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• W323784476 abstract "Introduction: The estimation of visceral abdominal fat in obese patients is very important because it allows to predict the metabolic risk of these patients. Background: To establish an equation that allows the estimation of visceral abdominal fat from the anthropometric parameters of our environment. Patients and methods: The population sample consists of 108 patients, 49 men (45.4%) and 59 women (54.6%), average age of 39.7 ± 13.8 years and BMI was 34.4 ± 4.5 kg/m2. Abdominal CT scan at level of L 4 was made, and cross sectional areas of abdominal fat compartments were determined by graphical analysis software developed by our group. Anthropometric evaluation was made to all sample determining: weight, height, waist circumference in bipedestation, sagital diameter, hip, wrist and thigh circumferences, and submandibular, bicipital, tricipital, subescapular, abdominal and suprailiac skin folds. The waist/hip ratio and BMI was calculated. As statistics methods were used the Pearson and ρ of Spearman correlation coefficients. For the CT variable a multiple linear regression model with the stepwise methodology was made. Results: After fitting by all the variables which were correlated with the visceral fat, and considering the age (in years) and sex, the variables that have an independent effect on the visceral fat are age (p < 0,000), waist (p < 0,000), and hip circumferences (p < 0,007). In case of the subcutaneous abdominal fat the variables that have an independent effect on the subcutaneous fat are the hip circumference (p < 0,000), sex (p < 0,000), age (p < 0,003) and BMI (p < 0,03). Discussion: In spite of the error percentage, the calculation of visceral fat from anthropometric variables can provide in clinical practice an accessible and economic method for the identification of patients who need more complete studies." @default.
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• W323784476 date "2008-01-01" @default.
• W323784476 modified "2023-09-26" @default.
• W323784476 title "Estimación de la grasa abdominal visceral y subcutánea en pacientes obesos a través de ecuaciones de regresión antropométricas" @default.
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