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Leveraging large-scale genetic data to assess the causal impact of COVID-19 on multisystemic diseases.

Authors :
Zhang, Xiangyang
Jiang, Zhaohui
Ma, Jiayao
Qi, Yaru
Li, Yin
Zhang, Yan
Liu, Yihan
Wei, Chaochao
Chen, Yihong
Liu, Ping
Peng, Yinghui
Tan, Jun
Han, Ying
Zeng, Shan
Cai, Changjing
Shen, Hong
Source :
Journal of Big Data; 9/12/2024, Vol. 11 Issue 1, p1-18, 18p
Publication Year :
2024

Abstract

Background: The long-term impacts of COVID-19 on human health are a major concern, yet comprehensive evaluations of its effects on various health conditions are lacking. Methods: This study aims to evaluate the role of various diseases in relation to COVID-19 by analyzing genetic data from a large-scale population over 2,000,000 individuals. A bidirectional two-sample Mendelian randomization approach was used, with exposures including COVID-19 susceptibility, hospitalization, and severity, and outcomes encompassing 86 different diseases or traits. A reverse Mendelian randomization analysis was performed to assess the impact of these diseases on COVID-19. Results: Our analysis identified causal relationships between COVID-19 susceptibility and several conditions, including breast cancer (OR = 1.0073, 95% CI = 1.0032–1.0114, p = 5 × 10 − 4), ER + breast cancer (OR = 0.5252, 95% CI = 0.3589–0.7685, p = 9 × 10 − 4), and heart failure (OR = 1.0026, 95% CI = 1.001–1.0042, p = 0.002). COVID-19 hospitalization was causally linked to heart failure (OR = 1.0017, 95% CI = 1.0006–1.0028, p = 0.002) and Alzheimer's disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). COVID-19 severity had causal effects on primary biliary cirrhosis (OR = 2.6333, 95% CI = 1.8274–3.7948, p = 2.059 × 10 − 7), celiac disease (OR = 0.0708, 95% CI = 0.0538–0.0932, p = 9.438 × 10–80), and Alzheimer's disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). Reverse MR analysis indicated that rheumatoid arthritis, diabetic nephropathy, multiple sclerosis, and total testosterone (female) influence COVID-19 outcomes. We assessed heterogeneity and horizontal pleiotropy to ensure result reliability and employed the Steiger directionality test to confirm the direction of causality. Conclusions: This study provides a comprehensive analysis of the causal relationships between COVID-19 and diverse health conditions. Our findings highlight the long-term impacts of COVID-19 on human health, emphasizing the need for continuous monitoring and targeted interventions for affected individuals. Future research should explore these relationships to develop comprehensive healthcare strategies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21961115
Volume :
11
Issue :
1
Database :
Complementary Index
Journal :
Journal of Big Data
Publication Type :
Academic Journal
Accession number :
179604262
Full Text :
https://doi.org/10.1186/s40537-024-00997-4