ReviewDiabetology & metabolic syndrome2026
Systematic review and meta-analysis on depression burden among Type 2 diabetes patients in India.
Review in Diabetology & metabolic syndrome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression and Type 2 Diabetes Mellitus (T2DM) are closely linked health challenges in India, which currently has more than 101 million people living with diabetes. This systematic review and meta-analysis aimed to determine the pooled prevalence of depression among Indian T2DM patients, highlight regional differences, and identify associated risk factors.
methodsFollowing PRISMA guidelines, a thorough search was conducted across PubMed, Embase, Scopus, and Web of Science for studies published until February 3, 2025. Random-effects models were applied to calculate pooled prevalence, while subgroup analyses assessed variations by geographic region, diagnostic tool, and study setting. Heterogeneity was quantified using I² statistics. Publication bias was evaluated using funnel plots and Egger’s regression, with trim-and-fill analysis performed when bias was detected. Meta-regression examined the impact of covariates such as sample size, mean age, diabetes duration, hypertension, and urban residence.
resultsA total of 59 studies with 24,073 participants were included. The pooled prevalence of depression among T2DM patients was 38% (95% CI: 33–42%), derived using a random-effects model to account for the substantial heterogeneity observed across studies (I² = 98.28%). This estimate reflects a statistical synthesis across studies with widely varying diagnostic tools, cutoff thresholds, and clinical settings, and should be interpreted as an approximation of the burden rather than a precise national prevalence figure. Mild, moderate, and severe depression accounted for 24%, 14%, and 14% of cases respectively. Regional variation was observed, with Western India showing the highest prevalence (48%) and multicenter studies the lowest (27%). Prevalence differed by diagnostic tool: CIDI-SF (20%), PHQ-9 (34%), and BDI (72% with lenient cutoffs). Hospital-based studies reported higher prevalence (42%) compared to community-based ones (28%). Females had a greater burden (39%) than males (31%). No significant differences were found between pre- and post-COVID-19 studies. Sensitivity analyses confirmed robustness of estimates. Meta-regression identified diabetes duration as a significant predictor (p = 0.026).
conclusionNearly two in five Indian T2DM patients experience depression, emphasizing the urgent need for standardized screening and integration of mental health care into India’s National Program for Non-Communicable Diseases.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.