Contact us Demo our platform

Blog

COVID-19 Severity Anticipated Via Multiomics Model For Predictive Biomarkers

COVID-19-Severity-Anticipated-Via-Multiomics-Model-For-Predictive-Biomarkers

Research has demonstrated certain correlations between COVID-19 severity and an array of comorbidities, and studies are now exploring the role of metabolomics and the microbiome in determining COVID-19 hospitalization.1 Previous research noted a connection between severity and specific cytokines, such as interleukin (IL)-6.2–4 Thus exploration of predictive biomarkers relating to COVID-19 severity could offer an opportunity to flag those prone to adverse outcomes.

Metabolomics and Multiomics Analysis for Predicting COVID-19 Severity

A retrospective cohort study comprised of 455 COVID-19 patients analyzed various predictive biomarkers for COVID-19 severity. For instance, the deep profiling included metabolites, lipids, circulatory cytokines, and other proteins.1 In particular, the study explored 1018 metabolites, 902 lipids, and 1463 cytokines and circulatory proteins to assess their connection to clinical and severe cases.1 Metabolon conducted the lipidomics for the study. Further, researchers leveraged proximity extension assays and tandem mass spectrometry for measurements of the biomarkers.1

In addition, the study included multiomics dataset analysis and machine learning algorithms for biomarker discovery. A select few previous studies combined machine learning with multiomics for COVID-19 severity research, but they were limited by smaller sample sizes than the current one.5,6

Machine-learning-based Prediction Model Identifies New Biomarkers

The research concluded that a machine-learning-based prediction model could accurately anticipate both clinical and severe COVID-19 outcomes with improved accuracy, as compared to traditional cytokine methods. Specifically, the study’s model noted a correlation with 102 predictive biomarkers for clinical and severe COVID-19 that included metabolites, lipids, and novel cytokines as well as additional proteins.1 In particular, these biomarkers included differing amounts of 2-hydroxydecanoate, C-type lectin domain family 6 member A (CLEC6A), and ether phosphatidylethanolamine (P-18:1/18:1).1

Further, the study’s metabolomic data showed that 12 of the 102 predictive biomarkers of COVID severity that changed were lipid-related.  Additional nonlipid metabolomic markers identified by the research were heme and polyamines, which showed increases with disease severity.  As well, the study combined proteomic cytokine storm markers with metabolomic markers, demonstrating the benefits of a complementary multiomic approach to biomarker discovery.

Additional research should explore validation of the findings that includes an independent control group cohort, but this study provides essential data supporting preliminary efficacy of the model and the key role of particular biomarkers in COVID-19 severity.

Study Implications for Multiomics Research

The findings suggest a benefit from exploring COVID-19 patients’ multiomic molecular signature from plasma prior to hospital admission.1 Doing so holds the potential to predict if the patient will suffer a more severe course of disease or not. Further, the study supports the idea that machine-learning approaches could potentially garner additional insights for clinical use by applying them to complex profiling data in additional disease areas.1

Compared to previous studies, this one was particularly well-powered with 455 cases and 182 controls. The study reported the important role of the microbiome and metabolites, as noted from the researchers’ identification of novel microbially-derived metabolite, imidazole propionate, which predisposes patients to poor COVID outcomes when levels are increased. In addition, the study has important implications regarding plasmalogens, which appear protective in this study. In summary, a multiomics analysis has the potential to drive additional research insights for COVID-19 and other diseases.

Ready to see what new insights metabolomics can help your research reveal?
Contact us today to discuss your project or study.

References

1. Byeon, S.K., Madugundu, A.K., Grapati, K., Ramarajan, M.G., Saraswat, M., Kumar-M, P., et al. Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity; a retrospective cohort study. The lancet. 2022; 4(9), E632-E645. DOI:https://doi.org/10.1016/S2589-7500(22)00112-1

2. Rosas IO, Bräu N, Waters M, et al. Tocilizumab in hospitalized patients with severe COVID-19 pneumonia. N Engl J Med. 2021; 384: 1503–16.

3. Horby P, Lim WS, Emberson JR, et al. Dexamethasone in hospitalized patients with COVID-19. N Engl J Med. 2021; 384: 693–704.

4. Group RC. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomized, controlled, open-label, platform trial. Lancet. 2021; 397: 1637–45.

5. Su Y, Chen D, Yuan D, et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell. 2020; 183: 1479–95.

6. Overmyer KA, Shishkova E, Miller IJ, et al. Large-scale multi-omic analysis of COVID-19 severity. Cell Syst. 2021; 12: 23–40.

Metabolon
Our team is made up of over 45 PhDs, has been published 4,000+ times, and is committed to hard work, excellence, and success through collaboration. With over 15,000 projects, Metabolon has been a trusted partner of researchers for over 25 years.

Topics

Academia / Medical Research | COVID-19

Share this article

Related Blog Posts

See All Blog Posts

GET STARTED

Talk with an expert

Request a quote, get detailed information on sample types, or learn how metabolomics can accelerate your research. Find our contact details are here.

Find us on:

Talk with a Metabolomics expert

✕

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.