Case Study

An Intervention for Type 2 Diabetes

High physical activity leads to metabolic changes that prevent type 2 diabetes.

This study’s results provide a detailed resource regarding the metabolic architecture of physical activity in humans and pinpoint specific pathways that are prognostically relevant. This offers novel avenues for future type 2 diabetes mechanistic investigation and intervention.

This study’s results provide a detailed resource regarding the metabolic architecture of physical activity in humans and pinpoint specific pathways that are prognostically relevant. This offers novel avenues for future type 2 diabetes mechanistic investigation and intervention.

An Intervention for Type 2 Diabetes

The Challenge: Physical Activity and Diabetes

Diabetes is among the top ten causes of death worldwide. Previous studies have shown that physical activity can prevent type 2 diabetes in individuals with a high risk of this disease.1 Consequently, harnessing the power of physical activity as a therapeutic tool could have far-reaching preventive health impacts. As with many areas in population health, the association between physical activity and type 2 diabetes is well documented; however, the mechanisms involved and their complex interactions are not yet fully elucidated. Studies that dissect the molecular changes related to physical activity will improve our understanding of how physical activity works as an intervention. Metabolomics offers a promising approach to exploring this relationship.

The Metabolon Insight: Metabolic Changes Related to Physical Activity

Metabolon helped investigate the association between the metabolite signature of physical activity and the risk for type 2 diabetes.The Metabolon Global Discovery Panel was used to analyze plasma samples from 7,271 male participants.

The Solution: Metabolomics Elucidates the Link Between Physical Activity and Diabetes

The current study demonstrated that increased physical activity is associated with a lower incidence of type 2 diabetes, increased insulin sensitivity, and insulin secretion. The group also found that an increase in physical activity leading up to a follow-up exam decreased glucose levels, increased insulin sensitivity, and insulin secretion compared to participants who did not increase their physical activity levels. On the other hand, participants who reduced their physical activity had a significant increase in their glucose levels and a decrease in their insulin sensitivity, and insulin secretion compared to those who did not change their physical activity.

Metabolomics can contribute significantly to the understanding of the effects of physical activity on metabolic pathways associated with the risk of type 2 diabetes. Using the Global Discovery Panel, the present study found 198 metabolites significantly associated with high physical activity.  Glycerophospholipids (28%), amino acids (15%), and glycerolipids (8%) were the most common metabolite groups associated with an increase in physical activity. Increased physical activity was significantly associated with high levels of choline plasmalogens, lysophosphatidylcholines, polyunsaturated fatty acids (PUFAs), carotenoids, long-chain acylcarnitines, imidazoles, bilirubins, aryl sulfates, hydroxy acids, indolepropionate, and indolelactate. Some of these metabolites have previously been associated with a decreased risk for type 2 diabetes and a healthy diet. This study suggests that the metabolite profile of increased physical activity includes multiple metabolic pathways that are associated with a healthy lifestyle.

The Outcome: Revealing Molecular Pathways that are Prognostically Relevant

This study’s results provide a detailed resource regarding the metabolic architecture of physical activity in humans and pinpoint specific pathways that are prognostically relevant. This offers novel avenues for future type 2 diabetes mechanistic investigation and intervention. Furthermore, it provides the potential for the identification of clinically useful biomarkers. Future studies could provide a comprehensive overview of changes in metabolite pathways dependent on different types of physical activity. Clinicians can potentially use findings from such studies to titrate exercise regimens based on individually tailored diabetes outcomes. These individualized exercise regimens can be prescribed even to healthy individuals, reducing the burden of type 2 diabetes in healthcare systems worldwide.

References

1. Merlotti, C.; Morabito, A.; Pontiroli, A.E. Prevention of type 2 diabetes; a systematic review and meta-analysis of different intervention strategies. Diabetes Obes. Metab. 2014, 16, 719–727. doi.org/10.1111/dom.12270

2. Kemppainen SM, Fernandez-Silva L, Lankinen MA, Schwab U, and Laakso M. Metabolite Signature of Physical Activity and the Risk of Type 2 Diabetes in 7271 Men. Metabolites. 2022;12(1):69. doi: 10.3390/metabo12010069.

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.