The incidence and mortality rates of chronic non-communicable diseases, such as cancer, are on the rise. Statistics reveal that these chronic diseases are responsible for 91% of deaths in China, imposing an ever-growing burden on national healthcare expenditures.
We will collect data on demographic characteristics, anthropometric measurements, biochemical tests, and multi-omics data from the UK Biobank database. Our research aims to identify biomarkers associated with these four chronic diseases, particularly cancer, with a focus on immune-related biomarkers. By analyzing these biomarkers, we seek to determine if there are common biomarkers across different chronic diseases and whether there are interactions between them. Examining the characteristic biomarkers of chronic diseases like cancer will enhance our understanding of their mechanisms and provide a scientific foundation for disease diagnosis and intervention.
Additionally, we plan to employ machine learning technology to develop and validate a statistical model that integrates basic demographic variables, such as age and gender, with omics data to predict the incidence and mortality risks associated with chronic diseases. The successful creation of this model could significantly improve early diagnosis and risk assessment for chronic diseases.
Completion of all work is anticipated within the next three years, during which we will continue to monitor the latest advancements in chronic disease prevention and control.
