Impact of metabolic syndrome criteria on cardiovascular disease risk in people with newly diagnosed type 2 diabetes.

AIMS/HYPOTHESIS: We investigated the prognostic implication of metabolic syndrome according to modified National Cholesterol Education Program criteria and the implication of individual features of metabolic syndrome on cardiovascular disease (CVD) and CHD in a 5-year community-based study of people with newly diagnosed type 2 diabetes.

METHODS

We entered 562 participants, aged 30-74 years, into a cross-sectional analysis and 428 participants (comprising those who were CVD-free at study entry) into a prospective analysis. In both analyses, the association of metabolic syndrome features with CVD/CHD was studied. Binary logistic regression, a Cox regression model and Fisher's exact test were used for statistical analyses.

RESULTS

At diagnosis of type 2 diabetes, metabolic syndrome was independently associated with CVD (odds ratio [OR] 2.54; p=0.006) and CHD (OR 4.06; p=0.002). In the 5-year follow-up, metabolic syndrome at baseline was an independent predictor of incident CVD (hazard ratio [HR] 2.05; p=0.019). An increase in the number of individual features of the metabolic syndrome present at the time of diagnosis of type 2 diabetes was associated with a linear increase in incident CVD risk (trend p=0.044) with an almost five-fold increase when all five features were present, compared with hyperglycaemia alone (HR 4.76; p=0.042). Increasing age (HR 1.07; p<0.001), female sex (HR 0.62; p=0.032), total cholesterol (HR 1.43; p=0.01) and lipid-lowering therapy (HR 0.32; p<0.001) were also independent predictors of risk.

CONCLUSIONS/INTERPRETATION: Metabolic syndrome at baseline is associated with an increased risk of incident CVD in the 5 years following diagnosis of type 2 diabetes. CVD-free survival rates declined incrementally as the presence of metabolic syndrome features increased. Thus, identifying the features of metabolic syndrome at diagnosis of type 2 diabetes is potentially a useful prognostic tool for identifying individuals at increased risk of CVD.