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Probing the factor structure of metabolic syndrome in Sardinian Genetic Isolates

Graziano, Francesca and Grassi, Mario and Sacco, S. and Concas, Maria Pina and Vaccargiu, Simona and Pirastu, Mario and Biino, Ginevra (2015) Probing the factor structure of metabolic syndrome in Sardinian Genetic Isolates. Nutrition, Metabolism and Cardiovascular Diseases, Vol. 25 (6), p. 548-555. ISSN 0939-4753. eISSN 1590-3729. Article.

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DOI: 10.1016/j.numecd.2015.02.004


Background and Aims: Owing to the multiplicity of metabolic syndrome (MetS) key components, its diagnosis is very complex. Lack of a unique definition is responsible for the prevalence variability observed among studies, therefore, a definition based on continuous variables was recommended. 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 metabolic syndrome. After selecting the best model, an algorithm was obtained using factor loadings/residual variances. The quality of MetS score was evaluated by Receiver Operating Characteristics curve and Area Under 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 analysis suggested that bifactor model was the most representative one and provided a score and a cut-point that are both clinically-accessible and interpretable measures for MetS diagnosis and likely useful for evaluating association with adverse cardiovascular disease and diabetes and for investigating MetS genetic component.

Item Type:Article
ID Code:10837
Uncontrolled Keywords:Metabolic Syndrome, score, confirmatory factor analysis, bifactor model
Subjects:Area 05 - Scienze biologiche > BIO/18 Genetica
Divisions:002 Altri enti e centri di ricerca del Nord Sardegna > CNR-Consiglio Nazionale delle Ricerche > Istituto di genetica delle popolazioni, Alghero
Deposited On:12 Mar 2015 15:11

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