Severe COVID-19 is associated with elevated serum IgA and antiphospholipid IgA-antibodies
Bibliographic record
Abstract
ABSTRACT Background While the pathogenesis of coronavirus disease 2019 (COVID-19) is becoming increasingly clear, there is little data on IgA response, the first line of bronchial immune defense. Objective To determine, whether COVID-19 is associated with a vigorous total IgA response and whether IgA autoantibodies are associated with complications of severe illness. Since thrombotic events are frequent in severe COVID-19 and resemble hypercoagulation of antiphospholipid syndrome (APS), our approach focused on antiphospholipid antibodies (aPL). Materials and methods In this retrospective cohort study we compared clinical data and aPL from 64 patients with COVID-19 from three independent centers (two in Switzerland, one in Liechtenstein). Samples were collected from April 9, 2020 to May 1, 2020. Total IgA and aPL were measured with FDA-approved commercially available clinical diagnostic kits. Results Clinical records of the 64 patients with COVID-19 were reviewed and divided into a cohort with mild illness (mCOVID, n =26 [41%]), a discovery cohort with severe illness (sdCOVD, n =14 [22%]) and a confirmation cohort with severe illness (scCOVID, n =24 [38%]). Severe illness was significantly associated with increased total IgA (sdCOVID, P =0.01; scCOVID, P <0.001). Total IgG levels were similar in both cohorts. Among aPL, both cohorts with severe illness significantly correlated with elevated anti-Cardiolipin IgA (sdCOVID and scCOVID, P <0.001), anti-Cardiolipin IgM (sdCOVID, P =0.003; scCOVID, P <0.001), and anti-Beta2 Glycoprotein-1 IgA (sdCOVID and scCOVID, P <0.001). Systemic lupus erythematosus was excluded from all patients as a potential confounder of APS. Conclusions Higher total IgA and IgA-aPL were consistently associated with severe illness. These novel data strongly suggest that a vigorous antiviral IgA-response triggered in the bronchial mucosa induces systemic autoimmunity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".