Rapid Engagement for Solutions to Population and Outcomes Through Networked Dialogue for Coronary Heart Disease
RESPOND
1 other identifier
observational
200
0 countries
N/A
Brief Summary
Cardiometabolic diseases are major causes of morbidity and mortality in the state of Wisconsin and are expected to pose an increasing burden over the next few decades. A crucial initial step in preventing or delaying the onset of these diseases is to assess disease risk at the individual level. However, the accuracy of risk prediction of disease events based on conventional risk factors remains modest. Incorporating a polygenic risk score (PRS) into risk equations improves risk prediction but there is uncertainty about how best to integrate PRSs into primary care settings, given the lack of familiarity with PRSs among patients and providers. Probabilistic estimates for risks of cardiometabolic diseases may be misunderstood, and genetic risk assessment may not be trusted by those in low resource rural or inner-city settings. The potential for using PRSs to refine disease risk estimates has led to numerous studies to assess their clinical utility; however, the vast majority have been conducted in tertiary academic medical centers, raising concern that communities with diminished access to care could be left behind. The study team will investigate how the use of PRSs for such diseases influences health outcomes in rural and inner-city settings. The study team will leverage prior experience in conducting the MIGENES randomized clinical trial (RCT) of disclosing polygenic risk of CHD in a preventive cardiology setting of an academic center. In the proposed study, the investigators will conduct a pragmatic RCT to extend the investigation to 'real-world' settings of primary care clinics in a rural medical center and an urban Federally Qualified Health Center (FQHC). The investigators will engage a Community Advisory Board (CAB) through focus groups to gather feedback on implementing PRS-guided screening, related medical and lifestyle interventions, and public health strategies to reduce CHD risk. Feedback will inform provider education, targeted outreach to Wisconsin residents, and identification of barriers to adoption. The study team will also assess primary care physicians' and patients' familiarity with polygenic risk, their attitudes toward PRS testing, and intended actions based on results, comparing responses across rural and urban settings. The 10-yr risk of CHD will be estimated based on pooled cohort equations (PCE). Participants will view a video describing how cardiovascular risk was estimated and how lifestyle changes and drug therapy could reduce such risk and, in those randomized to receive PRS, the probabilistic nature of genetic risk. Patients will then see their PCP to review the 10-yr CHD risk estimate and engage in shared decision-making regarding statin therapy. Patient and clinician understanding of polygenic risk information will be assessed, as well as health-related and behavioral outcomes.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2027
Typical duration for all trials
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
November 21, 2025
CompletedFirst Posted
Study publicly available on registry
December 3, 2025
CompletedStudy Start
First participant enrolled
June 1, 2027
ExpectedPrimary Completion
Last participant's last visit for primary outcome
May 31, 2030
Study Completion
Last participant's last visit for all outcomes
January 1, 2031
May 4, 2026
April 1, 2026
3 years
November 21, 2025
April 28, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The number of patients initiated on a statin medication
Primary outcome will be whether a participant was prescribed a statin medication.
Within 6 months of shared decision-making visit with primary care provider.
Secondary Outcomes (6)
A decrease in LDL cholesterol in patients who received the polygenic risk score
Within 6 months of shared decision making
Number of patients with new diagnosis of coronary heart disease in patients who received the polygenic risk score
Within 6 months of shared decision making
A decrease in body weight in patients who received the polygenic risk score
Within 6 months of shared decision making
A change in blood pressure in patient who received the polygenic risk score
Within 6 months of shared decision making
Number of patients that stop smoking who have received the polygenic risk score
Within 6 months of shared decision making with primary care provider
- +1 more secondary outcomes
Study Arms (1)
Adults aged 40-69 years
Adults aged 40-69 years without known coronary heart disease and who are not currently prescribed a statin medication.
Interventions
The polygenic risk score is a precision medicine screening tool to determine one's future risk of coronary heart disease.
Eligibility Criteria
Adults who receive their primary care in Milwaukee and Shawano Counties in the state of Wisconsin.
You may qualify if:
- Adults aged 40-69 years of age
- No prior history of coronary heart disease
- No prior use of statin medication
- Has primary care provider
You may not qualify if:
- Prior history of coronary heart disease
- current use of statin medication
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Related Publications (6)
Slunecka JL, van der Zee MD, Beck JJ, Johnson BN, Finnicum CT, Pool R, Hottenga JJ, de Geus EJC, Ehli EA. Implementation and implications for polygenic risk scores in healthcare. Hum Genomics. 2021 Jul 20;15(1):46. doi: 10.1186/s40246-021-00339-y.
PMID: 34284826BACKGROUNDKullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial). Circulation. 2016 Mar 22;133(12):1181-8. doi: 10.1161/CIRCULATIONAHA.115.020109. Epub 2016 Feb 25.
PMID: 26915630BACKGROUNDKullo IJ. Promoting equity in polygenic risk assessment through global collaboration. Nat Genet. 2024 Sep;56(9):1780-1787. doi: 10.1038/s41588-024-01843-2. Epub 2024 Aug 5.
PMID: 39103647BACKGROUNDKullo IJ. Clinical use of polygenic risk scores: current status, barriers and future directions. Nat Rev Genet. 2026 Mar;27(3):246-263. doi: 10.1038/s41576-025-00900-8. Epub 2025 Oct 10.
PMID: 41073616BACKGROUNDKullo IJ, Lewis CM, Inouye M, Martin AR, Ripatti S, Chatterjee N. Polygenic scores in biomedical research. Nat Rev Genet. 2022 Sep;23(9):524-532. doi: 10.1038/s41576-022-00470-z. Epub 2022 Mar 30.
PMID: 35354965BACKGROUNDKullo IJ, Trejo-Gutierrez JF, Lopez-Jimenez F, Thomas RJ, Allison TG, Mulvagh SL, Arruda-Olson AM, Hayes SN, Pollak AW, Kopecky SL, Hurst RT. A perspective on the New American College of Cardiology/American Heart Association guidelines for cardiovascular risk assessment. Mayo Clin Proc. 2014 Sep;89(9):1244-56. doi: 10.1016/j.mayocp.2014.06.018. Epub 2014 Aug 12.
PMID: 25131696BACKGROUND
Biospecimen
Blood and saliva specimens will be collected and retained for genotyping to determine one's polygenic risk score.
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Associate Professor
Study Record Dates
First Submitted
November 21, 2025
First Posted
December 3, 2025
Study Start (Estimated)
June 1, 2027
Primary Completion (Estimated)
May 31, 2030
Study Completion (Estimated)
January 1, 2031
Last Updated
May 4, 2026
Record last verified: 2026-04
Data Sharing
- IPD Sharing
- Will not share
Individual participant data will not be shared due to the risk of re-identification from genomic, clinical, and demographic variables, particularly given the modest sample size and inclusion of potentially identifiable combinations of data.