Exposome Modeling & Population Health

Exposome modeling in population health represents a revolutionary approach to understanding how the myriad environmental factors we encounter throughout our lives interact with our biology to influence health outcomes. Coined to complement the “genome,” the exposome encompasses the totality of human environmental exposures from conception to death – including everything from the air we breathe and the food we eat to our social interactions, lifestyle choices, and even internal biological responses like inflammation. Exposome modeling then involves leveraging advanced computational methods, data science, and “omics” technologies (like genomics and metabolomics) to measure, integrate, and analyze this vast, complex tapestry of exposures.

This holistic perspective is critically important for population health because it moves beyond studying single exposures in isolation to understand the cumulative and interactive effects of our “total environment.” By building sophisticated models, researchers can identify patterns, predict disease risk, and uncover the root causes of health disparities that might otherwise remain hidden. For instance, exposome modeling can reveal how a combination of air pollution, socioeconomic stress, and dietary patterns contributes to chronic diseases within specific communities. This deeper understanding empowers public health initiatives to develop more targeted and effective interventions, inform policy changes, and ultimately create healthier environments for entire populations, leading to more equitable and sustainable health outcomes for all.

One example is the research led by Dr. Khurram Nasir, a national leader in preventive cardiology at Houston Methodist. His team is advancing an ambitious effort to link real-world data, medical records from over 2 million patients, with environmental, social, imaging, genetic, and lab information. The goal is to better understand how a person’s surroundings, stress levels, access to healthy food, and even air quality can influence their risk of heart disease and other chronic conditions.

Using advanced AI and machine learning, this system can identify patterns that are often missed by traditional models. A key innovation is the creation of a PolySocial Risk Score, which functions like a genetic risk score but reflects the impact of social and environmental exposures on health. When combined with clinical and imaging data, it can help doctors predict and address disease earlier, before symptoms appear.

By supporting this kind of work, DHI is helping to advance a broader vision for proactive, personalized care. The focus is not just on treating illness, but on identifying risk, guiding early interventions, and improving health outcomes at scale, particularly in underserved communities across Texas and beyond.