ArticleJournal of translational medicine2026
Multilayer metabolomic integration reveals bioenergetic disruption in Long COVID.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers of post-acute infection syndrome: a systematic literature review.Frontiers in immunology · 2026Pooled it
- Systematic review of amino acid profiles among COVID-19 patients caused by SARS-CoV-2.Journal of diabetes and metabolic disorders · 2026Review
- Symptom-based clusters in people with post-COVID-19 condition (PCC).Journal of translational medicine · 2026Article
- Beyond plasma signatures: reframing bioenergetic disruption in long COVID as a neurological disorder.Journal of translational medicine · 2026Article
- Alpha-ketoglutarate Potentiates IL-1β Production and Suppressive Mechanisms of Myeloid-Derived Suppressor Cells by Altering Redox Metabolism and Inducing Autophagy.International journal of biological sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong 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.
methodsThe 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.
resultsUnivariate 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.
conclusionsMultilayer 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.