Critical COVID-19 disease explained by type I interferon autoantibodies found in patients within the Military Health System
Bibliographic record
Abstract
Abstract Neutralizing auto-antibodies (auto-Abs) that target type I interferons (IFN), a group of cytokines that induce innate immune responses upon viral infection, are found within approximately 10–20% of patients with critical COVID-19. We sought to determine if neutralizing type I IFN auto-Abs contribute to severe COVID-19 in patients within the Military Health System (MHS). The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) cohort collected demographic data, clinical data and sera from SARS-CoV-2 infected patients enrolled across 10 U.S. military treatment facilities. We screened sera collected <21 days post-symptom onset from 214 COVID-19 inpatients and 312 COVID-19 outpatients for IFN auto-Ab positivity and neutralizing activity using Luminex and intracellular flow cytometry, respectively. Similar to previous reports, we detected neutralizing auto-Abs against IFN-α and/or IFN-ω in a significantly higher frequency of inpatients (9 total, 4.2%) versus outpatients (1, 0.32%) (p=0.009). Remarkably, IFN auto-Abs persisted 6–12 months post-infection in most inpatients, including several with a prior history of autoimmune disease. Among inpatients, multivariate logistic regression analyses demonstrated that type I IFN auto-Abs were associated with a greater risk of severe and critical COVID-19 (adjusted odds ratio (aOR) = 16.40 and 6.44, respectively) after adjusting for age, sex and comorbidity burden. Our results confirm a robust association between critical COVID-19 and the presence of type I IFN auto-Abs, which may predispose to other severe respiratory viral infections that cause substantial morbidity and mortality in the MHS. This work was supported by awards from the Defense Health Program (HU00012020067) and the National Institute of Allergy and Infectious Disease (HU00011920111). The protocol was executed by the Infectious Disease Clinical Research Program (IDCRP), a Department of Defense (DoD) program executed by the Uniformed Services University of the Health Sciences (USUHS) through a cooperative agreement by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF).
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".