title: Validation of continuous clinical indices of cardiometabolic risk in a cohort of Australian adults.
abstract: BACKGROUND
  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).
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Spatial Epidemiology and Evaluation Research Group, School of Population Health and Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia. suzanne.carroll@unisa.edu.au.
authors.forename: Suzanne J
authors.initials: SJ
authors.lastname: Carroll
authors.forename: Catherine
authors.initials: C
authors.lastname: Paquet
authors.forename: Natasha J
authors.initials: NJ
authors.lastname: Howard
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Adams
authors.forename: Anne W
authors.initials: AW
authors.lastname: Taylor
authors.forename: Mark
authors.initials: M
authors.lastname: Daniel
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D062186:Arterial Pressure; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D002318:Cardiovascular Diseases / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D015994:Incidence; D008075:Lipoproteins, HDL / Q000097:blood; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D008875:Middle Aged; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013021:South Australia / Q000453:epidemiology; D013997:Time Factors; D014280:Triglycerides / Q000097:blood; D055105:Waist Circumference
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D008075:Lipoproteins, HDL; D014280:Triglycerides