Assessing the causal effects of type 2 diabetes and obesity-related traits on COVID-19 severity.

IF 3.8 3区医学 Q2 GENETICS & HEREDITY

Human Genomics Pub Date : 2025-04-22 DOI:10.1186/s40246-025-00747-4

Jieun Seo, Gaeun Kim, Seunghwan Park, Aeyeon Lee, Liming Liang, Taesung Park, Wonil Chung

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引用次数: 0

Abstract

Background: Type 2 diabetes (T2D) and obesity-related traits are highly comorbid with coronavirus disease 2019 (COVID-19), but their causal relationships with disease severity remain unclear. While recent Mendelian randomization (MR) studies suggest a causal link between obesity-related traits and COVID-19 severity, findings regarding T2D are inconsistent, particularly when adjusting for body mass index (BMI). This study aims to clarify these relationships.

Methods: We applied various MR methods to assess the causal effects of BMI-adjusted T2D (T2DadjBMI) and obesity-related traits (BMI, waist circumference, and waist-hip ratio) on COVID-19 severity. Genetic instruments were obtained from large-scale genome-wide association studies (GWAS), including 898K participants for T2D and 2M for COVID-19 severity. To address potential bias from sample overlap, we conducted large-scale simulations comparing MR results from overlapping and independent samples.

Results: Our MR analysis identified a significant causal relationship between T2DadjBMI and increased COVID-19 severity (OR = 1.057, 95% CI = 1.012-1.105). Obesity-related traits were also causally associated with COVID-19 severity. Simulations confirmed that MR results remained robust to sample overlap, demonstrating consistency between overlapping and independent datasets.

Conclusions: These findings highlight the causal role of T2D and obesity-related traits in COVID-19 severity, emphasizing the need for targeted prevention and management strategies for high-risk populations. The robustness of our MR analysis, even in the presence of sample overlap, strengthens the reliability of these causal inferences.

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评估2型糖尿病和肥胖相关特征对COVID-19严重程度的因果影响。

背景：2型糖尿病（T2D）和肥胖相关特征与2019冠状病毒病（COVID-19）高度共病，但它们与疾病严重程度的因果关系尚不清楚。虽然最近的孟德尔随机化（MR）研究表明，肥胖相关特征与COVID-19严重程度之间存在因果关系，但关于T2D的研究结果并不一致，特别是在调整体重指数（BMI）时。本研究旨在澄清这些关系。方法：我们应用各种磁共振方法评估BMI调整的T2D （T2DadjBMI）和肥胖相关特征（BMI、腰围和腰臀比）对COVID-19严重程度的因果影响。从大规模全基因组关联研究（GWAS）中获得遗传仪器，包括898K例T2D和2M例COVID-19严重程度的参与者。为了解决样本重叠带来的潜在偏差，我们进行了大规模模拟，比较了重叠样本和独立样本的MR结果。结果：我们的MR分析发现T2DadjBMI与COVID-19严重程度增加之间存在显著的因果关系（OR = 1.057, 95% CI = 1.012-1.105）。肥胖相关的特征也与COVID-19的严重程度有因果关系。模拟证实MR结果对样本重叠仍然保持鲁棒性，证明重叠和独立数据集之间的一致性。结论：这些发现突出了T2D和肥胖相关特征在COVID-19严重程度中的因果作用，强调了对高危人群有针对性的预防和管理策略的必要性。即使在样本重叠的情况下，我们的MR分析的稳健性也加强了这些因果推断的可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Genomics GENETICS & HEREDITY-

CiteScore

6.00

自引率

2.20%

发文量

审稿时长

11 weeks

期刊介绍： Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.