In this interview, we explored both theory-driven and data-driven points regarding disease predisposition. Theoretical insights highlighted the multifaceted nature of genetic predisposition, emphasizing that it is influenced by a combination of environmental, lifestyle, and genetic factors. By considering this complexity, predictions of disease risk can be made with less uncertainty compared to relying solely on one factor. On the other hand, concrete examples of data-driven approaches were also discussed. Megan Murray's research involves collecting data to evaluate individuals' risk factors for tuberculosis and diabetes, showcasing the practical application of data analysis in disease risk assessment. Similarly, Immaculata De Vivo's work with the Nurses' Health Study involves gathering updated data on women every two years to study endometrial cancer, providing valuable insights into disease dynamics. Additionally, wearable devices like the Apple Watch were highlighted for their continuous collection of health information, offering real-time insights into various health parameters and enhancing proactive health management strategies. These combined insights underscore the importance of integrating theory and data to deepen our understanding of disease risk and pave the way for more effective interventions.