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reported a genetic causal association between gout, one of the most prevalent autoimmune diseases, and severe COVID-19. Their study provided valuable guidance on managing COVID-19 and gout. Nevertheless, the relationship between another common autoimmune disease, systemic lupus erythematosus (SLE), and COVID-19 is contentious. While some studies had confirmed the association between SLE and COVID-19, other epidemiological investigations found no association.
Interference from confounding factors and reverse causation may disturb traditional epidemiological findings. Mendelian randomization (MR) uses genetic instrumental variables to determine the genetic association between exposures and outcomes, thereby excluding potential confounders from interfering. In this study, we attempted to reveal the causal association between COVID-19 and SLE by using bidirectional MR, which assessed the effects of COVID-19 on SLE and SLE on COVID-19, respectively.
We conducted MR studies using summary statistics of genome-wide association studies (GWAS) in the European population and replicated the results in individuals of East Asian descent. The largest GWAS data of COVID-19 were obtained from the latest r7 version based on COVID-19 Host Genetics Initiative (https://www.covid19hg.org/results/r7/), including three sets of genetic instruments, SARS-CoV-2 infection, hospitalized COVID-19, and severe COVID-19 (n = 1,086,211 to 2,297,856 individuals). For the outcome of SLE, the meta-GWAS yielded the largest GWAS summary statistics to date, consisting of 14,267 individuals.
Detailed information for the data sources was presented in Supplementary Table 1. We summarized the confounders influencing the incidence of COVID-19 and SLE from previous meta-analysis studies (Supplementary Table 2). To avoid potential pleiotropic effects caused by confounders, we removed potential confounding SNPs associated with these confounders of COVID-19 and SLE through the PhenoScanner website (http://www.phenoscanner.medschl.cam.ac.uk/), as presented in Supplementary Table 3. After removing potential confounders associated with outcomes, the used genetic instruments of exposures were shown in Supplementary Table 4. We chose inverse variance weighted (IVW) model as the main analysis method and applied additional sensitivity analysis methods, MR-Egger, weighted median, and weighted mode, to ensure the robustness of the results. The pleiotropy was estimated through the intercept from MR-Egger regression, and heterogeneity was assessed through Cochran's Q test in IVW approach. We also assessed whether bias existed due to individual SNP independently affecting the results by leave-one-out analysis.
The results suggested a non-causal association between of COVID-19 and risk of SLE. Neither the susceptibility, hospitalization, and severity of COVID-19 on SLE (IVW method: odds ratio (OR) = 0.89, 95% confidence interval (CI) 0.54–1.45, P = 0.63; OR = 0.94, 95% CI 0.75–1.18, P = 0.60; OR = 1.06, 95% CI 0.97–1.15, P = 0.18), nor SLE on susceptibility, hospitalization, and severity of COVID-19 (IVW method: OR = 0.99, 95% CI 0.99–1.00, P = 0.09; OR = 0.99, 95% CI 0.98–1.01, P = 0.49; OR = 0.99, 95% CI 0.96–1.02, P = 0.43) were genetically associated. The results from MR-Egger, weighted median, and weighted mode methods were consistent with IVW analysis (Fig. 1). Statistical power for MR analyses calculated based on sample size and explained variances was sufficient (90%) (Supplementary Table 5). No pleiotropy was identified in the sensitivity test, and no SNPs independently drove the results in the leave-one-out sensitivity, indicating the reliability of the causal effect estimates (Supplementary Tables 6 and 7). Replicated analyses in the East Asian population showed consistent results that genetically predicted susceptibility, hospitalization, and severity of COVID-19 were not associated with the risk of SLE (Fig. 2).
Fig. 1Genetic causal associations between COVID-19 and risk of SLE in European population. (A) MR estimates of genetically predicted susceptibility, hospitalization, and severity of COVID-19 on the risk of SLE. (B) MR estimates of genetically predicted risk of SLE on susceptibility, hospitalization, and severity of COVID-19. The inverse variance weighted method is considered the main method.
Fig. 2Genetic causal associations between COVID-19 and risk of SLE in East Asian population. (A) MR estimates of genetically predicted susceptibility, hospitalization, and severity of COVID-19 on the risk of SLE. (B) MR estimates of genetically predicted risk of SLE on susceptibility, hospitalization, and severity of COVID-19. The inverse variance weighted method is considered the main method.
This MR study revealed a non-genetic association between COVID-19 and SLE using the largest GWAS data to date; however, the onset and development of COVID-19 and SLE shared similar underlying mechanisms. Our previous study indicated the key roles of interferon-related genes involving in the crosstalk of SLE and severe COVID-19.
Immunity dysregulation and gut microbes are related to the pathogenesis of both COVID-19 and SLE. Consequently, the connection between SLE and COVID-19 may function through other shared pathways, as opposed to the diseases themselves.
In conclusion, our MR analysis results do not support the relationship between COVID-19 and SLE from a genetic perspective. Large-scale randomized controlled trials are needed to gain a deeper understanding of this association in the future.
Funding
This work was funded by the National College Students’ Innovation and Entrepreneurship Training Program (No. 202210366002) and the College Students’ Innovation and Entrepreneurship Training Program of Anhui Province (No. S202210366013, S202210366021, and S202210366017).
We thank the Mathematical Medicine Integration Innovation Training Program for Undergraduate Students (MITUS) for providing valuable guidance, support, research opportunities, and resources.
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