| Title: | Assessing the causal effects of type 2 diabetes and obesity-related traits on COVID-19 severity |
| Journal: | Human Genomics |
| Published: | 22 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40264243/ |
| DOI: | https://doi.org/10.1186/s40246-025-00747-4 |
Publication 15783
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundType 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.MethodsWe 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.ResultsOur 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.ConclusionsThese 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.</p>
14 Keywords
- Body Mass Index
- COVID-19
- Diabetes Mellitus, Type 2
- Female
- Genome-Wide Association Study
- Humans
- Male
- Mendelian Randomization Analysis
- Obesity
- Polymorphism, Single Nucleotide
- SARS-CoV-2
- Severity of Illness Index
- Waist Circumference
- Waist-Hip Ratio
7 Authors
- Jieun Seo
- Gaeun Kim
- Seunghwan Park
- Aeyeon Lee
- Liming Liang
- Taesung Park
- Wonil Chung
3 Applications
| Application ID | Title |
|---|---|
| 45052 | Genetic effect and genetic correlation among multiple related phenotypes and interaction with environmental exposures |
| 58105 | Pathway analysis and prediction analysis for various complex diseases |
| 77890 | Discovery of biomarkers for chronic and complex diseases risk predict based on biobank data |
Enabling scientific discoveries that improve human health