Associations between dietary pattern networks derived from machine learning algorithms and cardiovascular risk in the NutriNet-Santé cohort.

Abstract

Background: Major advances in the fields of data science and machine learning have enabled novel methods, like Gaussian Graphical Models (GGM) and the Louvain algorithm, for identifying dietary patterns (DP).

Objective: 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 years from the NutriNet-Santé cohort was used. Dietary intakes were assessed using ≥2 24-hour dietary records, which were then classified into 42 food groups (grams/day). CVD events were assessed using health questionnaires and subsequently validated based on medical records. GGM were employed with the Louvain algorithm to derive DP networks. GGM are network models that depict relationships among many variables (food groups) based on conditional correlation matrices. The Louvain algorithm extracts non-overlapping communities from large networks. The relationship between the DP networks and CVD incidence was evaluated using proportional hazard Cox models, adjusted for confounding variables.

Results: Analyses revealed five distinct DP networks reflecting consumption of: 1- appetizer foods, 2- breakfast foods, 3- plant-based foods, 4- ultra-processed sweets and snacks and 5- healthy foods. Among those, only the DP network of ultra-processed sweets and snacks was associated with greater CVD risk when adjusted for energy and potential confounders including overall diet quality (HR_Q5vsQ1=1.32, 95%CI=1.11 to 1.57, P for trend=0.0002).

Conclusions: Results suggest that a DP network reflecting the consumption of ultra-processed 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.

Study registration: NCT03335644,https://clinicaltrials.gov/study/NCT03335644.