Índices antropométricos estimadores de la distribución adiposa abdominal y capacidad discriminante para el síndrome metabólico en población española
- Bellido Guerrero, Diego
- López De la Torre, Martín
- Carreira Arias, J.
- Luis Román, Daniel Antonio de
- Bellido Castañeda, Virginia
- Soto González, Alfonso
- Luengo Pérez, Luis Miguel
- Hernández Martínez, Antonio
- Vidal Cortada, Josep
- Becerra Fernández, Antonio
- Ballesteros Pomar, María Dolores
ISSN: 0214-9168, 1578-1879
Argitalpen urtea: 2013
Alea: 25
Zenbakia: 3
Orrialdeak: 105-109
Mota: Artikulua
Beste argitalpen batzuk: Clínica e investigación en arteriosclerosis
Laburpena
Introduction: The metabolic syndrome (MS) carries an increased risk of cardiovascular disease and diabetes mellitus. Insulin resistance is probably the mechanism underlying the changes detected in lipid and carbohydrate metabolism in these patients, who have, as a common anthropometric feature, a predominantly increased abdominal fat distribution. Patients and methods: A total of 3316 patients were studied, of whom 63.40% were female and 36.60 male, with a mean age of 42.36 ± 14.63 years, and a body mass index (BMI) of 32.76 ± 6.81 kg/m2. Weight, height and waist circumference (CC) were measured using standard techniques. The waist/height (ICA) was calculated using two indicators, expressed as waist in cm divided by height in m2, and as waist divided by height, both in cm. The prevalence of metabolic syndrome in the sample was 33.70%. In order to assess the predictive ability of BMI, ICA and CC to detect the existence of MS, receiver operating curves (ROC) were constructed and the areas under the curve (AUC) calculated for each anthropometric parameter. Results: An AUC of 0.724 (95% CI: 0.706 to 0.742), P < .001, was obtained for CC, 0.709 (95% CI: 0.691 to 0.728), P < .001 for ICA with height in m2, and 0.729 (95% CI: 0.711 to 0.747), P < .001 for ICA with height in cm, and for the BMI it was 0.680 (95% CI 0.661-0.699), P < .001. Conclusions: Anthropometric indices that assess abdominal fat distribution have a better predictive capacity for detecting MS, compared to total adiposity indicators such as BMI.