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cc-by-nc-nd (c)  Mallol, R. et al., 2025
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/220451

Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID)

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Profound metabolomic alterations occur during COVID-19. Early identification of the subset of hospitalised COVID-19 patients at risk of developing severe disease is critical for optimal resource utilization and prompt treatment. This work explores the metabolomic profile of hospitalised adult COVID-19 patients with severe disease, and establishes a predictive signature for disease progression. Within 48 hours of admission, serum samples were collected from 148 hospitalised patients for nuclear magnetic resonance (NMR) spectroscopy. Lipoprotein profiling was performed using the 1H-NMR-based Liposcale test, while low molecular weight metabolites were analysed using one-dimensional Carr-Purcell-Meiboom-Gill pulse spectroscopy and an adaptation of the Dolphin method for lipophilic extracts. Severe COVID-19, per WHO's Clinical Progression Scale, was characterized by altered lipoprotein distribution, elevated signals of glyc-A and glyc-B, a shift towards a catabolic state with elevated levels of branched-chain amino acids, and accumulation of ketone bodies. Furthermore, COVID-19 patients initially presenting with moderate disease but progressing to severe stages exhibited a distinct metabolic signature. Our multivariate model demonstrated a cross-validated AUC of 0.82 and 72% predictive accuracy for severity progression. NMR spectroscopy-based metabolomic profiling enables the identification of moderate COVID-19 patients at risk of disease progression, aiding in resource allocation and early intervention.

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MALLOL, Roger, et al. Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID). Scientific Reports. 2025. Vol. 15, num. 1. ISSN 2045-2322. [consulted: 18 of June of 2026]. Available at: https://hdl.handle.net/2445/220451

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