Maaike Weijmans1, Paul I W de Bakker2, Yolanda van der Graaf3, Folkert W Asselbergs4, Ale Algra5, Gert Jan de Borst6, Wilko Spiering1, Frank L J Visseren7. 1. Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. 2. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Medical Genetics, Division of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. 3. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. 4. Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands; Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom. 5. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Neurology and Neursurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands. 6. Department of Vascular Surgery, University Medical Center Utrecht, The Netherlands. 7. Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: f.l.j.visseren@umcutrecht.nl.
Abstract
BACKGROUND: Several genetic markers are related to incidence of cardiovascular events. We evaluated whether a genetic risk score (GRS) based on 30 single-nucleotide-polymorphisms associated with coronary artery disease (CAD) can improve prediction of 10-year risk of new cardiovascular events in patients with clinical manifest vascular disease. METHODS: In 5742 patients with symptomatic vascular disease enrolled in the SMART study, we developed Cox regression models based on the SMART Risk Score (SRS) and based on the SRS plus the GRS in all patients, in patients with a history of acute arterial thrombotic events and in patients with a history of more stable atherosclerosis and without CAD. The discriminatory ability was expressed by the c-statistic. Model calibration was evaluated by calibration plots. The incremental value of adding the GRS was assessed by net reclassification index (NRI) and decision curve analysis. RESULTS: During a median follow-up of 6.5 years (IQR4.0-9.5), the composite outcome of myocardial infarction, stroke, or vascular death occurred in 933 patients. Hazard ratios of GRS ranging from 0.86 to 1.35 were observed. The discriminatory capacity of the SRS for prediction of 10-year risk of cardiovascular events was fairly good (c-statistic 0.70, 95%CI 0.68-0.72), similar to the model based on the SRS plus the GRS. Calibration of the models based on SRS and SRS plus GRS was adequate. No increase in c-statistics, categorical NRIs and decision curves was observed when adding the GRS. The continuous NRI improved only in patients with stable atherosclerosis (0.14, 95%CI 0.03-0.25), increasing further excluding patients with a history of CAD (0.21, 95%CI 0.06-0.36). CONCLUSIONS: In patients with symptomatic vascular disease, a GRS did not improve risk prediction of 10-year risk of cardiovascular events beyond clinical characteristics. The GRS might improve risk prediction of first vascular events in the subgroup of patients with a history of stable atherosclerosis.
BACKGROUND: Several genetic markers are related to incidence of cardiovascular events. We evaluated whether a genetic risk score (GRS) based on 30 single-nucleotide-polymorphisms associated with coronary artery disease (CAD) can improve prediction of 10-year risk of new cardiovascular events in patients with clinical manifest vascular disease. METHODS: In 5742 patients with symptomatic vascular disease enrolled in the SMART study, we developed Cox regression models based on the SMART Risk Score (SRS) and based on the SRS plus the GRS in all patients, in patients with a history of acute arterial thrombotic events and in patients with a history of more stable atherosclerosis and without CAD. The discriminatory ability was expressed by the c-statistic. Model calibration was evaluated by calibration plots. The incremental value of adding the GRS was assessed by net reclassification index (NRI) and decision curve analysis. RESULTS: During a median follow-up of 6.5 years (IQR4.0-9.5), the composite outcome of myocardial infarction, stroke, or vascular death occurred in 933 patients. Hazard ratios of GRS ranging from 0.86 to 1.35 were observed. The discriminatory capacity of the SRS for prediction of 10-year risk of cardiovascular events was fairly good (c-statistic 0.70, 95%CI 0.68-0.72), similar to the model based on the SRS plus the GRS. Calibration of the models based on SRS and SRS plus GRS was adequate. No increase in c-statistics, categorical NRIs and decision curves was observed when adding the GRS. The continuous NRI improved only in patients with stable atherosclerosis (0.14, 95%CI 0.03-0.25), increasing further excluding patients with a history of CAD (0.21, 95%CI 0.06-0.36). CONCLUSIONS: In patients with symptomatic vascular disease, a GRS did not improve risk prediction of 10-year risk of cardiovascular events beyond clinical characteristics. The GRS might improve risk prediction of first vascular events in the subgroup of patients with a history of stable atherosclerosis.
Authors: Christopher Labos; Sara C Martinez; Rui Hao Leo Wang; Petra A Lenzini; Louise Pilote; Peter Bogaty; James M Brophy; James C Engert; Sharon Cresci; George Thanassoulis Journal: Atherosclerosis Date: 2015-07-17 Impact factor: 5.162
Authors: Gad Abraham; Aki S Havulinna; Oneil G Bhalala; Sean G Byars; Alysha M De Livera; Laxman Yetukuri; Emmi Tikkanen; Markus Perola; Heribert Schunkert; Eric J Sijbrands; Aarno Palotie; Nilesh J Samani; Veikko Salomaa; Samuli Ripatti; Michael Inouye Journal: Eur Heart J Date: 2016-09-21 Impact factor: 29.983