Mendelian randomization and Bayesian model averaging of autoimmune diseases and Long COVID

TL;DR

This study finds that autoimmune diseases like IBD, CD, and UC may increase the risk of Long COVID, suggesting the need for targeted screening.

Contribution

The novel use of Mendelian randomization and Bayesian model averaging reveals causal links between autoimmune diseases and Long COVID.

Findings

IBD, CD, and UC show causal effects on Long COVID with statistically significant odds ratios.

UC is identified as the highest-ranked causal factor for Long COVID using MR-BMA analysis.

Abstract

Following COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated.

Summary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn’s disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA).

The evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00–1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01–1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03–1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD.

This MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations.

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