The global rise in chronic diseases like cardiovascular diseases, metabolic disease and chronic respiratory conditions demands an exploration of their multifaceted causes-genetic, environmental, lifestyle, and clinical. However, few studies have examined the risk factors for chronic diseases and the sequence of diseases caused by chronic diseases in terms of their temporal order of prevalence.
This project aims to explore the intricate causes and progression of chronic diseases by building networks that map their risk factors and co-morbidities, utilizing advanced statistical and machine learning methods. This research will provide vital insights for developing targeted prevention and treatment strategies.
We’ll use advanced statistical techniques such as generalized estimating equations, Cox proportional hazards models, logistic regression, and restricted cubic splines to model nonlinear relationships. Additionally, transcriptional, protein, metabolic, and epigenetic multi-omics revalidation was also performed using machine learning, survival, network, GWAS, temporal sequencing, and clustering pathway enrichment analyses.
Over the next three years, our program will focus on two research questions: what are the risk factors for chronic diseases and what is the sequence in which chronic diseases occur (i.e., constructing time-series co-morbidity networks for chronic diseases).
We will focus on Chronic Kidney Disease, Circulatory Disease (cardiovascular disease), Mental Disease (Depression, Anxiety, etc.), Motor System Disease (Osteoarthritis, Sarcopenia), Digestive Disease (NAFLD), etc.. We will construct a network of chronic disease co-morbidities as well as risk factors for chronic diseases from four major aspects: genes, lifestyle habits, environment, and biomarkers.
The results of the research will be disseminated in the form of publications.