Unlocking the complexities of disease: A multifaceted approach is required, and our research is dedicated to pioneering such an integrative methodology by using imaging and other comprehensive data. Our goal is to build a comprehensive framework for understanding various diseases by integrating socio-demographic data, imaging, genetic information, health records, and biomarkers. This framework will help reveal how macroscopic structural changes in various diseases interact with pathophysiological mechanisms at the cellular and molecular levels, and how these changes are influenced by an individual’s environmental exposures, behavioral habits, and responses to drug treatments. We will employ a range of analytical methods, including statistical models, bioinformatics, systems biology, and machine learning algorithms. These methods will enable us to extract critical information from large and complex data sets, identify disease risk factors, and predict disease progression. Specifically, we will use imaging technologies to monitor structural changes in different diseases, employ genome-wide association studies (GWAS) to explore genetic variation, and assess the causal role of environmental factors using Mendelian randomization. We expect this research to provide a scientific basis for prevention, early diagnosis, and personalized treatment of various diseases. By improving our understanding of diseases, we can develop more effective interventions to reduce incidence and mortality. In the long term, this research will contribute to improving public health standards, reducing healthcare costs, and promoting the widespread adoption of healthy lifestyles.
