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Long COVID, a post-infectious syndrome is caracterized by multitude interconnected pathophysiological mechanisms involving both genetic and environmental factors. Due to these complications, diagnosis remains an area of ongoing research because of the lack of reliable and robust biological markers. The development of biomarkers that reflect the biological mechanism(s) involved in the disease will enable accurate diagnosis, prevent misdiagnosis and, above all, facilitate the proper design of therapeutic trials based on these biomarkers and their modulation. We performed a multi-omics analysis of plasma samples from 222 participants: 100 well-characterised long-COVID patients (LC), 62 individuals with resolved-COVID (RC), and 60 uninfected controls (UC). We integrated state-of-the-art nuclear magnetic resonance metabolomics and lipoproteomics with multiplexed assays screening immune, vascular, and mitochondrial stress markers. LC patients exhibited a distinct signature of persistent thrombo-inflammation and bioenergetic alteration. We identified a systemic metabolic rewiring characterised by depleted pyruvate and lactate levels, indicating impaired glycolytic throughput, with evidence of altered alternative compensatory glutamine-dependent and enhanced ketone body-driven anapleurosis. The immunovascular profile revealed chronic platelet and neutrophil activation, as well as high levels of angiogenesis and matrix metalloproteinases, suggesting vascular damage. Furthermore, LC patients demonstrated a pro-atherogenic lipoproteomic profile. A XGBoost machine learning model integrating these markers could identify LC patients with 100% accuracy. Our results show that LC is defined by a breakdown in energy metabolism and microvascular integrity. The identification of accurate multi-omics signature provides a robust framework for objective clinical diagnosis and paves the way for targeted therapies to improve patient care and advance precision medicine.
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Laforge et al. (2026) studied this question.
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