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Record W4414217934 · doi:10.3389/fimmu.2025.1642250

Anti-neuronal and anti-mitochondrial autoantibodies are associated with lower functional status and more severe respiratory symptoms in post COVID syndrome

2025· article· en· W4414217934 on OpenAlexaboutno aff
Antje Vogelgesang, A. Steinmetz, Angela Stufano, Valentina Schino, Domenico Plantone, Agnes Flöel, Guglielmo Lucchese

Bibliographic record

VenueFrontiers in Immunology · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsAutoantibodyRespiratory systemCoronavirus disease 2019 (COVID-19)AutoimmunitySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Cognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.241
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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