How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study. (3rd February 2022)

• Record Type: Journal Article
• Authors: Widén, Elisabeth; Junna, Nella; Ruotsalainen, Sanni; Surakka, Ida; Mars, Nina; Ripatti, Pietari; Partanen, Juulia J.; Aro, Johanna; Mustonen, Pekka; Tuomi, Tiinamaija; Palotie, Aarno; Salomaa, Veikko; Kaprio, Jaakko; Partanen, Jukka; Hotakainen, Kristina; Pöllänen, Pasi; Ripatti, Samuli
• Is Part Of: Circulation. Volume 15:Number 2(2022)
• Page Start: e003459
• Publication Date: 2022-02-03
• Subjects: cardiovascular disease -- communication -- genomics -- risk factor -- weight loss; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular system -- Genetics -- Periodicals; Cardiovascular Diseases -- genetics; Precision Medicine; Periodical; Fulltext; Internet Resources; Periodicals; Electronic journals; Periodicals; 616.1042
• DOI: 10.1161/CIRCGEN.121.003459
• Languages: English
• ISSNs: 2574-8300

Abstract

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.