Author Interview: Dr. Raymond R. Townsend
Metabolic Syndrome, Components, and Cardiovascular Disease Prevalence in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study
Am J Nephrol. 2011 Apr 27;33(6):477-484.
What are the main findings of the study?
The main findings in this study are the substantial prevalence of metabolic syndrome in chronic kidney disease (about 2 in 3 patients with CKD), the frequency of cardiovascular disease (about 1 in 3 patients with CKD), and difference in performance of models that seek to help address the best way to characterize the “metabolic syndrome” either as a syndrome per se, or considering the various components individually and their association with prevalent CVD in a CKD population.
Were any of the findings unexpected?
We were impressed at the prevalence of metabolic syndrome in CKD. Among diabetics it was 87%, and among the non-diabetics it was 44%. In the adult US population the metabolic syndrome prevalence is generally around 30%. Another unexpected finding was that the fit of our models for metabolic syndrome’s relationship to prevalent CKD improved as we broke down metabolic syndrome in to components performed betted in those without diabetes. In the diabetics it appeard that much of the risk for prevalent cardiovascular disease disease was due to the presence of diabetes.
What should clinicians and patients take away from this study?
That patients with CKD frequently have treatable metabolic disturbances that characterize the metabolic syndrome, and that simply counting the number of components they have (using the standard ATP III designation to determine if a component is present) was the best way to estimate their cardiovascular disease risk. In our paper this was model 3. Each element of the syndrome, when present, increased the risk of prevalent cardiovascular disease by 9%.
What recommendations do you have for nephrology health care providers as a result of your study?
The Bard of the Yankees (Yogi Berra) probably said it best: “You can see a lot by just looking”. When evaluating a patient with CKD, given their substantial cardiovascular disease risk, it is useful to determine (or review) the standard metabolic syndrome components as they are all treatable and may be leveraged to reduce risk in the patient.
Abstract
Metabolic Syndrome, Components, and Cardiovascular Disease Prevalence in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.Am J Nephrol. 2011 Apr 27;33(6):477-484.
Townsend RR, Anderson AH, Chen J, Gadebegku CA, Feldman HI, Fink JC, Go AS, Joffe M, Nessel LA, Ojo A, Rader DJ, Reilly MP, Teal V, Teff K, Wright JT, Xie D.
Department of Medicine, University of Pennsylvania Medical Center, Philadelphia, Pa.
Background/Aims: Metabolic syndrome may increase the risk for incident cardiovascular disease (CVD) and all-cause mortality in the general population. It is unclear whether, and to what degree, metabolic syndrome is associated with CVD in chronic kidney disease (CKD).
We determined metabolic syndrome prevalence among individuals with a broad spectrum of kidney dysfunction, examining the role of the individual elements of metabolic syndrome and their relationship to prevalent CVD. Methods: We evaluated four models to compare metabolic syndrome or its components to predict prevalent CVD using prevalence ratios in the Chronic Renal Insufficiency Cohort (CRIC) Study.
Results: Among 3,939 CKD participants, the prevalence of metabolic syndrome was 65% and there was a significant association with prevalent CVD. Metabolic syndrome was more common in diabetics (87.5%) compared with non-diabetics (44.3%). Hypertension was the most prevalent component, and increased triglycerides the least prevalent. Using the bayesian information criterion, we found that the factors defining metabolic syndrome, considered as a single interval-scaled variable, was the best of four models of metabolic syndrome, both for CKD participants overall and for diabetics and non-diabetics separately.
Conclusion: The predictive value of this model for future CVD outcomes will subsequently be validated in longitudinal analyses.
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