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Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection
Ethan Cai1, Valentina L Kouznetsova2,3,4, Igor F Tsigelny2,3,4,5
1REHS Program, San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093, USA.
Metabolites
|December 24, 2025
Summary
Researchers developed a machine-learning model to identify Post-Acute Sequelae of SARS-CoV-2 Infection (PASC), or Long COVID, by analyzing metabolites. The model shows promise in differentiating PASC from similar conditions, aiding faster diagnosis.
Area of Science:
- Computational biology and bioinformatics
- Infectious disease research
- Metabolomics
Background:
- COVID-19 has caused millions of deaths globally and its long-term effects, known as Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) or Long COVID, remain poorly understood.
- PASC presents a significant public health challenge due to its widespread impact and complex symptomatology.
- Distinguishing PASC from other fatiguing illnesses is crucial for accurate diagnosis and effective treatment.
Purpose of the Study:
- To develop and validate a machine-learning model capable of distinguishing PASC from PASC-similar diseases based on metabolite profiles.
- To identify unique metabolic signatures associated with PASC.
- To explore the potential for accelerating PASC diagnosis and intervention.
Main Methods:
- A machine-learning model, specifically a multi-layer perceptron, was trained using molecular descriptors of PASC-dysregulated metabolites (p ≤ 0.05).
- The model was tested on its ability to recognize PASC-dysregulated metabolites and differentiate PASC from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Lyme disease, postural orthostatic tachycardia syndrome (POTS), and irritable bowel syndrome (IBS).
- Pairwise model testing was conducted to elucidate similarities between PASC and other conditions, particularly fibromyalgia (FM).
Main Results:
- The machine-learning model achieved an AUC-ROC of 0.8991 in recognizing PASC-dysregulated metabolites in an independent testing set.
- The model successfully differentiated PASC from ME/CFS, Lyme disease, POTS, and IBS.
- The model was unable to differentiate fibromyalgia (FM) from PASC, indicating a significant metabolic similarity between these two conditions.
Conclusions:
- The developed machine-learning approach offers a novel method for PASC diagnosis, potentially reducing the need for lengthy exclusion processes.
- The model's reliance on molecular descriptors allows for broad applicability across various metabolites.
- This study highlights the metabolic overlap between FM and PASC and presents a promising tool for faster PASC diagnosis, potentially leading to improved patient outcomes.
