The relationship between common mental disorders and incident diabetes among participants in the Kerala Diabetes Prevention Program (K-DPP)
Bibliographic record
Abstract
This study aims to describe the prevalence of depression and anxiety among a population sample of people at high risk for type 2 diabetes in Kerala, India, and examine the relationship between depressive symptoms, anxiety, and incident Type 2 Diabetes Mellitus (T2DM) over a two-year period. We used data from the Kerala Diabetes Prevention Program, a cluster-randomized controlled trial for diabetes prevention among 1007 high-risk individuals. The prevalence of depression and anxiety were estimated using the 9-item Patient Health Questionnaire and the Generalized Anxiety Disorder 7-item scale, respectively. We calculated proportions for depression and anxiety and performed generalized estimating equations (GEE) to examine the relationship between baseline mental health status and incident T2DM. The prevalence of depression and anxiety at baseline were 7.5% and 5.5%, respectively. Compared with those reporting none/low symptoms, the odds ratio for incident diabetes was 1.07 (95% CI 0.54-2.12) for participants with moderate to severe depression and 0.73 (95% CI 0.23-2.28) for participants with moderate to severe anxiety, after adjusting for potential confounders. Our findings suggest that the prevalence of depression and anxiety were higher than those previously reported in the general population in India. However, among this sample of community-based adults at high risk of developing T2DM, the presence of moderate to severe depression and/or anxiety symptoms was not significantly associated with the risk of developing T2DM. Trial registration: Australia and New Zealand Clinical Trials Registry ACTRN12611000262909. Registered 10 March 2011.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".