Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

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dc.contributor.author Lee, Jiwoo
dc.contributor.author Kiiskinen, Tuomo
dc.contributor.author Mars, Nina
dc.contributor.author Jukarainen, Sakari
dc.contributor.author Ingelsson, Erik
dc.contributor.author Neale, Benjamin
dc.contributor.author Ripatti, Samuli
dc.contributor.author Natarajan, Pradeep
dc.contributor.author Ganna, Andrea
dc.date.accessioned 2021-10-06T14:06:01Z
dc.date.available 2021-10-06T14:06:01Z
dc.date.issued 2021-08
dc.identifier.citation Lee , J , Kiiskinen , T , Mars , N , Jukarainen , S , Ingelsson , E , Neale , B , Ripatti , S , Natarajan , P & Ganna , A 2021 , ' Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome ' , Circulation-Genomic and precision medicine , vol. 14 , no. 4 , 003283 , pp. 409-417 . https://doi.org/10.1161/CIRCGEN.120.003283
dc.identifier.other PURE: 169208591
dc.identifier.other PURE UUID: e2667c0c-f625-4937-a85f-6cb78a55dcd9
dc.identifier.other WOS: 000686019100002
dc.identifier.other ORCID: /0000-0001-8169-8954/work/101083526
dc.identifier.other ORCID: /0000-0002-8147-240X/work/101084397
dc.identifier.uri http://hdl.handle.net/10138/335010
dc.description.abstract Background: Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS. Methods: We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen. Results: We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87x10(-8)) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38x10(-6)). Conclusions: In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease. en
dc.format.extent 9
dc.language.iso eng
dc.relation.ispartof Circulation-Genomic and precision medicine
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject acute coronary syndrome
dc.subject diagnosis
dc.subject genetics
dc.subject heart diseases
dc.subject HOSPITAL DISCHARGE REGISTER
dc.subject STABLE ANGINA-PECTORIS
dc.subject INDEX EVENT BIAS
dc.subject ARTERY-DISEASE
dc.subject RISK PREDICTION
dc.subject HEART-DISEASE
dc.subject 3121 General medicine, internal medicine and other clinical medicine
dc.title Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome en
dc.type Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization Helsinki Institute of Life Science HiLIFE
dc.contributor.organization Complex Disease Genetics
dc.contributor.organization Genomics of Neurological and Neuropsychiatric Disorders
dc.contributor.organization Centre of Excellence in Complex Disease Genetics
dc.contributor.organization Department of Public Health
dc.contributor.organization Samuli Olli Ripatti / Principal Investigator
dc.contributor.organization Faculty Common Matters
dc.contributor.organization Biostatistics Helsinki
dc.contributor.organization Data Science Genetic Epidemiology Lab
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1161/CIRCGEN.120.003283
dc.relation.issn 2574-8300
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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