Leveraging large-scale genetic data to assess the causal impact of COVID-19 on multisystemic diseases
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. 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. 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. 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.
Authors
- Ying Han (ORCID: https://orcid.org/0000-0001-8591-7894)
- Zhaohui Jiang (ORCID: https://orcid.org/0000-0002-4861-5819)
- Chaochao Wei
- Changjing Cai (ORCID: https://orcid.org/0000-0002-9567-5582)
- Yaru Qi
- Hong Shen (ORCID: https://orcid.org/0000-0001-5345-4709)
- Shan Zeng (ORCID: https://orcid.org/0000-0002-0988-723X)
- Jiayao Ma (ORCID: https://orcid.org/0000-0002-6146-9940)
- Xiangyang Zhang (ORCID: https://orcid.org/0009-0009-5015-9478)
- Yin Li (ORCID: https://orcid.org/0000-0001-8610-6970)
- Yan Zhang (ORCID: https://orcid.org/0000-0002-6682-9917)
- Yihong Chen (ORCID: https://orcid.org/0000-0002-7037-2227)
- Jun Tan (ORCID: https://orcid.org/0000-0001-9351-8340)
- Yihan Liu (ORCID: https://orcid.org/0000-0001-9094-0693)
- Yinghui Peng (ORCID: https://orcid.org/0000-0002-3676-3574)
- Ping Liu (ORCID: https://orcid.org/0009-0008-2025-9331)
Institutions
- Central South University (CN)
- Yueyang Second People's Hospital (CN)
- Xiangya Hospital Central South University (CN)
Publication Details
- Journal
- Journal Of Big Data
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1186/s40537-024-00997-4
- Citations
- 1
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.55
Funders
- National Natural Science Foundation of China
- Central South University
- China Postdoctoral Science Foundation
- Natural Science Foundation of Hainan Province
- Xiangya Hospital, Central South University
- Fundamental Research Funds for the Central Universities
- Fundamental Research Funds for Central Universities of the Central South University