Associations between Dietary Pattern Networks Derived from Machine Learning Algorithms and Cardiovascular Disease Risk in the NutriNet-Sante Cohort.

ABSTRACT:

Background

Major advances in the fields of data science and machine learning have enabled the use of novel methods, such as Gaussian graphical models (GGMs) and the Louvain algorithm, to identify dietary patterns (DP).

Objectives

The aim of this study was to identify DP networks using novel computational approaches and to investigate the associations between these DP networks and cardiovascular disease (CVD) risk in a sample of the French population.

Methods

A sample of 99,362 participants aged ≥15 y from the NutriNet-Santé cohort was used. Dietary intakes (reported as grams per day) were assessed using ≥2 24-h dietary records, which were then classified into 42 food groups. CVD events were assessed using health questionnaires and subsequently validated based on medic

SUBMITTER: Cote M 

PROVIDER: S-EPMC12799445 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature