When COVID-19 Helped to Advance Metabolic Phenotyping

The Collaborative Side of COVID-19

COVID-19 has put enormous strain on global healthcare systems, resulting in more than 7 million deaths worldwide. While the long-term impacts on health systems, economies, societies, and governance remain significant, the pandemic also triggered an unprecedented wave of scientific, clinical, and industrial collaboration. This collective effort accelerated the development of diagnostics, vaccines, and therapeutics.

The International COVID Research Network, as part of the International Phenome Center Network (IPCN), was established in April 2020 as a rapid international response to investigate the metabolic impact of SARS-CoV-2 infection using harmonized molecular phenotyping approaches.

Building on the IPCN’s expertise in population-scale phenotyping, the consortium brought together leading NMR centers and clinical partners to analyze samples from diverse cohorts and generate comparable data across laboratories.

NMR spectroscopy was selected as the lead technology because it enabled standardized, reproducible, and rapidly deployable analysis of both targeted and untargeted metabolic profiles.

Bruker BioSpin played a key role as a supporting partner and co-chair of the Task Force, providing the common Bruker Avance IVDr (for research use only) NMR analytical platform used across participating laboratories. Through coordinated data generation, validation, and scientific exchange, this International COVID Research Network contributed to a deeper understanding of COVID-19 as a systemic disease and strengthened preparedness for future pandemic research.

Metabolic Phenotyping: A Precision Lens on Disease

This collaborative momentum found a natural extension in metabolic phenotyping.

Using advanced NMR and mass spectroscopy (MS), researchers produced high-density molecular data from biological fluids and tissue samples. These profiles, analyzed through statistical modeling, allowed for comparisons at individual and population levels to identify key metabolic signatures of physiological and pathological conditions.^1,2

The ability to map such detailed biochemical profiles meant that researchers were able to identify personalized treatment targets and stratify disease risk.³ During the pandemic, these capabilities proved particularly powerful for understanding the metabolic effects of SARS-CoV-2.

The structure of the International COVID-19 Research Network team enhanced this impact.

Ten 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 supporting partner), all well experienced in population phenotyping, efficiently collected and analyzed small sample sets across clinical sites. Data was processed locally and then pooled to strengthen statistical significance.

All laboratories operated on a shared Bruker Avance IVDr NMR platform, which enabled both standardized targeted and untargeted modeling. This setup also facilitated rapid cross-validation of new biomarkers and methodological innovations.

This article highlights the significant contributions of the International COVID-19 Research Network in advancing COVID-19 research and outlines the implications for future studies into emerging diseases.

“COVID-19-Driven" Metabolic Discoveries

Lipoproteins – Vital Biomarkers for Prognosis, Disease Outcome & Recovery Rate

Ensuring complete standardization of sample preparation, NMR measurements, and analysis during studies meant spectra could be recorded on different spectrometers of the same type at different sites, in an archetypal manner. As a result, spectral data from different sites could, therefore, be combined for much broader-scale untargeted analysis.

One of the consortium's most consistent findings was the elevation of low-density lipoproteins (LDL) and very-low-density lipoprotein (VLDL) triglycerides in acutely infected patients, likely due to both overproduction and impaired clearance. This pattern may contribute to the observed connection between COVID-19 and insulin resistance.

Even more striking, prognosis models of COVID-19 showed that lipoproteins offered the best predictive markers of patient outcomes. Lipoproteins measured from serum samples from patients 8-61 days prior to death offered an important discriminatory marker that distinguished survivors from non-survivors with severe COVID infection.

Metabolic Variation During Recovery from SARS-CoV-2 Infection

The consortium also found that changes in molecular metabolites were consistent across populations in Europe and Australia, with hyperglycemia and an abnormal amino acid signature consistently observed in COVID-19 cohorts. This profile is consistent with that of a diabetic metabolic profile.⁴

While consortium collaborates Holmes et al. and Bizkarguenaga et al. discovered heterogeneity in recovery from SARS-CoV-2 infection, with some patients displaying biochemical abnormalities even 3-6 months post the acute infection.^5,6 These recovery studies found increased levels of the inflammatory markers GlycA and/or GlycB in some individuals.

Analysis of serum samples obtained from 263 patients hospitalized after positive PCR testing for SARS-CoV-2 infection, by another research group within the COVID-19 network, highlighted ketone bodies as a fundamental discriminatory metabolite for SARS-CoV-2 infection. Impaired mitochondrial functioning was indicated in patients still displaying raised levels of ketones and pyruvate. While symptoms of fatigue, inability to concentrate, and brain fog were specifically linked to increased Quinolinate (a neurotoxic metabolite) and decreased Tryptophan.⁷

Methodological Advances Beyond the Pandemic

Beyond serum-based analyses, the consortium also explored whether urine-based metabolic profiling could provide complementary, non-invasive insights into SARS-CoV-2 infection. It was demonstrated that non-invasive diagnostic and prognostic screening for COVID-19 using urine samples was possible. Patients suffering from COVID-19 had increased levels of glucose, acetone, taurine, and lactic acid, while hippuric acid, tartaric acid, and creatine were reduced, when compared to healthy control patients. Metabolic urinary concentrations could also be used to decipher the severity of the infection.⁸

Another major advancement was the development of the J-Edited Diffusional (JEDI) proton NMR spectroscopic method. Integrated into Bruker's IVDr platform and used in Bruker PhenoRiskPACS1.0 Data Analysis Module, JEDI enabled high-fidelity quantification of key inflammatory biomarkers Glycoproteins GlycA/B and Supramolecular Phospholipid Composite SPC1-3. The method isolates these markers by removing spectral signals from small molecules, large proteins, and specific lipoprotein peaks.

What’s interesting is that this technique’s utility extends far beyond that of COVID-19. It is also proving to be an important tool to measure SPC1-3 and GlycA/B for other disease conditions, including sepsis and obesity.^9,10

Conclusion

The research conducted by the International COVID Research Network provided novel and deeper insights into the multi-systemic organ pathologies of COVID-19 at the metabolite level, including identifying novel metabolic compounds predictive of viral infection. Thus, they offer important insights for patient monitoring throughout the SARS-CoV-2 infection period and during recovery.

The methodological advancements achieved by the consortium provide more accurate and augmented measurements of lipo- and glycoproteins and offer translatability to cost-effective field deployable benchtop NMR spectrometers. Their use of NMR multi-cohort studies ensured the rapid validation of these approaches.

References

1. Bales, J. R. et al. Metabolic profiling of body fluids by proton NMR: self-poisoning episodes with paracetamol (acetaminophen). Magn. Reson. Med. 6, 300–306 (1988).
2. Holmes, E., Wilson, I. D. & Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 134, 714–717 (2008).
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. Integrative modeling of plasma metabolic and lipoprotein biomarkers of SARS-CoV-2 infection in Spanish and Australian COVID-19 patient cohorts. J. Proteome Res. 20, 4139–4152 (2021).
5. Holmes, E., et al. 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 (2021).
6. Bizkarguenaga, M., et al. Uneven metabolic and lipidomic profiles in recovered COVID‐19 patients as investigated by plasma NMR metabolomics. NMR in Biomedicine. 35(2), p.e4637. (2022).
7. Bruzzone, C. et al. SARS-CoV-2 infection dysregulates the metabolomic and lipidomic profiles of serum. iScience 23, 101645 (2020).
8. Lodge, S. et al. Stratification of sepsis patients on admission into the Intensive Care Unit according to differential plasma metabolic phenotypes. J. Proteome Res. 23, 1328–1340 (2024).
9. Ahmad, M. S. et al. Exploring the interactions between obesity and diabetes: Implications for understanding metabolic dysregulation in a Saudi Arabian adult population. J. Proteome Res. 23, 809–821 (2024).
10. Nitschke, P. et al. Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum. Analyst 147, 4213–4221 (2022).