Validation of continuous clinical indices of cardiometabolic risk in a cohort of Australian adults

Abstract

BackgroundIndicators 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).MethodsData 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.ResultsBoth 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.ConclusionscCICRs 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.

Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population

“…General-FRS model showed a good discrimination in Australian population7, Spanish population8 and Tehran population9, in which the AUCs were higher than the findings in this study. However, in a Malaysia hospital-based study, the general-FRS had a c-statistic of 0.6310, and this maybe because the study subjects were actual patients from a clinic but not general population from community, whose overall CVD risk profile was already high even before CVD events occurring.…”

Sleep duration and the associated cardiometabolic risk scores in adults

“…28 Carroll et al 28 used the components of metabolic syndrome (ie, WC, SBP, DBP, TG, Glu, and HDL) in their study, and found that the continuous cardiometabolic risk score methodology was comparable to the Framingham Risk Score in predicting incident cardiovascular disease and type 2 diabetes. The use of the continuous cardiometabolic risk score in epidemiology instead of the binary definition of metabolic syndrome was, however, proposed in 2006 by Wijndaele et al 46 However, a standard formula for estimating the cardiometabolic risk score has not yet been developed for research or clinical purposes since the risk factors of cardiometabolic health are many.…”

“…26 The continuous cardiometabolic risk score method was recently validated in Australian adults. 28 Covariates Demographic variables used to describe the sample included age, sex, ethnicity, family poverty income ratio (PIR), education, alcohol intake, and smoking history. Age was categorized as 20 to b40 y, 40 to b65 y, and ≥65 y.…”

“…16,28 Participants' blood was used to obtain the levels of HDL, Insulin, Glu, and TG using standardized laboratory procedures. 26 Height, weight, WC, SBP and DBP were measured by trained medical professionals at the mobile examination sites.…”

Working (longer than) 9 to 5: are there cardiometabolic health risks for young Australian workers who report longer than 38-h working weeks?

“…Cardiometabolic risk has been quantified using calculation of a continuous clinical index of cardiometabolic (cCICR, e.g. Carroll et al, 2014;Okosun et al, 2010). Cardiometabolic risk factors were chosen for analysis based on previous variables used to calculate overall cardiometabolic and metabolic syndrome risk indices in adolescents (Okosun et al, 2010) and older adult populations (Carroll et al, 2014).…”