Association of Cystatin C and Depression in Healthy Elders: The Health, Aging and Body Composition Study
Bibliographic record
Abstract
BACKGROUND/AIMS: Depression is highly prevalent in individuals with advanced kidney disease, but is less well studied in individuals with milder disease. We evaluated the association between kidney function and depression in the Health, Aging and Body Composition (Health ABC) study. METHODS: The study enrolled 3,075 community-dwelling black and white adults aged 70-79 years. Kidney function was measured by cystatin C and estimated glomerular filtration rate (eGFR). The main outcome was incident treated depression. RESULTS: 52% of participants had low (≤1.0), 33% intermediate (>1-1.25) and 15% high cystatin C (>1.25). Kidney function and depression were not associated at baseline. Of 2,731 nondepressed participants at baseline, 95 developed incident depression during follow-up. In unadjusted Cox proportional hazard models, hazard ratios (HR) for incident depression were 1.89 (95% confidence interval (CI) 1.21-2.97) for the intermediate and 2.17 (CI 1.24-3.79) for the high cystatin C group. Intermediate (HR = 1.84) and high (HR = 2.1) serum cystatin C remained associated with incident depression in adjusted models. Chronic kidney disease, defined by an eGFR <60 ml/min/1.73 m(2), was not associated with depression. CONCLUSION: Participants with higher cystatin C had an increased likelihood of developing treated depression. Future studies should target this high-risk group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".