Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.

ABSTRACT:

Background

Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

Methods

We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirection

SUBMITTER: Hsu YH 

PROVIDER: S-EPMC7332400 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature