The longitudinal associations between C‐reactive protein and depressive symptoms: evidence from the English Longitudinal Study of Ageing (ELSA)
Bibliographic record
Abstract
OBJECTIVES: The inflammatory marker C-reactive protein (CRP) is associated with depression. We examined the directional relations between CRP and symptoms of depression among older adults. METHOD: The sample consisted of 3397 participants from the English Longitudinal Study of Ageing, a prospective study of community-dwelling older adults. CRP and depressive symptoms were measured at baseline and follow-up. A high CRP level was dichotomized as >3 mg/L. Elevated depressive symptomatology was defined as ≥4 using the 8-item Center for Epidemiologic Studies Depression Scale. Logistic regressions computed the association between high CRP levels at baseline with elevated depressive symptoms at follow-up, and vice versa. RESULTS: After adjusting for baseline depressive symptoms, baseline high CRP levels were associated with subsequent elevated symptoms of depression (OR = 1.49; 95% CI, 1.19-1.88). This relationship was no longer significant after simultaneous adjustments for metabolic and health variables. In the other direction, after adjusting for baseline CRP levels, baseline elevated depressive symptoms was not associated with subsequent high CRP levels (OR = 1.12; 95% CI, 0.88-1.42). CONCLUSION: High CRP levels at baseline are related to elevated depressive symptomatology at follow-up due to clinical factors. No association was found in the opposite direction.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| 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".