• KAFM
  • Contact us
  • E-Submission
ABOUT
ARTICLE CATEGORY
BROWSE ARTICLES
AUTHOR INFORMATION

Articles

Original Article

Antidepressant Use and Diabetes Mellitus Risk: A Meta-Analysis

Korean Journal of Family Medicine 2013;34(4):228-240.
Published online: July 24, 2013

Department of Family Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

Corresponding Author: Sang Min Park. Tel: +82-2-2072-3331, Fax: +82-2-2072-3276, smpark.snuh@gmail.com
Jae Moon Yoon and Eun-Geol Cho have contributed equally as the first author(s) for this article.
• Received: August 28, 2012   • Accepted: June 7, 2013

Copyright © 2013 The Korean Academy of Family Medicine

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • 6,854 Views
  • 57 Download
  • 51 Crossref
  • 63 Scopus
prev next
  • Background
    Epidemiologic studies have reported inconsistent findings regarding the association between the use of antidepressants and type 2 diabetes mellitus (DM) risk. We performed a meta-analysis to systematically assess the association between antidepressants and type 2 DM risk.
  • Methods
    We searched MEDLINE (PubMed), EMBASE, and the Cochrane Library (through Dec 31, 2011), including references of qualifying articles. Studies concerning the use of tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), or other antidepressants and the associated risk of diabetes mellitus were included.
  • Results
    Out of 2,934 screened articles, 3 case-control studies, 9 cohort studies, and no clinical trials were included in the final analyses. When all studies were pooled, use of antidepressants was significantly associated with an increased risk of DM in a random effect model (relative risk [RR], 1.49; 95% confidence interval [CI], 1.29 to 1.71). In subgroup analyses, the risk of DM increased among both SSRI users (RR, 1.35; 95% CI, 1.15 to 1.58) and TCA users (RR, 1.57; 95% CI, 1.26 to 1.96). The subgroup analyses were consistent with overall results regardless of study type, information source, country, duration of medication, or study quality. The subgroup results considering body weight, depression severity, and physical activity also showed a positive association (RR, 1.14; 95% CI, 1.01 to 1.28). A publication bias was observed in the selected studies (Egger's test, P for bias = 0.09).
  • Conclusion
    Our results suggest that the use of antidepressants is associated with an increased risk of DM.
Antidepressants are now one of the most frequently prescribed medications in outpatient medicine.1) They are used widely not only for treating depression but also for controlling fibromyalgia2) and postmenopausal problems.3) As use of antidepressants increases, so does interest in their potential side effects. It has been reported that tricyclic antidepressants can cause weight-gain4) and cardio-toxic effects when taken in overdose.5) Recently, it has been suggested that the use of tricyclic antidepressants and selective serotonin reuptake inhibitors (SSRIs) may increase the risk of mortality, and SSRIs the risk of hemorrhagic and fatal stroke.6) Furthermore, recent reports suggest that antidepressants may be associated with an increased risk of diabetes mellitus (DM).7)
There is controversy regarding the relationship between the use of antidepressants and the risk of DM. Some studies have found an increased risk of DM among antidepressant drug users,8,9) while others found no firm evidence.10,11) There is also disagreement regarding the reason for the association between the use of antidepressants and DM risk. Some studies propose that antidepressants may bio-pharmacologically affect glucose homeostasis and insulin sensitivity.12,13) On the other hand, it has been hypothesized that our understanding of the relationship between antidepressants and DM is confounded by depression, which has long been recognized to increase the incidence of DM.14) Therefore, in the present study, we aimed to investigate the association between the use of antidepressants and the risk of DM via a meta-analysis of cohort studies, case-control studies and randomized clinical trials (RCT).
1. Data Sources and Searches
Our review followed the Meta-analysis of Observational Studies in Epidemiology guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.15) We performed our search in MEDLINE (PubMed) (inception to Dec 31, 2011), EMBASE (inception to Dec 31, 2011), and the Cochrane Library (inception to Dec 31, 2011) by using selected common key words regarding antidepressants and diabetes mellitus in case-control studies, cohort studies, and RCTs.
In addition, we searched the bibliographies of relevant articles in order to identify additional studies of interest. As the keywords for the literature search, we used 'antidepressants' OR 'antidepressive agents' OR 'antidepressive drugs' OR 'antidepressive medications' OR 'selective serotonin reuptake inhibitors' OR 'SSRIs' OR 'tricyclic antidepressants' OR 'TCAs' for the exposure factors and 'diabetes' OR 'iabetes mellitus' OR 'DM' for the outcome factors.
2. Study Selection and Data Extraction
We searched case-control studies, cohort studies and RCTs reporting an association between antidepressive drugs and diabetes mellitus risk. Included studies had to contain both of the following: a risk estimate (odds ratio, relative risk, or hazard ratio) and its 95% confidence interval (CI). We only selected articles written in English and excluded those studies with no available data for outcome measures.
All studies retrieved from databases and bibliographies were independently reviewed by two authors, and disagreements were resolved by authors' consensus. Of the articles found in the three databases, duplicate articles and those that did not meet the selection criteria were excluded. We extracted the following data from the remaining studies: study name (first author), year of publication, country and design, study period, population characteristics, and type of antidepressants. Adjustment variables were also collected during data extraction. We obtained adjusted estimates with priority rather than those unadjusted.
3. Quality Assessment
We assessed the methodological quality of included studies using the Newcastle-Ottawa Scale (NOS) for quality of case control and cohort studies in meta-analyses.16) The NOS is quite comprehensive and has been partially validated for assessing the quality of non-randomized studies in meta-analysis. The NOS is judged on three broad subscales: the selection of the study groups (4 items), the comparability of the groups (1 item), and the ascertainment of the exposure or outcome of interest for case-control or cohort studies, respectively (3 items). A 'star system' (range, 0 to 9) has been developed for assessment. In the current study, we considered a study awarded 8 or more stars as a high-quality study, as standard criteria have not been established.
4. Statistical Analysis
The outcome of the meta-analysis was the risk for diabetes mellitus. We also conducted subgroup analysis by type of study design (case-controls studies, cohort studies), type of antidepressants (SSRIs, tricyclic antidepressants [TCAs]), duration of antidepressant use (within 12 months, greater than 12 months), source of drug information (self-report, database), country (USA, Europe), adjustment of dependent variables (body mass index [BMI], physical activity, depression symptoms) and study quality (high, low). We also performed subgroup analyses about a specific antidepressant if results of the individual antidepressant were reported by two or more studies (e.g., citalopram, paroxetine, trazodone).
We pooled the estimates with a 95% CI based on both fixed-effects and random-effects models. Heterogeneity was assessed by using Higgins I2 value, which measures the percentage of total variance across studies that is attributable to heterogeneity rather than chance.17) Negative I2 values are set at zero so that I2 falls between 0% (no observed heterogeneity) and 100% (maximal heterogeneity). We considered an I2 value greater than 50% to represent substantial heterogeneity and calculated based on the random-effects model.
We used the Woolf method (inverse variance method) for a fixed-effect analysis18) and the DerSimonian and Laird method for a random-effect analysis.19) Begg's funnel plot and Egger's test were used to identify publication bias. For studies with publication bias, the funnel plot was asymmetrical or the P-value was found to be less than 0.05 using Egger's test. We used Stata SE ver. 12.1 (Stata Co., College Station, TX, USA) for all statistical analysis.
1. Identification of Relevant Studies
Figure 1 shows a flow diagram of the study selection. A total of 2,934 articles were identified by searching the three databases and relevant bibliographies. Through review of titles and abstracts, we excluded 293 duplicate articles and 2,615 articles that did not satisfy the selection criteria. After the full text for the remaining 26 articles was reviewed, 14 articles were excluded, 8 demonstrated insufficient data,20-27) 3 were reviews or correspondences,28-30) and 3 were included totally or partially in another article.31-33) As a result, we included 12 observational studies (3 case-control studies, 9 cohort studies, no RCTs), which ultimately met our inclusion criteria.
2. Study Characteristics and Quality
Table 1 shows the main characteristics of the 12 reviewed studies. All studies were published in the 2000s. The countries in which the studies had been conducted were as follows: the United States (n = 6),7,8,14,33-35) Netherlands (n = 1),11) the UK (n = 1),36) Finland (n = 1),9) Norway (n = 1),10) Australia (n = 1),37) and multiple countries (n = 1).13) We identified 15 eligible estimates from 3 nested case control articles,9,13,36) 6 retrospective cohort studies,8,10,11,14,33,35) and 3 prospective cohort studies.7,34,37) Ten studies included both SSRIs and TCAs as antidepressants. Only one study was performed in the young.8) The mean value for the methodological quality of the included 12 studies using the NOS was 7.9 stars.
3. Overall Risk of DM by Using Antidepressants
As seen in Table 2, the use of antidepressants was significantly associated with an increased risk of DM in overall studies when using both a fixed-effect model (RR, 1.31; 95% CI, 1.26 to 1.37) and random-effect model (RR, 1.49; 95% CI, 1.29 to 1.71). And the overall heterogeneity of the studies was high (I2 = 85.8%). Figure 2 shows the association between the use of antidepressants and DM risk using a random-effect model.
4. Subgroup Meta-Analyses
As shown in Table 2, SSRI use was associated with an increased risk of DM8-10,33,35,36) (RR, 1.35; 95% CI, 1.15 to 1.58; n = 8; I2 = 75.5%) and TCA use was also associated with an increased risk of DM9,33,35,36) (RR, 1.57; 95% CI, 1.26 to 1.96; n = 6; I2 = 72.3%) when using the random-effect model. There are only three types of antidepressants (paroxetine, citalopram, and trazodone) which were estimated individually. However, all of them failed to show statistical significance in the random-effect model.
In the included studies, the major country was the USA. But the elevated risk of DM in the USA7,8,14,33-35) (RR, 1.50; 95% CI, 1.25 to 1.80) was similar to that in other countries9,10,13,23,36,37) (RR, 1.48; 95% CI, 1.18 to 1.85). According to type of study, the pooled estimate of cohort studies was slightly lower than that of case-control studies. The subgroup analyses by source of drug information were consistent with overall results.
Regarding the duration of medication, the risk of DM in the subgroups over 1 year of use7,9,13,36) (RR, 1.61; 95% CI, 1.30 to 1.99) was relatively higher than within 1 year of use13,36) (RR, 1.26; 95% CI, 1.01 to 1.57).
In a subgroup analysis of studies controlling specific risk factors, the associations between antidepressant use and risk of DM were consistent with the overall results. However, a pooled estimate of studies controlling physical activity7,10,14,33,37) (RR, 1.18; 95% CI, 1.09 to 1.27) was attenuated comparing the overall result in the random effect model.
When we grouped studies by quality, both subgroups showed significantly increased risk of DM associated with the use of antidepressants. The pooled risk ratio of high quality studies7-9,13,14,33-37) was particularly higher than overall results (RR, 1.67; 95% CI, 1.39 to 2.01).
5. Publication Bias
A publication bias was observed in the selected studies (Egger's test, P for bias = 0.09) (Figure 3).
Our meta-analysis suggests that the use of antidepressants is associated with an increased risk of DM. This finding is consistently observed in subgroup analyses by type of antidepressants (TCA, SSRI), study design, country and source of drug information. Generally, TCAs are known to increase the risk of cardiovascular disease as an adverse effect.5) Relatively, SSRIs were thought to have fewer side effects, less toxicity and be more safe to use.38) Thus, treatment with SSRIs has been increased to exceed the use of TCAs.39,40) Given the widespread use of anti-depressants, the implications of this increased risk are serious.
Several possible explanations exist for the association between the use of antidepressants and risk of DM. First, some anti-depressants may cause weight gain, and increased body weight may increase the risk of DM. Among antidepressants, TCA treatments are well known to be associated with weight gain41,42) through antihistaminergic effects.41,42) The association between SSRI treatment and weight change is complex. Some randomized controlled trials suggested that there were differences in weight increase caused by individual SSRIs.43) Paroxetine users reported an increase in body weight.43) From the results of subgroup analyses by individual antidepressants, we might explain these results as follows. Although there was no statistical significance, paroxetine showed a slightly higher risk ratio than citalopram, which was not reported to cause weight gain.41) But increased body weight does not fully explain the association between antidepressants and risk of DM. Although there was no study adjusting for change in body weight, the relationship between antidepressants and risk of DM in subgroup analysis by studies controlling basal BMI was positive. These findings suggest that there are additional mechanisms beyond weight gain which explain the association between antidepressants and DM.
Second, depression may act as a confounding variable in the relationship between antidepressant and risk of DM. It has been widely known that depression and DM were related to each other and increased risk reciprocally.44,45) Antidepressant users might have severe underlying depression that needs to be treated. Thus, the possible effect of depression on the relationship between antidepressant treatment and risk of DM should be considered. To examine this, some studies reported their results with adjustment of depressive symptom severity via variant depression scales. In subgroup analysis of these studies, our findings suggest that antidepressant drug treatment itself and not the depression increase the risk of DM.
Additionally, some explanations exist for the association between antidepressants and DM as the reflection of the depression working as a confounding factor. Antidepressant users might have underlying depression, and depressive patients might have an unfavorable lifestyle.26) Their lifestyle factors might make depressive patients predisposed to DM.46) A DM-predisposing lifestyle included low physical activity and a poor diet rich in carbohydrates and saturated fat. In the subgroup meta-analysis with adjustment for physical activity, the elevated risk of DM was lower than overall results. Unfortunately, other lifestyle factors such as diet or behavior were not considered in previous studies. As a result, we assumed that physical activity level, at least, would have an influence on the risk of DM in antidepressant use.
The results, considering all of the above (body weight, depression severity, and physical activity), were still positive. Moreover, the subgroup analyses were consistently positive regardless of study type, information source, country, and study quality. Consequently, it is not easy to deny the hypothesis that antidepressants themselves affect the risk of DM. This hypothesis is consistent with the result that the risk ratio in the subgroup with longer duration, one year or more, was higher than the subgroup with shorter duration, less than one year. This may reflect a mechanism in which antidepressants increase DM risk by their own neuroendocrine traits. It is assumed that antidepressants affect the hypothalamic-pituitary-adrenal axis, which results in an increase of plasma cortisol level and insulin resistance.22,47) Some SSRIs might work as inhibitors of insulin signaling and cellular insulin resistance by activation of insulin receptor substrate 1 kinases.48) TCAs have binding properties for the 5-HT2c receptor, H1 receptor and norepinephrine (NE) reuptake transporter. Inhibition of the NE reuptake transporter increases synaptic NE disposal directly by promoting gluconeogenesis and glycogenolysis.49) Some TCAs have high affinity for the M3-, α-1-adrenergic receptors. Blockage of M3 receptors in beta cells suppresses insulin secretion and induces hyperglycemia.50)
Lastly, only one study in the young reported similar results. Therefore, it is necessary to consider the risk of DM in young depressive patients and to perform further studies in the young of age.
Our meta-analysis has several limitations. First, all studies included in our meta-analysis were observational studies, and there seems to be publication bias surrounding this issue. To reduce publication bias, we performed a subsequent search for all relevant studies without any language restrictions, however, none of them met the inclusion criteria. Therefore, the uncertainty about these issues still remains until well-designed RCTs are performed. Secondly, almost all the included studies were from the USA or Europe. The only study which was performed globally did not describe the results by race. Therefore Asian or other racial studies should be performed for generalization of the results. Finally, the included studies were heterogeneous methodologically, and did not contain enough information about medication dosage, health behaviors, or other interventions which might be associated with DM risk. To determine optimal dosage without increasing risk of DM, dosage information will be helpful.
Our results suggest that the use of antidepressants is associated with an increased risk of DM. Physicians who prescribe antidepressants should consider carefully possible adverse effects in their patients, especially those who are already at risk of DM. Further studies are needed specifically to test the effect of individual antidepressants including newly developed antidepressants such as SNRI or norepinephrine-dopamine reuptake inhibitor on DM risk.
This work was supported by The Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology, No. 2012-0003761.

No potential conflict of interest relevant to this article was reported.

  • 1. Niska R, Bhuiya F, Xu J. National Hospital Ambulatory Medical Care Survey: 2007 emergency department summary. Natl Health Stat Report 2010;(26):1-31. PMID: 20726217.
  • 2. Moret C, Briley M. Antidepressants in the treatment of fibromyalgia. Neuropsychiatr Dis Treat 2006;2:537-548. PMID: 19412502.
  • 3. Grady D. Clinical practice: management of menopausal symptoms. N Engl J Med 2006;355:2338-2347. PMID: 17135587.
  • 4. Zimmermann U, Kraus T, Himmerich H, Schuld A, Pollmacher T. Epidemiology, implications and mechanisms underlying drug-induced weight gain in psychiatric patients. J Psychiatr Res 2003;37:193-220. PMID: 12650740.
  • 5. Blaber MS, Khan JN, Brebner JA, McColm R. "Lipid rescue" for tricyclic antidepressant cardiotoxicity. J Emerg Med 2012;43:465-467. PMID: 22244291.
  • 6. Smoller JW, Allison M, Cochrane BB, Curb JD, Perlis RH, Robinson JG, et al. Antidepressant use and risk of incident cardiovascular morbidity and mortality among postmenopausal women in the Women's Health Initiative study. Arch Intern Med 2009;169:2128-2139. PMID: 20008698.
  • 7. Ma Y, Balasubramanian R, Pagoto SL, Schneider KL, Culver AL, Olendzki B, et al. Elevated depressive symptoms, antidepressant use, and diabetes in a large multiethnic national sample of postmenopausal women. Diabetes Care 2011;34:2390-2392. PMID: 21911776.
  • 8. Jerrell JM. Neuroendocrine-related adverse events associated with antidepressant treatment in children and adolescents. CNS Neurosci Ther 2010;16:83-90. PMID: 19769598.
  • 9. Kivimaki M, Hamer M, Batty GD, Geddes JR, Tabak AG, Pentti J, et al. Antidepressant medication use, weight gain, and risk of type 2 diabetes: a population-based study. Diabetes Care 2010;33:2611-2616. PMID: 20823343.
  • 10. Raeder MB, Bjelland I, Emil Vollset S, Steen VM. Obesity, dyslipidemia, and diabetes with selective serotonin reuptake inhibitors: the Hordaland Health Study. J Clin Psychiatry 2006;67:1974-1982. PMID: 17194277.
  • 11. Knol MJ, Geerlings MI, Egberts AC, Gorter KJ, Grobbee DE, Heerdink ER. No increased incidence of diabetes in antidepressant users. Int Clin Psychopharmacol 2007;22:382-386. PMID: 17917558.
  • 12. McIntyre RS, Soczynska JK, Konarski JZ, Kennedy SH. The effect of antidepressants on glucose homeostasis and insulin sensitivity: synthesis and mechanisms. Expert Opin Drug Saf 2006;5:157-168. PMID: 16370964.
  • 13. Derijks HJ, Meyboom RH, Heerdink ER, De Koning FH, Janknegt R, Lindquist M, et al. The association between antidepressant use and disturbances in glucose homeostasis: evidence from spontaneous reports. Eur J Clin Pharmacol 2008;64:531-538. PMID: 18196226.
  • 14. Wilkins TL, Sambamoorthi U. Antidepressant use, depression, lifestyle factors, and new-onset diabetes. Int Clin Psychopharmacol 2011;26:159-168. PMID: 21471774.
  • 15. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008-2012. PMID: 10789670.
  • 16. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses In: Proceedings of the 3rd symposium on systematic reviews: beyond the basics; 2000 Jul 3-5; Oxford, UK. Oxford, Centre for Statistics in Medicine. 2000.
  • 17. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-560. PMID: 12958120.
  • 18. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. In: Buck C, Liopis A, Najera E, Terris M, editors. The challenge of epidemiology: issues and selected readings. Washington (DC): Pan American Health Organization; 2004. p. 533-553.
  • 19. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-188. PMID: 3802833.
  • 20. Brown LC, Majumdar SR, Johnson JA. Type of antidepressant therapy and risk of type 2 diabetes in people with depression. Diabetes Res Clin Pract 2008;79:61-67. PMID: 17714823.
  • 21. Campayo A, de Jonge P, Roy JF, Saz P, de la Camara C, Quintanilla MA, et al. Depressive disorder and incident diabetes mellitus: the effect of characteristics of depression. Am J Psychiatry 2010;167:580-588. PMID: 20123914.
  • 22. Carvalho F, Barros D, Silva J, Rezende E, Soares M, Fregoneze J, et al. Hyperglycemia induced by acute central fluoxetine administration: role of the central CRH system and 5-HT3 receptors. Neuropeptides 2004;38:98-105. PMID: 15223272.
  • 23. Knol MJ, Derijks HJ, Geerlings MI, Heerdink ER, Souverein PC, Gorter KJ, et al. Influence of antidepressants on glycaemic control in patients with diabetes mellitus. Pharmacoepidemiol Drug Saf 2008;17:577-586. PMID: 18449949.
  • 24. Manderbacka K, Sund R, Koski S, Keskimaki I, Elovainio M. Diabetes and depression? Secular trends in the use of antidepressants among persons with diabetes in Finland in 1997-2007. Pharmacoepidemiol Drug Saf 2010 11 11 [Epub]
  • 25. Shehatah A, Rabie MA, Al-Shahry A. Prevalence and correlates of depressive disorders in elderly with type 2 diabetes in primary health care settings. J Affect Disord 2010;123:197-201. PMID: 19804911.
  • 26. van Reedt Dortland AK, Giltay EJ, van Veen T, van Pelt J, Zitman FG, Penninx BW. Associations between serum lipids and major depressive disorder: results from the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry 2010;71:729-736. PMID: 20021996.
  • 27. van Reedt Dortland AK, Giltay EJ, van Veen T, Zitman FG, Penninx BW. Metabolic syndrome abnormalities are associated with severity of anxiety and depression and with tricyclic antidepressant use. Acta Psychiatr Scand 2010;122:30-39. PMID: 20456284.
  • 28. Antai-Otong D. The art of prescribing. Risks and benefits of non-benzodiazepine receptor agonists in the treatment of acute primary insomnia in older adults. Perspect Psychiatr Care 2006;42:196-200. PMID: 16916422.
  • 29. Jindal RD. Long-term antidepressant use and risk for diabetes: cause for concern and optimism. Am J Psychiatry 2009;166:1065-1066. PMID: 19723798.
  • 30. Zaharan NL, Bennett K. Antidepressants and new-onset diabetes in the irish primary care population. Pharmacoepidemiol Drug Saf 2010;19:657-658.
  • 31. Rubin RR, Knowler WC, Ma Y, Marrero DG, Edelstein SL, Walker EA, et al. Depression symptoms and antidepressant medicine use in Diabetes Prevention Program participants. Diabetes Care 2005;28:830-837. PMID: 15793181.
  • 32. Rubin RR, Gaussoin SA, Peyrot M, DiLillo V, Miller K, Wadden TA, et al. Cardiovascular disease risk factors, depression symptoms and antidepressant medicine use in the Look AHEAD (Action for Health in Diabetes) clinical trial of weight loss in diabetes. Diabetologia 2010;53:1581-1589. PMID: 20422396.
  • 33. Pan A, Sun Q, Okereke OI, Rexrode KM, Rubin RR, Lucas M, et al. Use of antidepressant medication and risk of type 2 diabetes: results from three cohorts of US adults. Diabetologia 2012;55:63-72. PMID: 21811871.
  • 34. Rubin RR, Ma Y, Marrero DG, Peyrot M, Barrett-Connor EL, Kahn SE, et al. Elevated depression symptoms, antidepressant medicine use, and risk of developing diabetes during the diabetes prevention program. Diabetes Care 2008;31:420-426. PMID: 18071002.
  • 35. Khoza S, Barner JC, Bohman TM, Rascati K, Lawson K, Wilson JP. Use of antidepressant agents and the risk of type 2 diabetes. Eur J Clin Pharmacol 2012;68:1295-1302. PMID: 22120432.
  • 36. Andersohn F, Schade R, Suissa S, Garbe E. Long-term use of antidepressants for depressive disorders and the risk of diabetes mellitus. Am J Psychiatry 2009;166:591-598. PMID: 19339356.
  • 37. Atlantis E, Browning C, Sims J, Kendig H. Diabetes incidence associated with depression and antidepressants in the Melbourne Longitudinal Studies on Healthy Ageing (MELSHA). Int J Geriatr Psychiatry 2010;25:688-696. PMID: 19806604.
  • 38. Geddes JR, Freemantle N, Mason J, Eccles MP, Boynton J. WITHDRAWN: Selective serotonin reuptake inhibitors (SSRIs) versus other antidepressants for depression. Cochrane Database Syst Rev 2007;(3):CD001851PMID: 17636689.
  • 39. Sclar DA, Robinson LM, Skaer TL, Galin RS. Trends in the prescribing of antidepressant pharmacotherapy: office-based visits, 1990-1995. Clin Ther 1998;20:871-884. PMID: 9737843.
  • 40. Olfson M, Klerman GL. Trends in the prescription of antidepressants by office-based psychiatrists. Am J Psychiatry 1993;150:571-577. PMID: 8465872.
  • 41. Fava M. Weight gain and antidepressants. J Clin Psychiatry 2000;61(Suppl 11):37-41. PMID: 10926053.
  • 42. Mann JJ. The medical management of depression. N Engl J Med 2005;353:1819-1834. PMID: 16251538.
  • 43. Fava M, Judge R, Hoog SL, Nilsson ME, Koke SC. Fluoxetine versus sertraline and paroxetine in major depressive disorder: changes in weight with long-term treatment. J Clin Psychiatry 2000;61:863-867. PMID: 11105740.
  • 44. Knol MJ, Twisk JW, Beekman AT, Heine RJ, Snoek FJ, Pouwer F. Depression as a risk factor for the onset of type 2 diabetes mellitus: a meta-analysis. Diabetologia 2006;49:837-845. PMID: 16520921.
  • 45. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care 2008;31:2383-2390. PMID: 19033418.
  • 46. Bonnet F, Irving K, Terra JL, Nony P, Berthezene F, Moulin P. Anxiety and depression are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis 2005;178:339-344. PMID: 15694943.
  • 47. Sugimoto Y, Inoue K, Yamada J. Involvement of serotonin in zimelidine-induced hyperglycemia in mice. Biol Pharm Bull 1999;22:1240-1241. PMID: 10598036.
  • 48. Levkovitz Y, Ben-Shushan G, Hershkovitz A, Isaac R, Gil-Ad I, Shvartsman D, et al. Antidepressants induce cellular insulin resistance by activation of IRS-1 kinases. Mol Cell Neurosci 2007;36:305-312. PMID: 17728140.
  • 49. Larsen P, Kronenberg M, Melmed S, Polonsky K. Wiliams textbook of endocrinology. 10th ed. Philadelphia: Saunders; 2003.
  • 50. Gilon P, Henquin JC. Mechanisms and physiological significance of the cholinergic control of pancreatic beta-cell function. Endocr Rev 2001;22:565-604. PMID: 11588141.
Figure 1
Flow diagram of selecting studies for inclusion in meta-analysis.
kjfm-34-228-g001.jpg
Figure 2
Meta-analyses and pooled relative risk (RR) of diabetes mellitus in antidepressant use comparing not in use. Weights are from random effects analysis. CI: confidence interval, HPFS: Health Professionals Follow-up Study, NHS: Nurses' Health Study, PLB: protective lifestyle behavior, ILS: intensive lifestyle intervention.
kjfm-34-228-g002.jpg
Figure 3
Funnel plots for publication bias. RR: relative risk.
kjfm-34-228-g003.jpg
Table 1
Characteristics of studies included in the final analysis of antidepressants and risk of diabetes mellitus

NOS: Newcastle-Ottawa Quality Assessment Scale, HPFS: Health Professionals Follow-up Study, RCS: Retrospective Cohort Study, SSRIs: selective serotonin reuptake inhibitors, TCAs: tricyclic antidepressants, FHx: family history, DM: diabetes mellitus, BMI: body mass index, NHS: Nurses' Health Study, SNRI: serotonin and norepinephrine reuptake inhibitor, PCS: Prospective Cohort Study, NCC: Nested Case-Control study, MAOI: monoamine oxidase inhibitor, PLB: protective lifestyle behavior, ILS: intensive lifestyle intervention, FBS: fasting blood glucose, NA: not available.

*Standard lifestyle group. Intensive lifestyle group.

kjfm-34-228-i001.jpg
Table 2
Overall and subgroup analyses for use of antidepressants and risk of diabetes mellitus

RR: relative risk, CI: confidence interval, SSRI: selective serotonin reuptake inhibitor, TCA: tricyclic antidepressant.

*Subgroup of studies including following risk factor as adjustment variable. Subgroup of studies including depression severity, body mass index, and physical activity as adjustment variable.

kjfm-34-228-i002.jpg

Figure & Data

References

    Citations

    Citations to this article as recorded by  
    • Incident diabetes in adolescents using antidepressant: a systematic review and meta-analysis
      Fatemeh Movahed, Ehsan Heidari, Dina Sadeghi, Aida Rezaei Nejad, Romina Abyaneh, Mehrshad Zarei, Farzan Beigi, Abolfazl Abdollahi, Arman Shafiee
      European Child & Adolescent Psychiatry.2025; 34(2): 599.     CrossRef
    • Comorbidity of Depression and Diabetes: A Literature Review on Systemic Flaws in Healthcare and the Benefits of Collaborative Diagnosis and Treatment in Primary Care Settings
      Pranay Wal, Pankaj Kumar, Harsh Bhardwaj, Komal Sharma, Arpan Kumar Tripathi, Arpit Gupta, Ankita Wal, Mukesh Chandra Sharma
      Current Diabetes Reviews.2025;[Epub]     CrossRef
    • Association between antidepressants and the risk of diabetic foot ulcers and amputation in antidepressant-naïve type 2 diabetes mellitus patients: A nested case-control study
      Jinhyun Kim, Kyungduk Hurh, Seokmoon Han, Hyunkyu Kim, Eun-Cheol Park, Suk-Yong Jang
      Diabetes Research and Clinical Practice.2024; 209: 111591.     CrossRef
    • Antidepressants and type 2 diabetes: highways to knowns and unknowns
      Nahi Sabih Alruwaili, Hayder M. Al-Kuraishy, Ali I. Al-Gareeb, Ali K. Albuhadily, Amany E. Ragab, Ahmad Awad Alenazi, Athanasios Alexiou, Marios Papadakis, Gaber El-Saber Batiha
      Diabetology & Metabolic Syndrome.2023;[Epub]     CrossRef
    • Non-drug interventions of traditional Chinese medicine in preventing type 2 diabetes: a review
      Jingying Liu, Chun Yao, Yitao Wang, Jinmin Zhao, Hua Luo
      Chinese Medicine.2023;[Epub]     CrossRef
    • Diabetes and mood disorders: shared mechanisms and therapeutic opportunities
      Laís Bhering Martins, Jenneffer Rayane Braga Tibães, Michael Berk, Antonio Lucio Teixeira
      International Journal of Psychiatry in Clinical Practice.2022; 26(2): 183.     CrossRef
    • Fluoxetine‐induced hepatic lipid accumulation is mediated by prostaglandin endoperoxide synthase 1 and is linked to elevated 15‐deoxy‐Δ12,14PGJ2
      Ahmed Ayyash, Alison C. Holloway
      Journal of Applied Toxicology.2022; 42(6): 1004.     CrossRef
    • The association between birth weight, ponderal index, psychotropic medication, and type 2 diabetes in individuals with severe mental illness
      Marie Kim Wium-Andersen, Terese Sara Høj Jørgensen, Martin Balslev Jørgensen, Jørgen Rungby, Carsten Hjorthøj, Holger J. Sørensen, Merete Osler
      Journal of Diabetes and its Complications.2022; 36(5): 108181.     CrossRef
    • Selective serotonin reuptake inhibitors and the risk of type 2 diabetes mellitus in youths
      Thi Xuan Dai Cao, Christopher Filliter, François Montastruc, Oriana Hoi Yun Yu, Emma Fergusson, Soham Rej, Laurent Azoulay, Christel Renoux
      Journal of Affective Disorders.2022; 318: 231.     CrossRef
    • Interventions for preventing type 2 diabetes in adults with mental disorders in low- and middle-income countries
      Masuma Pervin Mishu, Eleonora Uphoff, Faiza Aslam, Sharad Philip, Judy Wright, Nilesh Tirbhowan, Ramzi A Ajjan, Zunayed Al Azdi, Brendon Stubbs, Rachel Churchill, Najma Siddiqi
      Cochrane Database of Systematic Reviews.2021;[Epub]     CrossRef
    • Antidepressants use and the risk of type 2 diabetes mellitus: A systematic review and meta-analysis
      Yuqing Wang, Debiao Liu, Xuezhi Li, Yan Liu, Yili Wu
      Journal of Affective Disorders.2021; 287: 41.     CrossRef
    • Psychiatric disorders as risk factors for type 2 diabetes: An umbrella review of systematic reviews with and without meta-analyses
      Nanna Lindekilde, Femke Rutters, Jan Erik Henriksen, Mathias Lasgaard, Miranda T. Schram, Katrine Hass Rubin, Mika Kivimäki, Giesje Nefs, Frans Pouwer
      Diabetes Research and Clinical Practice.2021; 176: 108855.     CrossRef
    • Prevalence, awareness, treatment, and control of diabetes mellitus by depressive symptom severity: a cross-sectional analysis of NHANES 2011–2016
      Jaewon Lee, Kyae Hyung Kim, Joseph C Ahn, Jihoon Andrew Kim, Gyeongsil Lee, Joung Sik Son, Soo Jung Choi, Yun Hwan Oh, Sang Min Park
      BMJ Open Diabetes Research & Care.2021; 9(1): e002268.     CrossRef
    • Sleep disorders in people with type 2 diabetes and associated health outcomes: a review of the literature
      Samantha B. J. Schipper, Maaike M. Van Veen, Petra J. M. Elders, Annemieke van Straten, Ysbrand D. Van Der Werf, Kristen L. Knutson, Femke Rutters
      Diabetologia.2021; 64(11): 2367.     CrossRef
    • The Influence of CYP2D6 and CYP2C19 Genetic Variation on Diabetes Mellitus Risk in People Taking Antidepressants and Antipsychotics
      Isabelle Austin-Zimmerman, Marta Wronska, Baihan Wang, Haritz Irizar, Johan H. Thygesen, Anjali Bhat, Spiros Denaxas, Ghazaleh Fatemifar, Chris Finan, Jasmine Harju-Seppänen, Olga Giannakopoulou, Karoline Kuchenbaecker, Eirini Zartaloudi, Andrew McQuillin
      Genes.2021; 12(11): 1758.     CrossRef
    • Antidepressants and Risk of Type 2 Diabetes Mellitus
      Hsin-Ya Kuo, Hsiu-Min Chen, Ching-Chih Lee, Hsuan-Han Lee, Chuan-Jung Kuo, Chun-Sheng Hsu, Chih-Chuan Pan, Ning Su, Che-Sheng Chu
      Journal of Clinical Psychopharmacology.2020; 40(4): 359.     CrossRef
    • Increased risk of type 2 diabetes in antidepressant users: evidence from a 6‐year longitudinal study in the E3N cohort
      M. Azevedo Da Silva, A. Fournier, M.‐C. Boutron‐Ruault, B. Balkau, F. Bonnet, H. Nabi, G. Fagherazzi
      Diabetic Medicine.2020; 37(11): 1866.     CrossRef
    • Comorbid depression in medical diseases
      Stefan M. Gold, Ole Köhler-Forsberg, Rona Moss-Morris, Anja Mehnert, J. Jaime Miranda, Monika Bullinger, Andrew Steptoe, Mary A. Whooley, Christian Otte
      Nature Reviews Disease Primers.2020;[Epub]     CrossRef
    • Impact of Psychotropic Medication Effects on Obesity and the Metabolic Syndrome in People With Serious Mental Illness
      Victor Mazereel, Johan Detraux, Davy Vancampfort, Ruud van Winkel, Marc De Hert
      Frontiers in Endocrinology.2020;[Epub]     CrossRef
    • Kronik Ruhsal Hastalığı Olan Bireylerde Diyabet Yönetimi ve Psikiyatri Hemşiresinin Rolü
      Sevecen ÇELİK İNCE, Neslihan GÜNÜŞEN
      Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi.2020; 13(3): 195.     CrossRef
    • Physical activity for diabetes-related depression: A systematic review and meta-analysis
      Zui Narita, Takuma Inagawa, Andrew Stickley, Norio Sugawara
      Journal of Psychiatric Research.2019; 113: 100.     CrossRef
    • Relationship between sociodemographic factors and depression symptoms and level of diabetes acceptance
      A. Cyuńczyk, B. Misiak, K. Lewko, M. Dziekońska, J. Lewko
      Progress in Health Sciences.2019; 2: 21.     CrossRef
    • Antidepressant use during pregnancy and the risk of gestational diabetes mellitus: a nested case–control study
      Maëlle Dandjinou, Odile Sheehy, Anick Bérard
      BMJ Open.2019; 9(9): e025908.     CrossRef
    • The Impact of Antidepressant Therapy on Glycemic Control in Canadian Primary Care Patients With Diabetes Mellitus
      Justin Gagnon, Marie-Thérèse Lussier, Brenda MacGibbon, Stella S. Daskalopoulou, Gillian Bartlett
      Frontiers in Nutrition.2018;[Epub]     CrossRef
    • Leisure time physical activity and incident use of prescription tranquilizers: A longitudinal population-based study
      Mashhood Ahmed Sheikh
      Journal of Affective Disorders.2018; 238: 327.     CrossRef
    • Role of Serotonin Transporter in Antidepressant-Induced Diabetes Mellitus: A Pharmacoepidemiological–Pharmacodynamic Study in VigiBase®
      Thi Thu Ha Nguyen, Anne Roussin, Vanessa Rousseau, Jean-Louis Montastruc, François Montastruc
      Drug Safety.2018; 41(11): 1087.     CrossRef
    • Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource
      Rashmie Abeysinghe, Licong Cui
      BMC Medical Informatics and Decision Making.2018;[Epub]     CrossRef
    • Child maltreatment, psychopathological symptoms, and onset of diabetes mellitus, hypothyroidism and COPD in adulthood
      Mashhood Ahmed Sheikh
      Journal of Affective Disorders.2018; 241: 80.     CrossRef
    • Selective serotonin reuptake inhibitors affect structure, function and metabolism of skeletal muscle: A systematic review
      Diego Bulcão Visco, Raul Manhães-de-Castro, Wenicios Ferreira Chaves, Diego Cabral Lacerda, Sabrina da Conceição Pereira, Kelli Nogueira Ferraz-Pereira, Ana Elisa Toscano
      Pharmacological Research.2018; 136: 194.     CrossRef
    • The effect of selective serotonin re-uptake inhibitors on risk of type II diabetes mellitus and acute pancreatitis: a meta-analysis
      Shun Yao, Jian Li, XiuDe Fan, QingQuan Liu, JianQi Lian
      Bioscience Reports.2018;[Epub]     CrossRef
    • Increased incidence of non‐alcoholic fatty liver disease in male rat offspring exposed to fluoxetine during fetal and neonatal life involves the NLRP3 inflammasome and augmented de novo hepatic lipogenesis
      Nicole E. De Long, Daniel B. Hardy, Noelle Ma, Alison C. Holloway
      Journal of Applied Toxicology.2017; 37(12): 1507.     CrossRef
    • Non-communicable disease syndemics: poverty, depression, and diabetes among low-income populations
      Emily Mendenhall, Brandon A Kohrt, Shane A Norris, David Ndetei, Dorairaj Prabhakaran
      The Lancet.2017; 389(10072): 951.     CrossRef
    • The risk of new-onset diabetes in antidepressant users – A systematic review and meta-analysis
      Virginio Salvi, Ilaria Grua, Giancarlo Cerveri, Claudio Mencacci, Francesco Barone-Adesi, David Meyre
      PLOS ONE.2017; 12(7): e0182088.     CrossRef
    • Use of antidepressant medications not associated with A1C among individuals with diabetes in NHANES sample
      Jamie Kammer, Akiko S. Hosler, Emily Leckman-Westin, A. Gregory DiRienzo
      Primary Care Diabetes.2016; 10(5): 360.     CrossRef
    • Gender and race disparities in weight gain among offenders prescribed antidepressant and antipsychotic medications
      Madison L. Gates, Thad Wilkins, Elizabeth Ferguson, Veronica Walker, Robert K. Bradford, Wonsuk Yoo
      Health & Justice.2016;[Epub]     CrossRef
    • Sertraline inhibits increases in body fat and carbohydrate dysregulation in adult female cynomolgus monkeys
      Marnie G. Silverstein-Metzler, Carol A. Shively, Thomas B. Clarkson, Susan E. Appt, J.Jeffrey Carr, Stephen B. Kritchevsky, Sara R. Jones, Thomas C. Register
      Psychoneuroendocrinology.2016; 68: 29.     CrossRef
    • Use of antidepressants and the risk of myocardial infarction in middle-aged and older adults: a matched case-control study
      Raymond Noordam, Nikkie Aarts, Maarten J. G. Leening, Henning Tiemeier, Oscar H. Franco, Albert Hofman, Bruno H. Stricker, Loes E. Visser
      European Journal of Clinical Pharmacology.2016; 72(2): 211.     CrossRef
    • Use of antidiabetic and antidepressant drugs is associated with increased risk of myocardial infarction: a nationwide register study
      K. Rådholm, A.‐B. Wiréhn, J. Chalmers, C. J. Östgren
      Diabetic Medicine.2016; 33(2): 218.     CrossRef
    • Psychological and personality factors in type 2 diabetes mellitus, presenting the rationale and exploratory results from The Maastricht Study, a population-based cohort study
      Fleur E. P. van Dooren, Johan Denollet, Frans R. J. Verhey, Coen D. A. Stehouwer, Simone J. S. Sep, Ronald M. A. Henry, Stef P. J. Kremers, Pieter C. Dagnelie, Nicolaas C. Schaper, Carla J. H. van der Kallen, Annemarie Koster, Frans Pouwer, Miranda T. Sch
      BMC Psychiatry.2016;[Epub]     CrossRef
    • Effect of serotonin modulating pharmacotherapies on body mass index and dysglycaemia among children and adolescents: a systematic review and network meta-analysis protocol
      Reem A Al Khalifah, Nicole E De Long, Ivan D Florez, Lawrence Mbuagbaw, Katherine M Morrison
      BMJ Open.2016; 6(3): e009998.     CrossRef
    • Psychiatric referral and glycemic control of Egyptian type 2 diabetes mellitus patients with depression
      Mounir H. Fawzi, Nagwa S. Said, Maggie M. Fawzi, Ibrahim A. Kira, Mohab M. Fawzi, Hanaa Abdel-Moety
      General Hospital Psychiatry.2016; 40: 60.     CrossRef
    • The Effect of Long-Term Intranasal Serotonin Treatment on Metabolic Parameters and Hormonal Signaling in Rats with High-Fat Diet/Low-Dose Streptozotocin-Induced Type 2 Diabetes
      Kira V. Derkach, Vera M. Bondareva, Oxana V. Chistyakova, Lev M. Berstein, Alexander O. Shpakov
      International Journal of Endocrinology.2015; 2015: 1.     CrossRef
    • Depression in Persons with Diabetes by Age and Antidiabetic Treatment: A Cross-Sectional Analysis with Data from the Hordaland Health Study
      Line I. Berge, Trond Riise, Grethe S. Tell, Marjolein M. Iversen, Truls Østbye, Anders Lund, Ann Kristin Knudsen, Gianpaolo Reboldi
      PLOS ONE.2015; 10(5): e0127161.     CrossRef
    • Comorbidity between Type 2 Diabetes and Depression in the Adult Population: Directions of the Association and Its Possible Pathophysiological Mechanisms
      Line Iden Berge, Trond Riise
      International Journal of Endocrinology.2015; 2015: 1.     CrossRef
    • Screening SSRI-users for diabetes in a general practice
      Annabel Jane McDonald, Helen Towner
      Mental Health Review Journal.2015; 20(3): 177.     CrossRef
    • Brain Signaling Systems in the Type 2 Diabetes and Metabolic Syndrome: Promising Target to Treat and Prevent These Diseases
      Alexander O Shpakov, Kira V Derkach, Lev M Berstein
      Future Science OA.2015;[Epub]     CrossRef
    • Antidepressant medication use and trajectories of fasting plasma glucose, glycated haemoglobin, β-cell function and insulin sensitivity: a 9-year longitudinal study of the D.E.S.I.R. cohort
      Marine Azevedo Da Silva, Aline Dugravot, Beverley Balkau, Ronan Roussel, Frédéric Fumeron, Alexis Elbaz, Marianne Canonico, Archana Singh-Manoux, Hermann Nabi
      International Journal of Epidemiology.2015; 44(6): 1927.     CrossRef
    • Effects of antipsychotics, antidepressants and mood stabilizers on risk for physical diseases in people with schizophrenia, depression and bipolar disorder
      Christoph U. Correll, Johan Detraux, Jan De Lepeleire, Marc De Hert
      World Psychiatry.2015; 14(2): 119.     CrossRef
    • Does Mirtazapine Interfere With Naturalistic Diabetes Treatment?
      Hoo Rim Song, Young Sup Woo, Hee-Ryung Wang, In-hee Shim, Tae-Youn Jun, Won-Myong Bahk
      Journal of Clinical Psychopharmacology.2014; 34(5): 588.     CrossRef
    • Co-administration of paroxetine and pravastatin causes deregulation of glucose homeostasis in diabetic rats via enhanced paroxetine exposure
      Feng Li, Mian Zhang, Dan Xu, Can Liu, Ze-yu Zhong, Ling-ling Jia, Meng-yue Hu, Yang Yang, Li Liu, Xiao-dong Liu
      Acta Pharmacologica Sinica.2014; 35(6): 792.     CrossRef
    • Use of antidepressants and statins and short-term risk of new-onset diabetes among high risk adults
      Rituparna Bhattacharya, Mayank Ajmera, Sandipan Bhattacharjee, Usha Sambamoorthi
      Diabetes Research and Clinical Practice.2014; 105(2): 251.     CrossRef

    Download Citation

    Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

    Format:

    Include:

    Antidepressant Use and Diabetes Mellitus Risk: A Meta-Analysis
    Korean J Fam Med. 2013;34(4):228-240.   Published online July 24, 2013
    Download Citation
    Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

    Format:
    • RIS — For EndNote, ProCite, RefWorks, and most other reference management software
    • BibTeX — For JabRef, BibDesk, and other BibTeX-specific software
    Include:
    • Citation for the content below
    Antidepressant Use and Diabetes Mellitus Risk: A Meta-Analysis
    Korean J Fam Med. 2013;34(4):228-240.   Published online July 24, 2013
    Close

    Figure

    • 0
    • 1
    • 2
    Antidepressant Use and Diabetes Mellitus Risk: A Meta-Analysis
    Image Image Image
    Figure 1 Flow diagram of selecting studies for inclusion in meta-analysis.
    Figure 2 Meta-analyses and pooled relative risk (RR) of diabetes mellitus in antidepressant use comparing not in use. Weights are from random effects analysis. CI: confidence interval, HPFS: Health Professionals Follow-up Study, NHS: Nurses' Health Study, PLB: protective lifestyle behavior, ILS: intensive lifestyle intervention.
    Figure 3 Funnel plots for publication bias. RR: relative risk.
    Antidepressant Use and Diabetes Mellitus Risk: A Meta-Analysis

    Characteristics of studies included in the final analysis of antidepressants and risk of diabetes mellitus

    NOS: Newcastle-Ottawa Quality Assessment Scale, HPFS: Health Professionals Follow-up Study, RCS: Retrospective Cohort Study, SSRIs: selective serotonin reuptake inhibitors, TCAs: tricyclic antidepressants, FHx: family history, DM: diabetes mellitus, BMI: body mass index, NHS: Nurses' Health Study, SNRI: serotonin and norepinephrine reuptake inhibitor, PCS: Prospective Cohort Study, NCC: Nested Case-Control study, MAOI: monoamine oxidase inhibitor, PLB: protective lifestyle behavior, ILS: intensive lifestyle intervention, FBS: fasting blood glucose, NA: not available.

    *Standard lifestyle group. Intensive lifestyle group.

    Overall and subgroup analyses for use of antidepressants and risk of diabetes mellitus

    RR: relative risk, CI: confidence interval, SSRI: selective serotonin reuptake inhibitor, TCA: tricyclic antidepressant.

    *Subgroup of studies including following risk factor as adjustment variable. Subgroup of studies including depression severity, body mass index, and physical activity as adjustment variable.

    Table 1 Characteristics of studies included in the final analysis of antidepressants and risk of diabetes mellitus

    NOS: Newcastle-Ottawa Quality Assessment Scale, HPFS: Health Professionals Follow-up Study, RCS: Retrospective Cohort Study, SSRIs: selective serotonin reuptake inhibitors, TCAs: tricyclic antidepressants, FHx: family history, DM: diabetes mellitus, BMI: body mass index, NHS: Nurses' Health Study, SNRI: serotonin and norepinephrine reuptake inhibitor, PCS: Prospective Cohort Study, NCC: Nested Case-Control study, MAOI: monoamine oxidase inhibitor, PLB: protective lifestyle behavior, ILS: intensive lifestyle intervention, FBS: fasting blood glucose, NA: not available.

    *Standard lifestyle group. Intensive lifestyle group.

    Table 2 Overall and subgroup analyses for use of antidepressants and risk of diabetes mellitus

    RR: relative risk, CI: confidence interval, SSRI: selective serotonin reuptake inhibitor, TCA: tricyclic antidepressant.

    *Subgroup of studies including following risk factor as adjustment variable. Subgroup of studies including depression severity, body mass index, and physical activity as adjustment variable.

    TOP