Relation of Inflammation to Depression and Incident Coronary Heart Disease
Bibliographic record
Abstract
Numerous studies have found that depression was a strong independent risk factor for incident coronary heart disease (CHD), with increasing risk in those with higher levels of depressive symptoms. The association between measures of inflammation (C-reactive protein, interleukin-6, and soluble intracellular adhesion molecule-1), depressive symptoms, and CHD incidence was examined in 1,794 subjects of the population-based Canadian Nova Scotia Health Survey. There were 152 incident CHD events (8.5%; 141 nonfatal, 11 fatal) during the 15,514 person-years of observation (incidence rate 9.8 events/1,000 person-years). Depression and inflammation were correlated at baseline and each significantly predicted CHD in separate models. When both risk factors were in the same model, each remained significant. The association between depressed group by the Center for Epidemiological Studies-Depression scale (score > or =10 vs 0 to 9) and CHD incidence (hazard rate 1.60, 95% confidence interval 1.12 to 2.27) was not reduced by the addition of inflammatory markers to the model (hazard rate 1.59, 95% confidence interval 1.12 to 2.26). Findings were similar after adjustment for aspirin, lipid-lowering medication, or antidepressant use, and the association did not vary by gender, smoking status, age, obesity, cardiovascular medication use, or antidepressant use. In conclusion, increased inflammation explained only a very small proportion of the association between depression and incident CHD.
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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 | 0.004 |
| 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 | 0.001 |
| 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".