About
The incidence and mortality rates of non-communicable diseases (NCDs) to infectious diseases are increasing at an alarming rate around the world. Importantly, the onset and progression of these diseases are intrinsically influenced by genetic predispositions, lifestyle, and other factors plays, and the complex interactions between them. Therefore, understanding and analyzing the relationship between the disease population and the non-disease population in these factors plays an important role in the prevention, treatment, and prognosis of diseases.
In our study, the NCDs that I am particularly interested in researching are CVD and depression, as well as the progression between CVD and depression. For infectious diseases, I aim to explore the outcomes of diseases during the COVID-19 pandemic. Poisson regression and Cox proportional hazards models were used to explore the effects of multiple genetics, lifestyle, environment, and clinical factors on the morbidity and mortality of NCDs and hospitalizations of infectious diseases, and the complex interactions between them. The effects of genetic variants on the above diseases were investigated by Mendelian randomization. In addition, machine learning algorithms were used to construct risk prediction models. Exploring the independent and combined effects of risk factors on NCDs and infectious diseases will help clarify the etiology and pathogenesis of NCDs and infectious diseases, thereby help improve the prevention and control of these diseases.
We expect to finish this study within 36 months of receiving the data. By using extensive UK Biobank datasets and state-of-the-art computational tools, the research strives to unlock the complexity behind disease onset and progression. The anticipated findings are poised to significantly augment the existing strategies of screening, prevention, and early intervention for common complex diseases, thereby contributing to meet the diverse challenges of today's health landscape.