Depression, Diabetes Mellitus and Mortality in Older Adults: A National Cohort Study in Taiwan
Bibliographic record
Abstract
Purpose: Diabetes mellitus (DM) increases the risk of cardiovascular and all-cause mortality. The coexistence of depression and DM is associated with an increased risk of DM complications and functional morbidity. The independent effect of depression on mortality in patients with DM is unclear, and relevant Asian studies have provided inconsistent results. Accordingly, this study assessed the independent and additive effects of DM and depression on mortality in a nationally representative cohort of older adults in Taiwan over a 10-year observation period. Patients and Methods: A total of 5041 participants aged 50 years or older were observed between 1996 and 2007. We defined depression as a score of ≥8 on the 10-item Center for Epidemiologic Studies Depression (CES-D 10) scale. Additionally, we defined participants as having type 2 DM if they had received a diagnosis of type 2 DM from a health-care provider. Cox proportional hazard models were applied to analyze predictors of mortality in depression and DM comorbidity groups. Results: During the 10-year follow-up period, 1637 deaths were documented. After adjustment for potential confounders, the hazard ratios for mortality in participants with both depression and DM, DM only, and depression only were 2.47 (95% confidence interval [CI]: 2.02-3.03), 1.95 (95% CI: 1.63-2.32), and 1.23 (95% CI: 1.09-1.39), respectively. Conclusion: The co-occurrence of depression with DM in Asian adults increased overall mortality rates. Our results indicate that the increased mortality hazard in individuals with DM and depression was independent of sex.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".