Abhijith Biji was poring over data from more than 100,000 Americans when he noticed something striking: for millions of people, the conditions of their daily lives—where they live, how they feel, what they do—were just as predictive of disease as their DNA. At the Icahn School of Medicine at Mount Sinai in New York, Biji and his mentor, Dr. Samira Asgari, had set out to compare genetic risk with social determinants of health across six common diseases. What they found, published in The American Journal of Human Genetics, is reshaping how scientists think about health prediction. Using data from the NIH’s All of Us Research Program, the team analyzed genetic profiles, electronic health records, and detailed survey responses from participants nationwide. For asthma, chronic kidney disease, coronary heart disease, and high cholesterol, social, behavioral, and environmental factors were as predictive—or more so—than standard genetic risk scores. Even for conditions like breast and prostate cancer, where genetics have long dominated risk modeling, social determinants added significant predictive power. The study evaluated over 100 measures, from income and education to air quality and social connection. One of the most unexpected associations? Loneliness emerged as a notable factor linked to increased disease risk—a signal that emotional well-being may be biologically embedded in ways science is only beginning to understand. “Genes are an important part of the equation, but they do not determine destiny,” says Dr. Asgari. The research doesn’t claim causation—many survey responses were self-reported at a single point in time—but it offers a powerful framework for integrating life experience with biology. This holistic approach could transform preventive care, especially in diverse populations often underrepresented in genetic studies. By moving beyond DNA alone, researchers can build risk models that reflect real lives, not just inherited code. As health systems increasingly pursue personalized medicine, this study reminds us that personalization must include the places people live, the stress they endure, and the support they do or don’t have. The future of health prediction may not lie in a genome sequence, but in the full story of a person’s life.