How COVID-19 accelerated advances in metabolic phenotyping

The cross-sector collaborations that formed during the pandemic have supported metabolic studies into viral pathology and host response, and, in turn, enhanced pandemic preparedness.

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The collaborative side of COVID-19

COVID-19 placed significant strain on healthcare systems worldwide and led to more than seven million deaths worldwide. While the long-term effects on health systems, economies, societies, and governance remain substantial, the pandemic also triggered an unprecedented level of scientific, clinical, and industrial partnership.

This collaborative effort sped up the development of diagnostics, vaccines, and therapeutics.

The International COVID Research Network, as part of the International Phenome Center Network (IPCN), was founded in April 2020 as a rapid international response to research the metabolic effects of SARS-CoV-2 infection through the use of harmonized molecular phenotyping techniques.

Drawing on the IPCN’s expertise in population-scale phenotyping, the consortium brought together leading NMR centers and clinical partners to evaluate samples from diverse cohorts and produce comparable data across labs.

NMR spectroscopy was chosen as the primary technology due to its ability to provide standardized, reproducible, and rapidly deployable evaluation of both targeted and untargeted metabolic profiles.

Bruker BioSpin was a critical supporting partner and co-chair of the Task Force, supplying the common Bruker Avance IVDr (for research use only) NMR analytical platform adopted across participating labs.

By coordinating data generation, validation, and scientific exchange, this International COVID Research Network helped provide a more comprehensive understanding of COVID-19 as a systemic disease and enhanced readiness for future pandemic studies.

Metabolic phenotyping: A precision lens on disease

This collaborative momentum naturally extended into metabolic phenotyping.

Using advanced NMR and mass spectroscopy (MS), scientists generated high-density molecular data from biological fluids and tissue samples. These profiles, evaluated via statistical modeling, enabled individual and population-level comparisons to determine critical metabolic signatures of physiological and pathological conditions.^1,2

The ability to map such in-depth biochemical profiles enabled scientists to determine personalized treatment targets and stratify disease risk.³ These capabilities were especially useful during the pandemic for providing insight into the metabolic impacts of SARS-CoV-2.

The structure of the International COVID-19 Research Network team improved this effect.

With extensive experience in population phenotyping, 10 long-standing partners (ANPC, CIC-bioGUNE, Tübingen University, University of Lübeck, Graz University, CERM Florence, Trondheim University, Vanderbilt University, Cincinnati Children's Hospital, and Bruker as the supporting partner) efficiently gathered and evaluated small sample sets across clinical sites.

Following local processing, data was pooled to improve statistical significance.

All labs operated using a shared Bruker Avance IVDr NMR platform, which allowed for standardized targeted and untargeted modeling. This configuration also supported fast cross-validation of novel biomarkers and methodological innovations.

This article details the remarkable contributions of the International COVID-19 Research Network to advancing COVID-19 research and summarizes the implications for future research on novel diseases.

“COVID-19-driven" metabolic discoveries 

Lipoproteins: Crucial biomarkers for prognosis, disease outcome, and recovery rate

Ensuring complete standardization of sample preparation, NMR measurements, and analysis across the research enabled recording spectra on the same spectrometer type at multiple sites in an archetypal manner. Consequently, spectral data from different sites could be combined for much larger-scale untargeted evaluation.

Among the most consistent findings of the consortium was an increase in triglycerides in low-density lipoproteins (LDL) and very-low-density lipoproteins (VLDL) in patients with acute infection, most likely resulting from both overproduction and impaired clearance. This pattern may contribute to the observed association between COVID-19 and insulin resistance.

Even more intriguing, COVID-19 prognosis models demonstrated that lipoproteins provided the best predictive markers of patient outcomes. Lipoproteins measured from serum samples from patients eight to 61 days before death provided a valuable discriminatory marker, distinguishing survivors from non-survivors with severe COVID infection.

Metabolic variation during recovery from SARS-CoV-2 infection

Another finding of the consortium was that molecular metabolite variations were consistent across populations in Europe and Australia. Hyperglycemia and an abnormal amino acid signature were consistently observed in COVID-19 cohorts. This profile is consistent with a diabetic metabolic profile.⁴

Consortium collaborators Holmes et al. and Bizkarguenaga et al. identified heterogeneity in recovery from SARS-CoV-2 infection, with some patients exhibiting biochemical abnormalities even three to six months after the acute infection.^5,6 This recovery research found elevated levels of the inflammatory markers GlycA and/or GlycB in certain individuals.

Another research group within the COVID-19 network analyzed serum samples acquired from 263 patients hospitalized following positive PCR testing for SARS-CoV-2 infection and identified ketone bodies as a key discriminatory metabolite for SARS-CoV-2 infection.

Compromised mitochondrial function was indicated in patients who continued to display elevated ketone and pyruvate levels. Symptoms of fatigue, inability to concentrate, and brain fog were specifically associated with elevated quinolinate (a neurotoxic metabolite) and reduced tryptophan.⁷

Methodological developments beyond the pandemic

In addition to serum-based evaluations, the consortium also investigated whether urine-based metabolic profiling could deliver complementary, non-invasive insights into SARS-CoV-2 infection. The study demonstrated the feasibility of using urine samples for non-invasive diagnostic and prognostic screening of COVID-19.

Compared to healthy control patients, patients with COVID-19 exhibited elevated levels of glucose, acetone, taurine, and lactic acid, while hippuric acid, tartaric acid, and creatine were reduced. Metabolic urinary concentrations could also be used to determine the severity of the infection.⁸

Another significant advancement was the development of the J-Edited Diffusional (JEDI) proton NMR spectroscopic technique. JEDI was integrated into Bruker's IVDr platform and employed in Bruker’s PhenoRiskPACS1.0 Data Analysis Module, enabling high-fidelity quantification of critical inflammatory biomarkers Glycoproteins GlycA/B and Supramolecular Phospholipid Composite SPC1-3.

Using this technique, these markers are isolated by removing spectral signals from small molecules, large proteins, and specific lipoprotein peaks.

Notably, the utility of this method extends well beyond COVID-19. It is also proving valuable for measuring SPC1-3 and GlycA/B for other disease conditions, such as sepsis and obesity.^9,10

Conclusion

The study conducted by the International COVID Research Network delivered deeper, more novel insights into the multisystemic organ pathologies of COVID-19 at the metabolite level, including the identification of metabolic compounds that predict viral infection.

As a result, researchers provided valuable insights into monitoring patients during SARS-CoV-2 infection and throughout recovery.

The consortium's methodological developments deliver more precise and enhanced measurements of lipo- and glycoproteins, while also enabling translation to affordable, field-deployable benchtop NMR spectrometers. Their use of NMR multi-cohort research enabled rapid validation of these techniques.

References and further reading

1. Bales, J.R., et al. (1988). Metabolic profiling of body fluids by proton NMR: Self‐poisoning episodes with paracetamol (acetaminophen). Magnetic Resonance in Medicine, 6(3), pp.300–306. DOI:10.1002/mrm.1910060308. https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.1910060308.
2. Holmes, E., Wilson, I.D. and Nicholson, J.K. (2008). Metabolic Phenotyping in Health and Disease. Cell, 134(5), pp.714–717. DOI:10.1016/j.cell.2008.08.026. https://www.cell.com/fulltext/S0092-8674(08)01071-4?large_figure=true.
3. Nicholson, J., Darzi, A., Holmes, E. & Lindon, J. C. Metabolic Phenotyping in Personalized and Public Healthcare. (Academic Press, San Diego, CA, 2016).
4. Masuda, R., et al. (2021). Integrative Modeling of Plasma Metabolic and Lipoprotein Biomarkers of SARS-CoV-2 Infection in Spanish and Australian COVID-19 Patient Cohorts. Journal of Proteome Research, 20(8), pp.4139–4152. DOI:10.1021/acs.jproteome.1c00458. https://pubs.acs.org/jprobs/article/20/8/4139/797171/Integrative-Modeling-of-Plasma-Metabolic-and.
5. Holmes, E., et al. (2021). Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome. Journal of Proteome Research, 20(6), pp.3315–3329. DOI:10.1021/acs.jproteome.1c00224. https://pubs.acs.org/jprobs/article/20/6/3315/814880/Incomplete-Systemic-Recovery-and-Metabolic.
6. Bizkarguenaga, M., et al. (2021). Uneven metabolic and lipidomic profiles in recovered COVID‐19 patients as investigated by plasma NMR metabolomics. NMR in Biomedicine, 35(2). DOI:10.1002/nbm.4637. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4637.
7. Bruzzone, C., et al. (2020). SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum. iScience, 23(10), p.101645. DOI:10.1016/j.isci.2020.101645. https://www.cell.com/iscience/fulltext/S2589-0042(20)30837-3.
8. Lodge, S., et al. (2024). Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes. Journal of Proteome Research, 23(4), pp.1328–1340. DOI:10.1021/acs.jproteome.3c00803. https://pubs.acs.org/jprobs/article/23/4/1328/145743/Stratification-of-Sepsis-Patients-on-Admission.
9. Ahmad, M.S., et al. (2024). Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population. Journal of Proteome Research, 23(2), pp.809–821. DOI:10.1021/acs.jproteome.3c00717. https://pubs.acs.org/jprobs/article/23/2/809/145632/Exploring-the-Interactions-between-Obesity-and.
10. Nitschke, P., et al. (2022). Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum. The Analyst, 147(19), pp.4213–4221. DOI:10.1039/d2an01097f. https://pubs.rsc.org/an/article/147/19/4213/754836/Direct-low-field-J-edited-diffusional-proton-NMR.

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