Approved Research

Synthetic Design of Machine Learning for Cardiovascular Disease Process with Selected Pathways on Dynamics of Type 2 Diabetes Mellitus

Fu Jen Catholic University Affiliated Hospital

Lay summary

The aims:

In this study, we aim to identify the risk predictors for atrial fibrillation in individuals with prediabetes and elucidating the dynamics of metabolic syndrome-related transition.

Scientific rationale:

Atrial fibrillation (AF) is a common heart rhythm disorder that can lead to serious complications such as stroke, heart failure, and death. Prediabetes is an intermediate stage between normal glucose levels and type 2 diabetes, and MeS is a cluster of metabolic risk factors that increase the risk of cardiovascular disease. However, the relationship between prediabetes, MeS, and AF is not well understood. To address this research gap, we will use data from the UK Biobank, a large population-based cohort study, to identify individuals with prediabetes and MeS and follow them over time to investigate the development of AF.

Project duration and public health impact:

The study duration is expected to be three years. During this time, we will analyze data from the UK Biobank and conduct statistical analyses to identify risk predictors for AF in individuals with prediabetes and MeS. By identifying the risk predictors for AF in individuals with prediabetes and MeS, our study may provide early detection and intervention strategies to prevent or delay the onset of AF in this high-risk population.