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

Jieni Feng; Jiankun Chen; Xiaoya Li; Xiaolei Ren; Junxu Chen; Zuming Li; Yuan Wu; Zhongde Zhang; Rongyuan Yang; Jiqiang Li; Yue Lu; Yuntao Liu

doi:10.3389/fgene.2024.1383162

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

Front Genet. 2024 Jun 20:15:1383162. doi: 10.3389/fgene.2024.1383162. eCollection 2024.

Authors

Jieni Feng^#¹, Jiankun Chen^#^{1

2

3}, Xiaoya Li¹, Xiaolei Ren¹, Junxu Chen⁴, Zuming Li¹, Yuan Wu^{1

2}, Zhongde Zhang^{1

2

5}, Rongyuan Yang^{1

2

3

5}, Jiqiang Li^{1

2

3}, Yue Lu^{1

2}, Yuntao Liu^{1

2

3

5}

Affiliations

¹ The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
² The Second Affiliated Hospital (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou University of Chinese Medicine, Guangzhou, China.
³ Guangzhou Key Laboratory of Traditional Chinese Medicine for Prevention and Treatment of Emerging Infectious Diseases, Guangzhou, China.
⁴ The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
⁵ State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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).

Results: 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.

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

Keywords: Bayesian model averaging; Long Covid; Mendelian randomization; autoimmune diseases; causality.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by National Natural Science Foundation of China (82374392), National Key Research and Development Plan of China (2022YFC0867400), National Administration of Traditional Chinese Medicine project (2023ZYLCYJ02-21), Key Technologies Research and Development Program of Guangdong Province (2023B1111020003), Science and Technology Planning Project of Guangdong Province (No. 2023B1212060062), Basic and Applied Basic Research of Guangzhou City-University Joint Funding Project (202201020382), Open project of Guangdong Provincial Key Laboratory of Research on Emergency in TCM (KF2023JZ06), Research Fund for Zhaoyang Talents of Guangdong Provincial Hospital of Chinese Medicine (ZY2022KY10, ZY2022YL04), Clinical Research Project of Chinese Society of Traditional Chinese Medicine (2023 DEPLHGG-06).