Association between ideal cardiovascular health and depression incidence: a longitudinal analysis of ELSA‐Brasil
Bibliographic record
Abstract
OBJECTIVE: We investigated whether ideal cardiovascular health (ICH), a metric proposed by the American Heart Association, predicts depression development. METHODS: Cohort analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Adults with no current depression and other common mental disorders, cardiovascular diseases, and antidepressant drug use at baseline had their ICH (composite score of smoking, dietary habits, body mass index, blood pressure, fasting glucose, cholesterol, and physical activity) assessed and classified into poor, intermediate, and optimal. Depression was assessed using the Clinical Interview Schedule-Revised (CIS-R). Poisson regression models, adjusted for sociodemographic factors and alcohol consumption, were employed. Stratified analyses were performed for age and sex. RESULTS: We included 9214 participants (mean age 52 ± 9 years, 48.6% women). Overall depression incidence at 3.8-year follow-up was 1.5%. Intermediate and poor ICH significantly increased the risk rate (RR) of developing depression (2.48 [95%CI 1.06-5.78] and 3 [1.28-7.03], respectively) at a 3.8-year follow-up. Higher ICH scores decreased the rate of depression development (RR = 0.84 [0.73-0.96] per metric). Stratified analyses were significant for women and adults < 55 years old. CONCLUSIONS: Poor cardiovascular health tripled depression risk at follow-up in otherwise healthy adults. Ameliorating cardiovascular health might decrease depression risk development.
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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 | 0.001 |
| 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".