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Pollution Exposure and Skin Diseases

Multi-omics signatures reveal early signs of skin diseases associated with pollution exposure.

The metabolomic signatures identified by Metabolon revealed macromolecular alterations by pollution that could develop into clinical signs of early skin pigmentation and/or other imperfections.

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The metabolomic signatures identified by Metabolon revealed macromolecular alterations by pollution that could develop into clinical signs of early skin pigmentation and/or other imperfections.

Talk With An Expert About Your Project

The Challenge: No Mechanistic Link Between Pollution Exposure and Skin Diseases

Pollution is responsible for 15% of all deaths globally, and 275 million disability-adjusted life years. A major contributor to pollution is particulate matter (PM), inhalable particles found in the air that are 10 µm or smaller. PM is often associated with toxic chemicals such as heavy metals or polycyclic aromatic hydrocarbons (PAHs). A major mechanism by which PM exerts its detrimental effects is through the generation of oxidative stress which contributes to extrinsic skin aging.1 Previous studies of in vitro skin models have shown that exposure to PAHs leads to the disruption of several cellular processes.1 However, the molecular mechanisms that link pollution exposure and its impact on clinical manifestations of illness remain to be deciphered.

Metabolon Insight: Identified Differentially Regulated Metabolites in Individuals Living in a Polluted Environment

The participants of the study were assessed for the skin clinical parameters: acne, wrinkles, pigmented spots, etc., to assess the effect of chronic exposure to pollution on exposed areas of the skin. PAH can be identified in blood and hair cortex, so pollution exposure was determined by quantifying 39 parent PAHs and their monohydroxy metabolites in hair samples collected from the study participants. Non-invasive facial skin samples (D-Squame) were collected from 134 healthy women (24 to 45 years) living for at least 15 years in the Chinese cities of Baoding (used as a model of polluted area) and Dalian (control area with lower level of pollution).1 Women from the two cities (polluted and less polluted) showed distinct metabolic profiles and alterations in skin microbiome. The Metabolon Global Discovery Panel identified 350 metabolites using skin samples. Amino acid and fatty acid metabolism, gamma-glutamyl amino-acids, and urea cycle intermediates differed significantly between the Baoding and Dalian groups.

Additionally, there is complex interplay between the host metabolism and its microbiota occurring on the skin surface. Therefore, the skin microbiome was analyzed using bacterial 16S rRNA and fungal ITS1 amplicon sequencing using metagenomics analysis. Both exposure groups showed distinct metabolic profiles and alterations in skin microbiome.

The Solution: Pollution Effects Linked to Skin Microbiome Generated a Multi-omics Signature to Identify Effects of Pollution

The Global Discovery Panel identified 350 metabolites by comparing samples from patients that live in polluted and non-polluted areas. Metabolomic analysis complemented targeted proteomic analysis to characterize pollution-dependent biochemical events. Together these analyses suggest that chronic exposure to pollution induces a dynamic metabolic state that must simultaneously manage damage and repair mechanisms.

This multi-omics approach identified 39 PAH and 143 microbes and was used to develop a multiblock data analysis method to identify multi-omics signatures to predict skin dysfunctions (acne, wrinkles, pigmented spots etc.) in a polluted environment. The results pointed to a link between the microbiome and the impact of pollution exposure.

The Outcome: Multi-omics Signatures Could Identify Early Skin Imperfections in Clinic

The metabolomic signatures identified by Metabolon revealed macromolecular alterations by pollution that could develop into clinical signs of early skin pigmentation and/or other imperfections. This multi-omics signature approach can be used as a model to generate molecular maps for other human diseases.

References

1. Misra N, Clavaud C, Guinot F, et al. Multi-omics analysis to decipher the molecular link between chronic exposure to pollution and human skin dysfunction. Sci Rep. Sep 15 2021;11(1):18302. doi:10.1038/s41598-021-97572-1

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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.