Case Study

Identifying UPF Biomarkers and Their Impact on Health

Metabolon’s involvement in this study helped identify biomarkers of Ultra-Processed Food (UPF) intake and metabolic responses, shedding light on the effects of UPFs on human metabolism and their potential role in disease.

Metabolon proved instrumental in uncovering the effects of UPFs on metabolism, potentially offering valuable insights into their role in health outcomes and providing a basis for further research and strategies to address the health impact of UPFs.

Metabolon proved instrumental in uncovering the effects of UPFs on metabolism, potentially offering valuable insights into their role in health outcomes and providing a basis for further research and strategies to address the health impact of UPFs.

Identifying UPF Biomarkers and Their Impact on Health

The Challenge: Understanding the Health Impact of Ultra-Processed Foods

UPFs are food products that have undergone extensive processing and often contain hydrogenated oils, modified starches, preservatives, and artificial sweeteners. These foods are typically highly palatable, convenient, and have a long shelf life. However, consuming a diet high in UPFs has been associated with various health issues, including obesity, cardiovascular disease, type 2 diabetes, and certain cancers. In the United States, UPFs constitute more than 60% of the energy consumed by adults and children aged 2 to 18.

Currently, more research is needed to better understand the health effects of consuming UPFs. Addressing this knowledge gap is crucial for developing evidence-based strategies to reduce the consumption of UPFs and mitigate their potential adverse health impacts. In this study, Metabolon helped identify biomarkers of UPF intake and metabolic response to intake.1

Metabolon’s Insight: Identifying Metabolic Differences in UPF and Unprocessed Diet Patterns

This research team used the Metabolon Global Discovery Panel to profile plasma and urine samples from individuals in a domiciled, crossover, controlled-feeding trial. Twenty individuals consumed a UPF diet and an unprocessed diet for two weeks each. Using Metabolon’s services, the researchers identified metabolites that differed between dietary patterns (DP) high in or void of UPFs.

The Solution: Elucidating Key Metabolic Changes Induced by a UPF Diet

Plasma metabolomics showed that 257 out of 993 plasma and 606 out of 1279 urine metabolites differed between DPs. Overall, 21 known metabolites differed across biospecimen types. Six metabolites had higher levels, and fourteen had lower levels following a UPF diet. Acesulfame had the largest effect change between DPs, being higher after the UPF diet. Three metabolites related to benzoate metabolism (2-methozyhydroquinone sulfate, 4-ethylphenyl sulfate, and 4-vinylphenol sulfate) were consistently higher after the UPF diet in both plasma and urine samples. Most bile acids from plasma and urine were lower after the UPF diet. These results suggest that ingredients common to UPFs affect the human metabolome and justify further research as dietary biomarkers of a UPF diet.

The Outcome: Metabolomics Unveils the Effects of UPFs on Human Metabolism

Metabolomics provided a powerful tool for identifying metabolites that change with the intake of UPFs. Metabolomics analyses demonstrated that consuming a diet high in UPFs has a measurable impact on the human metabolome. These metabolites could serve as biomarkers of UPF intake or metabolic response to UPF intake. These biomarkers have the potential to estimate the association of UPFs with disease and provide insight into biological mechanisms linking UPF intake to human health.

References

1. O’Connor LE, Hall KD, Herrick KA, et al. Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr. Jun 03 2023;doi:10.1016/j.tjnut.2023.06.003

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.