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 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 | 0.030 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".