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

• Published in: Journal of big data Vol. 11; no. 1; pp. 129 - 18
• Main 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
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-12-2024; Springer Nature B.V; SpringerOpen
• Subjects: Autoimmune diseases; Breast cancer; Cancer; Communications Engineering; Computational Science and Engineering; Computer Science; Coronavirus disease-2019 (COVID-19); COVID-19; Data Mining and Knowledge Discovery; Database Management; Disease; Genome-Wide Association Study (GWAS); Heart failure; Heart failure Alzheimer’s disease; Heterogeneity; Impact analysis; Information Storage and Retrieval; Long-term effect; Mathematical Applications in Computer Science; Mendelian randomization; Multiple sclerosis; Networks; Randomization; Testosterone; Heart failure Alzheimer’s disease; Mendelian randomization; Coronavirus disease-2019 (COVID-19); Long-term effect; Genome-Wide Association Study (GWAS); Cancer
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-00997-4

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.