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How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior : an Observational Follow-up Study

Widén, Elisabeth ; Junna, Nella ; Ruotsalainen, Sanni ; Surakka, Ida ; Mars, Nina ; Ripatti, Pietari ; Partanen, Juulia J. ; Aro, Johanna ; Mustonen, Pekka and Tuomi, Tiinamaija LU orcid , et al. (2022) In Circulation: Genomic and Precision Medicine 15(2). p.003459-003459
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
cardiovascular disease, communication, genomics, risk factor, weight loss
in
Circulation: Genomic and Precision Medicine
volume
15
issue
2
pages
003459 - 003459
publisher
Lippincott Williams & Wilkins
external identifiers
  • pmid:35130028
  • scopus:85126479717
ISSN
2574-8300
DOI
10.1161/CIRCGEN.121.003459
language
English
LU publication?
yes
id
28265f85-bc1f-4997-9987-ffaeec746904
date added to LUP
2022-12-30 10:52:30
date last changed
2025-03-19 13:35:02
@article{28265f85-bc1f-4997-9987-ffaeec746904,
  abstract     = {{<p>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 (&gt;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&lt;0.001), with both high clinical (P&lt;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.</p>}},
  author       = {{Widén, Elisabeth and Junna, Nella and Ruotsalainen, Sanni and Surakka, Ida and Mars, Nina and Ripatti, Pietari and Partanen, Juulia J. and Aro, Johanna and Mustonen, Pekka and Tuomi, Tiinamaija and Palotie, Aarno and Salomaa, Veikko and Kaprio, Jaakko and Partanen, Jukka and Hotakainen, Kristina and Pöllänen, Pasi and Ripatti, Samuli}},
  issn         = {{2574-8300}},
  keywords     = {{cardiovascular disease; communication; genomics; risk factor; weight loss}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{2}},
  pages        = {{003459--003459}},
  publisher    = {{Lippincott Williams & Wilkins}},
  series       = {{Circulation: Genomic and Precision Medicine}},
  title        = {{How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior : an Observational Follow-up Study}},
  url          = {{http://dx.doi.org/10.1161/CIRCGEN.121.003459}},
  doi          = {{10.1161/CIRCGEN.121.003459}},
  volume       = {{15}},
  year         = {{2022}},
}