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. 2025 Jun 18:12:1607583.
doi: 10.3389/fmolb.2025.1607583. eCollection 2025.

Comprehensive clinical and metabolomics profiling of COVID-19 Mexican patients across three epidemiological waves

David Alejandro García-López  1 , Joel Monárrez-Espino  2 , Juan Carlos Borrego-Moreno  3 , Jiamin Zheng  4 , Rupasri Mandal  4 , Claudia Torres-Calzada  4 , Juan José Oropeza-Valdez  5 , Alanne Tenório Nunes  6 , Sergio Hugo Sánchez Rodríguez  7 , Jesús Adrián López  8 , Blanca Estela Calzada Rodríguez  3 , David S Wishart  4 , Yamilé López-Hernández  1   4
Affiliations

Comprehensive clinical and metabolomics profiling of COVID-19 Mexican patients across three epidemiological waves

David Alejandro García-López et al. Front Mol Biosci. .

Abstract

Introduction: As of mid-2024, COVID-19 has affected over 676 million people worldwide, leading to more than 6.8 million deaths. Numerous studies have documented metabolic changes occurring during both the acute phase of the disease and the recovery phase, which, in some cases, contribute to the development of long COVID syndrome.

Aims and methods: In this study, we aimed to evaluate clinical, laboratory, and comprehensive metabolomic data from hospitalized COVID-19 patients during the second, third and fourth waves (Alpha, Delta, and Omicron). A targeted, fully quantitative metabolomics assay (TMIC MEGA Assay) was used to measure 529 metabolites and lipids in plasma samples. The metabolomic profiles of these patients were compared according to different and relevant factors impacting COVID-19 outcome, such as age, sex, comorbidities, and vaccination status.

Results: Among the 21 classes of compounds evaluated in this study, amino acids and lipids were the most dysregulated when comparing age, sex, comorbidities, vaccination status, and the different epidemiological waves. This is the most comprehensive analysis in Mexico providing absolute quantitative data for 529 metabolites and lipids measured in hospitalized COVID-19 patients, which could be used to monitor their metabolic status and clinical outcomes associated with COVID-19 infection or with long COVID syndrome.

Keywords: COVID-19; biomarkers; mass spectrometry; metabolome; metabolomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision

Figures

FIGURE 1
FIGURE 1
Volcano plot of the plasma metabolome among different groups of COVID-19 patients. (A) Vaccinated vs. non-vaccinated. (B) Survivors vs. non-survivors. (C) Diagnosis of pneumonia vs. no pneumonia. (D) Male vs. female. (E) Younger than 60 years old vs. older than 60 years. (F) With obesity and diabetes vs. without obesity and diabetes. Fold change (FC) threshold >1.2 and p-value ≤0.05).
FIGURE 2
FIGURE 2
Multivariate analysis of plasma metabolome profile of patients from Alpha, Delta, and Omicron COVID-19 waves. (A) Principal component analysis (PCA) comparing the three epidemiological waves. (B) Rank of the different metabolites (the top 15) identified by the PLS-DA according to the VIP coefficient on the x-axis. The most discriminating metabolites are shown in descending order of their coefficient scores. The color boxes indicate whether metabolite concentration is increased (red) or decreased (blue).
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References

    1. Bagby G. J., Spitzer J. A. (1980). Lipoprotein lipase activity in rat heart and adipose tissue during endotoxic shock. Am. J. Physiology-Heart Circulatory Physiology 238 (3), H325–H330. 10.1152/ajpheart.1980.238.3.H325 - DOI - PubMed
    1. Bi C., He J., Yuan Y., Che S., Cui T., Ning L., et al. (2025). Metabolomic characteristics and related pathways in patients with different severity of COVID-19: a systematic review and meta-analysis. J. Glob. health 15, 04056. 10.7189/jogh.15.04056 - DOI - PMC - PubMed
    1. Biagini D., Oliveri P., Baj A., Gasperina D. D., Ferrante F. D., Lomonaco T., et al. (2023). The effect of SARS-CoV-2 variants on the plasma oxylipins and PUFAs of COVID-19 patients. Prostagl. and Other Lipid Mediat., 169. 10.1016/j.prostaglandins.2023.106770 - DOI - PubMed
    1. Bijlsma S., Bobeldijk I., Verheij E. R., Ramaker R., Kochhar S., Macdonald I. A., et al. (2006). Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation - PubMed. Anal. Chem. 78(2), 567–74. 10.1021/ac051495j - DOI - PubMed
    1. Bourgin M., Durand S., Kroemer G. (2023). Diagnostic, prognostic and mechanistic biomarkers of COVID-19 identified by mass spectrometric metabolomics. Metabolites 13 (3), 342. 10.3390/metabo13030342 - DOI - PMC - PubMed

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