About
Metabolic diseases pose a serious obstacle to the development of global public health, in which lifestyle exposure and multiomic imbalance play a crucial role. Therefore, it is of great significance to integrate the study of common exposures and multi-omics data analysis for the prevention and treatment of metabolic diseases. Multiple guidelines such as KDIGO also advocate lifestyle modifications to reduce the risk of metabolic diseases, while multi-omics techniques can help to fully understand the genetic and metabolite mechanisms behind high-risk life exposures, which can provide patients with more effective life interventions.
Based on the above situation, this study aims to link exposures such as diet, activity and sleep with the metabolic risk of multi-omics imbalance with the help of high-quality data from the UK Biobank, and deeply reveal the intrinsic relationship between life exposure factors and multi-omics imbalance through omics research methods, so as to formulate personalized prevention strategies for patients.
This study will use a linear mixed model to explore the interactions between omics, including life exposures, and their impact on the risk of metabolic disease morbidity. This research will help to understand the causes of metabolic diseases, identify more biomarkers, and inform individualized and highly effective life interventions, ultimately reducing the global public health burden.