Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.
Yu-Han H HsuChristina M AstleyJoanne B ColeSailaja VedantamJosep M MercaderAndres MetspaluKrista FischerKristen FortneyEric K MorgenClicerio GonzalezMaria E GonzalezTonu EskoJoel N HirschhornPublished in: International journal of obesity (2005) (2020)
These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.
Keyphrases
- mass spectrometry
- insulin resistance
- metabolic syndrome
- liquid chromatography
- weight loss
- rna seq
- high fat diet induced
- type diabetes
- single cell
- high resolution mass spectrometry
- weight gain
- gas chromatography mass spectrometry
- ms ms
- endothelial cells
- small molecule
- big data
- tandem mass spectrometry
- artificial intelligence
- climate change
- solid phase extraction