Anticardiolipin IgG Autoantibody Level Is an Independent Risk Factor for COVID‐19 Severity

Abstract

To the Editor: A growing body of evidence indicates that patients with cardiovascular complications are at a higher risk for developing severe manifestations of coronavirus disease 2019 (COVID-19) (1). In addition, the high incidence of thromboembolic events suggests that COVID-19–induced coagulopathy plays an important role in disease severity (2). Antiphospholipid autoantibodies (aPLs), which are essential markers of antiphospholipid syndrome, are also considered to be cardiovascular risk factors. The presence of aPLs has recently been described in 3 patients presenting with severe manifestations of COVID-19 (3). Such factors related to the severity of the disease may be relevant in the management of the COVID-19 pandemic, particularly as they pertain to the decision as to whether to keep a newly infected patient in the hospital. To this end, levels of IgG and IgM anticardiolipin antibodies (aCLs) and anti–β2-glycoprotein I (anti-β2GPI) autoantibodies were measured using real-time polymerase chain reaction in serum samples from 56 COVID-19 patients with severe acute respiratory syndrome coronavirus 2 (SARS–CoV-2). The cohort was divided into a moderate (n = 27) and a severe group of patients (n = 29) according to clinical presentation at sampling. A disease manifestation was defined as severe if at least one of the following criteria was met: respiratory rate >30 breaths/minute, oxygen saturation ≤93%, Pao2/Fio2 ratio ≤300 mm Hg, or cardiac shock or respiratory failure requiring admission to an intensive care unit. All samples were obtained from a declared biobank (DC 2020-4028) in compliance with ethics directives. Enzyme-linked immunosorbent assay kits were used to determine aCL and anti-β2GPI antibodies. The association between disease severity and the clinical and biologic features of the disease was analyzed by univariate and multivariate logistic regression analyses (generalized linear model function; R software version 4.0). A summary of our results is provided in Table 1. Additional information regarding materials, methods, and aPL levels are available online (Supplementary Table 1 and Supplementary Figure 1, available on the Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41409/abstract). No differences in term of age, sex, duration of symptoms, history of thrombosis, history of stroke, cardiovascular complications, diabetes, and chronic respiratory disease were observed between the 2 groups of patients. Differences in the aPL profile between the 2 groups were observed only for IgG aCL antibodies. Univariate analyses showed that the levels of IgG aCL were significantly associated with severe COVID-19 manifestations (odds ratio [OR] = 6.50; P = 0.009) with further confirmation by multivariate analysis (OR = 8.71; P = 0.017). These findings show, for the first time, that IgG aCL antibody levels are highly and independently associated with disease severity. Except for 1 patient who presented with a history of stroke, no other IgG aCL–positive patient with a severe manifestation of COVID-19 presented with a history of thrombosis, which suggests that positivity for aCL could be attributed to infection with SARS–CoV-2. Indeed, viral infections are known to induce aPL, especially aCL that may increase the risk of thrombosis and clot formation in the presence of another thrombophilic condition (5). Since patients with COVID-19 develop profound hypercoagulation (6), IgG aCL–positive patients are at a higher risk for developing thrombosis, and therefore further follow-up with clinical evaluations and biologic testing is recommended. Recent autopsy data on subjects who died of COVID-19 showed that ~50% of venous thromboembolic events were not recognized prior to death (2), suggesting that some patients may need anticoagulation therapy. While awaiting further investigation, aCL detection could serve as a simple strategy to help stratify COVID-19 patients according to disease severity and thereby help the therapeutic decision-making process. Univariate OR (95% CI) (P) Multivariate OR (95% CI) (P) Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Topics & Concepts