Latent Variable Models for Advancing Nutrition Epidemiology

Dietary patterns are essential for understanding dietary behaviors and their health implications in nutritional epidemiology, yet complexities in dietary assessments pose analytical challenges. Heterogeneity in dietary behaviors across populations and the multivariate nature of dietary assessment data underscore the need for latent variable models to uncover meaningful patterns. However, estimation of dietary patterns faces challenges of strong similarities and highly correlated acculturation exposure measures. Overlooking these challenges often results in numerical instability and inaccuracy that hinder scientific interpretation. We propose novel latent variable models to address these challenges by incorporating tree regularization and a multilayered modeling framework. Through studies of migrant populations in the US, we discuss improved identification of nuanced differences in dietary patterns in small subpopulations, and the interplay between acculturation and dietary behaviors. We also highlight insights into public health and nutrition interventions.