Prevalence of autoantibody responses in COVID-19 patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: An infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2) may significantly contribute to the pathogenesis of autoimmune rheumatic diseases; interactions between the virus and defence mechanisms may promote the development of autoimmune processes. Studies have reported elevated levels of autoimmune antibodies in patients with Coronavirus Disease-19 (COVID-19), however, the exact prevalence remains poorly undocumented. This study aims to evaluate the prevalence of autoantibodies in COVID-19 patients compared to unaffected individuals. METHODS: Electronic searches were conducted using the Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), WANFANG, and Chinese Weipu (VIP). The case-control studies investigating the presence of autoantibodies in the serum of COVID-19 patients and control subjects published were included in this meta-analysis. The search covered November 2019, to December 2025. Studies were rigorously screened based on predefined inclusion and exclusion criteria. Quality assessment was performed using a modified version of the Newcastle-Ottawa scale (NOS). The odds ratios (ORs) for autoantibody seropositivity were calculated using RevMan 5.3. Sensitivity analysis was conducted to evaluate the stability results, and publication bias was assessed using Egger’s test and funnel plot. RESULTS: A total of 17 studies involving 1652 COVID-19 patients and 1455 control subjects met eligibility criteria for inclusion in the meta-analysis. The overall OR for antinuclear antibodies (ANAs) was 2.90 [95% confidence interval (CI): 1.36–6.18], for anti-cardiolipin antibodies (ACAs) was 3.34 (95% CI: 2.14–5.21), for anti-β2-glycoprotein 1 antibodies (anti-β2GP1) was 1.87 (95% CI: 1.00–3.49) and that for anti-cytoplasmic neutrophil antibodies (ANCAs) was 8.49 (95% CI: 3.38–21.33). Among the 11 studies assessing ANAs, the quality varied widely, resulting in considerable heterogeneity in the meta-analysis results. Heterogeneity across studies on ANAs may partially stem from differences in assay manufacturers as well as variations in the working cut-off dilution used for IIF. Egger’s test reports indicated statistically significant publication bias among the included studies on ANAs. CONCLUSIONS: This study suggests a higher seroprevalence of autoantibodies, including ANAs, ACAs, anti-β2GP1, and ANCAs in COVID-19 patients compared to control subjects. The findings identified a possible association between SARS-CoV-2 infection and autoantibody positivity, contributing to the understanding of COVID-19’s role in autoimmune processes. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".