Depression-related weight change and incident diabetes in a community sample
Bibliographic record
Abstract
This cohort study aimed to compare the incidence of type 2 diabetes in adults with depression-related weight gain, depression-related weight loss, depression with no weight change, and no depression. The study sample included 59,315 community-dwelling adults in Ontario, Canada. Depression-related weight change in the past 12 months was measured using the Composite International Diagnostic Interview-Short Form. Participants were followed for up to 20 years using administrative health data. Cox proportional hazards models compared the incidence of type 2 diabetes in adults with depression-related weight change and in adults with no depression. Adults with depression-related weight gain had an increased risk of type 2 diabetes compared to adults no depression (HR 1.70, 95% CI 1.32-2.20), adults with depression-related weight loss (HR 1.62, 95% CI 1.09-2.42), and adults with depression with no weight change (HR 1.39, 95% CI 1.03-1.86). Adults with depression with no weight change also had an increased risk of type 2 diabetes compared to those with no depression (HR 1.23, 95% CI 1.04-1.45). Associations were stronger among women and persisted after adjusting for attained overweight and obesity. Identifying symptoms of weight change in depression may aid in identifying adults at higher risk of type 2 diabetes and in developing tailored prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".