Case Study

A Cross-platform Approach Identifies Genetic Regulators of Human Metabolism and Health

Integration of metabolite genome-wide association studies (mGWAS) across several metabolomic platforms unravels previously unknown gene-metabolite correlations.

In this study, researchers investigated the feasibility and insight gained from integrating gene-metabolite associations measured from distinct metabolomic platforms. Leveraging data obtained from several large cohort studies (the Fenland, INTERVAL, and EPIC-Norfolk studies) that used either BiocratesAbsoluteIDQ p180, 1H-NMR, or the Metabolon Global Discovery Panel, scientists conducted genome-wide meta-analyses for the genetic effects on 174 blood metabolite levels in up to 86,507 individuals¹. Their findings not only demonstrated synergy among different metabolomic assays but also revealed novel mechanisms underlying type-2 diabetes (T2D) and macular telangiectasia type 2, a rare degenerative retinal disease.

Talk With An Expert About Your Project

The Challenge: Limitations of a single metabolomic platform

Blood metabolite levels are highly heritable, and a growing number of studies have begun to characterize the genetic landscape of metabolite fluctuations. However, previous reports examining genome-metabolite associations are often limited in size and scope due to the utilization of a single metabolomic platform. By combining results across various large-cohort studies utilizing different metabolomic platforms, optimal statistical power provides improved understanding of gene-metabolite associations, human physiology, and disease.

Metabolon Insight: A more robust genome-metabolite data set

To build a cross-platform metabolomic dataset, researchers utilized metabolites measured by several metabolomic platforms, including Metabolon’s Global Discovery Panel. This provided a set of platform-specific metabolites, enabling genome-wide meta-analyses on 174 metabolites from seven biochemical classes. With increased power and scope, researchers mapped gene-metabolite associations across platforms and included genetic architecture, determining putative causal genes for metabolite fluctuations and links to disease.

The Solution: Integration across metabolomic platforms

Among the metabolites analyzed, cross-platform metabolomics revealed 499 gene-metabolite associations from 144 loci. To determine the statistical viability of cross-platform integration, researchers investigated the effects of cohort, measurement platform, metabolite class, and association strength. The variabilities between platforms were largely due to the overall strength of the signal, but the directionality of associations was consistent, demonstrating that pooling measurements across metabolomic platforms is not only feasible but also enables more powerful analyses. Notably, this approach revealed that the genetic effects on metabolite levels are more than threefold compared to what is typically seen in common genetic variants.

Since many of the metabolites targeted in this study are associated with T2D, the researchers sought to understand whether this cross-platform approach could provide novel insights into T2D pathophysiology. Interestingly, results revealed variants in a receptor called GLP2R, and while common variants are linked to increased T2D risk, one particular variant (rs17681684, previously considered benign) was not only associated with T2D phenotypes but also with elevated plasma citrulline. These findings were extended through in vitro studies, where impaired β-arrestin-specific signaling due to this GLP2R variant could be a potential mechanism underlying T2D pathophysiology.

This study further revealed a strong link between serine levels and the development of macular telangiectasia type 2. Risk scores generated by gene-metabolite associations revealed that a one standard deviation increase in serine levels is associated with a 95% lower risk of developing this rare degenerative retinal disease. These results suggest that blood metabolite levels can be utilized to treat these types of diseases via supplementation or pharmacological manipulations.

The Outcome: New insights in type-2 diabetes and retinal disease

With the advent of mGWAS, this report emphasizes the value of integrating metabolome data across various measurement platforms. With bolstered statistical power, these findings provide a deeper understanding of gene-metabolite interactions that underlie human physiology and demonstrate the translational utility of gene-metabolite associations toward clinical applications.

References

1. Lotta, LA, Pietzner, M, Stewart, ID, et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet 2021;(53):54-64.

Related Case Studies

See All Case Studies

Metabolomics finds PanK4 to be an Important Regulator of Glucose Uptake and Lipid Metabolism

Find out more $

Metabolomics provides insight into the pathology of sudden infant death syndrome and identifies biomarker candidates

Find out more $

Metabolomics Reveals Beneficial Changes to Metabolite Profiles During Diabetes Remission

Find out more $

References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.