An Assessment of Incremental Coronary Risk Prediction Using C-Reactive Protein and Other Novel Risk Markers

Johns Hopkins University, Baltimore, Maryland, United States
Archives of Internal Medicine (Impact Factor: 17.33). 08/2006; 166(13):1368-73. DOI: 10.1001/archinte.166.13.1368
Source: PubMed


There has been interest in recent years in whether additional, and in particular novel, risk factors or blood markers, such as C-reactive protein, can enhance existing coronary heart disease (CHD) prediction models.
Using a series of case-cohort studies, the prospective Atherosclerosis Risk in Communities (ARIC) Study assessed the association of 19 novel risk markers with incident CHD in 15,792 adults followed up since 1987-1989. Novel markers included measures of inflammation, endothelial function, fibrin formation, fibrinolysis, B vitamins, and antibodies to infectious agents. Change in the area under the receiver operating characteristic curve (AUC) was used to assess the additional contribution of novel risk markers to CHD prediction beyond that of traditional risk factors.
The basic risk factor model, which included traditional risk factors (age, race, sex, total and high-density lipoprotein cholesterol levels, systolic blood pressure, antihypertensive medication use, smoking status, and diabetes), predicted CHD well, as evidenced by an AUC of approximately 0.8. The C-reactive protein level did not add significantly to the AUC (increase in AUC of 0.003), and neither did most other novel risk factors. Of the 19 markers studied, lipoprotein-associated phospholipase A(2), vitamin B(6), interleukin 6, and soluble thrombomodulin added the most to the AUC (range, 0.006-0.011).
Our findings suggest that routine measurement of these novel markers is not warranted for risk assessment. On the other hand, our findings reinforce the utility of major, modifiable risk factor assessment to identify individuals at risk for CHD for preventive action.

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    • "Time-dependent area under the ROC curve methods [5] have also been used in the case-cohort setting [6,7] and C-statistics and the net reclassification index (NRI) have been applied by Vaarhorst et al [8]. However, any resulting bias in the estimates and their standard errors (SEs) has not been extensively investigated and the use of other measures of predictive ability has so far, to our knowledge, not been considered. "
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    ABSTRACT: Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted. We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics. The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available. Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI.
    Full-text · Article · Sep 2013 · BMC Medical Research Methodology
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    • "The ARIC sample is described in detail elsewhere (Folsom et al. 2006; ARIC Investigators 1989). Briefly, ARIC is a prospective epidemiologic cohort study sponsored by the National Heart, Lung, and Blood Institute to investigate the etiology of atherosclerotic disease. "
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    ABSTRACT: Multi-locus profiles of genetic risk, so-called "genetic risk scores," can be used to translate discoveries from genome-wide association studies into tools for population health research. We developed a genetic risk score for obesity from results of 16 published genome-wide association studies of obesity phenotypes in European-descent samples. We then evaluated this genetic risk score using data from the Atherosclerosis Risk in Communities (ARIC) cohort GWAS sample (N = 10,745, 55% female, 77% white, 23% African American). Our 32-locus GRS was a statistically significant predictor of body mass index (BMI) and obesity among ARIC whites [for BMI, r = 0.13, p<1 × 10(-30); for obesity, area under the receiver operating characteristic curve (AUC) = 0.57 (95% CI 0.55-0.58)]. The GRS predicted differences in obesity risk net of demographic, geographic, and socioeconomic information. The GRS performed less well among African Americans. The genetic risk score we derived from GWAS provides a molecular measurement of genetic predisposition to elevated BMI and obesity.[Supplemental materials are available for this article. Go to the publisher's online edition of Biodemography and Social Biology for the following resource: Supplement to Development & Evaluation of a Genetic Risk Score for Obesity.].
    Full-text · Article · May 2013 · Biodemography and Social Biology
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    • "A good example is C-reactive protein (CRP), an inflammatory marker. CRP level has been studied extensively as a potential biomarker that can improve CHD risk prediction, 16)22)31-36) but its clinical utility remains controversial. In the Women's Health Study, CRP was strongly associated with CHD risk and improved disease prediction.33)36)37) "
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    ABSTRACT: Coronary heart disease (CHD) is a significant cause of morbidity and mortality worldwide. Many risk prediction models have been developed in an effort to assist clinicians in risk assessment and the prevention of CHD. However, it is unclear whether the existing CHD prediction tools can improve clinical performance, and recently, there has been a lot of effort being made to improve the accuracy of the prediction models. A large number of novel biomarkers have been identified to be associated with cardiovascular risk, and studied with the goal of improving the accuracy and clinical utility of CHD risk prediction. Yet, controversy still remains with regard to the utility of novel biomarkers in CHD risk assessment, and in finding the best statistical methods to assess the incremental value of the biomarkers. This article discusses the statistical approaches that can be used to evaluate the predictive values of new biomarkers, and reviews the clinical utility of novel biomarkers in CHD prediction, specifically in the Korean population.
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