How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study
Elisabeth Widén, Nella Junna, Sanni Ruotsalainen, Ida Surakka, Nina Mars, Pietari Ripatti, Juulia Partanen, J. Aro, Pekka Mustonen, Tiinamaija Tuomi, Aarno Palotie, Veikko Salomaa, Jaakko Kaprio, Jukka Partanen, Kristina Hotakainen, Pasi Pöllänen, Samuli Ripatti
Abstract
Background: Prediction tools that combine polygenic risk scores with clinical factors provide a new opportunity for improved prediction and prevention of atherosclerotic cardiovascular disease, but the clinical utility of polygenic risk score has remained unclear. Methods: We collected a prospective cohort of 7342 individuals (64% women, mean age 56 years) and estimated their 10-year risk for atherosclerotic cardiovascular disease both by a traditional risk score and a composite score combining the effect of a polygenic risk score and clinical risk factors. We then tested how returning the personal risk information with an interactive web-tool impacted on the participants’ health behavior. Results: When reassessed after 1.5 years by a clinical visit and questionnaires, 20.8% of individuals at high (>10%) 10-year atherosclerotic cardiovascular disease risk had seen a doctor, 12.4% reported weight loss, 14.2% of smokers had quit smoking, and 15.4% had signed up for health coaching online. Altogether, 42.6% of persons at high risk had made one or more health behavioral changes versus 33.5% of persons at low/average risk such that higher baseline risk predicted a favorable change (OR [CI], 1.53 [1.37–1.72] for persons at high risk versus the rest, P <0.001), with both high clinical ( P <0.001) and genomic risk (OR [CI], 1.10 [1.03–1.17], P =0.003) contributing independently. Conclusions: Web-based communication of personal atherosclerotic cardiovascular disease risk-data including polygenic risk to middle-aged persons motivates positive changes in health behavior and the propensity to seek care. It supports integration of genomic information into clinical risk calculators as a feasible approach to enhance disease prevention.