Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome
Bibliographic record
Abstract
Introduction: A group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed. Methods: We identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC). Results: Through these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores. Conclusion: Our study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".