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

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Big Data Pub Date : 2024-09-12 DOI:10.1186/s40537-024-00997-4

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

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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.

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利用大规模遗传数据评估 COVID-19 对多系统疾病的因果影响

背景COVID-19对人类健康的长期影响是一个重大问题，但目前还缺乏对其对各种健康状况影响的全面评估。方法本研究旨在通过分析超过200万人的大规模人群的遗传数据，评估各种疾病与COVID-19的关系。研究采用了双向双样本孟德尔随机化方法，暴露包括 COVID-19 易感性、住院和严重程度，结果包括 86 种不同的疾病或性状。结果我们的分析确定了 COVID-19 易感性与几种疾病之间的因果关系，包括乳腺癌（OR = 1.0073, 95% CI = 1.0032-1.0114, p = 5 × 10 - 4）、ER + 乳腺癌（OR = 0.5252, 95% CI = 0.3589-0.7685, p = 9 × 10 - 4）和心力衰竭（OR = 1.0026, 95% CI = 1.001-1.0042, p = 0.002）。COVID-19住院与心力衰竭（OR = 1.0017，95% CI = 1.0006-1.0028，p = 0.002）和阿尔茨海默病（OR = 1.5092，95% CI = 1.1942-1.9072，p = 0.0006）有因果关系。COVID-19 严重程度对原发性胆汁性肝硬化（OR = 2.6333，95% CI = 1.8274-3.7948，p = 2.059 × 10-7）、乳糜泻（OR = 0.0708，95% CI = 0.0538-0.0932，p = 9.438 × 10-80）和阿尔茨海默病（OR = 1.5092，95% CI = 1.1942-1.9072，p = 0.0006）有因果效应。反向 MR 分析表明，类风湿性关节炎、糖尿病肾病、多发性硬化症和总睾酮（女性）会影响 COVID-19 的结果。我们评估了异质性和水平多向性，以确保结果的可靠性，并采用 Steiger 方向性检验来确认因果关系的方向。我们的研究结果突显了 COVID-19 对人类健康的长期影响，强调了对受影响人群进行持续监测和有针对性干预的必要性。未来的研究应探讨这些关系，以制定全面的医疗保健策略。

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.