A nutritionally informed model for Bayesian variable selection with metabolite response variables

Understanding the pathways through which diet affects human metabolism is a central task in nutritional epidemiology. This article proposes novel methodology to identify food items associated with blood metabolites in 2 cohorts of healthcare professionals. We analyze 244 metabolites characterized by statistical complexities that include skewness, left-censoring, and structural missingness. Though existing methods can address such factors in low-dimensional settings, they cannot exploit the nutritional or statistical relationships among the 30 considered food intake variables, and they are unsuitable for performing high-dimensional inference. To address these challenges, we develop a novel Bayesian variable selection framework for metabolite response variables based on a skew-normal censored mixture model, while exploiting substantive information on the considered food items via a Markov random field prior. Applying this methodology to the cohort data identifies multiple metabolite-diet associations that are consistent with previous research as well as several potentially novel associations that were not detected using standard methods. The proposed approach is implemented in the R package multimetab, facilitating its use in high-dimensional metabolomic analyses.