Can Metabolic Profiles Be Used as a Phenotypic Readout of the Genome to Enhance Precision Medicine?

The past decade has witnessed considerable advancements in sequencing technologies, which have allowed comprehensive investigation of genetic variation of individuals at a moderate cost and within a reasonable time frame. Personalized disease risk and drug response predictions based on genomic sequences are a cornerstone of preventive precision medicine, and have also been successful at informing therapeutic decisions. However, genomics is limited in predicting the onset of most common diseases (i.e., cancer, diabetes, and cardiovascular disorders) because genetic information is mostly static and does not account for dynamic environmental (i.e., diet and lifestyle) or gut microbiota influences. Metabolomics, the study of a large collection of metabolites, offers the advantage to measure the functional readout of activity and phenotype encoded in the genome. Hence, combining genetic and metabolic information provides a unique opportunity to gain further insights on how the genetic program is translated into biological function through metabolites, and how alterations in the program associate with the onset of diseases. This approach has already proven very useful at diagnosing and understanding the pathogenesis of rare inherited metabolic disorders. Furthermore, metabolic profiles are influenced by the environment and the gut microbes; thus metabolomics has the potential to unravel the impact of nongenetic factors on disease onset as well as reveal early biomarkers that may improve risk assessment and diagnosis of complex diseases. Thanks to rapid improvements in technology, mass spectrometry–based metabolomics can now robustly profile a broad spectrum of metabolites at a relatively low cost (1). The concept that genetic variations can be captured at the metabolite level in a population was first demonstrated in 2008 by Gieger et al. (2). That study gave a glimpse of the usefulness of combining genetic information with metabolic traits to understand the pathogenesis of common diseases and the influence of environment. In 2015, Guo …