ArticleScientific reports2025
A nutrient wide association study of cardiovascular disease prevalence in older adults from NHANES 2007 to 2018.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Choline status and related health outcomes in humans: A scoping review.Nutrition and health · 2026Article
- Choline-An Essential Nutrient with Health Benefits and a Signaling Molecule.International journal of molecular sciences · 2025Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, particularly among older adults. Identifying modifiable dietary factors associated with CVD prevalence is essential for prevention. This cross-sectional study analyzed 7,341 adults aged ≥ 65 years from NHANES 2007-2018. CVD was defined based on self-reported physician-diagnosed conditions, including coronary heart disease, heart failure, angina, heart attack, or stroke. A nutrient-wide association study (NWAS) evaluated associations between 56 dietary nutrients and CVD prevalence using multivariable logistic regression. Dose-response relationships were assessed using restricted cubic spline (RCS) models. Model discrimination was evaluated using receiver operating characteristic (ROC) analysis. Eight nutrients were significantly associated with CVD prevalence after adjusting for confounders and correcting for multiple comparisons: PUFAs 20:4 (OR 0.88, 95% CI 0.80-0.97, P = 0.024), 22:5 (OR 0.72, 95% CI 0.56-0.92, P = 0.019), and 22:6 (OR 0.92, 95% CI 0.87-0.99, P = 0.032); total choline (OR 0.96, 95% CI 0.93-0.99, P = 0.024); cholesterol (OR 0.89, 95% CI 0.82-0.96, P = 0.037); protein (OR 0.82, 95% CI 0.76-0.89, P = 0.040); vitamin A (OR 0.95, 95% CI 0.91-0.99, P = 0.045); and total sugars (OR 0.97, 95% CI 0.96-0.98, P = 0.049). Clustering analysis identified protective effects of fat-soluble vitamins and unsaturated fatty acids. The inclusion of these nutrients significantly improved model discrimination (P < 0.05). This study identified key dietary nutrients and clusters associated with CVD prevalence and characterized their dose-response relationships. Integrating these nutrients into predictive models enhances CVD risk discrimination, providing actionable dietary targets for prevention strategies in older adults.
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