Measures of depression and risk of type 2 diabetes: A systematic review and meta-analysis

Journal of Affective Disorders 265 (2020) 224–232

Contents lists available at ScienceDirect

Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Review article

Measures of depression and risk of type 2 diabetes: A systematic review and meta-analysis

T

⁎

Eva A Grahama,b, , Sonya S Deschênesc, Marina N Khalilb,d, Sofia Dannab, Kristian B Filiona,e,f, Norbert Schmitza,b,d a

Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada Douglas Mental Health University Institute, Montreal, QC, Canada c School of Psychology, University College Dublin, Dublin, Ireland d Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada e Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada f Department of Medicine, McGill University, Montreal, QC, Canada b

A R T I C LE I N FO

A B S T R A C T

Keywords: Diabetes Depression Depressive symptoms Measurement

Background: Depression is associated with an increased risk of type 2 diabetes. This study aimed to determine whether the association between depression and incident type 2 diabetes differs by measure of depression. Methods: Data sources included MEDLINE, EMBASE, PsycINFO, CINAHL, ProQuest Dissertations & Theses Global, Web of Science Emerging Sources Citation Index and Conference Proceedings Citation Index, Cochrane Library, the University of York Center for Reviews and Dissemination, abstracts from the PsychoSocial Aspects of Diabetes conference. Inclusion criteria: comparison of participants with and without depression, depression measured at age 18 or older, longitudinal follow-up with an outcome of type 2 diabetes, effect estimate adjusted for important confounders, full-text available in English or French, and study at overall low or moderate risk of bias. Two reviewers extracted data and assessed study quality. Results: Twenty-one studies reporting twenty-five effect estimates were included. Depressive symptom scales, clinical interviews, physician diagnoses, and use of antidepressants were all associated with an increased risk of incident type 2 diabetes. When all measures of depression were combined, the meta-analyzed risk ratio for type 2 diabetes was 1.18 (95% CI 1.12–1.24, I2=45.4%). Results did not provide conclusive evidence that the association between depression and incident diabetes differs by measure of depression. Limitations: Results showed heterogeneity and evidence of publication bias. Conclusions: Results suggest that various measures of depression may be used to identify individuals at higher risk of type 2 diabetes.

1. Introduction

particular, prior meta-analyses report that depression is associated with a 32% to 60% increased risk of type 2 diabetes (Hasan et al., 2013, 2014; Knol et al., 2006; Mezuk et al., 2008; Rotella and Mannucci, 2013; Yu et al., 2015). Depression may therefore be used to identify individuals at higher risk of type 2 diabetes in clinical practice, public health, and research settings. However, different measures may be used to identify individuals with depression, including depressive symptom scales, standardized diagnostic interviews, diagnoses of depression from health professionals, and treatment for depression. Measure of depression may be an important source of heterogeneity when examining the relationship between depression and risk of type 2 diabetes. Yu et al. found that differences between depression scales likely contributed to the

The prevalence of type 2 diabetes is increasing around the world, affecting approximately 422 million people in 2014 (World Health Organization, 2016). Type 2 diabetes can contribute to early mortality and micro- and macro-vascular complications, including myocardial infarction, stroke, blindness, and lower limb amputation (World Health Organization, 2016). There is a well-established association between depression and type 2 diabetes, with depression approximately twice as common in adults with diabetes compared to those without (Ali et al., 2006; Egede and Ellis, 2010). There is also a bidirectional association between depression and type 2 diabetes such that each increases the risk for the other (Egede and Ellis, 2010; Knol et al., 2006). In ⁎

Corresponding author at: Douglas Mental Health University Institute, Frank B. Common Pavilion, F-2116 6875 Boul. LaSalle Montreal, QC H4H 1R3 Canada E-mail address: [email protected] (E.A. Graham).

https://doi.org/10.1016/j.jad.2020.01.053 Received 21 July 2019; Received in revised form 17 November 2019; Accepted 13 January 2020 Available online 13 January 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

from 2007 to 2017 (S2 Supporting Information).

heterogeneity observed in their meta-analysis of depression and incident diabetes but did not quantitatively examine this hypothesis (Yu et al., 2015). Kan et al. report that depression identified through diagnostic interviews was more strongly associated with insulin resistance than depression identified from a short symptom scale (Kan et al., 2013). A meta-analysis by Rotella et al. found no significant difference between studies that used depression questionnaires compared to interviews and subsequent risk of type 2 diabetes (Rotella and Mannucci, 2013). However, they did not distinguish between interviews conducted as part of research studies and physician diagnoses of depression (Rotella and Mannucci, 2013). Further systematic evidence is needed on specific measures of depression and risk of type 2 diabetes. This will allow for improved identification of high-risk individuals in diverse research and public health contexts, where measures of depression may be drawn from surveys, diagnostic or billing information, or other sources of administrative data. Information is especially lacking on depression assessed through treatment with antidepressant medication and risk of diabetes. Treatment with antidepressant medication may be a useful proxy for depression when other measures are not available or not reliable, such as in databases that include prescription information. Three metaanalyses of depression and type 2 diabetes have included studies that identified depression with antidepressant treatment, but had significant methodological limitations (Hasan et al., 2013; Rotella and Mannucci, 2013; Yu et al., 2015). Two did not include antidepressants as search terms (Hasan et al., 2013; Yu et al., 2015). The third did not comprehensively search for antidepressant treatment and only searched a single database (Rotella and Mannucci, 2013). Thus, further evidence is needed on whether measuring depression through antidepressant treatment may be used to identify individuals at higher risk of type 2 diabetes. The primary goal of this systematic review and meta-analysis was to determine whether specific measures of depression are differentially associated with incident type 2 diabetes. As part of this analysis, a secondary goal was to determine the association between antidepressant use, considered as a proxy for depression, and risk of type 2 diabetes.

2.2. Study selection During the second stage of screening, full-text articles were assessed for six inclusion criteria. Firstly, the study compared participants with and without current depression or a history of depression at baseline. Depression at baseline could be identified using standardized interviews, depressive symptom scales, clinical diagnoses of depression, antidepressant use, or other methods including combinations of the above. Studies that compared people with depression to those with other psychiatric illnesses (e.g. schizophrenia) or to people taking other psychiatric medication (e.g. antipsychotic medication) were excluded, as these comparison groups were not representative of the general population (Suvisaari et al., 2016). In studies where depression was identified through antidepressant treatment, people who were taking antidepressants were considered to have depression and those who were not taking antidepressants were the comparison group without depression. Studies of antidepressant use where all participants had diagnoses of depression or where another measure of depression was a covariate in the analysis were excluded. Secondly, participants must have been 18 or older when responding to questions about depression to ensure that the measure of depression was appropriate for adults. Thirdly, the study included longitudinal follow-up with an outcome of incident type 2 or unspecified adult-onset diabetes. Studies that included participants with diabetes at baseline were excluded, as were studies with outcomes of type 1 diabetes or gestational diabetes only. Fourthly, a full-text article, report, or dissertation was available in English or French. Fifthly, the study reported an effect estimate of the association between depression and incident diabetes adjusted for age, sex, and socioeconomic status (e.g. education, income, employment) at minimum. Sixthly, the study received an overall score of moderate or lower overall risk of bias from the quality assessment, as detailed below. 2.3. Screening procedures In Stage 1, titles and abstracts were screened by two independent reviewers. Any publication deemed potentially relevant by either reviewer was carried forward to full-text review. Conference abstracts were used to identify subsequent full-length publications by comparing titles, authors, and abstracts. If no full-length publication was found, the authors of the conference abstract were emailed to determine whether this work had been published in full. Conference abstracts without a corresponding full-text publication were then excluded. In Stage 2 screening, two independent reviewers assessed all full-text publications for the first four inclusion criteria. Resolution from a third party was sought when necessary. If the publication met these inclusion criteria, it was carried forward for data extraction and quality assessment to assess the final two inclusion criteria.

2. Methods This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; www.crd.york.ac.uk/prospero) with the registry ID CRD42017072196. 2.1. Data sources and searches A literature search was conducted up to July 17, 2017 in the following databases: MEDLINE, EMBASE, PsycINFO, CINAHL, ProQuest Dissertations & Theses Global, Web of Science Emerging Sources Citation Index, Web of Science Conference Proceedings Citation Index, Cochrane Library, and the University of York center for Reviews and Dissemination. We searched titles, abstracts, keywords, and MeSH terms / Emtree headings for the concepts of depression, use of antidepressant medication, diabetes, and longitudinal analyses to identify potentially relevant studies. We did not specify the temporal order of depression and diabetes in longitudinal studies in our systematic search. Consequently, studies with depression as a risk factor for incident diabetes or diabetes as a risk factor for incident depression were identified. Studies that contained keywords for animals and not humans were excluded. No time or language restrictions were applied to the initial search. A complete search strategy for MEDLINE is presented in S1 Supporting Information, and search strategies for other databases are available on request. Proceedings from the PsychoSocial Aspects of Diabetes annual conference were hand searched from 2005 to 2015 (last available year). We also identified relevant studies included in previous meta-analyses of depression and incident diabetes published

2.4. Data extraction and quality assessment Information on study characteristics, sample characteristics, measures of depression and diabetes, and effect estimates were independently extracted by two reviewers. The extraction form was first piloted in five studies by each reviewer. Effect estimates were extracted using the original measure of association (i.e. risk ratio, hazard ratio, odds ratio, or rate ratio). Where depression was categorized into more than two categories, the extracted estimate was the most severe form of depression compared the lowest (Knol et al., 2006). Multiple estimates could be extracted from a single study or from two studies using the same dataset if reported associations used different measures of depression. Where two studies used the same dataset and measure of depression, priority was given to studies where the main research question focused on depression and incident diabetes, then to studies 225

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

with a larger sample size, studies with longer follow-up, and studies that were published more recently. Quality was assessed using the Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) tool (Sterne et al., 2016). Each study was given a score of low, moderate, serious, or critical risk of bias due to confounding, participant selection, classification of interventions, deviations from intended interventions, missing data, outcome measurement, and selection in reporting results (Sterne et al., 2016). To achieve a moderate score for confounding, studies must have included age, sex, and a measure of socioeconomic status (e.g. education, income, employment) as covariates, matching factors, or restriction criteria. The overall risk of bias was determined from the highest risk of bias across domains (Sterne et al., 2016). If there was not enough information to assess a source of bias, a score of uncertain was assigned, and the overall score excluded this domain. If information required for extraction or bias assessment was missing, the study authors were emailed twice. Following data extraction, studies were excluded that used the same depression measure and dataset as another analysis, included an unadjusted estimate only or did not adjust for important confounders, or were at an overall serious or critical risk of bias. This was determined by two assessors with resolution from a third party when necessary.

2.5. Data synthesis and analysis Data were meta-analyzed using DerSimonian and Laird random-effects models to estimate separate adjusted risk ratios (RRs) and 95% confidence intervals (95% CIs) for each measure of depression and risk of incident diabetes (DerSimonian and Laird, 1986). Hazard ratios, odds ratios, and rate ratios were assumed to approximate RRs in cohort studies (Knol et al., 2012). In case-control studies, odds ratios were assumed to approximate RRs if the study used incidence-density or riskset sampling or if the total incidence of diabetes was under 10% (i.e. under the rare disease assumption) (Vandenbroucke and Pearce, 2012). Studies that only presented effect measures stratified by population characteristics, such as age or sex, were combined using random-effects meta-analysis and included as one estimate (Mezuk et al., 2008). Heterogeneity was assessed using I2 and τ2 statistics (Borenstein et al., 2009). Publication bias was assessed using funnel plots and Egger's test, stratifying by measure of depression. The trim-and-fill method was used to estimate meta-analysed RRs taking into account potential publication bias (Weinhandl and Sue, 2012). Random effects meta-regression was planned to determine whether the association between depression and incident diabetes varies by depression measure, given at least two estimates per measure (Harbord and Higgins, 2009). As well, we calculated an overall RR that combined all measures of depression for comparison with prior metaanalyses. For meta-analyses that combined measures of depression, we restricted to one depression measure per dataset. Priority was given to studies that assessed depression using standardized interviews, followed by depressive symptom scales, clinical diagnoses of depression, and antidepressant use. Several sensitivity analyses were conducted. First, we included adjusted estimates from studies that were at serious risk of bias due to excluding important confounders. Second, we included all studies at serious or critical risk of bias. Third, meta-analyzed estimates were calculated for both adjusted and unadjusted associations. Fourth, we conducted stratified analyses that reported meta-analyzed associations grouped by type of effect measure (e.g. odds ratio, hazard ratio, rate ratio) and study characteristics (e.g. sample size, duration of follow-up, measured undiagnosed diabetes at follow-up). Fifth, we excluded studies in populations at high risk of type 2 diabetes and studies that only included self-reported measures of diabetes outcomes. All analyses were conducted in Stata version 15.

Fig. 1. Flow Diagram

3. Results 3.1. Study selection A PRISMA flow diagram for study inclusion is presented in Fig 1. In Stage 1, reviewers screened 20,858 titles and abstracts from database searches, 274 conference abstracts, and references from 9 meta-analyses. A total of 247 citations of articles, dissertations, and conferences abstracts were identified. Ten articles were then excluded, as 9 were published in languages other than English or French and 1 full-text article was not available. Seven conference abstracts lead to the inclusion of new full-texts, while the remaining abstracts were excluded due to already having been identified as published articles (n = 32) or no full-text publication (n = 11). In Stage 2 screening, 194 full-texts were assessed for eligibility and 72 met the inclusion criteria to be carried forward to data extraction and quality assessment. The kappa score for inter-rater reliability for full-text inclusion was 0.87 (95% CI 0.79–0.95) (Fleiss et al., 2003). The most common reasons for study exclusion after initial full-text assessment were not having an outcome of type 2 diabetes (n = 41), presenting findings from a study published elsewhere (e.g. commentary or review; n = 22), and measuring depression prior to age 18 (n = 15). After data extraction and quality assessment, 21 publications were excluded due to using the same sample as another study, 21 due to a serious or critical overall risk of bias, and 9 due to not reporting an adjusted estimate. The final set of publications comprised 20 journal articles and 1 dissertation and are listed in S3 Supporting Information. 3.2. Study characteristics Twenty-one publications reported 25 distinct estimates of the 226

General population, Spain General population, Australia

Latino population, USA

Low-income population, USA General population of women, USA General population of women, USA General population of women, USA General population without CVD, USA General population, Germany Male employee population, Japan Employee population, UK General population, UK High-risk for diabetes, USA General population, Taiwan Male urban population, Australia General population of women, USA General population, Sweden Employee population, Finland Employee population, UK Male health professionals, USA

Campayo et al., 2010

Bullard et al., 2008

Carnethon et al., 2007

227

Female health professionals, USA

Female health professionals, USA

High-risk for diabetes, USA

Pan et al., 2012

Pan et al., 2012

Rubin et al., 2010

Pan et al., 2012

Kivimaki et al., 2011

Kivimaki et al., 2010

Mezuk et al., 2013

Vimalananda et al., 2014

Tully et al., 2016

Tsai et al., 2015

Rubin et al., 2008

Laursen et al., 2017

Kumari et al., 2004

Kawakami et al., 1999

Icks et al., 2013

Golden et al., 2008

Frisard et al., 2015

Frisard et al., 2015

Everson-Rose et al., 2004

Atlantis et al., 2010

Sample, country

Authors, year

Table 1 Study Characteristics

2665

76,868

61,791

29,776

5804

2943

336,340

35,898

688

2995

3187

1170

8320

1594

3547

5201

68,169

52,326

2662

4681

1160

826

2326

N

25 and above

38.1

61.3

56.4

39–64

22–66

30 and above

25 and above

35 and above

53 and above

25 and above

50–91

35–55

18–53

45–75

45–84

50–79

50–79

42–52

65 and above

60 and above

65 and above

55 and above

Sample age (range or mean)

Symptom scale (CESD) Symptom scale (CESD)

7.5 (mean)

6 – 11 1–3

Symptom scale (CESD)

5.1 (mean)

10 (mean)

14 (maximum)

12 (maximum)

16 (maximum)

4 – 18

4 (mean)

7 (maximum)

12 (maximum)

5 (mean)

4 (mean)

3.2 (mean)

7.7 (mean)

10.5 (mean)

Antidepressant use (selfreport)

Antidepressant use (selfreport)

Antidepressant use (selfreport)

Physician diagnosis (administrative data) Antidepressant use (administrative data) Antidepressant use (selfreport) Antidepressant use (selfreport)

Symptom scale (CESD)

Symptom scale (BDI)

Symptom scale (CESD)

Symptom scale (BDI)

Symptom scale (CESD)

Symptom scale (GHQ)

Symptom scale (Zung)

Symptom scale (CESD)

2–5

8 (mean)

Symptom scale (CESD)

7.6 (median)

7.6 (median)

Symptom scale (CESD)

Standardized diagnostic interview Symptom scale (Psychogeriatric Assessment Scale) Symptom scale (CESD)

2.5 – 5.0 2 – 10

Depression measure

Years of follow-up

Clinical measures

Self-report

Self-report

Self-report

Self-report

Administrative data

Administrative data

Self-report, clinical measures Self-report

Self-report

Self-report, clinical measures Self-report, clinical measures Clinical measures

Self-report, clinical measures Self-report, clinical measures Clinical measures

Self-report

Self-report, clinical measures Self-report

Self-report, clinical measures, administrative data Self-report

Self-report

Self-report

Diabetes measure

HR 1.54 (0.66–3.57)*

HR 1.23 (1.11–1.37)

HR 1.10 (1.00–1.21)

HR 1.57 (1.07–2.29) OR 1.46 (0.90–2.36) HR 1.10 (1.00–1.20) HR 1.11 (1.02–1.21) HR 1.21 (0.87–1.67) OR 1.11 (0.74–1.65) HR 2.31 (1.03–5.20) OR 1.12 (0.82–1.53)* HR 1.20 (1.00–1.45) HR 0.99 (0.76–1.28)* OR 1.50 (1.05–2.12) OR 1.30 (0.74–2.26) RR 1.12, (1.03–1.20)† OR 1.11 (1.07–1.15) OR 2.19 (1.47–3.28)‡ OR 2.84 (1.45–5.54) HR 1.37 (1.07–1.76)

HR 1.51, (1.02–2.22)*

HR 1.65 (1.02–2.66) HR 2.29 (1.28–4.10)

Adjusted estimate

(continued on next page)

a,b,c,d,l,m, neighbourhood deprivation, country of origin, family history of depression a,b, socioeconomic position, temporary employment, hospital or municipality employer, geographic area a,b,f,g,h,i,k,l,q,r, low occupational position, antihypertensive medication, lipid-lowering medication a,e,f,g,h,i,k,l,m,r, multivitamin use, aspirin use, history of treatment for hypertension or hypercholesterolaemia, dietary score a,e,f,g,h,i,k,l,m,r, multivitamin use, aspirin use, history of treatment for hypertension or hypercholesterolaemia, dietary score, menopausal status, hormone use a,e,f,g,h,i,k,l,m,r, multivitamin use, aspirin use, history of treatment for hypertension or hypercholesterolaemia, dietary score, menopausal status, hormone use, oral contraceptive use a,b,c,f, fasting glucose, weight, weight change

a,c,g,h,i,j,k,m, health care use, tv watching, study cycle

a,b,c,g,h,i,k,l, Betel quid chewing, activities of daily living, heart disease, chronic kidney disease, gout a,d,e,g,h,i,k,l,n,o,q,r, CVD, arthritis, sleep apnea

a,b,c,f,n, fasting glucose, weight, weight change

a,c,g,h,i,k,m, occupation, shift work, circulatory or metabolic conditions a,f,g,i,k,l,m, employment grade, length of follow-up, height, ECG abnormalities, life events a,b,d,f,g,h,i,k

a,b,c,e,g,i

a,b,c,d,f,g,h,i,j,k,l,p,q,r, exam site, fasting insulin, IL-6

a,c,f,g,i,j,k,m,n, hormone therapy

a,c,f,g,i,j,k,m,n, hormone therapy

a,c,f,i,k,n,study site

a,b,c,e,f,g,h,i,k,p

a,b,c,d,e,i,p

e, get-up-and-go test, social activity, problems with feet/legs

a,b,c,e,g,h,k,l,m,n,o, statin use, disability

Covariates

E.A. Graham, et al.

Journal of Affective Disorders 265 (2020) 224–232

Journal of Affective Disorders 265 (2020) 224–232

Self-report, clinical measures 9 – 10 25–74 919 Tsenkova et al., 2016

⁎ Combined estimate from subgroups using DerSimonian and Laird random-effects models. †Estimated rate ratio. ‡95% CI calculated by authors. Covariates: a:age b:sex c:education d:income e:marital status/ cohabiting f:race/ethnicity g:smoking h:alcohol consumption i:physical activity j:energy intake k:obesity/BMI/waist circumference/waist-to-hip ratio l:hypertension/blood pressure m:family history of diabetes n:antidepressant medication o:other psychiatric medication p:C-reactive protein q:triglycerides r:cholesterol.

a,b,c,f,i,k, sleep problems, interaction between education and gender

a,c,g,h,i,j,k,m, health care use, tv watching, study cycle

RR 1.26 (1.11–1.43) † RR 1.13 (0.70–1.83) † Self-report

Antidepressant use (selfreport) Other (self-report) 12 (mean) 25 and above

General population of women, USA General population, USA Vimalananda et al., 2014

35,898

Sample, country Authors, year

Table 1 (continued)

N

Sample age (range or mean)

Years of follow-up

Depression measure

Diabetes measure

Adjusted estimate

Covariates

E.A. Graham, et al.

association between depression and risk of new-onset type 2 diabetes (Table 1; S4 Table). Three estimates were extracted from observational analyses of randomized controlled trials (Frisard et al., 2015; Rubin et al., 2008, 2010), one was from a case control study (Kivimaki et al., 2010), and the remaining 21 were from cohort studies. Most study populations were from North America (n = 14), with others from Europe (n = 7), Asia (n = 2), and Australia (n = 2). Nearly all estimates were from populations at low risk of type 2 diabetes, such as general population samples or employee populations. Two estimates were from participants at high risk of diabetes enrolled in a diabetes prevention program (Rubin et al., 2008, 2010). Only three studies included fewer than 1000 participants, while 14 included between 1000 and 10,000, and eight included over 10,000 participants. Depressive symptom scales were most commonly used to measure depression (n = 15). The center for Epidemiologic Studies – Depression (CESD) scale (n = 10) and the Beck Depression Inventory (n = 2) were used most frequently. Seven estimates assessed antidepressant use, of which six measured self-reported antidepressant use and one assessed antidepressant purchases in administrative data. In two cases, separate studies reported estimates of depressive symptoms and incident diabetes or antidepressant use and incident diabetes using the same dataset (Kivimaki et al., 2010; Kumari et al., 2004; Rubin et al., 2008, 2010). One study contributed estimates of both depressive symptoms and antidepressant use and incident diabetes in the same dataset (Vimalananda et al., 2014). For these studies, the estimates of antidepressant use were included in stratified analyses but excluded from analyses that combined or compared different measures of depression (Kivimaki et al., 2011; Rubin et al., 2010; Vimalananda et al., 2014). One study assessed depression using a standardized diagnostic interview (Campayo et al., 2010). One study assessed physician-diagnosed depression using administrative health records to identify diagnoses (Mezuk et al., 2013). One study was categorized as an other measure of depression and used an unstructured telephone survey that followed DSM-III-R criteria for depressive episodes (Tsenkova and Karlamangla, 2016). Nearly all studies compared participants with and without current or recent depression, ranging from depressive symptoms in the past week (Carnethon et al., 2007) to regular use of antidepressant medication in the past two years (Pan et al., 2012). One study assessed diagnoses of depression over a 7-year period (Mezuk et al., 2013). Type 2 diabetes was assessed using self-report of physician-diagnosed diabetes or use of glucose-lowering medication (n = 12), clinical glucose measures (n = 3), both self-report and clinical measures (n = 7), administrative data (n = 2), or a combination of self-report, clinical measures, and administrative data (n = 1). When using clinical measures, ten estimates assessed fasting plasma glucose levels, seven of which also assessed either 2-hour glucose tolerance, random glucose testing, or HbA1c levels. Nine studies identified people with diabetes using cut-off values for glucose levels consistent with the World Health Organization (World Health Organization, 2006, 2011). These cut-off values were 126 mg/dl for fasting plasma glucose levels, 200 dl/l for other glucose measures, or 6.5% for HbA1c levels (World Health Organization, 2006, 2011). One study identified diabetes using cut-off values of 130 mg/dl for fasting plasma glucose and 6.2% for HbA1c levels (Tully et al., 2016). The last study screened first for elevated glucose in urine samples followed by confirmation of diabetes in patients with glycosuria (Kawakami et al., 1999). Confirmation included a fasting plasma glucose test with a cut-off of 110 mg/dl followed by an oral glucose tolerance test with an unspecified cut-off (Kawakami et al., 1999). Follow-up time ranged from a minimum of 1 year (EversonRose et al., 2004) to a maximum of 16 years (Pan et al., 2012), with most estimates drawn from studies with a mean follow-up time of 5 years or longer (n = 21). All extracted estimates accounted for age, sex, and socioeconomic status through covariate adjustment, matching, or restriction. Twenty-two estimates were also adjusted for at least one behavioural risk factor for diabetes, such smoking, alcohol 228

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

Table 2 Adjusted Associations Between Depression and Incident Type 2 Diabetes Stratified by Depression Measure Depression measure Stratified analyses N estimates RR (95% CI) I2 τ2

Combined analyses N estimates Overall RR (95% CI) I2

Depressive symptom scale

Antidepressant use

Standardized diagnostic interview

Physician-diagnosed depression

Other

15 1.17 (1.10–1.25)* 28.1% 0.004

7 1.33 (1.15–1.52)* 70.3% 0.018

1 1.65 (1.02–2.66) – –

1 1.11 (1.07–1.15) – –

1 1.13 (0.70–1.83) – –

15

4

1

1

1

1.18 (1.12–1.24)* 45.4% 0.004

τ2

⁎

Meta-analyzed using DerSimonian and Laird random-effects models.

studies that reported odds ratios (S8 Table). Results were similar when excluding high-risk populations and studies that included only self-reported measures of incident diabetes (S8 Table).

consumption, or lower levels of physical activity. Many estimates were further adjusted for a measure of obesity (e.g. body mass index, n = 18) or clinical risk factors for diabetes (e.g. hypertension, elevated cholesterol, or elevated triglycerides, n = 10). All estimates were drawn from studies at moderate risk of bias, rather than low risk, due to risk of confounding between groups with and without depression (Sterne et al., 2016).

4. Discussion The primary goal of this review was to determine whether the association between depression and new-onset type 2 diabetes differs by measure of depression. Risk ratios for different measures of depression ranged from an 11% increased risk of diabetes (95% CI 1.07–1.15) in one study that assessed physician-diagnosed depression to a 65% increased risk (95% CI 1.02–2.66) in one study that used a standardized diagnostic interview (Mezuk et al., 2013, Campayo et al., 2010). The two most common measures of depression, depressive symptom scales and antidepressant use, were associated with a 17% increased risk (95% CI 1.10–1.25) and a 33% increased risk (95% CI 1.15–1.52) of type 2 diabetes, respectively. While point estimates suggested that some measures of depression may be more strongly associated with incident diabetes, confidence intervals often overlapped. As well, a meta-regression analysis did not find differences between studies using depressive symptom scales and antidepressant use, though statistical power was likely low. Quantitative analysis of other measures of depression was not possible due to limited evidence. Overall, these findings did not provide conclusive evidence that the relationship between depression and new-onset diabetes varies by depression measure. On the other hand, these results do provide strong evidence that nearly all measures of depression are associated with an increased risk of type 2 diabetes. Methods to identify individuals with depression at higher risk of diabetes may therefore include survey-based scales, often used in epidemiologic research, or measures more easily implemented in public health contexts, such as diagnoses of depression in administrative data. A secondary goal of this study was to establish the relationship between depression and incident diabetes when considering antidepressant treatment as a proxy for depression. Results suggest that depression defined by antidepressant use is associated with a 33% (95% CI 1.15–1.52) increased risk of type 2 diabetes. It may therefore be useful to consider antidepressant treatment as an indicator of diabetes risk when more accurate information on depressive symptoms or diagnoses is not available. Nonetheless, this should be carefully weighed against the likelihood of misclassification of depression, as evidence suggests that up to 50% of adults taking antidepressants may not have a depressive disorder (Aarts et al., 2016; Wong et al., 2016). As well, the association reported here considers antidepressant use only as a proxy for depression, and these results do not provide evidence that antidepressant medication itself increases the risk of type 2 diabetes. Results should be interpreted in the context of the varying concordance and agreement between different measures of depression. When using standardized diagnostic interviews as the reference standard, common symptom scales show sensitivity values between 0.78 and 0.87 and specificity values between 0.70 and 0.87 (Moriarty et al., 2015; Vilagut et al., 2016). Conversely, evidence consistently

3.3. Meta-Analysis results When combining all measures of depression, depression was associated with an 18% (95% CI 1.12–1.24) increased risk of type 2 diabetes (Table 2). Heterogeneity was moderate for the overall effect (I2=45.4%, τ2= 0.004). In stratified analyses, all depression measures except the single study categorized as an other measure of depression were associated with an increased risk of type 2 diabetes (Table 2). Qualitatively, the strength of the association between depression and type 2 diabetes differed between measures of depression, though confidence intervals frequently overlapped. The strongest association was observed in the single study that used a standardized diagnostic interview (RR 1.65, 95% CI 1.20–2.66) and the weakest association was observed in the single study that used physician-diagnosed depression (RR 1.11, 95% CI 1.07–1.15) (Campayo et al., 2010; Mezuk et al., 2013). There was too little data to quantitatively compare these measures. Meta-analyses were performed for measures of antidepressant use and depressive symptom scales. Forest plots for these associations are shown in Fig 2. Depression identified through treatment with antidepressants was associated with a 33% increased risk of type 2 diabetes (95% CI 1.15–1.52). Heterogeneity and between-study variance were high (I2 = 70.3%, τ2= 0.018). There was also some evidence of publication bias observed through funnel plots (S12 Fig) and Egger's test statistic (Egger's test p = 0.023). Using the trim-and-fill method, the association was slightly attenuated (RR 1.22, 95% CI 1.04–1.42). Depression identified through symptom scales had a lower meta-analyzed risk ratio of 1.17 (95% CI 1.10–1.25). Heterogeneity and between-study variance were low (I2=28.1%, τ2= 0.004), though there was evidence of publication bias (Egger's test p = 0.002, S13 Fig). Using the trimand-fill method, the meta-analyzed risk ratio decreased to 1.12 (95% CI 1.03–1.21). Meta-regression did not provide evidence that antidepressant use and depressive symptom scales were differentially associated with type 2 diabetes risk (meta-regression estimate of antidepressant use compared to depressive symptoms 1.05, 95% CI 0.88–1.26). However, statistical power was likely limited due to the small number of studies included (S5 Table). Sensitivity analyses showed that unadjusted estimates were generally higher than adjusted estimates (S6 Table). Slightly stronger associations between depression and incident diabetes were observed when including estimates from studies at serious or critical risk of bias (S7 Table). Estimates were also somewhat increased in studies with lower sample size, less than 5 years of mean follow-up time, and in 229

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

Fig. 2. Forest Plots of Adjusted Associations Between Depression and Incident Type 2 Diabetes Stratified by Depression Measure.

review of the literature with no time restrictions and included both published and unpublished works. It is noteworthy that our search strategy identified 51 publications when we did not impose restrictions on study quality or confounders, which can be compared to 33 publications and 16 publications in the two most recent meta-analyses (Hasan et al., 2014; Yu et al., 2015). We searched a wide range of medical and psychosocial databases, while some prior meta-analyses were restricted to the MEDLINE database (Mezuk et al., 2008; Rotella and Mannucci, 2013). As well, this is the first meta-analysis to search for textwords in the title and abstract, rather than solely relying on keywords such as MeSH terms. It is also the first to search for observational analyses of randomized controlled trials. We likely minimized bias in our results by restricting to estimates that were adjusted for important confounders and were at moderate risk of bias as defined by the ROBINS-I risk assessment tool. We further conducted several sensitivity analyses to assess potential sources of heterogeneity in our results.

demonstrates that only 50% or fewer individuals with major depressive disorder have been diagnosed by a primary care physician or other health professional (Mitchell et al., 2009; Pelletier et al., 2016). As noted above, studies in Canada and the Netherlands indicate that approximately half of antidepressants are prescribed for reasons other than depressive disorders (Aarts et al., 2016; Wong et al., 2016). Thus, while all measures of depression were associated with an increased risk of type 2 diabetes, they may identify somewhat different populations. When combining all measures of depression, depression was associated with a 18% increased risk of developing type 2 diabetes (95% CI 1.12–1.24). This is smaller than other pooled estimates, which range from 1.32 to 1.60 (Mezuk et al., 2008; Yu et al., 2015). This is likely due to our restriction to studies at moderate risk of bias and to studies that adjusted for critical confounders. Depression may increase the risk of diabetes through biological pathways, such as increased activity in the hypothalamic-pituitary-adrenal axis and chronic inflammation, as well through health behaviours such as physical inactivity and poor adherence with dietary recommendations (Golden et al., 2008; Tabak et al., 2014). There is no evidence comparing mechanisms between measures of depression, though biological changes may differ between adults with depressive symptomology and adults receiving antidepressant treatment (Reus et al., 2017).

4.2. Limitations There were several limitations of this analysis. First, there was substantial heterogeneity between studies, even among those with the same measure of depression. The most heterogeneity was observed when defining depression through antidepressant use (I2 = 70.3%). Definitions of antidepressant use were diverse, and included measures from both self-reported and administrative data with duration of antidepressant use ranging from the past year to continuous use in the past 10 years (Kivimaki et al., 2010; Rubin et al., 2010). Heterogeneity may have also arisen from the inclusion of different covariates, differences in study settings, or recency of depressive episodes (Hunter et al., 2018;

4.1. Strengths This meta-analysis provides a novel perspective of whether the association between depression and new-onset diabetes varies by measure of depression. It is the first meta-analysis to quantitatively examine the association between depression and incident diabetes when identifying depression through antidepressant use. We conducted a thorough 230

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

editing, Methodology, Validation. Norbert Schmitz: Formal analysis, Writing - review & editing, Methodology, Validation.

Meng et al., 2018). Second, misclassification of depression was likely present due to imperfect measures of major depressive disorder, particularly when using antidepressant treatment as a proxy for depression. Similarly, most studies assessed depression solely at baseline and did not account for changes in depression over time. Third, some studies did not exclude participants with undiagnosed diabetes at baseline. As undiagnosed diabetes is more common among people with depression, this may have spuriously increased associations between depression and new-onset diabetes (Meurs et al., 2016). However, we did not observe meaningful differences in effect estimates between studies of depressive symptoms that included and excluded participants with undiagnosed diabetes at baseline (S11 Table). Fourth, there was some evidence of publication bias. Publication bias could lead to a spuriously increased association between depression and incident diabetes. Nonetheless, estimates calculated using the trim-and-fill method were similar to our main results. We also limited the impact of publication bias by including conference abstracts in the first stage of screening and by including unpublished research, such as dissertations.

Declarations of Competing Interest None. Acknowledgements None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.01.053. References Aarts, N., Noordam, R., Hofman, A., Tiemeier, H., Stricker, B.H., Visser, L.E., 2016. Selfreported indications for antidepressant use in a population-based cohort of middleaged and elderly. Int. J. Clin. Pharm. 38, 1311–1317. Ali, S., Stone, M.A., Peters, J.L., Davies, M.J., Khunti, K., 2006. The prevalence of comorbid depression in adults with type 2 diabetes: a systematic review and metaanalysis. Diabet. Med. 23, 1165–1173. Borenstein, M., Hedges, L.V., Higgins, J.P.T., Rothstein, H.R., 2009. Introduction to metaanalysis. John Wiley & Sons, Chichester, U.K. Campayo, A., de Jonge, P., Roy, J.F., Saz, P., de la Camara, C., Quintanilla, M.A., Marcos, G., Santabarbara, J., Lobo, A., 2010. Depressive disorder and incident diabetes mellitus: the effect of characteristics of depression. Am. J. Psychiatr. 167, 580–588. Carnethon, M.R., Biggs, M.L., Barzilay, J.I., Smith, N.L., Vaccarino, V., Bertoni, A.G., Arnold, A., Siscovick, D., 2007. Longitudinal association between depressive symptoms and incident type 2 diabetes mellitus in older adults: the cardiovascular health study. Arch. Intern. Med. 167, 802–807. DerSimonian, R., Laird, N., 1986. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188. Egede, L.E., Ellis, C., 2010. Diabetes and depression: global perspectives. Diabetes Res. Clin. Pract. 87, 302–312. Everson-Rose, S.A., Meyer, P.M., Powell, L.H., Pandey, D., Torrens, J.I., Kravitz, H.M., Bromberger, J.T., Matthews, K.A., 2004. Depressive symptoms, insulin resistance, and risk of diabetes in women at midlife. Diabetes Care 27, 2856–2862. Fleiss, J.L., Levin, B.A., Paik, M.C., 2003. Chapter 18: The measurement of interrater agreement. statistical methods for rates and proportions. Wiley-Interscience, Hoboken, N.J., pp. 598–626. Frisard, C., Gu, X., Whitcomb, B., Ma, Y., Pekow, P., Zorn, M., Sepavich, D., Balasubramanian, R., 2015. Marginal structural models for the estimation of the risk of diabetes mellitus in the presence of elevated depressive symptoms and antidepressant medication use in the Women's Health Initiative observational and clinical trial cohorts. BMC Endocr. Disord. 15, 56. Golden, S.H., Lazo, M., Lee, H.B., Lyketsos, C., Carnethon, M., Bertoni, A.G., Schreiner, P.J., Diez Roux, A.V., 2008. Examining a bidirectional association between depressive symptoms and diabetes. JAMA 299, 2751–2759. Harbord, R.M., Higgins, J.P.T., 2009. Meta-regression in Stata. Stata J 8, 493–519. Hasan, S.S., Clavarino, A.M., Mamun, A.A., Doi, S.A., Kairuz, T., 2013. Population impact of depression either as a risk factor or consequence of type 2 diabetes in adults: a meta-analysis of longitudinal studies. Asian J. Psychiatr. 6, 460–472. Hasan, S.S., Clavarino, A.M., Mamun, A.A., Kairuz, T., 2014. Incidence and risk of diabetes mellitus associated with depressive symptoms in adults: evidence from longitudinal studies. Diabetes Metab. Syndr. 8, 82–87. Hunter, J.C., DeVellis, B.M., Jordan, J.M., Sue Kirkman, M., Linnan, L.A., Rini, C., Fisher, E.B., 2018. The association of depression and diabetes across methods, measures, and study contexts. Clin. Diabetes Endocrinol. 4, 1. Kan, C., Silva, N., Golden, S.H., Rajala, U., Timonen, M., Stahl, D., Ismail, K., 2013. A systematic review and meta-analysis of the association between depression and insulin resistance. Diabetes Care 36, 480–489. Kawakami, N., Takatsuka, N., Shimizu, H., Ishibashi, H., 1999. Depressive symptoms and occurrence of type 2 diabetes among Japanese men. Diabetes Care 22, 1071–1076. Kivimaki, M., Batty, G.D., Jokela, M., Ebmeier, K.P., Vahtera, J., Virtanen, M., Brunner, E.J., Tabak, A.G., Witte, D.R., Kumari, M., Singh-Manoux, A., Hamer, M., 2011. Antidepressant medication use and risk of hyperglycemia and diabetes mellitus: a noncausal association? Biol. Psychiatr. 70, 978–984. Kivimaki, M., Tabak, A.G., Lawlor, D.A., Batty, G.D., Singh-Manoux, A., Jokela, M., Virtanen, M., Salo, P., Oksanen, T., Pentti, J., Witte, D.R., Vahtera, J., 2010. Antidepressant use before and after the diagnosis of type 2 diabetes: a longitudinal modeling study. Diabetes Care 33, 1471–1476. Knol, M.J., Le Cessie, S., Algra, A., Vandenbroucke, J.P., Groenwold, R.H., 2012. Overestimation of risk ratios by odds ratios in trials and cohort studies: alternatives to logistic regression. CMAJ 184, 895–899. Knol, M.J., Twisk, J.W., Beekman, A.T., Heine, R.J., Snoek, F.J., Pouwer, F., 2006. Depression as a risk factor for the onset of type 2 diabetes mellitus. A meta-analysis. Diabetologia 49, 837–845.

5. Conclusions The results of this systematic review and meta-analysis suggest that depression is associated with an increased risk of diabetes when measured using a variety of methods, including survey-based depression measures and depression identified in administrative data. Results also provide evidence that antidepressant use, considered as a proxy for depression, is associated with an increased risk of type 2 diabetes. There were insufficient data to determine whether the association between depression and diabetes differs substantially by measure of depression. These findings can inform the identification of individuals at increased risk of type 2 diabetes in research, clinical practice, and public health settings and can be applied to initiatives such as risk score development and surveillance. Contributors E.G. conceived the study, searched the databases, performed screening of titles and abstracts, performed screening of full-texts, extracted data, performed all analyses, and wrote the manuscript. S.Deschênes performed screening of full-texts, extracted data, and edited the final manuscript. M.K. and S.Danna performed screening of titles and abstracts. K.B.F. provided advice on systematic review and meta-analysis methodology and edited the manuscript. N.S. provided advice on systematic review and meta-analysis methodology and edited the manuscript. All authors have approved the final article. Funding E.G. was supported by a Frederick Banting and Charles Best Canada Graduate Scholarship. S.Deschênes was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. M.K. was supported by a Graduate Excellence Fellowships in Mental Health Research award, Department of Psychiatry, McGill University. K.B.F. is supported by a Junior II salary support award from the Fonds de recherche du Québec – santé (Quebec Foundation for Health Research). Part of this study was funded by a grant from the Canadian Institutes of Health Research (CIHR; PCG-155452). The funding sources had no involvement in the conduct of the research or preparation of this article. CRediT authorship contribution statement Eva A Graham: Conceptualization, Validation, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Sonya S Deschênes: Validation, Data curation, Formal analysis, Writing - review & editing. Marina N Khalil: Validation. Sofia Danna: Validation. Kristian B Filion: Formal analysis, Writing - review & 231

Journal of Affective Disorders 265 (2020) 224–232

E.A. Graham, et al.

T.D., Ramsay, C.R., Regidor, D., Rothstein, H.R., Sandhu, L., Santaguida, P.L., Schünemann, H.J., Shea, B., Shrier, I., Tugwell, P., Turner, L., Valentine, J.C., Waddington, H., Waters, E., Wells, G.A., Whiting, P.F., Higgins, J.P., 2016. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355. Suvisaari, J., Keinanen, J., Eskelinen, S., Mantere, O., 2016. Diabetes and schizophrenia. Curr. Diab. Rep. 16, 16. Tabak, A.G., Akbaraly, T.N., Batty, G.D., Kivimaki, M., 2014. Depression and type 2 diabetes: a causal association? Lancet Diabetes Endocrinol 2, 236–245. Tsenkova, V.K., Karlamangla, A., 2016. Depression amplifies the influence of central obesity on 10-Year incidence of diabetes: findings from MIDUS. PLoS One 11, e0164802. Tully, P.J., Baumeister, H., Martin, S., Atlantis, E., Jenkins, A., Januszewski, A., O'Loughlin, P., Taylor, A., Wittert, G.A., 2016. Elucidating the biological mechanisms linking depressive symptoms with type 2 diabetes in men: the longitudinal effects of inflammation, microvascular dysfunction, and testosterone. Psychosom. Med. 78, 221–232. Vandenbroucke, J.P., Pearce, N., 2012. Case–control studies: basic concepts. Int. J. Epidemiol. 41, 1480–1489. Vilagut, G., Forero, C.G., Barbaglia, G., Alonso, J., 2016. Screening for depression in the general population with the center for epidemiologic studies depression (CES-D): a systematic review with meta-analysis. PLoS One 11, e0155431. Vimalananda, V.G., Palmer, J.R., Gerlovin, H., Wise, L.A., Rosenzweig, J.L., Rosenberg, L., Ruiz-Narvaez, E.A., 2014. Depressive symptoms, antidepressant use, and the incidence of diabetes in the Black Women's Health Study. Diabetes Care 37, 2211–2217. Weinhandl, E.D., Sue, D., 2012. Generalization of trim and fill for application in meta‐regression. Res Synth Methods 3, 51–67. Wong, J., Motulsky, A., Eguale, T., Buckeridge, D.L., Abrahamowicz, M., Tamblyn, R., 2016. Treatment indications for antidepressants prescribed in Primary Care in Quebec, Canada, 2006-2015. JAMA 315, 2230–2232. World Health Organization, 2006. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF consultation, Geneva, Switzerland. https://www.who.int/diabetes/publications/Definition%20and%20diagnosis%20of %20diabetes_new.pdf. World Health Organization, 2011. Use of glycated haemoglobin (HbA1c) in diagnosis of diabetes mellitus: abbreviated report of a WHO consultation, Geneva, Switzerland. https://www.who.int/diabetes/publications/report-hba1c_2011.pdf. World Health Organization, 2016. Global report on diabetes. http://apps.who.int/iris/ bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid= 427173489D8354BE298C2FB213B02881?sequence=1. Yu, M., Zhang, X., Lu, F., Fang, L., 2015. Depression and risk for diabetes: a meta-analysis. Can. J. Diabetes 39, 266–272.

Kumari, M., Head, J., Marmot, M., 2004. Prospective study of social and other risk factors for incidence of type 2 diabetes in the Whitehall II study. Arch. Intern. Med. 164, 1873–1880. Meng, R., Liu, N., Yu, C., Pan, X., Lv, J., Guo, Y., Bian, Z., Yang, L., Chen, Y., Chen, Z., Pan, A., Li, L., 2018. Association between major depressive episode and risk of type 2 diabetes: a large prospective cohort study in Chinese adults. J. Affect. Disord. 234, 59–66. Meurs, M., Roest, A.M., Wolffenbuttel, B.H., Stolk, R.P., de Jonge, P., Rosmalen, J.G., 2016. Association of depressive and anxiety disorders with diagnosed versus undiagnosed diabetes: an epidemiological study of 90,686 participants. Psychosom. Med. 78, 233–241. Mezuk, B., Chaikiat, A., Li, X., Sundquist, J., Kendler, K.S., Sundquist, K., 2013. Depression, neighborhood deprivation and risk of type 2 diabetes. Health Place 23, 63–69. Mezuk, B., Eaton, W.W., Albrecht, S., Golden, S.H., 2008. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care 31, 2383–2390. Mitchell, A.J., Vaze, A., Rao, S., 2009. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet 374, 609–619. Moriarty, A.S., Gilbody, S., McMillan, D., Manea, L., 2015. Screening and case finding for major depressive disorder using the Patient Health Questionnaire (PHQ-9): a metaanalysis. Gen. Hosp. Psychiatr. 37, 567–576. Pan, A., Sun, Q., Okereke, O.I., Rexrode, K.M., Rubin, R.R., Lucas, M., Willett, W.C., Manson, J.E., Hu, F.B., 2012. Use of antidepressant medication and risk of type 2 diabetes: results from three cohorts of US adults. Diabetologia 55, 63–72. Pelletier, L., O'Donnell, S., Dykxhoorn, J., McRae, L., Patten, S.B., 2016. Under-diagnosis of mood disorders in Canada. Epidemiol. Psychiatr. Sci. 1–10. Reus, G.Z., dos Santos, M.A.B., Strassi, A.P., Abelaira, H.M., Ceretta, L.B., Quevedo, J., 2017. Pathophysiological mechanisms involved in the relationship between diabetes and major depressive disorder. Life Sci 183, 78–82. Rotella, F., Mannucci, E., 2013. Depression as a risk factor for diabetes: a meta-analysis of longitudinal studies. J. Clin. Psychiatr. 74, 31–37. Rubin, R.R., Ma, Y., Marrero, D.G., Peyrot, M., Barrett-Connor, E.L., Kahn, S.E., Haffner, S.M., Price, D.W., Knowler, W.C., 2008. Elevated depression symptoms, antidepressant medicine use, and risk of developing diabetes during the diabetes prevention program. Diabetes Care 31, 420–426. Rubin, R.R., Ma, Y., Peyrot, M., Marrero, D.G., Price, D.W., Barrett-Connor, E., Knowler, W.C., 2010. Antidepressant medicine use and risk of developing diabetes during the diabetes prevention program and diabetes prevention program outcomes study. Diabetes Care 33, 2549–2551. Sterne, J.A., Hernán, M.A., Reeves, B.C., Savović, J., Berkman, N.D., Viswanathan, M., Henry, D., Altman, D.G., Ansari, M.T., Boutron, I., Carpenter, J.R., Chan, A.-.W., Churchill, R., Deeks, J.J., Hróbjartsson, A., Kirkham, J., Jüni, P., Loke, Y.K., Pigott,

232