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Communicating polygenic and non-genetic risk for atherosclerotic cardiovascular disease - An observational follow-up study

By Elisabeth Widen, Nella Junna, Sanni Ruotsalainen, Ida Surakka, Nina Mars, Pietari Ripatti, Juulia Partanen, Johanna Aro, Pekka Mustonen, Tiinamaija Tuomi, Aarno Palotie, Veikko Salomaa, Jaakko Kaprio, Jukka Partanen, Kristina Hotakainen, Pasi Pollanen, Samuli Ripatti

Posted 18 Sep 2020
medRxiv DOI: 10.1101/2020.09.18.20197137

Background. Algorithms including both traditional risk factors and polygenic risk scores (PRS) can significantly improve prediction of atherosclerotic cardiovascular disease (ASCVD). However, the clinical benefit of adding PRS to clinical risk evaluation remains unclear. Objectives. The study evaluated the attitudes of 7,342 individuals (64% women, mean age 56 yrs) upon receiving personal genome-enhanced ASCVD risk information, and prospectively assessed the impact on the participants' health behavior. Methods. The participant's 10-year risk for ASCVD was estimated using both a traditional clinical risk score and a PRS-enhanced score, and both scores were communicated directly to study participants with an interactive web-tool. Results. When reassessed after 1.5 years by a clinical visit and questionnaires, 20.8% of individuals at high (>10%) ASCVD 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 individuals at high risk had made some health behavioral change compared to 33.5% of persons at low/average risk such that a higher baseline risk predicted a favorable change (p<0.001), with both clinical (p<0.001) and genomic factors (p=0.003) contributing independently. Seeing a doctor and weight loss both resulted in clinically significant improvement of lipid profiles (lower LDL-cholesterol and triglycerides) and lower systolic blood pressure (p<0.01). Conclusions. Web-based communication of personal ASCVD risk-data including polygenic risk to middle-aged persons can motivate positive changes in health behavior. It supports integration of genomic information into clinical risk calculators as a feasible approach to enhance disease prevention.

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