Genomic prediction of coronary heart disease.
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ABSTRACT: Aims
Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.Methods and results
We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.Conclusions
A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.
SUBMITTER: Abraham G
PROVIDER: S-EPMC5146693 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectiveThe objectives of this study were to develop a coronary heart disease (CHD) risk model among the Korean Heart Study (KHS) population and compare it with the Framingham CHD risk score.DesignA prospective cohort study within a national insurance system.Setting18 health promotion centres nationwide between 1996 and 2001 in Korea.Participants268 315 Koreans between the ages of 30 and 74 years without CHD at baseline.Outcome measureNon-fatal or fatal CHD events between 1997 and 2011. During an 11.6-year median follow-up, 2596 CHD events (1903 non-fatal and 693 fatal) occurred in the cohort. The optimal CHD model was created by adding high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol and triglycerides to the basic CHD model, evaluating using the area under the receiver operating characteristic curve (ROC) and continuous net reclassification index (NRI).ResultsThe optimal CHD models for men and women included HDL-cholesterol (NRI=0.284) and triglycerides (NRI=0.207) from the basic CHD model, respectively. The discrimination using the CHD model in the Korean cohort was high: the areas under ROC were 0.764 (95% CI 0.752 to 0.774) for men and 0.815 (95% CI 0.795 to 0.835) for women. The Framingham risk function predicted 3-6 times as many CHD events than observed. Recalibration of the Framingham function using the mean values of risk factors and mean CHD incidence rates of the KHS cohort substantially improved the performance of the Framingham functions in the KHS cohort.ConclusionsThe present study provides the first evidence that the Framingham risk function overestimates the risk of CHD in the Korean population where CHD incidence is low. The Korean CHD risk model is well-calculated alternations which can be used to predict an individual's risk of CHD and provides a useful guide to identify the groups at high risk for CHD among Koreans.
| S-EPMC4039825 | biostudies-literature
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| S-EPMC10218435 | biostudies-literature
Project description:An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.
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Project description:Background The long-term risk of coronary heart disease (CHD) and clinical models that predict this risk remain understudied in blood or marrow transplantation (BMT) recipients. Objectives This study sought to examine the risk of CHD after BMT and identify the associated risk factors. Methods Participants included patients transplanted between 1974 and 2014 at City of Hope, University of Minnesota, or University of Alabama at Birmingham and those who survived ≥2 years after BMT. Multivariable logistic regression models assessed CHD risk in BMT survivors compared with a sibling cohort. A self-reported questionnaire and medical records provided information regarding sociodemographics, comorbidities, and therapeutic exposures, which were used to develop a CHD risk prediction nomogram. Results Overall, 6,677 BMT recipients participated; the mean age at BMT was 43.9 ± 17.7 years, 58.3% were male, and 73.3% were non-Hispanic Whites. The median length of follow-up was 6.9 years (range: 2-46.2 years) from BMT. CHD was reported in 249 participants, with a 20-year cumulative incidence of 5.45% ± 0.39%. BMT survivors had a 1.6-fold greater odds of CHD compared with a sibling cohort (95% CI: 1.09-2.40). A nomogram was then developed to predict the risk of CHD at 10 and 20 years after BMT including age at BMT (HR: 1.06/y; 95% CI: 1.04-1.08), male sex (HR: 1.89; 95% CI: 1.15-3.11), a history of smoking (HR: 1.61; 95% CI: 1.01-2.58), diabetes (HR: 2.45; 95% CI: 1.23-4.89), hypertension (HR: 2.02; 95% CI: 1.15-3.54), arrhythmia (HR: 1.90; 95% CI: 0.89-4.06), and pre-BMT chest radiation (yes vs no: HR: 2.83; 95% CI: 1.20-6.67; unknown vs no: HR: 0.88; 95% CI: 0.34-2.28). The C-statistic was 0.77 in the test set (95% CI: 0.70-0.83). Conclusions This study identified BMT recipients at high risk for CHD, informing targeted screening for early detection and aggressive control of risk factors. Central Illustration
| S-EPMC10443117 | biostudies-literature
Project description:BackgroundCoronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.MethodsIn this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.ResultsResults demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.ConclusionThe CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.
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Project description:ObjectiveThe roles of microRNAs (miRNAs) in coronary heart disease (CHD) have not been well characterized. This study sought to systematically characterize the complex genomic architecture of CHD by integrating whole blood miRNA and mRNA expression with genetic variation in 186 CHD cases and 186 controls.Approach and resultsAt false discovery rate <0.2, 15 miRNAs were differentially expressed between CHD cases and controls. To explore regulatory mechanisms, we integrated miRNA and mRNA expression with genome-wide genotype data to investigate miRNA and mRNA associations and relationships of genetic variation with miRNAs. We identified a large number of correlated miRNA-mRNA pairs and genetic loci that seem to regulate miRNA levels. Subsequently, we explored the relationships of these complex molecular associations with CHD status. We identified a large difference in miRNA-mRNA associations between CHD cases and controls, as demonstrated by a significantly higher proportion of inversely correlated miRNA-mRNA pairs in cases versus controls (80% versus 30%; P<1×10(-16)), suggesting a genome-wide shift in the regulatory structure of the transcriptome in CHD. The differentially coexpressed miRNA-mRNA pairs showed enrichment for CHD risk genetic variants affecting both miRNA and mRNA expression levels, implicating a putatively causal role in CHD. Furthermore, 3 miRNAs (miR-1275, miR-365a-3p, and miR-150-5p) were associated with an mRNA coexpression module that was causally linked to CHD and reflected the dysregulation of B-cell centered immune function.ConclusionsOur results provide novel evidence that miRNAs are important regulators of biological processes involved in CHD via genetic control and via their tight coexpression with mRNAs.
| S-EPMC4376567 | biostudies-literature
Project description:BackgroundGuidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS.MethodsAmong 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization.ResultsDuring 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS.ConclusionsThe FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.
| S-EPMC3314613 | biostudies-literature
Project description:Although both inflammatory and atherosclerosis markers have been associated with coronary heart disease (CHD) risk, data directly comparing their predictive value are limited. The authors compared the value of 2 atherosclerosis markers (ankle-arm index (AAI) and aortic pulse wave velocity (aPWV)) and 3 inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-alpha)) in predicting CHD events. Among 2,191 adults aged 70-79 years at baseline (1997-1998) from the Health, Aging, and Body Composition Study cohort, the authors examined adjudicated incident myocardial infarction or CHD death ("hard" events) and "hard" events plus hospitalization for angina or coronary revascularization (total CHD events). During 8 years of follow-up between 1997-1998 and June 2007, 351 participants developed total CHD events (197 "hard" events). IL-6 (highest quartile vs. lowest: hazard ratio = 1.82, 95% confidence interval: 1.33, 2.49; P-trend < 0.001) and AAI (AAI < or = 0.9 vs. AAI 1.01-1.30: hazard ratio = 1.57, 95% confidence interval: 1.14, 2.18) predicted CHD events above traditional risk factors and modestly improved global measures of predictive accuracy. CRP, TNF-alpha, and aPWV had weaker associations. IL-6 and AAI accurately reclassified 6.6% and 3.3% of participants, respectively (P's < or = 0.05). Results were similar for "hard" CHD, with higher reclassification rates for AAI. IL-6 and AAI are associated with future CHD events beyond traditional risk factors and modestly improve risk prediction in older adults.
| S-EPMC2842214 | biostudies-literature
Project description:Cardiovascular risk models developed may have limitations when applied to rural Chinese. This study validated and compared the Framingham Risk Score (FRS) and Prediction for Atherosclerotic Cardiovascular Disease Risk in China (PAR) models in predicting 10-year risk of coronary heart disease (CHD) in a rural cohort in Ningxia, China from 2008 to 2019. The FRS and PAR models were validated by estimating predicted events, C index, calibration χ2 and plots. 1381 adults without CHD at baseline were followed up for 9.75 years on average. 168 CHD cases were observed. The FRS and PAR underestimated CHD events by 22 % and 46 % for the total population, while overestimated for males by 152 % and 78 %, respectively. The C index was slightly higher for PAR than FRS. Both models showed weak calibration with chi-square values above 20 (p < 0.001). Bland-Altman plots indicated FRS predicted higher CHD risk than PAR, lacking consistency. Overall, FRS and PAR demonstrated limited performance in predicting 10-year CHD risk in this rural population. PAR had slightly better discrimination than FRS, but require further improvement in calibration and individual risk estimation to suit the rural population in Northwest China.
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