Diabetes, depressive symptoms, and mortality risk in old age: The role of inflammation
Bibliographic record
Abstract
BACKGROUND: Both diabetes and depression increase the mortality risk in the elderly. In this study, we evaluated mortality risk associated with the comorbidity between depression and diabetes. We also assessed the moderating role of inflammation in the mortality risk in this population. METHODS: We included a total of 1,183 community-dwelling older adults, divided into four groups: "neither diabetes nor depression"; "diabetes only"; "depression only," and "both diabetes and depression," and followed-up for a median of 13.5 years. We evaluated the inflammatory status by the high-sensitivity C-reactive protein (hs-CRP) levels. Date of death was computed by reviewing death certificates. We used Cox's proportional hazards models and additive interactions to evaluate the risk of mortality in the subject groups and the moderating effect of hs-CRP. RESULTS: Participants with both diabetes and depression had higher death risk (hazard ratio [HR]: 2.33; 95% confidence interval [CI]: 1.59-3.42) than those with each condition alone (HR diabetes: 2.08 95% CI: 1.56-2.76 HR depression: 1.26; 95% CI: 1.03-1.54). High level of hs-CRP, indicative of high inflammatory status, significantly moderated the risk of mortality in subjects with both diabetes and depression (Bonferroni-adjusted p = 0.0116). CONCLUSIONS: The coexistence of diabetes and depression symptoms is associated with the highest death risk in this population. This risk is moderated by inflammatory status.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".