Erschienen in:
01.08.2013 | Original Contribution
Confirmatory factor analysis to assess the measure of adiposity that best fits the diagnosis of metabolic syndrome and relationship to physical activity in adults
verfasst von:
Manuel A. Gómez-Marcos, María C. Patino-Alonso, José I. Recio-Rodríguez, Juanjo Antón-Alvarez, Alfredo Cabrejas-Sánchez, Carmen Fernandez-Alonso, Javier Rubio-Galán, Verónica Arce, Luís García-Ortiz
Erschienen in:
European Journal of Nutrition
|
Ausgabe 5/2013
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Abstract
Purpose
The objective of the study is to assess the goodness of fit for the diagnosis of metabolic syndrome in adults of four models with different measures of adiposity using confirmatory factor analysis, to develop a cardio metabolic risk index and to analyze its relationship to physical activity.
Methods
Cross-sectional descriptive multicenter study including 636 patients from the EVIDENT study. Considering as fixed variables, triglycerides/HDL-C ratio, HOMA-IR index and mean arterial pressure, we will compare which single-factor model of metabolic syndrome shows better goodness of fit. The models only differ by the measure of adiposity used: waist circumference, waist circumference/height, body mass index or adiposity index. With the factorial weights obtained, we created a quantitative metabolic index and analyzed its relationship to physical activity, quantified with the accelerometer for 1 week and measured at counts/min.
Results
The single-factor model including waist circumference in women and body mass index in men were those that were better indicators of goodness of fit. The estimated quantitative metabolic index shows a mean value in men of −0.022 ± 1.29 with a range of values between −3.36 and 4.57 and in women of 0.0001 ± 1.53 with a range of values between −3.17 and 5.55. The quantitative index shows an inverse relationship to physical activity.
Conclusions
Waist circumference in women and body mass index in men are the measures of adiposity that were better indicators goodness of fit. This quantitative index may be useful to quantify the risk of metabolic syndrome in clinical practice.