Psychological Medicine (2014), 44, 2017–2028. © Cambridge University Press 2013 doi:10.1017/S0033291713002778

REVIEW ARTICLE

Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables D. Vancampfort1,2*, C. U. Correll3,4, M. Wampers1, P. Sienaert1, A. J. Mitchell5,6, A. De Herdt2, M. Probst1,2, T. W. Scheewe7 and M. De Hert1 1

University Psychiatric Centre KU Leuven, Kortenberg, Belgium Department of Rehabilitation Sciences, KU Leuven, Belgium 3 The Zucker Hillside Hospital, Glen Oaks, NY, USA 4 Albert Einstein College of Medicine, Bronx, NY, USA 5 Department of Psycho-oncology, Leicestershire Partnership Trust, Leicester, UK 6 Department of Cancer and Molecular Medicine, University of Leicester, UK 7 Windesheim University of Applied Sciences, Zwolle, The Netherlands 2

Background. Individuals with depression have an elevated risk of cardiovascular disease (CVD) and metabolic syndrome (MetS) is an important risk factor for CVD. We aimed to clarify the prevalence and correlates of MetS in persons with robustly defined major depressive disorder (MDD). Method. We searched Medline, PsycINFO, EMBASE and CINAHL up until June 2013 for studies reporting MetS prevalences in individuals with MDD. Medical subject headings ‘metabolic’ OR ‘diabetes’ or ‘cardiovascular’ or ‘blood pressure’ or ‘glucose’ or ‘lipid’ AND ‘depression’ OR ‘depressive’ were used in the title, abstract or index term fields. Manual searches were conducted using reference lists from identified articles. Results. The initial electronic database search resulted in 91 valid hits. From candidate publications following exclusions, our search generated 18 studies with interview-defined depression (n = 5531, 38.9% male, mean age = 45.5 years). The overall proportion with MetS was 30.5% [95% confidence interval (CI) 26.3–35.1] using any standardized MetS criteria. Compared with age- and gender-matched control groups, individuals with MDD had a higher MetS prevalence [odds ratio (OR) 1.54, 95% CI 1.21–1.97, p = 0.001]. They also had a higher risk for hyperglycemia (OR 1.33, 95% CI 1.03–1.73, p = 0.03) and hypertriglyceridemia (OR 1.17, 95% CI 1.04–1.30, p = 0.008). Antipsychotic use (p < 0.05) significantly explained higher MetS prevalence estimates in MDD. Differences in MetS prevalences were not moderated by age, gender, geographical area, smoking, antidepressant use, presence of psychiatric co-morbidity, and median year of data collection. Conclusions. The present findings strongly indicate that persons with MDD are a high-risk group for MetS and related cardiovascular morbidity and mortality. MetS risk may be highest in those prescribed antipsychotics. Received 1 July 2013; Revised 15 August 2013; Accepted 14 October 2013; First published online 21 November 2013 Key words: Depression, dislipidemia, hyperglycemia, metabolic syndrome, obesity.

Introduction Depression is thought to be an independent risk factor for cardiovascular disease (CVD) (Niranjan et al. 2012). Meta-analyses suggest that individuals with depressive disorders have almost twice the risk of developing CVD (Wulsin & Singal, 2003; Van der Kooy et al. 2007; Pan et al. 2011; Rugulies, 2002). Moreover, depression

* Address for correspondence: Dr D. Vancampfort, University Psychiatric Centre KU Leuven, Campus Kortenberg, Leuvensesteenweg 517, 3070 Kortenberg, Belgium. (Email: [email protected])

is known to increase the risk for cardiac mortality two to four times, irrespective of CVD history (Wulsin et al. 1999; Penninx et al. 2001; Barth et al. 2004; Nicholson et al. 2006; Whang et al. 2009). In later life, depression has been associated with several cardiovascular risk factors, such as obesity, in addition to CVD, diabetes and stroke (Valkanova & Ebmeier, 2013). To help clinicians to identify and focus more on patients with increased risk for CVD, the concept of the metabolic syndrome (MetS) has been introduced. MetS is defined by a combination of central obesity, high blood pressure, low levels of high density

2018 D. Vancampfort et al. lipoprotein cholesterol (HDL-C), elevated triglycerides and hyperglycemia (Expert Panel, 2001). In the general population, these clustered risk factors have been associated with the development of CVD (Galassi et al. 2006; Gami et al. 2007; Bayturan et al. 2010; Mottillo et al. 2010). Although several definitions have been proposed for MetS, the most often cited ones are those formulated by the National Cholesterol Education Program (NCEP), that is the Adult Treatment Panel III (ATP-III) and adapted ATP-III (ATP-III-A) criteria (Expert Panel, 2001; Grundy et al. 2005), by the International Diabetes Federation (IDF; Alberti et al. 2006) and by the World Health Organization (WHO Consultation, 1999). Current definitions for MetS are aimed at being easy to use in clinical settings and share similar diagnostic thresholds. However, the role of abdominal obesity is central to the IDF definition, with provision of ethnic specific thresholds for waist circumference (Alberti et al. 2009), whereas central obesity is not a mandatory NCEP/ATP MetS criterion. As a prevalent condition and predictor of CVD across racial, gender and age groups, MetS provides a unique opportunity to identify high-risk populations and prevent the progression of some of the major causes of morbidity and mortality (Galassi et al. 2006; Gami et al. 2007; Bayturan et al. 2010; Mottillo et al. 2010). Patients with major depressive disorder (MDD) are subject to background socio-economic and lifestyle conditions that may influence the development and course of CVD (Atlantis et al. 2012) and MetS. These include poor receipt of high-quality physical health care (Mitchell et al. 2009; De Hert et al. 2011a), reduced uptake of mass screening (Lord et al. 2010), reduced compliance with medical recommendations (Ziegelstein et al. 2000; Swardfager et al. 2011) and adverse medication treatment effects (McIntyre et al. 2007, 2010; Gartlehner et al. 2011; De Hert et al. 2011b), along with the presence of modifiable behavioral risk factors, such as smoking and physical inactivity (De Hert et al. 2011b). In addition, MDD is associated with physiological changes, including dysregulation of autonomic nervous system activity, impaired hypothalamic–pituitary–adrenal (HPA) axis function, dysregulation of immune mechanisms, coagulation abnormalities and vascular endothelial dysfunction (Brown et al. 2009; McIntyre et al. 2009; Lamers et al. 2013). In a meta-analysis of 29 cross-sectional studies, Pan et al. (2012) reported indications of a bidirectional association between MetS and depression. Information on the overall prevalence of MetS and its components, and on the effect of moderating variables on MetS frequency, was not presented in that study because it focused exclusively on the strength and directionality

of the association between MetS and depression. Therefore, to assess the prevalence of MetS and its components in persons with MDD and to explore the effect of geographical region, gender, age, treatment setting, psychiatric co-morbidity, illness duration and antidepressant and antipsychotic use and type, we conducted a systematic review and meta-analysis. We also aimed to assess the differences in the prevalence of MetS in studies comparing persons with MDD with age- and gender-matched healthy comparison groups. We hypothesized that persons with MDD are at an increased risk for MetS compared with matched healthy controls. Method Inclusion and exclusion criteria The systematic review was executed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard (Moher et al. 2009). The focus was on individuals with syndromal depressive episode, irrespective of age and clinical setting (in-patient, out-patient or mixed). Inclusion criteria were: (a) an interview-defined MDD according to DSM-IV-TR (APA, 2000) or clinical depression according to the ICD (WHO, 1993); and (b) MetS diagnosis according to non-modified ATP-III (Expert Panel, 2001), modified ATP-III-A (Grundy et al. 2005), IDF (Alberti et al. 2006) or WHO criteria (WHO Consultation, 1999). We included case–control studies, prospective cohort studies, cross-sectional studies and comparisons of study populations with age standardization. For estimation of the MetS prevalence, we excluded studies with: (a) non-interview-defined diagnoses of MDD, (b) non-standardized definitions of MetS, (c) insufficient data for extraction of MetS proportions, (d) a sample size below 50, and (e) restriction to patients at risk for or without CVD. In the case of multiple publications from the same study, only the most recent paper or article with the largest sample was included. Search criteria and critical appraisal Two independent reviewers (A.D.H. and D.V.) searched Medline, PsycINFO, EMBASE and CINAHL from database inception until June 2013. Key words used were ‘metabolic’ OR ‘diabetes’ or ‘cardiovascular’ or ‘blood pressure’ or ‘glucose’ or ‘lipid’ AND ‘depression’ OR ‘depressive’ in the title, abstract or index term fields. Manual searches were also conducted using the reference lists from identified articles. Data were abstracted by the same two independent reviewers. Methodological appraisal of each study was performed according to PRISMA standards

MetS and metabolic abnormalities in MDD patients (Moher et al. 2009), including evaluation of bias (confounding, overlapping data, publication bias). A funnel graph (Egger et al. 1997) was created, in which the study-specific effect estimates are displayed in relation to the standard error, to assess the potential presence of publication bias. In addition, publication bias was tested using the Egger regression method (Egger et al. 1997) and the Begg–Mazumdar test (Begg & Mazumdar, 1994), with a p value < 0.05 suggesting presence of bias. Lastly, a trim-and-fill approach (Duvall & Tweedie, 2000) was used to determine the adjusted MetS prevalence after figuring into the analyses results from potentially missing studies.

Statistical analysis A meta-analysis, based on the above-described available studies, was performed to obtain an optimal estimation of the prevalence of MetS in the population with MDD. The effect size used for the prevalence of MetS was the proportion, but all analyses were performed, converting proportions into logits. As indicated by Lipsey & Wilson (2001), logits are preferred over proportions because the mean proportion across studies underestimates the size of the confidence interval (CI) around the mean proportion (because of the compression of the standard error as p approaches 0 or 1) and overestimates the degree of heterogeneity across effect sizes. Lipsey & Wilson (2001) note that this is especially the case when the observed proportions are < 0.2 or > 0.80, as was the case in some of the included studies. The logit method circumvents these problems and is the preferred method, especially given our interest in between-study differences. However, for ease of interpretation, all final results were back-converted into proportions. To examine the homogeneity of the effect size distribution, a Q statistic (Hedges & Olkin, 1985) was used. A mixed random-effects model was used, implying that the observed variance stems from three sources: (a) variance due to subject-level sampling error, (b) variance from study characteristics that we could identify (e.g. geographical region), and (c) variance from other systematic, random or unmeasured sources. In these analyses, several study characteristics were incorporated including median year of data collection, geographical area, mean age of the study sample, criteria used for MetS (ATP-III, ATP-III-A, IDF or WHO), percentage of patients with psychiatric co-morbidity, and percentage of antidepressant and antipsychotic use, both in general and by class. Lastly, we pooled data from individual studies to calculate odds ratios (ORs) to statistically compare the prevalence of MetS

2019

between individuals with MDD and age- and gendermatched general population control subjects.

Results Search results and included participants The initial electronic database search resulted in 91 valid hits. From candidate publications following exclusions, our search generated 18 studies fulfilling the inclusion and exclusion criteria. The list of included studies is presented in Appendix 1 in the online Supplementary Material. The dataset comprised 5531 unique individuals (38.9% male, mean age = 45.5 years) with a diagnosis of MDD (Fig. 1). Published studies involved sample sizes that ranged from 60 to 1028 participants. Details on the included studies are presented in Appendix 2 online. Of the 18 studies in persons with a diagnosis of MDD, eight were conducted among in-patients (n = 2393), five in out-patient settings (n = 558), four in mixed samples (n = 2303) and one included community patients (n = 277). Six studies (n = 1380) reported smoking, and 41.7% of persons with MDD in these studies were smokers. In addition, five of the included studies compared individuals with MDD (n = 4427, 47% male, mean age = 47.2 years) with healthy control subjects (n = 5172, 48% male, mean age = 49.6 years). A list of studies that were excluded and the reasons for exclusion are presented in Appendix 3 online. Publication bias The funnel plot of the 18 studies included in our meta-analysis was asymmetrical, as shown in Fig. 2. Only the Egger test (p = 0.02), and not the Begg– Mazumdar test (p = 0.12), showed any evidence of publication bias. The trim-and-fill method demonstrated that adjusting for publication bias had little effect on the pooled MetS estimate. Prevalence of MetS in MDD Based on a meta-analysis involving 5531 unique individuals with a MDD diagnosis, the estimated weighted mean prevalence of MetS defined according to either ATP-III, ATP-III-A, IDF or WHO standards in a random-effects model was 30.5% (95% CI 26.3–35.1). After adjustment for publication bias, the estimated weighted mean prevalence of MetS in a random-effects model was 29.7% (95% CI 25.6–34.1). Fig. 3 shows the estimated MetS prevalences of each individual study together with the weighted mean MetS prevalence rate. The significant Q statistic of the fixed-effects model indicated that the distribution of MetS prevalence rate of individual studies was not

2020 D. Vancampfort et al. Valid search hits (N =91)

Reasons for exclusion (n=73): Appendix 3. ATP-III (N=18) ATP-III-A (N=9)

Valid metabolic syndrome studies (N =18)

IDF (N=9) WHO (N =1)

Main groupings

Metabolic components

Region of origin

In-patients only (N=8)

Waist (N=7)

Europe (N=10)

Out-patients only (N=5)

Blood pressure (N=7)

Northern America (N=5)

Triglycerides (N=7)

Asia (N=3)

In- and out-patients (N=4)

High density lipoproteins (N=7) General population (N=1)

Fasting glucose (N=7)

Fig. 1. Quality of reporting of meta-analyses (Quorom) search results.

0.0

Standard Error

0.1

0.2

0.3

0.4 –3

–2

–1

0

1

2

3

Logit event rate Fig. 2. Publication bias assessment for metabolic syndrome (MetS) studies in depression. Begg–Mazumdar: Kendall’s tau-b with continuity correction = 0.20, z = 1.17, p = 0.12. Egger’s bias = 4.11 [95% confidence interval (CI) 0.86–7.36], t = 2.68, p = 0.02.

homogeneous (Q14 = 173.39, p < 0.001), implying that the variability in the prevalence rates of MetS between studies was larger than can be expected on the basis of sampling error. Consequently, in a next step, we

examined the potential moderating role of several study, population and treatment characteristics to explain systematic differences in the observed prevalence rates of MetS between studies.

MetS and metabolic abnormalities in MDD patients Study (first author)

Event rate

Lower Upper limit limit Z-Value p-Value

Kloiber 2010 Carroll 2009 Van Reedt 2010 Goethe 2009 Zeugmann 2010 Goldbacher 2009 Richter 2010 Grimaldi 2009 Ifteni 2009 Blank 2010 Sagud 2013 Aggarwal 2012 Hatt 2011 Kahl 2012 Lehto 2010 Correll 2006 Dutt 2011 Sili 2012

0.174 0.206 0.223 0.234 0.243 0.245 0.250 0.258 0.292 0.293 0.335 0.366 0.375 0.413 0.414 0.434 0.451 0.455 0.305

0.152 0.162 0.198 0.210 0.157 0.183 0.157 0.210 0.207 0.257 0.273 0.278 0.271 0.351 0.305 0.328 0.377 0.376 0.263

0.199 –18.557 0.257 –9.087 0.250 –16.027 0.261 –16.075 0.356 –4.079 0.320 –5.947 0.374 –3.685 0.313 –7.660 0.395 –3.797 0.331 –9.681 0.403 –4.612 0.464 –2.653 0.492 –2.098 0.478 –2.624 0.532 –1.427 0.547 –1.144 0.528 –1.247 0.537 –1.078 0.351 –7.792

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0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.036 0.009 0.154 0.253 0.212 0.281 0.000 –1.00

–0.50

0.00

0.50

1.00

Fig. 3. Summary of metabolic syndrome (MetS) rates in major depressive disorder (MDD). (See Appendix 1 online for study citations.)

Prevalence of individual metabolic abnormalities in subjects with MDD Six studies reported on the rate of abdominal obesity defined as a waist circumference > 102 cm in males and > 88 cm in females (ATP-III or ATP-III-A), and one study reported on abdominal obesity defined as a waist circumference > 94 cm in males and > 80 cm in females (IDF). The estimated proportion of patients with abdominal obesity by ATP definitions was 38.0% (N = 6, n = 2827, 95% CI 30.9–45.6) and 40.5% according to IDF (N = 1, n = 988, 95% CI 25.2–57.9). Of studies reporting on hyperglycemia (5110 mg/dl; ATP-III) the estimated prevalence was 18.8% (N = 4, n = 2600, 95% CI 13.0–26.5), being 8.6% (N = 3; n = 1215, 95% CI 5.2–14.0) for those studies using a threshold of 5100 mg/dl (ATP-III-A and IDF). The prevalence of hypertriglyceridemia was 30.1% (N = 7, n = 3699, 95% CI 22.6–38.8) and the prevalence of abnormally low HDL-C was 31.1% (N = 7, n = 3699, 95% CI 23.1– 40.4). Seven studies reported on high blood pressure, which was present in 36.7% (N = 7, n = 4202, 95% CI 22.7–53.3). Moderating variables of MetS prevalence in patients with MDD No significant differences between the different criteria for MetS were observed. Using the ATP-III-A criteria, the MetS prevalence was 38.8% (N = 5, n = 672, 95% CI 30.6–47.8), and estimated MetS prevalences using ATP-III, IDF and WHO definitions were 26.7% (N = 7,

n = 3187, 95% CI 21.3–32.9), 30.8% (N = 5, n = 1395, 95% CI 23.6–39.1) and 20.6% (N = 1, n = 277, 95% CI 10.6– 36.1) respectively. Antipsychotic medication use explained part of the heterogeneity of the prevalence estimates of MetS between the included studies, being associated with significantly higher MetS prevalences (N = 4, n = 1745, p < 0.05). Furthermore, differences in prevalence estimates of different studies could not be explained by age, gender, geographical area, smoking, selective serotonin reuptake inhibitor (SSRI) use, presence of psychiatric co-morbidity, year of data collection and use of antidepressants (data not shown). Because of limited data, we were not able to investigate the effect of ethnicity, individual antipsychotic and antidepressant medications and illness duration as potential moderators.

ORs of MetS and metabolic abnormalities in MDD compared with age- and gender-matched general population controls Compared with healthy control subjects (N = 5, n = 3297, 33.1% male, mean age = 44.1 years), those with MDD (N = 5, n = 3118, 33.2% male, mean age = 43.7 years) had a significantly increased risk of MetS (23.8%, 95% CI 19.6–28.6 v. 16.7%, 95% CI 12.3–22.4; OR 1.54, 95% CI 1.21–1.97, p < 0.001). Four studies reported prevalence figures of individual MetS criteria in individuals with MDD (n = 3188, 34.8% male, mean age = 43.7 years) compared with

2022 D. Vancampfort et al. healthy control subjects (n = 3118, 34.9% male, mean age = 43.9 years). Those with MDD had significantly more fasting hyperglycemia (11.8%, 95% CI 6.9–19.4 v. 8.7%, 95% CI 4.2–17.4; OR 1.33, 95% CI 1.03–1.73, p = 0.03) and hypertriglyceridemia (22.9%, 95% CI 17.1–30.0 v. 20.9%, 95% CI 15.9–28.4; OR 1.17, 95% CI 1.04–1.30, p = 0.008). However, no significant differences in the prevalence of hypertension (29.1%, 95% CI 13.8–51.3 v. 31.5%, 95% CI 16.7–51.5; OR 0.89, 95% CI 0.62–1.28), abnormally low HDL-C (29.2%, 95% CI 20.1–40.4 v. 22.9%, 95% CI 16.1–31.4; OR 1.41, 95% CI 0.94–2.12) and abdominal obesity (41.6%, 95% CI 35.3–48.2 v. 34.5%, 95% CI 22.5–48.9; OR 1.37, 95% CI 0.92–2.05) were observed.

Discussion General findings To our knowledge, this is the first meta-analysis on the proportion of MetS and its components in individuals with MDD. We found 18 publications including 5531 subjects with clearly defined MDD, all published between 2004 and June 2013 (see Fig. 1). This finding indicates that cardiometabolic risk in people with MDD has only been a focus of attention for the past 8 years and remains somewhat overlooked compared with patients with schizophrenia (Mitchell et al. 2013b). We found that 30.5% (95% CI 26.3–35.1) of individuals with MDD had MetS. Our meta-analysis adds to the current literature by showing that the odds for MetS are 1.5 times higher for persons with MDD compared with general population controls. Regarding individual MetS criteria, about 40% of individuals with MDD had abdominal obesity or were hypertensive, about 30% had abnormal HDL-C or triglycerides, and 20% had significant prediabetes (using the > 110 mg/dl fasting glucose threshold for hyperglycemia). Importantly, persons with MDD were at a significantly increased risk for MetS, hyperglycemia and hypertriglyceridemia when compared to matched healthy controls. However, in the available studies we found no differences relative to healthy controls for waist circumference, HDL-C levels and hypertension. The observation that the presence of abnormally elevated blood pressure was not increased in MDD patients contradicts a recent meta-analysis of nine cohort studies linking depression with incident hypertension (OR 1.42, 95% CI 1.09–1.86, p = 0.009) (Meng et al. 2012). A possible reason might be that we were not able to control for antihypertensive drugs in our study and that the threshold for elevated blood pressure in MetS is lower than that for hypertension. The observation that waist circumference and HDL-C levels did not differ from

those of matched controls warrants further investigation. Identifying those who currently have, or are at high risk for, metabolic disorders is a clinical imperative. Although knowledge about factors that are associated with the highest MetS proportions can help to identify persons at greater risk, we were able to identify only one significant moderating variable. Consistent with population studies (Ford et al. 2002; Tillin et al. 2005; Hwang et al. 2006), there was no significant difference between men and women, indicating that both sexes need the same attention and care. In addition, in the current meta-analysis, age did not explain differences in prevalence estimates, indicating that the high risk for metabolic abnormalities should be a concern across the lifespan. This finding also indicates that depression and/or related biological processes may override even the strong, known risk factor of age for MetS (North & Sinclair, 2012), at least within the restricted age range of the included cohorts (mean age range 38.3– 54.3 years) and on a study level of analysis. Because of lack of sufficient data, we were unable to investigate whether illness duration had a moderating effect. It might be hypothesized that the cumulative long-term effects of poor health behaviors or of illness-related biological factors (McIntyre et al. 2007, 2009) place persons with MDD with a longer illness duration at a greater risk for cardiometabolic risk factors and disorders. The high co-occurrence between depressive symptoms and MetS suggests a possible pathophysiological overlap (Pan et al. 2012). Although the precise mechanisms mediating the pathophysiological overlap between MetS and MDD have not yet been elucidated, an elevated cortisol secretion due to hyperactivity of the HPA axis, (pro)-inflammatory processes involving interleukin-6 and C-reactive protein, oxidative stress, autonomic nervous system dysregulation including an increase in sympathetic and decrease in parasympathetic activity, and insulin resistance are all interacting biological mechanisms that may mediate the association between depression and MetS (McIntyre et al. 2007, 2009). Although biological processes might be important, background lifestyle and socio-economic factors are probably equally relevant (Patton et al. 1998; Whooley et al. 2008; McIntyre et al. 2009; Patten et al. 2009). For example, MDD increased the odds for developing hyperglycemia and hypertriglyceridemia, which could be due to depression or related changes in diet and exercise (Luppino et al. 2010) but clearly increases the risk for MetS. We also did not find a relationship between antidepressant use and MetS prevalence. However, because of the limited data available on specific antidepressants and on duration of treatment, it is premature to draw

MetS and metabolic abnormalities in MDD patients

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any firm conclusions. Previous studies have found that some antidepressants may, in some circumstances, reduce hyperglycemia (McIntyre et al. 2006; Atlantis et al. 2012; Hennings et al. 2012), normalize glucose homeostasis and also increase insulin sensitivity, whereas others, including tricyclic antidepressants, may exacerbate glycemic dyscontrol or have little effect on glucose homeostasis (Knol et al. 2008; Mojtabai, 2013). Nevertheless, we did find that use of antipsychotics in patients with MDD was a significant moderator for increased prevalence of MetS. Many groups have previously found that use of antipsychotic medication is a likely risk factor for MetS, obesity and diabetes in schizophrenia (Smith et al. 2008) and bipolar disorder (Vancampfort et al. 2013). Adjunctive treatment with antipsychotic medications has been endorsed for treatment-resistant depression by several clinical practice guidelines (Davidson, 2010) and was supported by a recent meta-analysis (Farahani & Correll, 2012). It is also clear that different antipsychotics differ in their cardiometabolic risk profile (Correll et al. 2011; De Hert et al. 2011c). It is therefore advisable for future studies to report MetS proportions in patients with depression by individual medication classes and groups, including different antidepressant and antipsychotic classes and, possibly, agents or groups of agents with similar cardiometabolic risk profiles. We note that the pooled MetS proportion of 30.5% (95% CI 26.3–35.1) using either ATP-III, ATP-III-A, IDF or WHO definitions in patients with MDD is very similar to the recently reported pooled MetS proportion of 32.5% (95% CI 30.1–35.0) across 77 studies and 25 692 patients with schizophrenia (Mitchell et al. 2013b), and lower than the 37.3% (95% CI 36.1–39.0) found across 37 studies involving 6983 bipolar disorder patients (Vancampfort et al. 2013) using the same MetS criteria. We do, however, advise caution in interpreting these data, as patients with schizophrenia and bipolar disorder are more likely to receive long-term treatment with antipsychotics that have been associated with significant adverse effects on body weight, glucose and lipid metabolism and MetS risk (Correll et al. 2011; De Hert et al. 2011c; Mitchell et al. 2013a), and as study populations were assessed with varying methodologies.

MetS and CVD risk factors. Given the mean age of our sample, this proactive monitoring should not be reserved for older depressed patients over 65 years of age. Considering the high cardiometabolic risks observed, we suggest that patients with MDD should be screened for cardiovascular risk factors at least annually (even if they have normal baseline values). In addition, we suggest that assessments in these patients should be performed at baseline and repeated at 6 and 12 weeks after initiation of any high-risk treatment, such as antipsychotic medication (De Hert et al. 2011a). An additional 6-week assessment has been endorsed in the European Psychiatric Association guideline (De Hert et al. 2009), but its advantages have not yet been tested. Given the high MetS risk observed across all treatment settings, we propose that a minimum standard of monitoring for MDD patients not treated with antipsychotics, even in those with normal baseline tests, should include blood pressure, smoking status and waist circumference or body mass index (BMI) at annual intervals. We further propose that optimal monitoring for MDD patients, and standard monitoring for those taking antipsychotics, should also include fasting lipids and hemoglobin A1C (HbA1c) and/or fasting blood glucose. HbA1c has the advantage of not requiring a fasting sample and is reported to identify a larger number of patients with early, only post-prandial hyperglycemia/pre-diabetes (Manu et al. 2012, 2013). A recent study (Mitchell et al., unpublished observations) proposes the optimal testing protocol with a HbA1c threshold 55.7% followed by conventional testing with an oral glucose tolerance test and fasting blood glucose in patients who test positive. An important second step regarding the management of MetS is treatment for any detected abnormality In addition, psychiatrists, physicians and other members of the multidisciplinary team should educate and help motivate individuals with MDD to improve their lifestyle through the use of effective behavioral interventions, including smoking cessation, dietary measures and exercise. If lifestyle interventions do not succeed, preferential use of lower-risk medication or the addition of a medication known to reduce weight and/or metabolic abnormalities should be considered (De Hert et al. 2009; Maayan et al. 2010).

Clinical implications

Limitations

Our data support the recently developed Canadian Network for Mood and Anxiety Treatments (CANMAT) recommendations (McIntyre et al. 2012) that individuals with MDD, and in particular those taking antipsychotics, should be considered a vulnerable group that should be screened proactively for

We acknowledge several limitations in the primary data and in this meta-analysis. First, there was considerable methodological heterogeneity across studies. This heterogeneity may be attributable to the differences in study design, sample size, participant characteristics, diagnostic criteria for depression and different

2024 D. Vancampfort et al. MetS definitions. To account for the heterogeneity, we chose random-effects models for the meta-analyses. Second, there was marked variation in the quantity and quality of analyzable studies, some of which had limited sample sizes, a reliance largely on crosssectional and retrospective studies, and insufficient pre-treatment information on MetS in enrolled participants. However, to reduce heterogeneity and increase the generalizability of the analyzed studies, we excluded those with less than 50 participants. Third, moderator variables were not always reported, reducing the power for these analyses. In particular, ethnicity, duration of treatment and lifestyle behaviors, all relevant variables for cardiometabolic health, were recorded insufficiently, precluding the meta-analytic assessment of these factors as moderating or mediating variables. Fourth, the included patients were predominantly in-patients, which limits the generalizability of our findings. Fifth, as the data were too limited regarding individual prescribed antidepressants and antipsychotics, we were not able to assess the risk-moderating effects of specific medications. Nevertheless, despite these limitations, this is the largest study of MetS proportions and its moderators in MDD and the first formal meta-analysis of this important topic. Future research Variables, such as clinical subtypes of MDD and concomitant or previous use of antidepressants, mood stabilizers, such as lithium and valproate, or antipsychotics, were not reported or were insufficiently reported or controlled for in most available studies. Future studies should examine in more detail whether different clinical subtypes of depression (i.e. melancholic, atypical or undifferentiated MDD) are at equal risk for developing MetS. Very recent data from the Netherlands Study of Depression and Anxiety (NESDA) sample (Lamers et al. 2013) did demonstrate that persons with atypical depression had significantly higher levels of inflammatory markers, BMI, waist circumference and triglycerides, and lower HDL-C than persons with melancholic depression. In the same way, future studies should investigate to what extent risk for MetS in drug-naive and untreated persons with MDD is lower than in those treated with specific pharmacological regimens. Future studies should also examine whether there is an underlying genetic risk for the development of metabolic abnormalities after pharmacotherapy initiation. Examining whether cardiometabolic outcomes are moderated by genetic factors, but also by clinical characteristics, or are mediated by individual treatments should become a clinical research priority. Interventions that target the individual MetS components should also be evaluated.

Furthermore, future research should undertake a comprehensive assessment of MetS risk factors following, at the very least, recommended monitoring guidelines and evaluate the optimal monitoring regimen and interventions in patients treated with antidepressants. To date, audits (De Hert et al. 2011d) of metabolic monitoring conducted in patients with bipolar disorder and schizophrenia show that most patients do not receive adequate medical surveillance. Lastly, prospective studies are needed to investigate the direct relationship between individual medications and MetS, and long-term follow-up studies are required to accurately document the emergence of some more distal outcomes, such as ischemic heart disease. Conclusions Our meta-analysis has clearly demonstrated that MetS, a significant constellation of risk factors for cardiovascular illness, is highly prevalent in individuals with MDD. Those taking antipsychotics should be considered as a particularly vulnerable population. We recommend that treating mental health professionals, general practitioners and medical specialists should be responsible for giving preventive and proactive lifestyle advice, implementing the necessary screening assessments, and orchestrating or conducting the appropriate treatment of clinically relevant, abnormal findings. Supplementary material For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291713002778. Acknowledgments We are very grateful to the following authors for sending us additional data or information: Dr K. Blank, Memory Disorders Center, Braceland Center for Mental Health and Aging Institute of Living, Hartford Hospital, Hartford, CT, USA; Dr F. Bonnet, Department of Medicine, University of Lyon, and Centre for Research in Human Nutrition, Hôpital Edouard Herriot, Lyon, France; Dr J. A. Dunbar and B. Philpot, Greater Green Triangle University Department of Rural Health, Flinders University and Deakin University, Warrnambool, Victoria, Australia; Dr S. Grover, Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India; Dr T. A. Hartley, Biostatistics and Epidemiology Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA and Department of Community Medicine, School of Medicine, West

MetS and metabolic abnormalities in MDD patients Virginia University, Morgantown, WV, USA; Dr P. Ifteni, Department of Medicine, Transilvania University, Brasov, Romania; Dr R. Kobrosly, Department of Community and Preventive Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; Dr F. Lamers, Department of Psychiatry, VU University Medical Center Amsterdam, The Netherlands; Dr P. D. Loprinzi, Department of Exercise Science, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, KY, USA; Dr T. Partonen, National Institute for Health and Welfare, Helsinki, Finland; Dr J. Miettola, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; and Dr F. Thomas-Jean, Centre Investigations Préventives et Cliniques (IPC), Paris, France.

Declaration of Interest C. U. Correll has been a consultant and/or advisor to or has received honoraria from: Actelion, Alexza, American Academy of Child and Adolescent Psychiatry, AstraZeneca, Biotis, Bristol-Myers Squibb, Cephalon, Desitin, Eli Lilly, GersonLehrman Group, GSK, IntraCellular Therapies, Lundbeck, Medavante, Medscape, Merck, National Institute of Mental Health (NIMH), Novartis, Ortho-McNeill/Janssen/J&J, Otsuka, Pfizer, ProPhase, Sunovion, Takeda, and Teva. He has received grant support from BMS, Feinstein Institute for Medical Research, Janssen/J&J, NIMH, National Alliance for Research in Schizophrenia and Depression (NARSAD), and Otsuka. P. Sienaert has been on the speakers/advisory boards of AstraZeneca, Lundbeck JA, Janssen-Cilag, Eli Lilly, Servier, Glaxo-SmithKline, and Bristol-Myers Squibb. A. De Herdt reports no financial relationships with commercial interests. M. De Hert has been a consultant for, received grant/ research support and honoraria from, and has been on the speakers/advisory boards of: AstraZeneca, Lundbeck JA, Janssen-Cilag, European Diabetes Foundation/Lilly, Otsuka, Pfizer, Sanofi-Aventis, Bristol-Myers Squibb, and Takeda.

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