Serum uric acid and the risk of major adverse cardiovascular events and death among older adults: a population-based prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The relationship between serum uric acid (SUA) and adverse outcomes in advanced age remains poorly understood. Our population-based prospective cohort study assessed the potential association between SUA levels and the risk of major adverse cardiovascular events (MACE) and all-cause mortality among community-dwelling older adults. METHODS: We used data from the Berlin Initiative Study linked to administrative claims and vital statistics. Cohort members were followed from cohort entry (2009) until the occurrence of a study outcome or the end of the study period (2021). We created three exposure groups according to the baseline SUA distribution (in mg/dL; lower: 1.68–5.16, intermediate: 5.17–6.83, higher: 6.84-13.0); SUA levels were updated biennially. Time-dependent Cox models yielded hazard ratios (HRs) and 95% confidence intervals (CIs) of MACE and all-cause mortality adjusted for potential confounders. Sensitivity analyses addressed time-dependent confounding. RESULTS: Our cohort included 2,058 individuals (mean age 80 years, 53% female). Lower vs. intermediate SUA levels were not associated with the risk of MACE (HR, 1.16; 95% CI, 0.88–1.54) or all-cause mortality (HR, 1.06; 95% CI, 0.86–1.31). Higher vs. intermediate SUA levels were not associated with the risk of MACE (HR, 1.11; 95% CI, 0.85–1.45) but with an increased risk of all-cause mortality (HR, 1.26; 95% CI, 1.03–1.53). Sensitivity analyses showed no statistically significant associations between higher vs. intermediate SUA levels and the risk of mortality (HR [95% CI]: 1.09 [0.89–1.34] & 1.07 [0.86–1.34]). CONCLUSION: Lower or higher SUA levels are not associated with the risk of MACE or all-cause mortality in older adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".