Case Study

Metabolite Biomarkers for Type 2 Diabetes (T2D)

The Metabolon Global Discovery Panel was used to discover distinctive alterations in metabolic components of signaling pathways underlying different T2D subtypes.

This study associated T2D etiopathogenesis with metabolites that could be targeted for novel therapies. These metabolites can also diagnose the correct T2D subtype to improve care and outcomes for individuals with T2D. Monitoring these metabolites in patients with T2D via targeted metabolomic profiling may inform the efficacy of a therapeutic intervention.

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Metabolite Biomarkers for Type 2 Diabetes (T2D)

The Challenge: Type 2 Diabetes (T2D) Has a Heterogeneous Etiology

Type 2 diabetes (T2D) is a highly heterogeneous, multifactorial condition affecting over 400 million people worldwide. T2D’s heterogeneous etiology influences its progression, treatment, and complications. Therefore, a study recently conducted a data-driven cluster analysis in European individuals with T2D and stratified subjects into four clusters representing T2D subtypes: severe insulin-deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes.¹

Given that clinical discrepancies between racial/ethnic groups have been confirmed, a more thorough validation of different populations is still warranted. Furthermore, individualized therapies targeting the underlying pathophysiology of T2D should be a primary goal in treating these patients. In this study, metabolomics helped characterize the broader spectrum of physiological perturbations associated with T2D to enable subtype-specific individualization of therapies.

Metabolon Insight: Metabolomics Characterizes T2D Subtypes

The Metabolon Global Discovery Panel was used in this study to profile plasma samples from an Arab population with T2D (n = 420) and without T2D (n = 1735).² The goal was to discover distinctive alterations in metabolic components of signaling pathways underlying different T2D subtypes.

The Solution: Elucidating T2D Subtype-Specific Metabolites

Here, the clustering approach was applied to subjects with T2D from an Arab population. Further, the team used nontargeted metabolomics and affinity proteomics profiling to identify cluster-specific physiological and biochemical processes. This study identified 214 proteins and 194 metabolites associated with T2D. In relation to plasma metabolites, this study confirmed previous findings that associate T2D with sugars (glucose, mannose, 1,5-anhydroglucitol (1,5-AG), etc.), branched-chain amino acids (BCAAs), and several lipids and markers of kidney function.

Cluster-specific signatures of proteins and metabolites were established, revealing T2D subtype-specific molecular mechanisms. As in the case of metabolites, subjects in the SIDD group had the lowest 1,5-AG levels of all other T2D subtypes. Blood sugars (eg, mannose, glucose, and fructose), cortisone, and cortisol were considerably higher in the SIDD cluster, indicating dysregulated glucose metabolism and many chronic complications of T2D. Furthermore, the SIDD cluster had lower levels of gamma-glutamyl amino acids, indicating perturbed glutathione metabolism. The levels of two sphingomyelin species were also lower in the SIDD cluster. Downregulated sphingolipid metabolism can affect insulin sensitivity. The SIRD cluster had elevated levels of 12,13-DiHOME, which can be used as an alternative energy source for SIRD individuals given their potentially limited access to glucose due to insulin resistance. The MOD cluster had high levels of hydroxyasparagine, 5-(galactosylhydroxy)-L-lysine, and 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-Hoca), which play a role in lipid metabolism and obesity. The MARD cluster was the healthiest of the four T2D subtypes, with metabolomic profiles most similar to the controls.

The Benefit: Improving Type 2 Diabetes Diagnosis and Therapeutic Strategies and Efficacy

This group translated the T2D subtypes to an Arab population and discovered distinct molecular signatures to further elucidate these T2D subtypes’ etiology. The research team identified many T2D subtype-specific metabolite and protein signatures that could help identify pathways involved in the etiology of T2D and enable more personalized treatment approaches. Metabolites supported as potentially causal for T2D in this data could be particularly interesting as novel therapeutic targets. These metabolites could potentially help diagnose the correct T2D subtype to improve care and outcomes for individuals with T2D. Monitoring these metabolites in patients with T2D via targeted metabolomic profiling may inform the efficacy of new therapeutic interventions.

References

1. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-9. Epub 20180305. doi: 10.1016/S2213-8587(18)30051-2. PubMed PMID: 29503172.

2. Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, et al. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun. 2022;13(1):7121. Epub 20221119. doi: 10.1038/s41467-022-34754-z. PubMed PMID: 36402758; PubMed Central PMCID: PMC9675829.

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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.