Composição corporal e taxa metabólica basal em mulheres com diabetes mellitus tipo 2

• Autor(a) principal: Ferreira, Marina de Figueiredo
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/7301
• topic: Indirect calorimetry; Diabetes Mellitus; Basal metabolism; Diabetes mellitus; Calorimetria indireta; Diabetes Mellitus tipo 2; Metabolismo basal; Avaliação nutricional; CNPQ::CIENCIAS DA SAUDE::NUTRICAO::ANALISE NUTRICIONAL DE POPULACAO

Subjects presenting type 2 diabetes mellitus (T2DM) are mostly obese people. Therefore, to reduce body mass and maintain glycemic control, a dietary planning is necessary, which is based on the energy requirement estimation. The basal metabolic rate (BMR) is the main contributor to total energy expenditure, and body composition is its major determinant. The study aim to indicate among the selected equations, one that is closest to the measured value and to check that best predicts the measured basal metabolic rate using anthropometric, biochemical, body composition and age as variables.This cross-sectional study had the participation of 28 adult women with T2DM, aged between 37 and 59 years, assisted by the Unified Health System from Rio de Janeiro s city. The observed variables were: BMR measured by indirect calorimetry, BMR estimated by predictive equations proposed in the literature, weight and height to determine body mass index, waist circumference, body composition by the dual-emission X-ray absorptiometry (DXA) method, glycemia, glycated hemoglobin, total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglyceride levels. We used the paired t test to determine statistical significance on the difference of measured BMR and the estimated ones, calculated the percent deviation between the measured and estimated BMR, and the Pearson linear correlation. Correlations considered statistically significant (pvalue < 0.05) were used to estimate BMR by simple and multiple linear regression models. There was significant difference between the measured and estimated BMR by the FAO/WHO/UNU and Huang et al equations. The equation that most overestimated the BMR was the Huang et al. (11.26%, 4 to 18) followed by the FAO/WHO/UNU equation (10.58%, 3 to 18). However, the one that most underestimated BMR was the Mifflin et al. equation (-2.58%; -8 to 3). The correlation matrix identified as BMR positive significant correlations with BMR, the weight (0.729), body mass index (0.640), waist circumference (0.705), fat mass (0.705) and lean body mass (0.642). Those which were used in linear regression model were lean body mass and fat mass. This model reached a value of 53%, which is an equal index to the simple linear regression when the BMR is estimated by weight. The procedures made it possible to indicate that the Owen et al. equation was the one closest to the measured basal metabolic rate and that body mass was the variable that best explained the basal metabolic rate