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. 2013 Feb;36(2):362-8.
doi: 10.2337/dc11-2546. Epub 2012 Aug 29.

Metabolic syndrome model definitions predicting type 2 diabetes and cardiovascular disease

Cécile M Povel  1 , Joline W Beulens, Yvonne T van der Schouw, Martijn E T Dollé, Annemieke M W Spijkerman, W M Monique Verschuren, Edith J M Feskens, Jolanda M A Boer
Affiliations

Metabolic syndrome model definitions predicting type 2 diabetes and cardiovascular disease

Cécile M Povel et al. Diabetes Care. 2013 Feb.

Abstract

Objective: Metabolic syndrome (MetS) is a cluster of abdominal obesity, hyperglycemia, hypertension, and dyslipidemia, which increases the risk for type 2 diabetes and cardiovascular diseases (CVDs). Some argue that MetS is not a single disorder because the traditional MetS features do not represent one entity, and they would like to exclude features from MetS. Others would like to add additional features in order to increase predictive ability of MetS. The aim of this study was to identify a MetS model that optimally predicts type 2 diabetes and CVD while still representing a single entity.

Research design and methods: In a random sample (n = 1,928) of the EPIC-NL cohort and a subset of the EPIC-NL MORGEN study (n = 1,333), we tested the model fit of several one-factor MetS models using confirmatory factor analysis. We compared predictive ability for type 2 diabetes and CVD of these models within the EPIC-NL case-cohort study of 545 incident type 2 diabetic subjects, 1,312 incident CVD case subjects, and the random sample, using survival analyses and reclassification.

Results: The standard model, representing the current MetS definition (EPIC-NL comparative fit index [CFI] = 0.95; MORGEN CFI = 0.98); the standard model excluding blood pressure (EPIC-NL CFI = 0.95; MORGEN CFI = 1.00); and the standard model extended with hsCRP (EPIC-NL CFI = 0.95) had an acceptable model fit. The model extended with hsCRP predicted type 2 diabetes (integral discrimination index [IDI]: 0.34) and CVD (IDI: 0.07) slightly better than did the standard model.

Conclusions: It seems valid to represent the traditional MetS features by a single entity. Extension of this entity with hsCRP slightly improves predictive ability for type 2 diabetes and CVD.

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Figures

Figure 1
Figure 1
A: The standard second-order one-factor MetS model in the random sample of EPIC-NL. B: The standard second-order one-factor MetS model in the subset of the MORGEN study. C: The standard second-order one-factor MetS model extended with hsCRP in the random sample of EPIC-NL. Data are presented as factor loading (SE). All factor loadings are significant (P < 0.05). The first-order factors are waist circumference (WC), lipids, HbA1c, FPG, and blood pressure. The second-order factors are triglyceride (TG), HDL, systolic blood pressure (SBP), and diastolic blood pressure (DBP).
See this image and copyright information in PMC

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