Heart Care Info - Heart Disease Prevention & Treatment

Exploring clinical predictors of cardiovascular disease in a central australian aboriginal cohort

JN Luke, AD Brown, L Brazionis... - European Journal of ..., 2012 - Abstract 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 ...

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.

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 mm Hg and diastolic blood pressure ≥70 mm Hg 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 mm Hg; diastolic ≥70 mm Hg; albumin:creatinine ratio ≥5.7 mg/mmol and smoking) had sensitivity of 82.0% and specificity of 59.9% for predicting incident CVD.