Multilayer metabolomic integration reveals bioenergetic disruption in Long COVID

Abstract

Background Long COVID represents a significant health challenge, with 10–20% of patients with COVID-19 experiencing persistent multiorgan symptoms. The heterogeneity of clinical manifestations, combined with an incomplete understanding of the underlying molecular mechanisms, limits the improvement of patient management. Circulating metabolomic profiling constitutes a promising tool to address these limitations. In this context, we aim to investigate long-term metabolic disruptions in Long COVID through multilayer integration of plasma metabolites.

Methods The study population included 42 survivors of critical COVID-19 who attended a comprehensive clinical evaluation conducted 12 months postdischarge. Plasma biochemicals, including lipoproteins, lipids, glycoproteins and metabolites were quantified using proton nuclear magnetic resonance spectroscopy (H-NMR). Circulating tricarboxylic acid (TCA) cycle intermediates and protein damage markers were detected by gas chromatography‒mass spectrometry (GC/MS). A machine learning-based feature selection approach was employed to identify the multilayered metabolic signature. Generalized additive models (GAMs) were used to explore associations between individual metabolites and specific dimensions of Long COVID.

Results Univariate analysis revealed significantly elevated levels of alpha-ketoglutarate (aKG) and reduced levels of creatine in patients with Long COVID. A nine-metabolite and damage marker signature [aKG, carboxymethyl-cysteine (CMC), carboxymethyl-lysine (CML), creatine, fumarate, lactate, low density lipoprotein particle size (LDL-Z), 2-succinyl-cysteine (2SC) and tyrosine] was identified through the integration of Random Forest with Boruta and Sparse Partial Least Squares regression. This signature effectively classified patients with Long COVID (a cross-validated AUC of 0.91). In the GAM models, aKG, CMC, CML and creatine were associated with distinct Long COVID dimensions, including cognitive, functional and respiratory impairments.

Conclusions Multilayer metabolomic integration reveals persistent bioenergetic disruption in patients with Long COVID. The identified metabolic profile offers promising biomarkers for medical decision-making. Modulating key metabolites could potentially mitigate specific symptoms of long COVID.

The current project was financially supported by SEPAR (1284/2022). This study was funded by the Instituto de Salud Carlos III (ISCIII) through the project “PI23/01205” and cofunded by the European Union. The COVIDPonent study is funded by Institut Català de la Salut and Gestió de Serveis Sanitaris (GSS). We were further supported by: Program “estar preparados”; UNESPA (Madrid, Spain), Fundación Eugenio Rodríguez Pascual (FERP-2023- 076), Programa de Becas Gilead a la Investigación Biomédica (GLD23_00063). Research by the authors was also supported by the Spanish Ministry of Science, Innovation, and Universities (Ministerio de Ciencia, Innovación y Universidades, PID2023-152233OB-I00) and the Generalitat of Catalonia: Agency for Management of University and Research Grants (2021SGR00990) to RP. This study was co-financed by FEDER funds from the European Union (“A way to build Europe”). MCGH held predoctoral fellowship Ayudas al Personal Investigador en Formación from IRBLleida/Diputación de Lleida. FB is supported by ICREA Academia program. DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Union.