Our research project aims to understand how different factors, such as genetics, environment, and socio-economic status, contribute to age-related diseases. Age-related diseases are health problems that occur as we get older, like hypertension, diabetes, cancer, Alzheimer’s disease, Parkinson’s disease, and osteoporosis. These diseases can affect various systems in our body and have a significant impact on our health and the healthcare system. We know that certain genetic variations, lifestyle factors (like smoking and diet), and socio-economic status can increase the risk of these diseases. Additionally, recent studies have shown that imbalances in different types of biological molecules in our body, called omics (like genes, proteins, and metabolites), can also play a role in age-related diseases. By combining all these factors, we can improve the accuracy of risk assessment and prevention strategies.
In this project, we will use machine learning techniques to analyze data from multiple sources, including genetic information, environmental exposure data, socio-economic data, and omics data. By studying these factors together, we hope to understand their individual and combined effects on age-related diseases. This will help us predict the long-term outcomes of these diseases more accurately. Ultimately, our research will provide valuable evidence to prevent age-related diseases and promote healthy aging.
The project will last for 36 months, during which we will analyze large datasets and develop models to assess the risk of age-related diseases. The findings from this study will have a significant impact on public health by improving our understanding of the factors that contribute to age-related diseases. This knowledge can be used to develop targeted prevention strategies and interventions, leading to better health outcomes for individuals as they age.