DEPRESSIVE SYMPTOMS, C-REACTIVE PROTEIN, AND INCIDENCE OF DIABETES
Bibliographic record
Abstract
Introduction Depressive symptoms are associated with increased risk of incident diabetes. Inflammatory mechanisms have been suggested to be involved in depression and diabetes. Specifically, increased levels of C-reactive protein (CRP), a biomarker for inflammation, is associated with depression and have also been linked to risk of developing diabetes. Objective To assess the association of both CRP and depressive symptomatology with diabetes incidence in a representative sample of English people ≥50 years old. Methods Participants were 5475 community-dwelling men and women without diabetes at baseline from the English Longitudinal Study of Ageing (ELSA). Wave 2 of ELSA was used as baseline (first assessment of CRP), with assessment of diabetes incidence at waves 3, 4, and 5. Elevated depressive symptoms were based on a score ≥4 using the 8-item Center for Epidemiologic Studies Depression (CES-D) scale, and high CRP level was defined as >3 mg/L. Diabetes incidence was indicated by self-reported doctor diagnosis. Association of diabetes incidence with baseline CRP and depressive symptomatology groups was examined using multivariate logistic regression adjusted for socio-demographic, lifestyle, metabolic, and health variables. Results In comparison to participants with normal CRP levels and low depressive symptoms, those with both high CRP and elevated depressive symptoms were more likely to develop diabetes over 6 years of follow-up (adjusted OR: 1.90, 95% CI: 1.10–3.30). Individuals with high CRP and low depressive symptoms (adjusted OR: 1.26, 95% CI: 0.89–1.78) and those with normal CRP and elevated depressive symptoms (adjusted OR: 1.55, 95% CI: 0.88–2.73) were not associated with diabetes incidence. Conclusion People with high CRP and elevated depressive symptoms are more likely to develop diabetes. Further analyses will examine the possible interactions between CRP and depression with diabetes incidence.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".