Joint Association of Multiple Dietary Components on Cardiovascular Disease Risk: A Machine Learning Approach.

Human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1928 healthy participants of the Coronary Artery Risk Development in Young Adults cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk for atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian Kernel Machine Regression (BKMR). Employing BKMR, we found among women, unprocessed red meat was most strongly related to the outcome: an interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when other dietary components were at their median (similar results when other components were at their 25th and 75th). Among men, fruits had the strongest association: an interquartile range increase in fruit consumption was associated with -0.09 (95% credible interval: -0.16, -0.02), -0.10 (-0.16, -0.03), and -0.11 (-0.18, -0.04)-unit lower ASCVD risk, when other dietary components were at their 25th, median, and 75th percentiles. Using BKMR to explore the complex structure of diet totality, we found distinct gender-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit.