Metabolomic Analysis Flags Rare and Common Genetic Health Determinants

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A person’s circulating metabolites characterize their unique chemistry and physiology, and factors ranging from genetics to diet to drug-related and disease-related influences contribute.1 Exploring causal correlations between these factors and metabolites thus offers the potential to improve precision medicine.1 A recent study published in Nature Medicine analyzed the relationship between high-confidence causal genes and the metabolites they regulate by leveraging untargeted mass spectrometry methods, noting some key metabolites are impacted by several high-confidence causal genes.1

Leveraging Metabolomics for Genetic Analysis

The results published in the November 2022 cover story of Nature Medicine examined the clinical relevance of various genes across 1,400 phenotypes.1 In particular, the study explored data from 19,994 patients and included plasma levels of over 900 metabolites.1 Classes of metabolites analyzed included nucleotides, peptides, amino acids, lipids, xenobiotics, and carbohydrates as well as others. The researchers leveraged Metabolon’s Global Discovery Panel as part of the untargeted mass spectrometry analysis. Several key findings emerged. Within 330 genomic regions, the study found 2,599 variant-metabolite associations with 646 metabolites.1 Of note, the largest genetic study previously to leverage the Metabolon assay did not report on 225 of the 330 associated genomic regions reported on in this study.2,3

The results from this new study attributed rare variants to 9.4% of associations.1 The study found the highest level of annotated metabolites—94 lipids—associated with the FADS1/FADS2 locus.1 In addition, results detailed pleiotropy both across-class (ABCC1/PLA2G10, ABCG2/PPM1K, AGPAT1, GCKR, and SLC22A1) as well as within-class (MFSD2A and PCSK9).1 Further, the study noted the potential to flag likely adverse drug effects based on specific metabolite-guided discovery.1

Potential Root Causes of Rare Diseases 

Metabolic diseases due to rare genetic variants which result in metabolite accumulation or deficiency are called inborn errors of metabolism (IEMs), and whether untreated or undetected can lead to deleterious phenotypic impacts.4,5 The study noted an eightfold increase of genes among causal genes that result in IEMs.1 In particular, the data showed 88 regions that contained one or more of 97 IEM genes.1 These results have implications for how researchers identify and treat IEMs. 

Implications of Metabolomics for Health Determinants

In sum, this study stands to impact future research by providing a viable pathway for metabolomic analysis to mitigate some of the inherent risks of potential treatments and disease courses seen in past experiments via insight into an individual’s unique chemical individuality.1,6,7

In addition, correlations of known key genes to regulating metabolites have potentially far-reaching implications for scientific research in fields ranging from oncology to rare diseases. Moreover, such data supports efforts toward precision medicine for an array of indications. 

Ready to see what new insights metabolomics can help your research reveal? Contact us today to learn more.

References

  1. Surendran P, Stewart ID., Au Yeung VPW, et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med (2022). https://doi.org/10.1038/s41591-022-02046-0
  2. Long T, Hicks M, Yu HC, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet (2017); 49:568–578. https://doi.org/10.1038/ng.3809
  3. Shin, SY., Fauman, E., Petersen, AK. et al. An atlas of genetic influences on human blood metabolites. Nat Genet (2014); 46:543–550. https://doi.org/10.1038/ng.2982
  4. Campeau PM, Scriver CR, Mitchell JJ. A 25-year longitudinal analysis of treatment efficacy in inborn errors of metabolism. Mol. Genet. Metab (2008). https://doi.org/10.1016/j.ymgme.2008.07.001
  5. Garrod AE. The Incidence of Alkaptonuria: A study in Chemical Individuality. Mol Med (1996); 2:274–282. https://doi.org/10.1007/BF03401625
  6. Zheng JS. et al. Plasma vitamin C and type 2 diabetes: genome-wide association study and Mendelian randomization analysis in European populations. Diabetes Care (2021). https://doi.org/10.2337/dc20-1328
  7. Yarmolinsky J, et al. Circulating selenium and prostate cancer risk: a Mendelian randomization analysis. J. Natl Cancer Inst. (2018). https://doi.org/10.1093/jnci/djy081

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