Nella Junna1, Sanni Ruotsalainen1, Elisabeth Widén1, Ida Surakka1,2, Nina Mars1, Pietari Ripatti1, Juulia J Partanen1, Johanna Aro1, Pekka Mustonen3, Tiinamaija Tuomi1,4,5,6, Aarno Palotie1,7, Veikko Salomaa8, Jaakko Kaprio1, Jukka Partanen9, Kristina Hotakainen10, Pasi Pöllänen11,12, Samuli Ripatti1,13,7. 1. Institute for Molecular Medicine Finland, FIMM, HiLIFE (E.W., N.J., S.R., I.S., N.M., P.R., J.J.P., J.A., T.T., A.P., J.K., S.R.), University of Helsinki, Helsinki, Finland. 2. Department of Internal Medicine, University of Michigan, Ann Arbor (I.D.). 3. Duodecim Publishing Company Ltd, Helsinki, Finland. (P.M.). 4. Research Program Unit, Clinical and Molecular Metabolism (T.T.), University of Helsinki, Helsinki, Finland. 5. Folkhälsan Research Center, Helsinki, Finland (T.T.). 6. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden (T.T.). 7. Analytic and Translational Genetics Unit, Massachusetts General Hospital & Harvard Medical School, Boston & Broad Institute of MIT & Harvard, Cambridge (A.P., S.R.). 8. Finnish Institute for Health and Welfare, Helsinki, Finland (V.S.). 9. Finnish Red Cross Blood Service, Helsinki, Finland (J.P.). 10. Mehiläinen Oy, Helsinki (K.H.). 11. Clinicum (P.P.), University of Helsinki, Helsinki, Finland. 12. CAREA - Kymenlaakso social and health care services, Kotka, Finland (P.P.). 13. Department of Public Health, Clinicum (S.R.), University of Helsinki, Helsinki, Finland.
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.
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.
Entities:
Keywords:
cardiovascular disease; communication; genomics; risk factor; weight loss
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