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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".