Seo Jieun, Kim Gaeun, Park Seunghwan, Lee Aeyeon, Liang Liming, Park Taesung, Chung Wonil
Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, Korea.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Hum Genomics. 2025 Apr 22;19(1):43. doi: 10.1186/s40246-025-00747-4.
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.
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.
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.
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.
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,但其与疾病严重程度的因果关系仍不明确。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联系,但关于T2D的研究结果并不一致,尤其是在调整体重指数(BMI)时。本研究旨在阐明这些关系。
我们应用各种MR方法来评估经BMI调整的T2D(T2DadjBMI)和肥胖相关特征(BMI、腰围和腰臀比)对COVID-19严重程度的因果效应。基因工具来自大规模全基因组关联研究(GWAS),其中包括89.8万名T2D参与者和200万名COVID-19严重程度参与者。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,比较重叠样本和独立样本的MR结果。
我们的MR分析确定了T2DadjBMI与COVID-19严重程度增加之间存在显著因果关系(OR = 1.057,95%CI = 1.012 - 1.105)。肥胖相关特征也与COVID-19严重程度存在因果关联。模拟证实,MR结果对样本重叠具有稳健性,表明重叠数据集和独立数据集之间具有一致性。
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,强调了针对高危人群制定有针对性的预防和管理策略的必要性。即使存在样本重叠,我们的MR分析的稳健性也增强了这些因果推断的可靠性。