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

backgroundMajor 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).

objectivesThe 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.

methodsA 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 medical records. GGMs were employed with the Louvain algorithm to derive DP networks. GGMs are network models that depict relationships among many variables (food groups) based on conditional correlation matrices. The Louvain algorithm extracts nonoverlapping communities from large networks. The relationship between DP networks and CVD incidence was evaluated using proportional hazard Cox models, adjusted for confounding variables.

resultsAnalyses revealed 5 distinct DP networks reflecting consumption of 1) appetizer foods, 2) breakfast foods, 3) plant-based foods, 4) ultraprocessed sweets and snacks, and 5) healthy foods. Among these, only the DP network of ultraprocessed sweets and snacks was associated with greater CVD risk when adjusted for energy and potential confounders including overall diet quality (hazard ratio of quintile 5 compared with quintile 1: 1.32; 95% confidence interval: 1.11, 1.57; P-trend = 0.0002).

conclusionsThe 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.