Abstract
BackgroundThe severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. Our aim was to evaluate whether MetS severity conferred additional discrimination to existing scoring systems for cardiovascular disease (CVD) and diabetes risk.MethodsWe assessed Cox proportional hazard models of CHD- and diabetes risk among 13,141 participants of the Atherosclerosis Risk in Communities Study and the Jackson Heart Study, using the Framingham Risk Calculator, the American Heart Association’s Atherosclerotic CVD calculator, the American Diabetes Association diabetes risk score and an additional diabetes risk score derived from ARIC data. We then added a MetS-severity Z-score to these models and assessed for added risk discrimination by assessing Akaike information criterion, c-statistic, integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI).ResultsThe MetS severity score appears to add to the predictive ability of individual CHD and diabetes risk scores. Using the IDI, MetS improved risk prediction for diabetes but not CHD risk. In all 4 scoring systems, MetS severity had a significant non-event NRI, improving the ability to exclude individuals without events. Assessing interactions between risk scores and MetS severity revealed that MetS severity was more highly associated with disease risk among those in the lowest quintiles of risk score, suggesting that MetS was particularly able to identify risk among individuals judged to be of low risk by existing algorithms.ConclusionsMets severity improved prediction of diabetes more so than CHD. Incorporation of multiple risk predictors into electronic health records may help in better identifying those at high disease risk, who can then be placed earlier on preventative therapy.
Highlights
The severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components
We recently formulated a sex- and race/ethnicity-specific MetS severity Z-score with differential weighting of the individual MetS components based on how these components correlated together by sex- and racial/ethnic sub-group [12, 13]. While this MetS severity score was not formulated to be a risk predictor, we demonstrated that this score remained significantly associated with long-term risk for coronary heart disease (CHD) [14, 15] and Type 2 diabetes mellitus (T2DM) [16, 17], even in models that included the individual MetS components [15, 17]—contributing to the notion that the presence of the underlying processes driving MetS may confer additional risk for CHD and T2DM
We found that a MetS severity Z-score, when added to predictive models alongside existing T2DM risk scores, consistently improved the models’ discrimination performance for future T2DM
Summary
The severity of the metabolic syndrome (MetS) predicts future coronary heart disease (CHD) and diabetes independent of the individual MetS components. The continued need for tools to identify risk for future cardiovascular disease (CVD) and Type 2 diabetes mellitus (T2DM) has driven the design of risk-prediction scoring systems, which can be used to identify and motivate high-risk patients toward preventative treatments and lifestyle improvement These predictive scoring systems include the Framingham calculator from D’Agostino et al [1, 2] and the more recent atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equations scoring system by Goff et al [3] (Table 1). A predictive score for risk of current T2DM by Bang et al [5] has been endorsed by the American Diabetes Association (ADA) [6], potentially because of its reliance on a relatively small number of variables and no need for blood testing (Table 1) These T2DM scoring systems detect T2DM risk with an area-under-the-curve of 0.80 for the Schmidt equation [4] 0.74 for the Bang equation [6]
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