Aims: Our research aims to improve the prediction and understanding of diseases that occur more frequently as people age, such as Alzheimer’s disease and heart disease. We will do this by combining genetic information with various types of biological data to create a comprehensive risk assessment model.
Scientific Rationale: Genetic information, known as polygenic risk scores (PRS), can indicate an individual’s genetic risk for certain diseases. However, genetics alone does not capture the full picture, especially for age-related diseases influenced by lifestyle, age, and environmental factors. By integrating PRS with various types of biological data-such as imaging, blood markers, proteins, and metabolites-we can develop a more complete and dynamic understanding of disease risk and progression.
Project Duration: The project is expected to last for three years. During this time, we will collect and process the necessary data, develop and refine our risk assessment model using advanced deep learning techniques, and validate the model to ensure its accuracy and reliability.
Public Health Impact: This research has the potential to significantly impact public health by providing better tools for predicting and preventing age-related diseases. Early and accurate risk assessment can lead to more effective interventions, personalized prevention strategies, and improved health outcomes for the aging population. By addressing the complex interplay of genetic and non-genetic factors, our model will offer valuable insights that can inform public health policies and strategies aimed at reducing the burden of age-related diseases.