 28.80 6.1 1.36 81.33
0.30 73 279 352 20.74 12 2.37 254.12
0.14 59 277 336 17.56 17.6 3.78 264.00
0.15 63 311 374 16.84 14.7 3.09 240.55
0.08 142 162 304 46.71 16.6 3.69 221.33
0.02 163 101 264 61.74 20.3 4.81 221.45
0.13 49 470 519 9.44 16.4 3.93 173.65
0.24 79 234 313 25.24 38 8.44 506.67
0.22 98 259 357 27.45 11.8 2.68 146.48
0.06 131 207 338 38.76 12.6 3.05 129.60
0.13 149 155 304 49.01 3.6 0.84 41.81
0.30 206 213 419 49.16 13.5 3.13 156.77
0.03 98 328 426 23.00 <2
0.08 162 202 364 44.51 3 0.68 37.24
136
Diabetes mellitus
Age Fasting
glucose
(mg/dL)
Post
prandial
glucose
(mg/dL)
Height
(m)
Weight
(kg)
BMI
(kg/m2)
Waist
circumference
(cm)
Hip
circumference
(cm)
Waist-hip
ratio
39 136 238 1.66 64 23.2 84 87.5 0.96
40 190 296 1.78 102 32.2 100 102 0.98
49 394 805 1.64 63 23.4 92 93 0.99
40 143 304 1.65 65 23.9 83 89 0.93
58 135 150 1.57 62 25.2 93 95 0.98
50 180 286 1.57 66.2 26.9 89 91 0.98
33 173 300 1.58 82 32.8 96 101 0.95
47 149 297 1.68 69 24.4 86.5 90.5 0.96
49 163 310 1.64 78.6 29.2 103 106 0.97
47 143 206 1.52 70 30.3 97 95 1.02
48 192 330 1.65 70.7 26 96 97 0.99
47 155 212 1.64 69 25.7 86 91 0.95
40 198 291 1.71 73.6 25.2 90 95 0.95
45 283 303 1.6 56 21.9 78 85 0.92
52 153 287 1.65 73 26.8 95.5 91.5 1.04
48 309 515 1.75 65 21.2 78 72 1.08
35 319 329 1.65 52.5 19.3 75 78 0.96
52 138 178 1.74 90.5 29.9 103 105 0.98
42 129 270 1.61 61 23.5 74 72 1.03
36 257 393 1.65 83.5 30.7 99 99 1
48 222 354 1.73 70.6 23.6 70 66 1.06
137
Hb(%) RBC
count
(106
cells/μL)
MCV
(fL)
MCH
(pg)
Reticulocyte
Hb(pg)
Reticulocyte
count(106/μL)
Reticulocyte
(%)
CRP
(mg/L)
Ferritin
(ng/mL)
Hepcidin
(ng/mL)
15.8 5.63 78.2 28.1 30.5 0.0816 1.45 <3.28 389 25.92
16.1 5.7 79.8 28.2 32.1 0.126 2.21 3.78 118 7.42
16.7 5.24 94.5 31.9 35.2 0.0398 0.76 3.88 113 6.97
15.5 4.8 92.1 32.3 35.6 0.1118 2.33 <3.28 182 15.27
15.4 5.48 82.7 28.1 31.4 0.0581 1.06 7.79 284.5 20.64
13.9 4.73 83.1 29.4 33.6 0.053 1.12 <3.19 136 64.84
16.2 5.49 82.9 29.5 34 0.0565 1.03 <3.19 211 25.92
14.8 5.36 81 27 32 0.0477 0.89 4.09 101 8.33
13.9 4.88 84.2 28.5 33 0.0752 1.54 4.25 43 13.15
16.4 5.78 81.5 28.4 33.2 0.1142 1.98 <3.17 131 50.12
14.4 5.26 84.4 27.4 31.7 0.0663 1.26 3.41 115 15.75
15.2 5.27 84.3 28.5 32.9 0.0838 1.59 <3.19 137 13.15
17.9 6.4 81.7 28 32.6 0.0576 0.90 8.43 249 12.52
17.9 6.22 78.1 28.8 33.3 0.0827 1.33 <3.19 184 12.05
15.2 4.79 85 31.7 34.9 0.0632 1.32 5.79 734 17.68
15.9 5.59 77.5 28.4 33.3 0.0827 1.48 <3.17 147 9.90
16.1 5.75 77.9 28 32.3 0.0512 0.89 <3.17 177 8.48
16.5 5.32 89.3 31 34.7 0.0782 1.47 <3.17 81 5.74
15.6 5.06 86.8 30.8 34.8 0.0724 1.43 <3.17 254 27.50
16.1 5.67 76.7 28.4 31.2 0.1287 2.27 3.34 64 4.95
15.4 4.92 89 31.3 33.9 0.0964 1.96 <3.17 292 25.15
138
Hepcidin-
ferritin ratio
Iron
(μg/dl)
UIBC
(μg/dl)
TIBC
(μg/dl)
Transferrin
saturation(%)
Insulin
(μIU/ml)
HOMA-IR HOMA-B
0.07 80 213 293 27.30 10 3.36 49.32
0.06 100 267 367 27.25 17.8 8.35 50.46
0.06 84 56 140 60.00 2.1 2.04 2.28
0.08 122 189 311 39.23 13.4 4.73 60.30
0.07 141 200 341 41.35 12.7 4.23 63.50
0.48 113 220 333 33.93 5.5 2.44 16.92
0.12 104 252 356 29.21 15.6 6.66 51.05
0.08 94 301 395 23.80 7.95 2.92 33.28
0.31 69 310 379 18.21 15.5 6.24 55.80
0.38 126 211 337 37.39 18.2 6.43 81.90
0.14 61 291 352 17.33 8.9 4.22 24.84
0.10 84 218 302 27.81 7.9 3.02 30.91
0.05 79 287 366 21.58 15.4 7.53 41.07
0.07 90 262 352 25.57 <2
0.02 87 227 314 27.71 27.5 10.39 110.00
0.07 141 215 356 39.61 <2
0.05 93 195 288 32.29 <2
0.07 118 249 367 32.15 20.3 6.92 97.44
0.11 291 72 363 80.17 7.8 2.48 42.55
0.08 122 262 384 31.77 11.9 7.55 22.08
0.09 94 256 350 26.86 8.7 4.77 19.70
139