Associations between Dietary Pattern Networks Derived from Machine Learning Algorithms and Cardiovascular Disease Risk in the NutriNet-Santé Cohort
The results suggest that a DP network reflecting the consumption of ultraprocessed sweets and snacks is associated with incident CVD in a sample of the French population, independent of diet quality. The innovative approach to derive empirical DP networks may assist in the identification of food groups that are likely to be consumed together in a population, thereby helping to identify dietary habits to target for the prevention of CVD. This trial was registered at clinicaltrials.gov as NCT03335644.
Authors
- Bernard Srour (ORCID: https://orcid.org/0000-0002-1277-3380)
- Benoı̂t Lamarche (ORCID: https://orcid.org/0000-0002-4443-5378)
- Laurent Bourhis
- Mélina Côté (ORCID: https://orcid.org/0000-0003-2206-9124)
- Joy M. Hutchinson (ORCID: https://orcid.org/0000-0002-1615-7964)
- Léopold Fezeu (ORCID: https://orcid.org/0000-0002-7589-3179)
- Mathilde Touvier (ORCID: https://orcid.org/0000-0002-8322-8857)
Institutions
- Conservatoire National des Arts et Métiers (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Sorbonne Université (FR)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Université Sorbonne Paris Nord (FR)
- Sorbonne Paris Cité (FR)
- Université Laval (CA)
- Centre de Recherche Épidémiologie et Statistique (FR)
Publication Details
- Journal
- Journal of Nutrition
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1016/j.tjnut.2025.09.014
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00