Renal Dysfunction, Metabolic Syndrome and Cardiovascular Disease Mortality

Abstract EN

Background.

Renal disease is commonly described as a complication of metabolic syndrome (MetS) but some recent studies suggest that Chronic Kidney disease (CKD) may actually antecede MetS.

Few studies have explored the predictive utility of co-clustering CKD with MetS for cardiovascular disease (CVD) mortality.

Methods.

Data from a nationally representative sample of United States adults (NHANES) was utilized.

A sample of 13115 non-pregnant individuals aged ≥35 years, with available follow-up mortality assessment was selected.

Multivariable Cox Proportional hazard regression analysis techniques explored the relationship between co-clustered CKD, MetS and CVD mortality.

Bayesian analysis techniques tested the predictive accuracy for CVD Mortality of two models using co-clustered MetS and CKD and MetS alone.

Results.

Co-clustering early and late CKD respectively resulted in statistically significant higher hazard for CVD mortality (HR = 1.80, CI = 1.45–2.23, and HR = 3.23, CI = 2.56–3.70) when compared with individuals with no MetS and no CKD.

A model with early CKD and MetS has a higher predictive accuracy (72.0% versus 67.6%), area under the ROC (0.74 versus 0.66), and Cohen's kappa (0.38 versus 0.21) than that with MetS alone.

Conclusion.

The study findings suggest that the co-clustering of early CKD with MetS increases the accuracy of risk prediction for CVD mortality.