Case Study

Genetic Studies of Paired Metabolomes Reveal Enzymatic and Transport Processes at the Interface of Plasma and Urine

Metabolites in bodily fluids reflect a person’s unique chemistry and physiology. Genetics, environment, health and disease state all contribute to a person’s metabolic profile.1

A study published in Nature Genetics used Metabolon’s untargeted Global Discovery Panel to analyze the blood plasma and urine metabolomes of over 5,000 participants from the German Chronic Kidney Disease (GCKD) study. Parallel genome-wide screens were used to uncover genetic associations in kidney function that would have been missed by looking at plasma alone.2

A recent study published in Nature Genetics used Metabolon’s untargeted Global Discovery Panel to analyze the blood plasma and urine metabolomes of over 5,000 participants from the German Chronic Kidney Disease (GCKD) study. Parallel genome-wide screens were used to uncover genetic associations in kidney function that would have been missed by looking at plasma alone2.

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine

Characterizing Kidney Function Using Parallel Metabolomes

The kidneys play a crucial role in removing waste from the body while retaining essential molecules like amino acids and sugars. With many of their functions still unknown, hundreds of transport proteins and enzymes involved in this process form the dark matter of the kidney. In the past, discoveries about specific kidney proteins were made by studying human diseases caused by single gene mutations. For instance, the sodium-glucose transporter SGLT2 was discovered in a rare genetic disease called familial renal glucosuria.3 Today, SGLT2 is a target for therapies treating diabetes and chronic kidney disease.

However, not all mutations in transport proteins lead to obvious diseases—this is where the combination of metabolomics and genomics comes into play. Schlosser and colleagues used Metabolon’s untargeted Global Discovery Panel to analyze the levels of 1,296 metabolites in plasma and 1,399 metabolites in urine. Combined with a genome-wide association approach, this study revealed 677 genetic loci in plasma and 622 in urine associated with 760 metabolites.2 All participants in the study had chronic kidney disease (CKD); however, most genetic effects on urine metabolites in people with CKD are also present in healthy adults.4

When Two Metabolomes are Better than One

Combining metabolomics with genomics isn’t new; however, previous genomics-metabolomics studies have primarily focused on metabolites from a single compartment, either plasma or urine.1,4,5 Among metabolites examined in both plasma and urine, about half (49%) of significantly associated genomic loci were identified only in one compartment or the other, and almost 40% of significant associations with a metabolite would have been missed by only studying plasma.2 For example, AQP-7, an aquaporin protein that transports water and glycerol, is localized to the apical membrane in the proximal tubule of the kidney, where its activity affects metabolites in urine but not plasma. On the other hand, SLC13A3, a transport protein facing the plasma, exclusively influences its associated metabolites in the plasma. And still, other proteins like the bile acid transporter SLC10A2 have distinct effects on both sides of the kidney interface and show genetic associations with their corresponding metabolites in both plasma and urine.

Implications for Health

The metabolome has been considered an avenue to more sensitive and accurate views of health and disease, both in the kidney and elsewhere, based on blood or urine metabolites.2,6,7 This study identified two urine metabolites that could be linked to cardiometabolic disease. Galactosylglycerol and 1,6-anhydroglucose were linked to FUT2 mutations that are also associated with lipid metabolism disorders, hypertension, and gallstones. These findings invite future studies to validate these associations and advance precision medicine through metabolomics.2,6

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;28(11):2321-2332. doi:10.1038/s41591-022-02046-0

2. Schlosser P, Scherer N, Grundner-Culemann F, et al. Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine. Nat Genet. 2023;55:995-1008. doi:10.1038/s41588-023-01409-8

3. Gyimesi G, Pujol-Giménez J, Kanai Y, Hediger MA. Sodium-coupled glucose transport, the SLC5 family, and therapeutically relevant inhibitors: from molecular discovery to clinical application. Pflugers Arch. 2020;472(9):1177-1206. doi:10.1007/s00424-020-02433-x

4. Schlosser P, Li Y, Sekula P, et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet. 2020;52(2):167-176. doi:10.1038/s41588-019-0567-8

5. Lotta LA, Pietzner M, Stewart ID, et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet. 2021;53(1):54-64. doi:10.1038/s41588-020-00751-5

6. Davies R. The metabolomic quest for a biomarker in chronic kidney disease. Clin Kidney J. 2018;11(5):694-703. doi:10.1093/ckj/sfy037

7. Aderemi AV, Ayeleso AO, Oyedapo OO, Mukwevho E. Metabolomics: a scoping review of its role as a tool for disease biomarker discovery in selected non-communicable diseases. Metabolites. 2021;11(7):418. doi:10.3390/metabo11070418

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.