Abstract
Background and aims
Owing to the multiplicity of the key components of metabolic syndrome (MetS), its
diagnosis is very complex. The lack of a unique definition is responsible for the
prevalence variability observed among studies; therefore, a definition based on continuous
variables was recommended. The aim of this study was to compare competing models of
the MetS factor structure for selecting the one that explains the best clustering
pattern and to propose an algorithm for computing MetS as a continuous variable.
Methods and results
Data were from isolated Sardinian populations (n = 8102). Confirmatory factor analysis (CFA) and two-group CFA by gender were performed
to evaluate the sex-specific factor structure of MetS. After selecting the best model,
an algorithm was obtained using factor loadings/residual variances. The quality of
the MetS score was evaluated by the receiver operating characteristics curve and the
area under the curve. Cross-validation was performed to validate the score and to
determine the best cut point. The best fit model was a bifactor one with a general
factor (MetS) and three specific factors (f1: obesity/adiposity trait; f2: hypertension/blood
pressure trait; and f3: lipid trait). Gender-specific algorithms were implemented
to obtain MetS scores showing a good diagnostic performance (0.80 specificity and
0.80 sensitivity for the cut point). Furthermore, cross-validation confirmed these
results.
Conclusion
These analyses suggested that the bifactor model was the most representative one.
In addition, they provided a score and a cut point that are both clinically accessible
and interpretable measures for MetS diagnosis and likely useful for evaluating the
association with adverse cardiovascular disease and diabetes and for investigating
the MetS genetic component.
Keywords
Abbreviations:
AHA/NHLBI (International Diabetes Federation and American Heart Association/National Heart, Lung and Blood Institute), AIC (Akaike information criterion), ATPIII (National Cholesterol Education Program – Third Adult Treatment Panel), AUC (area under the curve), BIC (Bayesian information criterion), BLUP (best linear unbiased predictor), BMI (body mass index), CFA (confirmatory factor analysis), CFI (comparative fit index), DBP (diastolic blood pressure), EFA (exploratory factor analysis), IDF (International Diabetes Federation), MetS (metabolic syndrome), RMSEA (root mean square of approximation), ROC (receiver operating characteristics), SBP (systolic blood pressure), SRMR (standardized root mean square residual), WC (waist circumference)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: February 20, 2015
Accepted:
February 12,
2015
Received in revised form:
February 5,
2015
Received:
December 16,
2014
Identification
Copyright
© 2015 Elsevier B.V. Published by Elsevier Inc. All rights reserved.
