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

Authors :
Xiangyang Zhang
Zhaohui Jiang
Jiayao Ma
Yaru Qi
Yin Li
Yan Zhang
Yihan Liu
Chaochao Wei
Yihong Chen
Ping Liu
Yinghui Peng
Jun Tan
Ying Han
Shan Zeng
Changjing Cai
Hong Shen
Source :
Journal of Big Data, Vol 11, Iss 1, Pp 1-18 (2024)
Publication Year :
2024
Publisher :
SpringerOpen, 2024.

Abstract

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.

Details

Language :
English
ISSN :
21961115
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Big Data
Publication Type :
Academic Journal
Accession number :
edsdoj.37c358585b4f48ae81803b52f33be2c5
Document Type :
article
Full Text :
https://doi.org/10.1186/s40537-024-00997-4