The prevalence of comorbid depression in patients with type 2 diabetes: an updated systematic review and meta-analysis on huge number of observational studies
Abstract
Aims Depression is a common co-morbidity in patients with type 2 diabetes mellitus (T2DM). Untreated depression in these patients adversely affects self-care activities and other diabetes complications. The aim of this study is to estimate the prevalence of depression among patients with T2DM by conducting a meta-analysis of observational studies.Methods MEDLINE, Web of Science, Science Direct, and Google Scholar databases were searched for all observational studies that assessed depression in T2DM. Relevant articles were searched using the combination of Medical Subject Heading (MeSH) terms of "depression", "depressive disorder", and "diabetes mellitus" published between January 2007 and July 2018. Random effects model was used to estimate the weighted prevalence rates and 95% CI using "metaprop program in STATA 11".Results In total, the 248 included studies (with 273 reported prevalence) identified 83,020,812 participants; of them, 23,245,827 (28%; 95% CI 27, 29) suffered from different severity levels of depressive disorders. The prevalence of depression was separately reported in 137,372 males and 134,332 females. Of them, 31,396 males (23%, 95% CI: 20, 26) and 45,673 females (34%, 95% CI: 31, 38) were depressed. Compared with global estimate, depression prevalence was lower in Europe (24%) and Africa (27%), but higher in Australia (29%) and Asia (32%). The prevalence in America was equal to the estimated prevalence in the world (28%). Depression was more common in subjects younger than 65 compared with elderlies (31% vs. 21%).Conclusion Our findings demonstrated that almost one in four adults with T2DM experienced depression. Given the high prevalence of depressive disorders in diabetic patients, screening these patients for co-morbid depression and its relevant risk factors is highly recommended.
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People with diabetes are more likely to suffer from clinically significant psychological disorders than those without the disease.
“People with diabetes are more likely to suffer from clinically significant psychological disorders than those without the disease 1 , 2 , 3 , 4 .”
From Effects of mindfulness-based intervention on glycemic control and psychological outcomes in people with diabetes: a systematic review and meta-analysis (2021) · cites this paper as [3]
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- Authors
- Khaledi, M., Haghighatdoost, F., Feizi, A., & Aminorroaya, A.
- Affiliations
- Isfahan University of Medical Sciences (IR)
- Corresponding author
- Aminorroaya, Ashraf
- Venue
- Acta Diabetologica
- Published
- 2019-03-22
- Volume
- 56
- Issue
- 6
- Pages
- 631-650
- ISSN
- 0940-5429 (ISSN-L)
- 1432-5233
- OpenAlex
- W2925054441
- Cited by
- 359
- References
- 284
- Citations by year
- 2026: 42 · 2025: 53 · 2024: 59 · 2023: 75 · 2022: 56 · 2021: 39 · 2020: 27 · 2019: 8
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Acta Diabetologica (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Isfahan University of Medical Sciences (397089)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- depression, type 2 diabetes, comorbidities, prevalence, depressive disorders, sex differences, age differences, depression screening, risk factors
- MeSH terms
- Depression, Diabetes Mellitus, Type 2, Humans, Comorbidity, Diabetes Complications, Observational Studies as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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