Severe Coronavirus Disease 2019 (COVID-19) is Associated With Elevated Serum Immunoglobulin (Ig) A and Antiphospholipid IgA Antibodies
Bibliographic record
Abstract
BACKGROUND: Severe coronavirus disease 2019 (COVID-19) frequently entails complications that bear similarities to autoimmune diseases. To date, there are little data on possible immunoglobulin (Ig) A-mediated autoimmune responses. Here, we aim to determine whether COVID-19 is associated with a vigorous total IgA response and whether IgA antibodies are associated with complications of severe illness. Since thrombotic events are frequent in severe COVID-19 and resemble hypercoagulation of antiphospholipid syndrome, our approach focused on antiphospholipid antibodies (aPL). METHODS: In this retrospective cohort study, clinical data and aPL from 64 patients with COVID-19 were compared from 3 independent tertiary hospitals (1 in Liechtenstein, 2 in Switzerland). Samples were collected from 9 April to 1 May 2020. RESULTS: Clinical records of 64 patients with COVID-19 were reviewed and divided into a cohort with mild illness (mCOVID; 41%), a discovery cohort with severe illness (sdCOVID; 22%) and a confirmation cohort with severe illness (scCOVID; 38%). Total IgA, IgG, and aPL were measured with clinical diagnostic kits. Severe illness was significantly associated with increased total IgA (sdCOVID, P = .01; scCOVID, P < .001), but not total IgG. Among aPL, both cohorts with severe illness significantly correlated with elevated anticardiolipin IgA (sdCOVID and scCOVID, P < .001), anticardiolipin IgM (sdCOVID, P = .003; scCOVID, P< .001), and anti-beta 2 glycoprotein-1 IgA (sdCOVID and scCOVID, P< .001). Systemic lupus erythematosus was excluded from all patients as a potential confounder. CONCLUSIONS: Higher total IgA and IgA-aPL were consistently associated with severe illness. These novel data strongly suggest that a vigorous antiviral IgA response, possibly triggered in the bronchial mucosa, induces systemic autoimmunity.
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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 | 0.001 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".