Abstract 10005: Sodium Intake and Risk of Cardiovascular Disease: A Pooled Analysis of Individual Data from Six Cohort Studies with Multiple 24-Hour Urine Collections
Bibliographic record
Abstract
Background: The relationship between sodium intake and cardiovascular disease (CVD) remains controversial, in part, due to inaccurate assessment of sodium intake. 24-hour urinary excretion over multiple days is considered the optimal method. Methods: We included individual participant data from 6 prospective cohorts among generally healthy adults with sodium and potassium excretion assessed by at least two 24-hour urine collections. Out of 10,709 participants (54% women; mean [SD] age, 51.5 [12.6] years), 571 incident CVD events (including myocardial infarction, coronary revascularization, and stroke) were ascertained during a median follow-up of 8.8 years. We analyzed each cohort using consistent methods and combined the results using random-effects meta-analysis. Results: Median 24-hour urinary sodium excretion (10 th -90 th percentile) was 3,270 (2,099-4,899) mg. Higher sodium excretion, lower potassium excretion and higher sodium-to-potassium ratio were all associated with higher risk of CVD events after controlling for confounding factors (all P values for trend≤0.02); there was no evidence of nonlinearity. The hazard ratio [HR] comparing top with bottom quartiles was 1.67 [95% confidence interval [CI]: 1.21-2.30] for sodium, 0.77 [0.57-1.02] for potassium and 1.72 [1.27-2.32] for sodium-to-potassium ratio. Each 1,000 mg/d increment in sodium excretion was associated with an 18% increase in CVD risk (95%CI: 5%-32%) and each 1,000 mg/d increment in potassium excretion was associated with 18% lower risk (95%CI: 6%-28%). Conclusions: Higher sodium and lower potassium intakes, measured in multiple 24-hour urine samples, were associated with higher risk of CVD in a dose-response manner. These findings support current recommendations to reduce sodium and increase potassium intakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".