ON-DEMAND WEBINAR

Multiomic Integration of the Metabolome to Inform Biological Aging and Precision Medicine in Autism and Asthma

Research into biological age is crucial for improving personalized medicine, preventing age-related diseases, and enhancing overall quality of life by identifying individuals at risk and offering targeted interventions. It can also help optimize aging interventions and alleviate the societal impact of aging populations. However, the challenges are significant, including the complexity of aging, the lack of consensus on biomarkers, the need for long-term studies, individual variability in aging processes, and the integration of multiomic data. In this webinar, Dr. Jessica Lasky-Su will discuss her work developing the OMICmAge.

Leveraging the rich, longitudinal resource of the Mass General Brigham (MGB) Biobank, Dr. Lasky-Su and her team integrated over two decades of electronic health records (EHRs) with multiomic data—including DNA methylation, proteomics, and untargeted metabolomics—to advance the understanding of biological aging and precision medicine. They developed OMICmAge, the first multiomic aging biomarker trained across these complementary omic layers, capturing dimensions of molecular aging that outperform single-omic clocks. It further demonstrates that the metabolome enables the extraction of individual nutritional signatures, which show distinct associations with OMICmAge and other aging biomarkers. Additionally, they were able to highlight novel links between metabolomic features and circulating antibody profiles, suggesting immune-metabolic crosstalk relevant to aging and disease. As a case study in precision medicine, Dr. Lasky-Su will show how metabolite ratios can predict asthma exacerbations and stratify patients likely to respond to anti-IgE biologics, underscoring the metabolome’s value in guiding targeted interventions.

Together, these findings establish the metabolome as a powerful bridge between aging, exposome and clinical outcomes in aging and disease.

In This Webinar You Will Learn:

  • How multiomic data, including DNA methylation, proteomics, and metabolomics, advances our understanding of biological aging and precision medicine.
  • The development of OMICmAge, the first multiomic aging biomarker that captures molecular aging dimensions beyond traditional single-omic clocks.
  • The role of the metabolome in identifying individual nutritional signatures and its connections to aging biomarkers.
  • How integrating the exposome with clinical outcomes can revolutionize patient stratification and individualized medicine.

Speakers

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”Dr.

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Dr. Natasa Giallourou, Ph.D.

Field Metabolomic Scientist at Metabolon

Dr. Natasa Giallourou is a Field Metabolomics Scientist supporting Metabolon’s International Business activities. She provides scientific counsel for metabolomics applications in the biopharma and academic sectors. Natasa obtained her Ph.D. in Metabolomics from the University of Reading and holds an M.Sc. in Nutrition and Health from Wageningen University and a B.Sc. in Biology from the University of Leeds.

Prior to joining Metabolon, Natasa served as a Marie Skłodowska-Curie Postdoctoral Fellow at biobank.cy. Her research projects involved integrating metabolomic data with other omics data in population-based studies, with a focus on identifying biomarkers for complex diseases. She has also worked as a postdoctoral research associate at Imperial College London, where she specialized in utilizing metabolic phenotyping to address global health challenges, particularly in the field of public health nutrition.

Natasa sits on the Board of Directors of the International Metabolomics Society and is also an advisor to the Early-career Member’s Network for young metabolomics scientists.

WATCH WEBINAR

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.