Investigating the Link between Type 2 Diabetes and Epstein-Barr Virus: a Machine Learning and Mendelian Randomization.

Abstract

Background: Epstein-Barr virus (EBV) is a ubiquitous herpesvirus that is known to cause infectious mononucleosis and is associated with several autoimmune diseases and cancers through immune system dysregulation and chronic inflammatory mechanisms.

Methods: The authors collected 3,624 samples containing EBV DNA test results and 1,872 samples containing EBV antibody test results from Qingdao Central Hospital. The machine learning model was trained using CatBoost classifier, and the data imbalance problem was dealt with using SMOTE method. For the EBV antibody data, normality was assessed using the Shapiro-Wilk test, and the Welch's t-test and Mann-Whitney U test were used to compare the differences between the type 2 diabetic and non-diabetic groups. Finally, the causal relationship between EBV antibodies and type 2 diabetes was verified by Mendelian randomization.

Results: Machine learning modeling showed 70% prediction accuracy of EBV DNA in immunoendocrine diseases. Type 2 diabetic patients had significantly higher VCA IgG levels than non-diabetic patients (p < 0.05). Mendelian randomization analysis further validated the positive correlation between type 2 diabetes mellitus and VCA IgG levels (p < 0.05), suggesting that patients with type 2 diabetes mellitus may have higher VCA IgG levels.

Conclusions: This study found a significant association between type 2 diabetes and EBV VCA IgG levels, emphasizing the potential relationship between EBV infection and diabetes. Machine learning and Mendelian randomization methods played an important role in determining disease associations, which provides new ideas for future clinical management and prevention strategies.