Exploring clinical predictors of cardiovascular disease in a central Australian Aboriginal cohort

Introduction: For Aboriginal populations, predicting individuals at risk of cardiovascular disease (CVD) is difficult due to limitations and inaccuracy in existing risk-prediction algorithms. We examined conventional and novel risk factors associated with insulin resistance and the metabolic syndrome and assessed their relationships with subsequent CVD events. Design: Longitudinal cohort. Methods: Aboriginal people (n = 739) from Central Australia completed population-based risk-factor surveys in 1995 and were followed up in 2005. Principal components analysis (PCA), regression and univariate analyses (using ROC defined cut-off points) were used to identify useful clinical predictors of primary CVD. Results: PCA yielded five components: (1) lipids and liver function; (2) insulin resistance; (3) blood pressure and kidney function; (4) glucose tolerance; and (5) anti-inflammatory (low fibrinogen, high HDL cholesterol). Components 2, 3 and 4, and age were significant independent predictors of incident CVD, and smoking approached significance. In univariate analysis fasting glucose >= 4.8 mmol/l, total: HDL cholesterol ratio >= 5.7, non-HDL cholesterol >= 4.3 mmol/l, gamma-glutamyl transferase >= 70 U/l, albumin creatinine ratio >= 5.7 mg/mmol, systolic blood pressure >= 120 mmHg and diastolic blood pressure >= 70 mmHg were useful predictors of CVD. The co-occurrence of three or more risk variables (fasting glucose >= 4.8 mmol/l, total: HDL cholesterol ratio >= 5.7, blood pressure (systolic >= 120 mmHg; diastolic >= 70 mmHg; albumin: creatinine ratio >= 5.7 mg/mmol and smoking) had sensitivity of 82.0% and specificity of 59.9% for predicting incident CVD. Conclusion: Age is the strongest predictor of CVD for this population. For clinical identification of individuals at high risk, screening for the combination of three or more of hyperglycaemia, dyslipidaemia, hypertension, albuminuria and smoking may prove a useful and efficient strategy