Nita Limdi, Pharm.D., Ph.D., MSPHUAB researchers are leading a large national effort to determine whether delivering personalized genetic risk information to patients and their doctors can meaningfully improve preventive healthcare for common chronic diseases
Chronic diseases, including heart disease, diabetes, kidney disease, and cancer, affect the majority of Americans and account for more than 90 percent of U.S. healthcare spending. Historically, healthcare has focused on treating disease after it develops, rather than identifying people at high risk early enough to prevent illness or delay its onset.
A recent study, published March 23 in The American Journal of Human Genetics, outlines the design and analysis framework of one of the largest real-world evaluations to date of genome-informed risk assessment in clinical care. The research is led by Nita Limdi, Pharm. D., Ph.D., MSPH, Ray L. Watts Heersink Endowed Chair and professor in the UAB Department of Neurology, and brings together investigators from the Electronic Medical Records and Genomics (eMERGE) Network, a National Institutes of Health–funded consortium of 10 major U.S. health systems including UAB, Vanderbilt University Medical Center, the University of Washington, Harvard/ Mass General, Northwestern, Mount Sinai, Mayo Clinic, Columbia University, Cincinnati Children's Hospital Medical Center, and Children’s Hospital of Philadelphia.
Advances in genomics have made it possible to estimate an individual’s inherited risk for certain conditions by analyzing many small genetic differences across the genome, commonly referred to as polygenic risk scores (PRS), as well as rarer genetic variants with stronger effects. While these tools are increasingly available, there has historically been limited evidence showing whether providing this information improves real-world medical decision-making and clinical outcomes.
To address this gap, the eMERGE study has delivered genome-informed risk assessment reports to 23,840 adults and children, along with their healthcare providers, across the participating sites nationwide. Each report combined genetic data with clinical factors and family health history to estimate risk for 11 common chronic conditions: asthma, atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, colorectal cancer, hypercholesterolemia, obesity, prostate cancer, and type 1 and 2 diabetes. The reports also included evidence-based recommendations for follow-up care, such as earlier screening, lifestyle changes, or additional clinical evaluation and/or treatment.
“The eMERGE study’s overarching goal is to determine whether PRS can be evaluated and applied to stratify individuals’ risk for multiple chronic conditions,” Limdi explained. “Can PRS data, combined with other risk factors, be effectively incorporated into clinical workflows alongside recommended healthcare interventions? Will both providers and patients adopt these recommendations, and will such adoption improve clinical outcomes across a range of chronic diseases?”
The study is designed to track measurable outcomes within healthcare systems, including whether patients and providers follow recommended preventive actions, whether conditions are diagnosed earlier, and whether treatments are started or adjusted based on risk information.
To rigorously evaluate these outcomes, Limdi and the research team will use innovative quasi-experimental methods, including regression discontinuity, to help determine the impact of genetic information itself outside a randomized clinical trial setting.
As genetic testing becomes more common, healthcare systems will need to integrate and facilitate the use of genomic risk information to guide care. Findings from this study will help inform future clinical guidelines, health system policies, and research on the role of genomics in disease prevention.
“The eMERGE Network is well-positioned to advance understanding of the clinical utility of genomics for the prevention of common chronic diseases,” Limdi said. “Conducting a study across multiple conditions, incorporating diverse types of risk information, although challenging, provides a unique opportunity to clarify study design, define critical endpoints, and propose innovative analytic methods to disentangle intervention effects in a real-world setting.”
She continued: “Through this work, we are establishing a scalable implementation framework for integrating genomic information into routine care and generating critical evidence on its effectiveness for population risk stratification for the purpose of preventing common chronic diseases.”
UAB authors on the March 23 study include Nita Limdi, Pharm. D., Ph.D., MSPH (Neurology); T. Mark Beasley, Ph.D. (Biostatistics); Josh Cortopassi, Pharm.D. (Neurology); Brittney Davis, Pharm.D. (Neurology); James Cimino, M.D. (Biomedical Informatics and Data Science); Margurite R. Irvin, Ph.D. (Epidemiology); Bruce Korf, M.D., Ph.D. (Genetics); Hemant Tiwari (Biostatistics).