a systematic review and meta-analysis of the association...
TRANSCRIPT
A Systematic Review andMeta-analysisof the Association Between Depressionand Insulin ResistanceCAROL KAN, MA, MBBS, MRCPSYCH
1
NAOMI SILVA, BSC1
SHERITA HILL GOLDEN, MD, MHS2,3
ULLA RAJALA, MD, PHD4
MARKKU TIMONEN, MD, PHD4
DANIEL STAHL, PHD5
KHALIDA ISMAIL, MRCP, MRCPSYCH, PHD1
OBJECTIVEdDepression is associated with the onset of type 2 diabetes. A systematic reviewand meta-analysis of observational studies, controlled trials, and unpublished data was conduc-ted to examine the association between depression and insulin resistance (IR).
RESEARCH DESIGN AND METHODSdMedline, EMBASE, and PsycINFO weresearched for studies published up to September 2011. Two independent reviewers assessedthe eligibility of each report based on predefined inclusion criteria (study design and measureof depression and IR, excluding prevalent cases of diabetes). Individual effect sizes were stan-dardized, and a meta-analysis was performed to calculate a pooled effect size using randomeffects. Subgroup analyses and meta-regression were conducted to explore any potential sourceof heterogeneity between studies.
RESULTSdOf 967 abstracts reviewed, 21 studies met the inclusion criteria of which 18studies had appropriate data for the meta-analysis (n = 25,847). The pooled effect size (95%CI) was 0.19 (0.11–0.27) with marked heterogeneity (I2 = 82.2%) using the random-effectsmodel. Heterogeneity between studies was not explained by age or sex, but could be partlyexplained by the methods of depression and IR assessments.
CONCLUSIONSdA small but significant cross-sectional association was observed betweendepression and IR, despite heterogeneity between studies. The pathophysiology mechanismsand direction of this association need further study using a purposively designed prospective orintervention study in samples at high risk for diabetes.
Diabetes Care 36:480–489, 2013
Depression is at least twice as com-mon among those with diabetescompared with the general popula-
tion (1) and is associated with adverse ef-fects on diabetes outcomes includingsuboptimal glycemic control (2), compli-cations (3), and higher rates of mortality(4,5). Depression appears to be presenteven at the prediabetes stage of the type2 diabetes (T2DM) continuum withpooled data suggesting that depression
in the nondiabetic population is indepen-dently associated with a 37–60% in-creased prospective risk of developingT2DM (6).
Insulin resistance (IR) is a prediabetesstage. There have now been several stud-ies examining the association betweendepression and IR. These studies havehad mixed findings. The aim of this re-view is to conduct a systematic synthesisand ameta-analysis of the evidence for the
association between depression and IR. Apositive association would increase theplausibility of a biological link betweendepression and diabetes and suggest apotentially modifiable target for the pre-vention of T2DM.
RESEARCH DESIGN ANDMETHODSdOur systematic reviewand meta-analysis was conducted accord-ing to Preferred Reporting Items for Sys-tematic Reviews and Meta-Analyses(PRISMA) guidelines (7).
Data sources and study selectionThe following electronic librariesdMED-LINE (1948 to September 2011), EMBASE(1947 to September 2011), and PsycINFO(1806 to September 2011)dweresearched to identify relevant studies. Thesearch items were based on establishedterminology using Cochrane definitionswhere possible and were “diabetes,” “de-pression,” “insulin resistance,” and “insu-lin sensitivity” (Supplementary Table 1).The titles and/or abstracts were reviewedto exclude any clearly irrelevant studies.The full texts of the remaining studieswere then retrieved and read in full bytwo authors (C.K. and N.S.) indepen-dently to determine whether the studiesmet inclusion criteria. Disagreement wasresolved by a third author (K.I.) who in-dependently examined the studies. Thereference lists of studies that examine thetopic of interest were checked for addi-tional publications while correspondingauthors were contacted for additional in-formation on published and unpublishedstudies.
Criteria for inclusion into the reviewAbstracts were considered eligible for fullmanuscript data extraction if the studymet all the following criteria: a) they re-ported an association between depressionand IR (including its reverse measure, lowinsulin sensitivity); b) sample consisted ofadults ($18 years of age); and c) the de-sign was cross-sectional, observational,or a randomized controlled trial. Studiesthat excluded patients with depression atbaseline or consisted solely of patients
c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c
From the 1Institute of Psychiatry, King’s College London, London, U.K.; the 2Department of Medicine, JohnsHopkins School of Medicine, Baltimore, Maryland; the 3Department of Epidemiology, Johns Hopkins Uni-versity, Bloomberg School of PublicHealth, Baltimore,Maryland; the 4Institute ofHealth Sciences,University ofOulu, Oulu, Finland; and the 5Department of Biostatistics, Institute of Psychiatry, King’s College London,London, U.K.
Corresponding author: Carol Kan, [email protected] 20 July 2012 and accepted 17 September 2012.DOI: 10.2337/dc12-1442This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10
.2337/dc12-1442/-/DC1.The views expressed are those of the authors and not necessarily those of the National Health Service, the
National Institute for Health Research, or the Department of Health.© 2013 by the American Diabetes Association. Readers may use this article as long as the work is properly
cited, the use is educational and not for profit, and thework is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
480 DIABETES CARE, VOLUME 36, FEBRUARY 2013 care.diabetesjournals.org
M E T A - A N A L Y S I S
with diabetes (or where it was not possi-ble to separate diabetic and nondiabeticparticipants) were not included.
Data extractionUsing a standardized data extractionsheet, the following information (if avail-able) was extracted and recorded fromstudies: authors; year of publication;country of origin; study design; totalsample size of nondiabetic participants;age; sex; methods of IR assessment; meth-ods of depression assessment; and type ofconfounders. Authors were contacted toclarify whether prevalent cases of diabeteswere excluded at baseline. An attempt toretrieve missing or incomplete data in thepublished study was made by e-mail to atleast two coauthors on at least two occa-sions. If multiple risk estimates werepresented in a given manuscript, the un-adjusted estimate was selected for theprimary meta-analysis as some studieswere adjusted for prominent confound-ing variables, such as family history andadiposity, while others were not, re-ndering a direct comparison of estimatesto be questionable. Reporting unadjustedestimates also reduces the bias of selectivereporting of adjusted estimates in primarystudies and the potentiality of overadjust-ment with multiple confounders, whichmay also be on the causal pathway for theeffect of depression on IR, such as obesity(8). Studies written in a foreign languagewere translated by mental health professio-nals fluent in that language.
Quality assessmentThere is no consensus as to the beststandardized method for assessing thequality of observation studies, and thePRISMA guidelines for randomized con-trolled trials (7) and Meta-analysis Of Ob-servational Studies in Epidemiology(MOOSE) guidelines for observationalstudies in epidemiology (9) were used toexamine the quality of the studies. Theseinclude adequacy of study design (pro-spective cross-sectional, observational,and randomized controlled trial with anadequate control group); recruitment ofsample; ascertainment of depression andIR; and control for cofounding variables,such as age, sex, socioeconomic status,and BMI. The quality of the studies wasnot summarized with a score, as this ap-proach has been criticized for allocatingequal weight to different aspects of meth-odology (10), but a formal assessment ofthe risk of bias and strength of evidencesaccording to the Agency for Healthcare
Research and Quality (AHRQ) guidelineswas conducted (11). A study was consid-ered to be of high quality if the study de-sign was prospective in nature; consecutiveor random sampling method was used; theascertainment of depression was through astructural diagnostic interview based onthe ICD (12) or the DSM (13); and co-founders for diabetes and depression (age,sex, ethnicity, BMI/waist circumference,socioeconomic status, physical activity)were accounted for.
Data synthesis and analysisMeta-analyses were carried out usingStata 10.1 and 11.1 (14,15), with user-contributed commands for meta-analyses:metan, metainf, metabias, metatrim andmetareg (16). The Cohen d approachwas used to calculate the primary effectsize, as it allows data from different plat-forms to be combined without the use ofnormalization and can be converted fromdifferent effect sizes. It was calculated forthe majority of the datasets by the meandifference in IR between depressed andnondepressed groups divided by thepooled SD. The SE of each study’s stan-dardized effect estimate was calculatedfrom the estimated effect and the study’sgroup sizes according to a formula pro-vided by Cooper and Hedges (17). If Pear-son correlation coefficient (rr) wasreported instead, it was transformed intoCohen d using Cohen’s conversion for-mula (1988): d ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
4r2=ð12 r2Þp. The
variance was calculated by Vd = 4Vr /(1-r2)3 where Vr is the variance of rp. Thesame conversion was applied to Spearmancorrelation coefficients (rs) since rr isequivalent to rs using rank data or isslightly smaller if the data are binomialdistributed (18). In one study (19), the zstatistic of a Mann-Whitney U test wasused to transform z to r using Fischertransformation r ¼ z=
ffiffiffin
p(20) and then
converting r to Cohen d using the aboveformula. Results reported in odds ratioswere transformed in Cohen d using themethod recommended by Borensteinet al. (21), d ¼ lnðORÞ3 ffiffiffi
3p
=p. The asso-ciated variance of d would then beVðdÞ ¼ VlnðORÞ33=p2and the SE
ffiffiffiffiffiffiffiffiffiVðdÞp
.The effect sizes and SEs of the studies
were pooled using random-effects mod-els. The random-effects meta-analysismodels were chosen as heterogeneity isexpected given the differences in studypopulations and procedures. The as-sumption of homogeneity of true effectsizes was assessed by the Cochran Q test(22), and the degree of inconsistency
across studies was calculated I2 (23). I2
describes the percentage of total variationacross studies that is due to heterogeneityrather than sampling error and ranges be-tween 0% (no inconsistency) and 100%(high heterogeneity) with values of 25,50, and 75% suggesting low, moderate,and high heterogeneity (23). A priorimeta-regression analysis was then per-formed to assess whether conclusionswere sensitive to restricting studies tosubgroups that might modify the effectsize: i) mean age; ii) sex; iii) method ofdepression assessment; and iv) method ofIR assessment. Random-effects modelswere used to allow for the residual hetero-geneity among attrition rates, which werenot modeled by the explanatory variables(24). A secondary analysis of adjusted andcorresponding unadjusted data whenavailable was also conducted using therandom-effects models.
Sensitivity analyses were conductedto weigh up the relative influence of eachindividual study on the pooled effect sizeusing STATA’s user-written function,metainf (16). The presence of publishingbias for the hypothesis of an associationbetween depression and IR was assessedinformally by visual inspections of funnelplots (25) and corroborated by Begg ad-justed rank correlation (26) as implemen-ted in metabias. The nonparametric “trimand fill”method was also used to estimatethe number of hypothetical studies thatwere missing due to possible publicationbias and was implemented in STATA’suser-written command, metatrim (16).It is a sensitivity analysis since it relied onstrong symmetrical assumption and couldbe influenced in the presence of strongbetween-group heterogeneity (27).
RESULTS
Study selectionThe flowchart is shown in Fig. 1. The lit-erature search resulted in 962 studies. Af-ter review of their titles and abstracts, 38studies met the inclusion criteria andwere retrieved for full text. Of these, 21studies were excluded from the system-atic review as they no longer met the in-clusion criteria. The search for additionalstudies among the reference lists of in-cluded articles yielded five more studies,with four meeting inclusion criteria. A to-tal of 21 studies were included in the sys-tematic review, and the extracted data aresummarized in Table 1.
Three studies were excluded from themeta-analysis; one study was published as
care.diabetesjournals.org DIABETES CARE, VOLUME 36, FEBRUARY 2013 481
Kan and Associates
an abstract (28) and did not include anyinformation about sampling method,baseline clinical characteristics of thesample population, and depression mea-sure. The data of two large cohort studieswere presented in quartiles (29,30), andraw data were not available to generate astandardized effect size. This resulted in18 studies being included in the meta-analysis.
Upon further examination, one studywas found to be made up of two separatestudies using two different study popula-tions (31), which were therefore sepa-rated into two different datasets. Thesample for six studies were separatedinto normal/impaired glucose tolerance(32,33) and men/women (19,34–36),yielding an additional six datasets. The totalnumber of datasets in the meta-analysiswas therefore 25.
Qualitative summaryOf the 25 datasets include in the meta-analysis, one was a prospective longitudi-nal cohort study (37), six were case-controlstudies (31,38–41), and 18 were cross-sectional studies (19,32–36,42–47). Fourdatasets were based on clinical diagnosisusing DSM-IV, six used semistructureddiagnostic interviews, and 15 used self-report depressive scales. IR was reportedin 18 datasets while insulin sensitivity wasmeasured in seven datasets. Descriptivedata from the datasets are summarized inTable 1.
Meta-analysisA total of 25 datasets (n = 25,847) pro-vided unadjusted data on the associationbetween depression and IR in adults with-out diabetes. A random-effects meta-analysis revealed a small pooled estimate
of the mean standardized effect sizes (d =0.19 [95% CI: 0.11–0.27]) (Fig. 2), withthe effect sizes ranging from d = -0.56 tod = 1.37. Heterogeneity between the stud-ies was statistically significant (Q (24) =134.83, P , 0.0001) and large in magni-tude (I2 = 82.2%).Subgroup analysis and meta-regression.A series of random-effects subgroup anal-yses and meta-regression was conductedto examine whether the association be-tween depression and IR varied acrossdemographic groups and the methods ofdepression and IR assessments. Age (b =-0.002 per year, t = -0.50; P = 0.62) or sex(b = 0.0006, t = 0.33; P = 0.74) did notsignificantly change the observed associ-ation between depression and IR. Withthe random-effects model, a much greatereffect size was observed for diagnostic in-terviews than self-report measures (0.46[0.22–0.71] vs. 0.13 [0.05–0.21]) and thedifference was statistically significant inthe meta-regression (z = 2.22, P ,0.0001). A larger effect size with insulinsensitivity as an IR measure was found incomparison with studies using homeosta-sis model assessment of insulin resistance(HOMA-IR) or HOMA2-IR test (0.32[0.12–0.53] vs. 0.17 [0.08–0.26]), andthe difference was also significant (z =4.70, P , 0.0001). The observed associ-ation between depression and IR re-mained statistically significant in allsubgroup analyses.Secondary analysis. Of the 17 datasetswith confounders being included, threestudies were excluded from the secondaryanalysis. Depression and IR were not themain outcome of interest in one study(40) and thus, its association was not ad-justed for the confounder being mea-sured, while two studies presented theirdata in quartiles (45,46) and raw datawere not available to generate a standard-ized effect size. This resulted in 14 data-sets being included in the random-effectssecondary meta-analysis. The estimate ofthe mean standardized effect sizes was0.11 (0.04–0.17) for theunadjusteddatasets(n = 22,545) and 0.02 (20.02 to 0.07) forthe adjusted datasets (n = 21,826) (Fig. 3).Sensitivity analysis and publicationbias. The robustness of the estimate wasexamined by sequentially removing eachstudy and reanalyzing the remaining da-tasets. The estimated effect sizes rangedfrom d = 0.14 to d = 0.21, with all effectsizes being significantly different from 0,suggesting that the significant effect size isnot determined by a single study. Sensi-tivity analysis for the secondary analysis
Figure 1dFlowchart of systematic review. *Further information in regards to excluded studiescan be found in Supplementary Table 1.
482 DIABETES CARE, VOLUME 36, FEBRUARY 2013 care.diabetesjournals.org
Depression and insulin resistance
Table
1dSu
mmarytableof
prim
arystud
iesinclud
edin
thesystem
icreview
Firstauthor,
year,cou
ntry
Setting,
study
population
Stud
ydesign
,sample
selection
Sample
size
a
Age
c ,mean(SD)d
orrange
Men/
Wom
enc
Evidenceof
selectionbias
IRmeasure
Depression
assessment
(measure)
Adjustedfor
confoun
ders
Adriaanse,200
6,Netherlands
Com
mun
ity,
IGT
Cross-section
al,
rando
m16
455
–75
84/75
Non
participation
atfollo
w-up
unaccoun
tedfor
HOMA-IR
SR(CES-D)
Non
e
Com
mun
ity,N
GT
Cross-section
al,
rando
m26
055
–75
129/12
9Non
participation
atfollo
w-up
unaccoun
tedfor
HOMA-IR
SR(CES-D)
Non
e
Chiba,2
000,
Japan
Hospital,depressed
andhealthy
control
subjects
Case-control,
consecutiv
e20
846
.8(12.8)
130/78
Non
representative
controlgroup
HOMA-IR
CI
Non
e
Outpatients,NGT
Case-control,
consecutiv
e22
048
.7(8.6)
138/82
Non
participation
atfollo
w-up
unaccoun
tedfor
HOMA-IR
SR(ZSD
S)Non
e
Everson
-Rose,
2004
,U.S.
Com
mun
ity,
wom
enat
midlife
Prospective
longitudinal
coho
rt,rando
msupplem
ented
bysnow
ball
techniqu
e
2,66
246
.4(2.7)
0/2,66
2Non
participation
atfollo
w-up
unaccoun
tedfor
HOMA-IR
SR(CES-D)
Age,ethnicity,w
aist
circumference,
education
,phy
sical
activity,antidepressants,
medications
fornervou
sdisorders,site
Gilb
ey*,
2011
,U.K.
Obesity
clinic,
obese
Cross-section
al,
consecutiv
e53
*NA
NA
Noinform
ationabou
tsamplingmetho
dor
baselin
eclinical
characteristics
HOMA-IR
CI
Non
e
Golden,
2007
,U.S.
Com
mun
ity,local
residents
Cross-section
al,
rando
mand
consecutiv
e,supplem
ented
bysnow
ball
techniqu
e
5,79
061
.7(10.3)
2,68
4/3,10
6HOMA-IR
SR(CES-D,
antid
epressant
use)
Age,sex,ethnicity
,BMI,
SES,
lifestyle,m
etabolic,
inflam
matory,site
Holtb,
2009
,U.K.
Com
mun
ity,m
enBirthcohort,
consecutiv
e98
665
.7(2.9)
986/0
Non
participation
atfollo
w-upun
accoun
ted
for;lownu
mberof
subjectswith
depression
(17/1,57
8b)
HOMA-IR
SR(H
AD-D
)Age,B
MI,SE
S,sm
oking,alcoho
l
Com
mun
ity,
wom
enBirthcohort,
consecutiv
e62
566
.6(2.7)
0/62
5Non
participation
atfollo
w-up
unaccoun
tedfor;
lownu
mberof
subjectswith
depression
(20/1,41
7b)
HOMA-IR
SR(H
AD-D
)Age,B
MI,SE
S,sm
oking,alcoho
l
Contin
uedon
p.484
care.diabetesjournals.org DIABETES CARE, VOLUME 36, FEBRUARY 2013 483
Kan and Associates
Tab
le1d
Con
tinu
ed
Firstauthor,
year,cou
ntry
Setting,
stud
ypo
pulation
Stud
ydesign
,sample
selection
Sample
size
a
Age
c ,mean(SD)d
orrange
Men/
Wom
enc
Evidenceof
selectionbias
IRmeasure
Depression
assessment
(measure)
Adjusted
for
confou
nders
Hung,2
007,
Taiwan
Inpatients
andstaffs
Case-control,
consecutive
3523
.3(0.12)
35/0
Non
representative
controlgroup
Minim
almod
elCI
Non
e
Kahl,20
05,
Germany
Patientsand
university
recruited
Case-control,
consecutive
3828
.8(5.4)
0/38
Non
representative
controlgroup
HOMA-IR
DI(SCID
I/II)
Age
Kauffman,
2005
,U.S.
Outpatients,
gynecological
wom
en
Case-control,
consecutive
2118
–45
0/21
HOMA-IR
CI
Non
e
Krishnam
urthy,
2008
,U.S.
Com
mun
ity,
prem
enop
ausal
wom
en
Cross-sectio
nal,
consecutive
8035
.6(7.0)
0/80
HOMA-IR
DI(SCID
)Non
e
Lawlor*,
2003
,U.K.
Com
mun
ity,
wom
enCross-sectio
nal,
consecutive
4,28
6*60
–79
0/4,28
6Rationalefor
presenting
results
inqu
artileswas
notprovided
HOMA-IR
SR(EQ5D
,past
depression
,antidepressant
use)
Age,B
MI,WHR,
SES,
physicalactiv
ity,
smok
ing,alcoho
l
Lawlor*,
2005
,U.K.
Com
mun
ity,men
Prospective
coho
rt,
consecutive
2,51
2*45
–59
2,51
2/0
Rationaleforpresenting
resultsin
quartiles
was
notprovided
HOMA-IR
SR(GHQ)
Age,SES,
physical
activ
ity,smok
ing,
alcoho
l,samplingtime
Okamura,
2000
,Japan
Patientsand
staffs
Case-control,
consecutive
3344
.8(13.2)
20/13
Non
representative
controlgroup
Minim
almod
elCI
Non
e
Pan,
2008
,China
Com
mun
ity,
localresidents
Cross-sectio
nal,
consecutive
2,83
858
.4(6.0)
1,23
6/1,60
2HOMA2-IR
SR(CES-D)
Age,sex,B
MI,
comorbidity,education,
physicalactivity,
smok
ing,alcoho
l,geographicallocation,
residentialregion
Pearson,
2010
,Australia
Com
mun
ity,men
Cross-sectio
nal,
consecutive
833
31.1
(2.5)
833/0
Highno
nparticipation
atfollo
w-up(25%
oforiginalcoho
rtsampled);
lownu
mberof
subjects
withdepression
(45/83
3)
HOMA-IR
DI(CID
I)Age,education,
physical
activ
ity,smok
ing,
alcoho
l,antid
epressants,
fish
consumption
Com
mun
ity,
wom
enCross-sectio
nal,
consecutive
899
30.9
(2.7)
0/89
9Highno
nparticipation
atfollo
w-up(25%
oforiginalcoho
rtsampled)
HOMA-IR
DI(CID
I)Age,education,
physical
activ
ity,smok
ing,
alcoho
l,antid
epressants,
oralcontraceptives,fi
shconsum
ptio
n,PC
OS
Roo
s,20
07,
Sweden
Com
mun
ity,
wom
enwith
T2D
Mrisk
factors
Cross-sectio
nal,
consecutive
1,04
756
.0(5.1)
0/1,04
7Rationaleforpresenting
resultsin
quartiles
was
notprovided
HOMA-IR
SR(SRSD
)Age,B
MI,WHR,
smok
ing,alcoho
l,ph
ysicalactivity,
timedelay
Contin
uedon
p.485
484 DIABETES CARE, VOLUME 36, FEBRUARY 2013 care.diabetesjournals.org
Depression and insulin resistance
Tab
le1d
Con
tinu
ed
Firstauthor,
year,cou
ntry
Setting,
stud
ypo
pulation
Study
design
,sample
selection
Sample
size
a
Age
c ,mean(SD)d
orrange
Men/
Wom
enc
Evidenceof
selectionbias
IRmeasure
Depression
assessment
(measure)
Adjusted
for
confou
nders
Schlotzb,
2007
,U.K.
Com
mun
ity,
localresidents
Cross-section
al,
consecutive
1,19
664
.4(2.6)
618/57
8HOMA-IR
SR(H
RQoL
-MH)
Age,B
MI,CHD,
depression
,SES,
smok
ing,alcoho
lSh
en,2
011,
U.S.
Com
mun
ity,
men
Stratified
cross-
sectional,
consecutive
279
29.6
(0.0)
279/0
Selectionof
subsam
plewas
not
described;
low
numberof
subjects
withdepression
(16/27
9)
HOMA-IR
DI(CID
I)Age,ethnicity,w
aist
circum
ference,
smok
ing,sBP,
triglycerides
Com
mun
ity,
wom
enStratified
cross-
sectional,
consecutive
358
29.7
(0.0)
0/35
8Selectionof
subsam
ple
was
notdescribed;
lownu
mberof
subjectswith
depression
(18/35
8)
HOMA-IR
DI(CID
I)Age,ethnicity,w
aist
circum
ference,
smok
ing,
sBP,
triglycerides
Tim
onen,
2005
,Finland
Com
mun
ity,
NGT
Cross-section
al,
consecutive
336
61.8
(0.7)
130/20
6Non
participation
atfollo
w-up
unaccoun
tedfor
QUICKI
SR(BDI)
Sex,
BMI,education,
physicalinactivity,
smok
ing,alcoho
lCom
mun
ity,
IGT
Cross-section
al,
consecutive
9261
.7(0.7)
35/57
Non
participation
atfollo
w-up
unaccoun
tedfor
QUICKI
SR(BDI)
Sex,
BMI,education,
physicalinactivity,
smok
ing,alcoho
lTim
onen,
2006
,Finland
Com
mun
ity,
men
Cross-section
al,
consecutive
2,74
831
.3(0.4)
2,74
8/0
QUICKI
SR(H
SCL-25
)BM
I,comorbidity,S
ES,
physicalinactivity,
smok
ing,alcoho
l,CRP,
cholesterol
Com
mun
ity,
wom
enCross-section
al,
consecutive
3,01
3e31
.3(0.4)
0/3,01
3QUICKI
SR(H
SCL-25
)BM
I,comorbidity,S
ES,
physicalinactivity,
smok
ing,alcoho
l,CRP,
cholesterol
Tim
onen,
2007
,Finland
Military
conscripts,m
enCross-section
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also revealed that no single study has sub-stantial influence on the effect size for theadjusted and unadjusted datasets.
There was some evidence of publish-ing bias regarding studies of depressionand IR from visual inspection of thefunnel plot (Supplementary Fig. 1) andthe Begg coefficient (z = 2.22, P =0.027). The trim and fill sensitivitymethod imputed estimates from nine hy-pothesized negative unpublished studies.The “publication bias” corrected effectsize attenuated to 0.07 (20.02 to 0.16),and this was not significant (P = 0.117).
Risk of bias and strength of evidenceGiven that most studies were cross-sectional and all were observational, theoverall risk of bias was medium to high
and the study quality was fair. The overallmagnitude of association was small andthere was substantial heterogeneity be-tween studies, but the estimate was pre-cise as reflected by the narrow CIs and themagnitude of association for diagnosticcriteria for depression was larger than forself-report depression measures. Thissuggests that the strength of evidence islow to moderate.
CONCLUSIONS
Main findingsTo our knowledge, this study representsone of the first systematic reviews andmeta-analysis of the evidence for an asso-ciation between depression and IR usingdata from observational studies, controlled
trials, and unpublished data. A small butsignificant association between depressionand IR was observed that was attenuated inanalyses adjusted for body weight andother confounders. The magnitude of theassociation increased when a diagnosticinterview for depression was used to definedepression or insulin sensitivity was a mea-sure of IR.
Strengths and limitationsThe primary strength of this meta-analy-sis is the expansive literature search but ithas several limitations, mainly stemmingfrom the quality of the included studies assummarized in Table 1. There was sub-stantial evidence of heterogeneity and po-tential publication bias. The observedfunnel plot asymmetry could be partly
Figure 2dForest plots showing the effect size of the association between depression and insulin resistance. Estimates are at the center of the boxesand drawn in proportion to the SEs. Lines indicate 95% CIs. Diamond shows the pooled effect size at its center and 95% CI at its horizontal points.IGT, impaired glucose tolerance; NGT, normal glucose tolerance.
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explained by the heterogeneity in depres-sion measure as clinical/diagnostic inter-views were the depression assessments infive out of six datasets with a sample sizebelow 100. The random-effects modelwas chosen to account for heterogeneityand the association remained significantin all subgroup analyses. There was sub-stantial evidence of heterogeneity and po-tential publication bias.
A further limitation of the study is theinconsistent reporting of results, makingit necessary to convert different effectsizes into a common one. The conversionof correlation and odds ratio into Cohend rely on the assumptions that the distri-bution of the underlying trait is continu-ous (21). Association also does not implycausation and the temporal relationshipbetween depression and IR could not bedelineated since the present meta-analysisis mainly based on cross-sectional data.Nearly every individual study was a sec-ondary analysis of a study designed totest a different primary hypothesis andthis inevitably will result in some mea-surement bias and residual confounding.The strength of systematic reviews is thatby systematically identifying these limita-tions, future designs can be improved.
InterpretationThese results suggest that the associationbetween depression and diabetes may
start at an earlier stage since prevalentcases of diabetes were excluded from thisstudy and IR is on the casual pathway ofdeveloping T2DM. This is supported byother recent reviews (6,48) and a recentmeta-analysis, which provides evidencefor a bidirectional association betweendepression and metabolic syndrome(49) with hyperglycemia being one ofthe diagnostic criteria of metabolic syn-drome. These results indicate a smallbut significant association between de-pression and prediabetes or other bio-markers of glucose dysregulation.
There are several possible pathophys-iological mechanisms that may explainthe observed relationship. Depression is asso-ciated with disruption to the hypothalamic-pituitary adrenal axis, causing an increasein cortisol and catecholamine, hormonesresponsible for antagonizing the hypogly-cemic effects of insulin and resulting in IR(50). People with diagnostic depressionhave increased levels of inflammation(51), and psychological stresses havebeen shown to activate the innate inflam-matory response with chronic cytokinae-mia leading to IR and b-cell apoptosis,antecedents to the development ofT2DM (52). Depression can also have in-fluences on lifestyle behaviors associatedwith diabetes risk factors such as dietaryintake, exercise, and medication adher-ence (53,54). Findings from the secondary
analysis using data adjusted for confound-ers, such as obesity, might explain some ofthe observed associations between depres-sion and IR, although it should be inter-preted with caution as body weight hasbeen postulated to be on the casual path-way for depression and IR, raising the po-tentiality of overadjustment.
The type of depression assessmentmakes a substantive difference in theobserved association between depressionand IR, which may in part reflect a greatersensitivity of clinical interviews in detect-ing depression. Self-report measures suchas the Center for Epidemiologic StudiesDepression Scale (CES-D) have been val-idated in epidemiological studies (55) butuncertainty remains in regards to theirrelation to clinically diagnosed depres-sion. Estimates of depression have beensuggested to differ depending on the useof dimensionally verses categoricallybased depression assessment tools (56).The method at which IR is being mea-sured also has an impact upon the find-ing. There are several different methodsto measure depression and IR. Thehyperinsulinemic-euglycemic clamp iscurrently the gold standard but is un-suitable for large-scale cross-sectionalstudies for practical reasons. Good cor-relation has been demonstrated betweenestimates from HOMA and euglycemicclamp (Rs = 0.88, P , 0.0001) (57),
Figure 3dForest plots of the unadjusted and adjusted association between depression and insulin resistance for studies with confounders included.Confounders adjusted for A: age, ethnicity, waist circumference, education, physical activity, antidepressants, medications for nervous conditions,site; B: age, sex, ethnicity, BMI; C: weight, BMI, waist-to-hip ratio; D: weight, BMI, waist-to-hip ratio; E: age, sex, BMI, comorbidity, education,physical activity, smoking, alcohol, geographical location, residential region; F: age, education, physical activity, smoking, alcohol, anti-depressants, fish consumption; G: age, education, physical activity, smoking, alcohol, antidepressants, oral contraceptives, fish consumption,polycystic ovary disease; H: age, ethnicity, waist circumference, smoking, systolic blood pressure, triglyceride; I: age, ethnicity, waist circumference,smoking, systolic blood pressure, triglyceride; J: sex, BMI, education, physical inactivity, smoking, alcohol; K: sex, BMI, education, physicalinactivity, smoking, alcohol; L: BMI, comorbidity, socioeconomic status, physical inactivity, smoking, alcohol, C-reactive protein, cholesterol level;M: BMI, comorbidity, socioeconomic status, physical inactivity, smoking, alcohol, C-reactive protein, cholesterol level; and N: waist circumference,education, physical inactivity, alcohol, smoking. IGT, impaired glucose tolerance; NGT, normal glucose tolerance.
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whereas the quantitative insulin sensi-tivity check index (QUICKI) has beensuggested to be superior to HOMA-IR(58). Some studies have, however,shown that minimal model analysisfrom frequently sampled intravenousglucose tolerance tests underestimatesinsulin sensitivity (59).
ImplicationsThis review suggests that it is now time tomove from repeating cross-sectionalstudies to studies examining causal rela-tionships. The ideal study design couldeither be a prospective design of patientspotentially at high risk for T2DM (e.g.,positive family history of diabetes); withor without diagnostic depression;matched for at least age, sex, obesity,and change in IR measured over time;or a randomized controlled trial in a sam-ple of depressed patients testing whetherintensive treatment of depression (phar-macological or psychological) leads toimprovements in IR and other markersof glucose dysregulation. Further sec-ondary analyses are unlikely to con-tribute further to the field unless thereis adequate assessment of potentialconfounding.
ConclusionThis systematic review and meta-analysiscontributes to the growing evidence of asmall but persistent association betweendepression and the onset of T2DM.
AcknowledgmentsdC.K. receives salarysupport from the National Institute for HealthResearch Mental Health Biomedical ResearchCentre at South London, Maudsley NationalHealth Service Foundation Trust, and King’sCollege London.No potential conflicts of interest relevant to
this study were reported.C.K., N.S., S.H.G., and K.I. designed the
protocol with contributions from U.R. andM.T. C.K. and N.S. extracted the data whileC.K. wrote the manuscript. D.S. supervisedthe statistical analysis. All authors reviewedand edited the manuscript.The authors would like to thank An Pan,
National University of Singapore; Jari Joke-lainen, University of Oulu, Finland; HollySyddall and Karen Jameson, MRC LifecourseEpidemiology Unit, University of South-ampton, UK, for providing further data tocomplete the meta-analysis, and GiovanniCizza, National Institute of Diabetes and Di-gestive and Kidney Diseases; MichaelDeuschle, Central Institute of Mental Health;Susan Everson-Rose, University of Min-nesota; and Sue Pearson, University of
Tasmania, for clarifying the methodology oftheir studies.
References1. Anderson RJ, Freedland KE, Clouse RE,
Lustman PJ. The prevalence of comorbiddepression in adults with diabetes: a meta-analysis. Diabetes Care 2001;24:1069–1078
2. Lustman PJ, Clouse RE. Depression indiabetic patients: the relationship betweenmood and glycemic control. J DiabetesComplications 2005;19:113–122
3. de Groot M, Anderson R, Freedland KE,Clouse RE, Lustman PJ. Association ofdepression and diabetes complications:a meta-analysis. Psychosom Med 2001;63:619–630
4. Ismail K, Winkley K, Stahl D, Chalder T,Edmonds M. A cohort study of peoplewith diabetes and their first foot ulcer: therole of depression on mortality. DiabetesCare 2007;30:1473–1479
5. Katon WJ, Rutter C, Simon G, et al. Theassociation of comorbid depressionwith mortality in patients with type 2diabetes. Diabetes Care 2005;28:2668–2672
6. Mezuk B, Eaton WW, Albrecht S, GoldenSH. Depression and type 2 diabetes overthe lifespan: a meta-analysis. DiabetesCare 2008;31:2383–2390
7. Moher D, Liberati A, Tetzlaff J, AltmanDG; PRISMA Group. Preferred reportingitems for systematic reviews and meta-analyses: the PRISMA statement. PLoSMed 2009;6:e1000097
8. Morris AA, Ahmed Y, Stoyanova N, et al.The association between depression andleptin is mediated by adiposity. Psycho-som Med 2012;74:483–488
9. Stroup DF, Berlin JA, Morton SC, et al.Meta-analysis of observational studies inepidemiology: a proposal for reporting.Meta-analysis of observational studies inepidemiology (MOOSE) group. JAMA2000;283:2008–2012
10. J€uni P, Altman DG, Egger M. Systematicreviews in health care: assessing thequality of controlled clinical trials. BMJ2001;323:42–46
11. OwensDK, LohrKN,AtkinsD, et al. AHRQseries paper 5: grading the strength of abody of evidence when comparing medicalinterventionsdagency for healthcare re-search and quality and the effective health-care program. J Clin Epidemiol 2010;63:513–523
12. World Health Organization. The ICD-10classification of mental and behaviouraldisorders: clinical descriptions and diagnosticguidelines. World Health Organization,1992
13. American Psychiatric Association. Di-agnostic and statistical manual of mentaldisorders: DSM-IV-TR. Washington, DC,American Psychiatric Association, 2000
14. StataCorp, Stata Statistical Software: Re-lease 11. 2009
15. StataCorp, Stata Statistical Software: Re-lease 10. 2007
16. Sterne JAC, Newton HJ, Cox NJ. Meta-Analysis in Stata: An Updated Collectionfrom the Stata Journal. College Station, TX,Stata Press, 2009
17. Cooper HM, Hedges LV. The Handbook ofResearch Synthesis. New York, Russell SageFoundation, 1994
18. Gilpin AR. Table for conversion ofKendall’s Tau to Spearman’s Rho withinthe context of measures of magnitude ofeffect for meta-analysis. Educ PsycholMeas 1993;53:87–92
19. Shen Q, Bergquist-Beringer S, Sousa VD.Major depressive disorder and insulin re-sistance in nondiabetic young adults inthe United States: the National Health andNutrition Examination Survey, 1999-2002. Biol Res Nurs 2011;13:175–181
20. Rosenthal, R.Meta-analytic Procedures ForSocial Research. Newbury Park, CA, SagePublications, 1991
21. Borenstein M, Hedges LV, Higgins JPT,Rothstein HR. Introduction to Meta-Analysis.New York, John Wiley & Sons, 2009
22. Cochran WG. The comparison of per-centages in matched samples. Biometrika1950;37:256–266
23. Higgins JP, Thompson SG, Deeks JJ,Altman DG. Measuring inconsistency inmeta-analyses. BMJ 2003;327:557–560
24. Thompson SGS, Sharp SJ. Explainingheterogeneity in meta-analysis: a compar-ison of methods. Stat Med 1999;18:2693–2708
25. Egger M, Davey Smith G, Schneider M,Minder C. Bias in meta-analysis detectedby a simple, graphical test. BMJ 1997;315:629–634
26. Begg CB, Mazumdar M. Operating char-acteristics of a rank correlation test forpublication bias. Biometrics 1994;50:1088–1101
27. Higgins JPT, Green S. Cochrane Handbookfor Systematic Reviews of Interventions. NewYork, John Wiley & Sons, 2011
28. Gilbey MP, Casale C, Lei S, et al. Preva-lence and treatment of depression aredifferentially associated with insulin re-sistance and obesity. 1st Joint Meeting ofthe International Society for AutonomicNeuroscience and the American Auto-nomic Society (ISAN/AAS 2011), Buzios,Rio de Janeiro, Brazil, September 12–16,2011( Abstracts). Clin Auton Res 2011;21:215–308
29. Lawlor DA, Smith GD, Ebrahim S; BritishWomen’s Heart and Health Study. Asso-ciation of insulin resistance with de-pression: cross sectional findings from theBritish Women’s Heart and Health Study.BMJ 2003;327:1383–1384
30. Lawlor DA, Ben-Shlomo Y, Ebrahim S,et al. Insulin resistance and depressivesymptoms in middle aged men: findings
488 DIABETES CARE, VOLUME 36, FEBRUARY 2013 care.diabetesjournals.org
Depression and insulin resistance
from the Caerphilly prospective cohortstudy. BMJ 2005;330:705–706
31. Chiba M, Suzuki S, Hinokio Y, et al.Tyrosine hydroxylase gene microsatellitepolymorphism associated with insulinresistance in depressive disorder. Metab-olism 2000;49:1145–1149
32. Timonen M, Laakso M, Jokelainen J,Rajala U, Meyer-Rochow VB, Keinänen-Kiukaanniemi S. Insulin resistance anddepression: cross sectional study. BMJ2005;330:17–18
33. Adriaanse MC, Dekker JM, Nijpels G,Heine RJ, Snoek FJ, Pouwer F. Associa-tions between depressive symptoms andinsulin resistance: the Hoorn Study. Dia-betologia 2006;49:2874–2877
34. Holt RI, PhillipsDI, JamesonKA,CooperC,Dennison EM, Peveler RC; HertfordshireCohort Study Group. The relationship be-tween depression and diabetes mellitus:findings from the Hertfordshire CohortStudy. Diabet Med 2009;26:641–648
35. Timonen M, Rajala U, Jokelainen J,Keinänen-Kiukaanniemi S, Meyer-RochowVB, Räsänen P. Depressive symptoms andinsulin resistance in young adult males: re-sults from the Northern Finland 1966 birthcohort. Mol Psychiatry 2006;11:929–933
36. Pearson S, Schmidt M, Patton G, et al. De-pression and insulin resistance: cross-sectionalassociations in young adults. Diabetes Care2010;33:1128–1133
37. Everson-Rose SA, Meyer PM, Powell LH,et al. Depressive symptoms, insulin re-sistance, and risk of diabetes in women atmidlife. Diabetes Care 2004;27:2856–2862
38. Hung YJ, Hsieh CH, Chen YJ, et al.Insulin sensitivity, proinflammatorymarkers and adiponectin in young maleswith different subtypes of depressivedisorder. Clin Endocrinol (Oxf) 2007;67:784–789
39. Kauffman RP, Castracane VD, White DL,Baldock SD, Owens R. Impact of the se-lective serotonin reuptake inhibitor cit-alopram on insulin sensitivity, leptin andbasal cortisol secretion in depressed andnon-depressed euglycemic women of re-productive age. Gynecol Endocrinol2005;21:129–137
40. Kahl KG, Bester M, Greggersen W, et al.Visceral fat deposition and insulin sensi-tivity in depressed women with and
without comorbid borderline personalitydisorder. Psychosom Med 2005;67:407–412
41. Okamura F, Tashiro A, Utumi A, et al.Insulin resistance in patients with de-pression and its changes during the clin-ical course of depression: minimal modelanalysis. Metabolism 2000;49:1255–1260
42. Golden SH, Lee HB, Schreiner PJ, et al.Depression and type 2 diabetes mellitus:the multiethnic study of atherosclerosis.Psychosom Med 2007;69:529–536
43. Krishnamurthy P, Romagni P, Torvik S,et al.; P.O.W.E.R. (Premenopausal, Osteo-porosis Women, Alendronate, Depression)Study Group. Glucocorticoid receptor genepolymorphisms in premenopausal womenwith major depression. Horm Metab Res2008;40:194–198
44. Pan A, Ye X, Franco OH, et al. Insulinresistance and depressive symptoms inmiddle-aged and elderly Chinese: find-ings from the Nutrition and Health ofAging Population in China Study. J AffectDisord 2008;109:75–82
45. Roos C, Lidfeldt J, Agardh CD, et al. In-sulin resistance and self-rated symptomsof depression in Swedish womenwith riskfactors for diabetes: the Women’s Healthin the Lund Area study. Metabolism 2007;56:825–829
46. Schlotz W, Ambery P, Syddall HE, et al.;Hertfordshire Cohort Study Group. Spe-cific associations of insulin resistance withimpaired health-related quality of life inthe Hertfordshire Cohort Study. Qual LifeRes 2007;16:429–436
47. Timonen M, Salmenkaita I, Jokelainen J,et al. Insulin resistance and depressivesymptoms in young adult males: findingsfrom Finnish military conscripts. Psy-chosom Med 2007;69:723–728
48. Nouwen A, Winkley K, Twisk J, et al.;European Depression in Diabetes (EDID)Research Consortium. Type 2 diabetesmellitus as a risk factor for the onset ofdepression: a systematic review and meta-analysis. Diabetologia 2010;53:2480–2486
49. Pan A, Sun Q, Czernichow S, et al. Bi-directional association between de-pression and obesity in middle-aged andolder women. Int J Obes (Lond) 2012;36:595–602
50. Musselman DL, Betan E, Larsen H,Phillips LS. Relationship of depression todiabetes types 1 and 2: epidemiology, bi-ology, and treatment. Biol Psychiatry2003;54:317–329
51. Howren MB, Lamkin DM, Suls J. Associ-ations of depression with C-reactive pro-tein, IL-1, and IL-6: a meta-analysis.Psychosom Med 2009;71:171–186
52. Black PH. The inflammatory response isan integral part of the stress response:implications for atherosclerosis, insulinresistance, type II diabetes and metabolicsyndrome X. Brain Behav Immun 2003;17:350–364
53. DiMatteo MR, Lepper HS, Croghan TW.Depression is a risk factor for non-compliance with medical treatment:meta-analysis of the effects of anxiety anddepression on patient adherence. ArchIntern Med 2000;160:2101–2107
54. Marcus MD, Wing RR, Guare J, Blair EH,Jawad A. Lifetime prevalence of majordepression and its effect on treatmentoutcome in obese type II diabetic patients.Diabetes Care 1992;15:253–255
55. McDowell I. Measuring Health: A Guide toRating Scales and Questionnaires. Newell C,Ed. New York, Oxford University Press,1987
56. Kessler RC. The categorical versus di-mensional assessment controversy in thesociology of mental illness. J Health SocBehav 2002;43:171–188
57. Matthews DR, Hosker JP, Rudenski AS,Naylor BA, Treacher DF, Turner RC. Ho-meostasis model assessment: insulin re-sistance and beta-cell function fromfasting plasma glucose and insulin con-centrations inman. Diabetologia 1985;28:412–419
58. Rabasa-Lhoret R, Bastard JP, Jan V, et al.Modified quantitative insulin sensitivitycheck index is better correlated to hy-perinsulinemic glucose clamp thanother fasting-based index of insulinsensitivity in different insulin-resistantstates. J Clin Endocrinol Metab 2003;88:4917–4923
59. Cobelli C, Caumo A, Omenetto M. Mini-mal model SG overestimation and SI un-derestimation: improved accuracy by aBayesian two-compartment model. Am JPhysiol 1999;277:E481–E488
care.diabetesjournals.org DIABETES CARE, VOLUME 36, FEBRUARY 2013 489
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