Additional value of a combined genetic risk score to standard cardiovascular stratification
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.1590/1678-4685-gmb-2017-0173 |
Resumo: | The utility of genetic risk scores (GRS) as independent risk predictors remains inconclusive. Here, we evaluate the additive value of a multi-locus GRS to the Framingham risk score (FRS) in coronary artery disease (CAD) risk prediction. A total of 2888 individuals (1566 coronary patients and 1322 controls) were divided into three subgroups according to FRS. Multiplicative GRS was determined for 32 genetic variants associated to CAD. Logistic Regression and Area Under the Curve (AUC) were determined first, using the TRF for each FRS subgroup, and secondly, adding GRS. Different models (TRF, TRF+GRS) were used to classify the subjects into risk categories for the FRS 10-year predicted risk. The improvement offered by GRS was expressed as Net Reclassification Index and Integrated Discrimination Improvement. Multivariate analysis showed that GRS was an independent predictor for CAD (OR = 1.87; p<0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p<0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories. In conclusion, GRS provided a better incremental value in intermediate subgroup. In this subgroup, inclusion of genotyping may be considered to better stratify cardiovascular risk. |
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Additional value of a combined genetic risk score to standard cardiovascular stratificationCoronary artery diseaseFramingham scoreGenetic risk scoreRisk factorsRisk predictionMolecular BiologyGeneticsSDG 3 - Good Health and Well-beingThe utility of genetic risk scores (GRS) as independent risk predictors remains inconclusive. Here, we evaluate the additive value of a multi-locus GRS to the Framingham risk score (FRS) in coronary artery disease (CAD) risk prediction. A total of 2888 individuals (1566 coronary patients and 1322 controls) were divided into three subgroups according to FRS. Multiplicative GRS was determined for 32 genetic variants associated to CAD. Logistic Regression and Area Under the Curve (AUC) were determined first, using the TRF for each FRS subgroup, and secondly, adding GRS. Different models (TRF, TRF+GRS) were used to classify the subjects into risk categories for the FRS 10-year predicted risk. The improvement offered by GRS was expressed as Net Reclassification Index and Integrated Discrimination Improvement. Multivariate analysis showed that GRS was an independent predictor for CAD (OR = 1.87; p<0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p<0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories. In conclusion, GRS provided a better incremental value in intermediate subgroup. In this subgroup, inclusion of genotyping may be considered to better stratify cardiovascular risk.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNPereira, Andreia MicaelaMendonca, Maria IsabelBorges, SofiaSousa, Ana CéliaFreitas, SóniaHenriques, EvaRodrigues, MarianaFreitas, Ana IsabelGuerra, GraçaFreitas, CarolinaPereira, DécioBrehm, AntónioDos Reis, Roberto Palma2019-01-03T14:45:36Z2018-10-012018-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9application/pdfhttps://doi.org/10.1590/1678-4685-gmb-2017-0173eng1415-4757PURE: 11102505http://www.scopus.com/inward/record.url?scp=85058932229&partnerID=8YFLogxKhttps://doi.org/10.1590/1678-4685-gmb-2017-0173info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:27:08Zoai:run.unl.pt:10362/56379Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:32:53.926674Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
title |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
spellingShingle |
Additional value of a combined genetic risk score to standard cardiovascular stratification Pereira, Andreia Micaela Coronary artery disease Framingham score Genetic risk score Risk factors Risk prediction Molecular Biology Genetics SDG 3 - Good Health and Well-being |
title_short |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
title_full |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
title_fullStr |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
title_full_unstemmed |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
title_sort |
Additional value of a combined genetic risk score to standard cardiovascular stratification |
author |
Pereira, Andreia Micaela |
author_facet |
Pereira, Andreia Micaela Mendonca, Maria Isabel Borges, Sofia Sousa, Ana Célia Freitas, Sónia Henriques, Eva Rodrigues, Mariana Freitas, Ana Isabel Guerra, Graça Freitas, Carolina Pereira, Décio Brehm, António Dos Reis, Roberto Palma |
author_role |
author |
author2 |
Mendonca, Maria Isabel Borges, Sofia Sousa, Ana Célia Freitas, Sónia Henriques, Eva Rodrigues, Mariana Freitas, Ana Isabel Guerra, Graça Freitas, Carolina Pereira, Décio Brehm, António Dos Reis, Roberto Palma |
author2_role |
author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
dc.contributor.author.fl_str_mv |
Pereira, Andreia Micaela Mendonca, Maria Isabel Borges, Sofia Sousa, Ana Célia Freitas, Sónia Henriques, Eva Rodrigues, Mariana Freitas, Ana Isabel Guerra, Graça Freitas, Carolina Pereira, Décio Brehm, António Dos Reis, Roberto Palma |
dc.subject.por.fl_str_mv |
Coronary artery disease Framingham score Genetic risk score Risk factors Risk prediction Molecular Biology Genetics SDG 3 - Good Health and Well-being |
topic |
Coronary artery disease Framingham score Genetic risk score Risk factors Risk prediction Molecular Biology Genetics SDG 3 - Good Health and Well-being |
description |
The utility of genetic risk scores (GRS) as independent risk predictors remains inconclusive. Here, we evaluate the additive value of a multi-locus GRS to the Framingham risk score (FRS) in coronary artery disease (CAD) risk prediction. A total of 2888 individuals (1566 coronary patients and 1322 controls) were divided into three subgroups according to FRS. Multiplicative GRS was determined for 32 genetic variants associated to CAD. Logistic Regression and Area Under the Curve (AUC) were determined first, using the TRF for each FRS subgroup, and secondly, adding GRS. Different models (TRF, TRF+GRS) were used to classify the subjects into risk categories for the FRS 10-year predicted risk. The improvement offered by GRS was expressed as Net Reclassification Index and Integrated Discrimination Improvement. Multivariate analysis showed that GRS was an independent predictor for CAD (OR = 1.87; p<0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p<0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories. In conclusion, GRS provided a better incremental value in intermediate subgroup. In this subgroup, inclusion of genotyping may be considered to better stratify cardiovascular risk. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-01 2018-10-01T00:00:00Z 2019-01-03T14:45:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.1590/1678-4685-gmb-2017-0173 |
url |
https://doi.org/10.1590/1678-4685-gmb-2017-0173 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1415-4757 PURE: 11102505 http://www.scopus.com/inward/record.url?scp=85058932229&partnerID=8YFLogxK https://doi.org/10.1590/1678-4685-gmb-2017-0173 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137950919819264 |