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

• Published in: International Journal of Obesity 2020-07, Vol.44 (7), p.1596-1606
• Main Authors: Hsu, Yu-Han H., Astley, Christina M., Cole, Joanne B., Vedantam, Sailaja, Mercader, Josep M., Metspalu, Andres, Fischer, Krista, Fortney, Kristen, Morgen, Eric K., Gonzalez, Clicerio, Gonzalez, Maria E., Esko, Tonu, Hirschhorn, Joel N.
• Format: Article
• Language: English
• Subjects: 101/58; 45/43; 631/443/319/1642/393; 692/699/2743/393; Analysis; Bioinformatics; Body Mass Index; Body size; Causality; Cause-effect relationships; Computational Biology; Datasets; Epidemiology; Genetic analysis; Genetic research; Genotypes; Glycine; Health problems; Health Promotion and Disease Prevention; Humans; Inference; Internal Medicine; Medicine; Medicine & Public Health; Metabolic Diseases; Metabolites; Metabolome; Metabolomics; Obesity; Obesity - genetics; Obesity - metabolism; Phenotypes; Phenotypic variations; Public Health; Systematic review; Valine
• ISSN: 0307-0565; 1476-5497
• DOI: 10.1038/s41366-020-0603-x

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 bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS. Results We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched ( p