Anti-neuronal and anti-mitochondrial autoantibodies are associated with lower functional status and more severe respiratory symptoms in post COVID syndrome
Bibliographic record
Abstract
Introduction: )-as markers of severe acute COVID-19. This study investigates whether the same autoantibodies contribute to the pathophysiology of Post COVID Syndrome (PCS). Methods: in serum samples from 45 PCS patients and 30 post-COVID controls without long-term symptoms. We employed generalized linear mixed models (GLMM) and nonlinear principal component analysis (CATPCA) to explore associations between antibody levels and clinical variables, including functional status (PCFS), respiratory symptoms, fatigue, cognitive impairment (as assessed by the Montreal Cognitive Assessment, MoCA), and mood. Results: Higher IgG levels against the three autoantigens significantly predicted PCS at 3 months postinfection (t=2.21, p=0.03), whereas antibodies against a control peptide (polio) showed no such association. CATPCA identified a principal component capturing respiratory symptoms and functional impairment (PCFS), which was also significantly predicted by autoantibody levels (t=2.04, p=0.04). MoCA scores did not correlate with autoantibody levels, and subjective cognitive complaints were paradoxically linked to lower antibody titers and fewer physical symptoms. Conclusions: The findings from the present explorative study, although largely correlative, appear to suggest a sustained autoimmune response targeting neuronal and mitochondrial proteins in PCS, particularly associated with respiratory dysfunction and reduced functional capacity. The results also highlight potential limitations of standard cognitive screening tools like the MoCA in detecting subtle deficits in PCS. The identified autoantibodies may serve as biomarkers for persistent post-COVID disability. Future research replicating present results on larger samples and specifically investigating a causal link between occurrence of the Auto-Abs and PCS is needed for shaping future immunomodulatory therapeutic strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".