Dietary Minerals and Incident Cardiovascular Outcomes among Never-Smokers in a Danish Case–Cohort Study
Bibliographic record
Abstract
Background: Diet is known to impact cardiovascular disease (CVD) risk, but evidence for the essential minerals of magnesium (Mg), calcium (Ca), and potassium (K) is inconsistent. Methods: We conducted a case–cohort study within a non-smoking subgroup of the Danish Diet, Cancer and Health cohort, a prospective study of 50–64-year-olds recruited between 1993–1997. We identified incident heart failure (HF), acute myocardial infarction (AMI) and stroke cases through 2015 with an 1135-member subcohort. We measured the dietary intake of minerals, also known as elements, and calculated a combined dietary intake (CDI) score based on joint Ca, Mg and K intakes (mg/d) from Food Frequency Questionnaires. We estimated adjusted hazard ratios (HRs) with Cox proportional hazard models. Results: Most HRs examining associations between CDI score and CVD were null. However, the third quartile of CDI was associated with a lower risk for heart failure (HR: 0.89; 95% CI: 0.67, 1.17), AMI (HR: 0.79; 95% CI: 0.60, 1.04), and stroke (HR: 0.63; 95% CI: 0.44, 0.88). Conclusions: We did not find consistent evidence to suggest that higher levels of essential minerals are associated with incident HF, AMI, and stroke, though results suggest a potential U-shaped relationship between select minerals and CVD outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".