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      A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses

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          Summary

          Background

          Comparison of patients with coronary heart disease and controls in genome-wide association studies has revealed several single nucleotide polymorphisms (SNPs) associated with coronary heart disease. We aimed to establish the external validity of these findings and to obtain more precise risk estimates using a prospective cohort design.

          Methods

          We tested 13 recently discovered SNPs for association with coronary heart disease in a case-control design including participants differing from those in the discovery samples (3829 participants with prevalent coronary heart disease and 48 897 controls free of the disease) and a prospective cohort design including 30 725 participants free of cardiovascular disease from Finland and Sweden. We modelled the 13 SNPs as a multilocus genetic risk score and used Cox proportional hazards models to estimate the association of genetic risk score with incident coronary heart disease. For case-control analyses we analysed associations between individual SNPs and quintiles of genetic risk score using logistic regression.

          Findings

          In prospective cohort analyses, 1264 participants had a first coronary heart disease event during a median 10·7 years' follow-up (IQR 6·7–13·6). Genetic risk score was associated with a first coronary heart disease event. When compared with the bottom quintile of genetic risk score, participants in the top quintile were at 1·66-times increased risk of coronary heart disease in a model adjusting for traditional risk factors (95% CI 1·35–2·04, p value for linear trend=7·3×10 −10). Adjustment for family history did not change these estimates. Genetic risk score did not improve C index over traditional risk factors and family history (p=0·19), nor did it have a significant effect on net reclassification improvement (2·2%, p=0·18); however, it did have a small effect on integrated discrimination index (0·004, p=0·0006). Results of the case-control analyses were similar to those of the prospective cohort analyses.

          Interpretation

          Using a genetic risk score based on 13 SNPs associated with coronary heart disease, we can identify the 20% of individuals of European ancestry who are at roughly 70% increased risk of a first coronary heart disease event. The potential clinical use of this panel of SNPs remains to be defined.

          Funding

          The Wellcome Trust; Academy of Finland Center of Excellence for Complex Disease Genetics; US National Institutes of Health; the Donovan Family Foundation.

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          Most cited references 25

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          Time-dependent ROC curves for censored survival data and a diagnostic marker.

          ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
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            The Malmo Diet and Cancer Study. Design and feasibility.

            The Malmö Diet and Cancer study is a 10-year prospective case-control study in 45-64-year-old men and women (n = 53,000) living in a city with 230,000 inhabitants. One objective is to clarify whether a western diet is associated with certain forms of cancer whilst taking other life-style factors into account. Another broad question is whether oxidative stress and the activity in DNA-repairing systems influence the impact of diet on the development of all or certain forms of cancer. The study is also to act as a resource available for testing new hypotheses emanating from other studies. Initially food intake, heredity, socio-economic factors, life-style pattern, occupational situation, previous and current diseases, symptoms and medications, will be determined. Viable lymphocytes, granulocytes, erythrocytes, and plasma/serum will be stored in a biological bank together with tumour specimens gathered from cases. The incidence and mortality of all cancer forms will then be followed for 10 years by existing registries. Data from the initial examination in these cases will then be compared with those of control subjects not having developed any form of cancer. A biomarker programme, utilizing the biological bank, has been developed and is aimed at finding predictors and/or precursors of cancer. A high participation rate (> 70%) and a high quality biological bank are prerequisites for a successful project. The experience gathered so far indicates that these goals are feasible.
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              Polymorphisms associated with cholesterol and risk of cardiovascular events.

              Common single-nucleotide polymorphisms (SNPs) that are associated with blood low-density lipoprotein (LDL) or high-density lipoprotein (HDL) cholesterol modestly affect lipid levels. We tested the hypothesis that a combination of such SNPs contributes to the risk of cardiovascular disease. We studied SNPs at nine loci in 5414 subjects from the cardiovascular cohort of the Malmö Diet and Cancer Study. We first validated the association between SNPs and either LDL or HDL cholesterol and subsequently created a genotype score on the basis of the number of unfavorable alleles. We used Cox proportional-hazards models to determine the time to the first cardiovascular event in relation to the genotype score. All nine SNPs showed replication of an association with levels of either LDL or HDL cholesterol. With increasing genotype scores, the level of LDL cholesterol increased from 152 mg to 171 mg per deciliter (3.9 to 4.4 mmol per liter), whereas HDL cholesterol decreased from 60 mg to 51 mg per deciliter (1.6 to 1.3 mmol per liter). During follow-up (median, 10.6 years), 238 subjects had a first cardiovascular event. The genotype score was associated with incident cardiovascular disease in models adjusted for covariates including baseline lipid levels (P<0.001). The use of the genotype score did not improve the clinical risk prediction, as assessed by the C statistic. However, there was a significant improvement in risk classification with the use of models that included the genotype score, as compared with those that did not include the genotype score. A genotype score of nine validated SNPs that are associated with modulation in levels of LDL or HDL cholesterol was an independent risk factor for incident cardiovascular disease. The score did not improve risk discrimination but did modestly improve clinical risk reclassification for individual subjects beyond standard clinical factors. Copyright 2008 Massachusetts Medical Society.
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                Author and article information

                Journal
                Lancet
                Lancet
                Lancet
                Lancet Publishing Group
                0140-6736
                1474-547X
                23 October 2010
                23 October 2010
                : 376
                : 9750
                : 1393-1400
                Affiliations
                [a ]Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
                [b ]National Institute for Health and Welfare, Helsinki, Finland
                [c ]Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
                [d ]Broad Institute, Cambridge, MA, USA
                [e ]Division of Cardiology, Department of Medicine, Helsinki University Central Hospital (HUCH), Helsinki, Finland
                [f ]Transplantation Laboratory, Haartman Institute, University of Helsinki, Helsinki, Finland
                [g ]Wellcome Trust Sanger Institute, Hinxton, UK
                [h ]Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
                [i ]Department of Medicine, Harvard Medical School, Boston, MA, USA
                [j ]Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
                Author notes
                [* ]Correspondence to: Dr Samuli Ripatti, FIMM, P O Box 20, FIM-00014 University of Helsinki, Finland samuli.ripatti@ 123456fimm.fi
                [‡]

                Prof Peltonen died in March, 2010

                Article
                LANCET61267
                10.1016/S0140-6736(10)61267-6
                2965351
                20971364
                © 2010 Elsevier Ltd. All rights reserved.

                This document may be redistributed and reused, subject to certain conditions.

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                Medicine

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