The online version of this article (doi:10.1186/1471-2261-14-27) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
SJC, MD, CP, NJH, AWT, and RJA conceived and designed the study. SJC analysed the data under guidance of CP. SJC, CP, MD contributed to interpretation of results. SJC wrote the manuscript, and CP, NJH, and MD revised it critically for important intellectual content. All authors approved the final manuscript.
Indicators of cardiometabolic risk typically include non-clinical factors (e.g., smoking). While the incorporation of non-clinical factors can improve absolute risk prediction, it is impossible to study the contribution of non-clinical factors when they are both predictors and part of the outcome measure. Metabolic syndrome, incorporating only clinical measures, seems a solution yet provides no information on risk severity. The aims of this study were: 1) to construct two continuous clinical indices of cardiometabolic risk (cCICRs), and assess their accuracy in predicting 10-year incident cardiovascular disease and/or type 2 diabetes; and 2) to compare the predictive accuracies of these cCICRs with existing risk indicators that incorporate non-clinical factors (Framingham Risk Scores).
Data from a population-based biomedical cohort (n = 4056) were used to construct two cCICRs from waist circumference, mean arteriole pressure, fasting glucose, triglycerides and high density lipoprotein: 1) the mean of standardised risk factors (cCICR-Z); and 2) the weighted mean of the two first principal components from principal component analysis (cCICR-PCA). The predictive accuracies of the two cCICRs and the Framingham Risk Scores were assessed and compared using ROC curves.
Both cCICRs demonstrated moderate accuracy (AUCs 0.72 – 0.76) in predicting incident cardiovascular disease and/or type 2 diabetes, among men and women. There were no significant differences between the predictive accuracies of the cCICRs and the Framingham Risk Scores.
cCICRs may be useful in research investigating associations between non-clinical factors and health by providing suitable alternatives to current risk indicators which include non-clinical factors.
Woodward M, Brindle P, Tunstall-Pedoe H: Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007, 93: 173-176.
IDF: IDF Diabetes Atlas. 2011, Brussels: International Diabetes Federation, 5
Adult Treatment Panel III: Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002, 106 (25): 3143-3421.
Agarwal S, Jacobs DR, Vaidya D, Sibley CT, Jorgensen NW, Rotter JI, Chen YDI, Liu Y, Andrews JS, Kritchevsky S, Goodpaster B, Kanaya A, Newman AB, Simonsick EM, Herrington DM: Metabolic syndrome derived from principal component analysis and incident cardiovascular events: the multi ethnic study of atherosclerosis (MESA) and health, aging, and body composition (Health ABC). Cardiol Res Pract. 2012, 1: 919425-
Paquet C, Dube L, Gauvin L, Kestens Y, Daniel M: Sense of mastery and metabolic risk: moderating role of the local fast-food environment. Psychosoc Med. 2010, 72: 324-331. 10.1097/PSY.0b013e3181cdf439. CrossRef
Hillier TA, Rousseau A, Lange C, Lepinay P, Cailleau M, Novak M, Calliez E, Ducimetiere P, Balkau B: Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia. 2006, 49 (7): 1528-1535. 10.1007/s00125-006-0266-8. CrossRefPubMedPubMedCentral
Grant J, Chittleborough C, Taylor A, Dal Grande E, Wilson D, Phillips P, Adams R, Cheek J, Price K, Gill T, Ruffin R: The North West Adelaide Health Study: detailed methods and baseline segmentation of a cohort for chronic diseases. Epidemiol Perspect Innov. 2006, 3 (4): 4- CrossRefPubMedPubMedCentral
IEC: International Expert Committee Report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009, 32 (7): 1327-1334. CrossRef
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