OBJECTIVE: To evaluate the impact of diabetes status and race, in addition to other covariables, on the estimation of resting energy expenditure (REE). RESEARCH DESIGN AND METHODS: Demographic, anthropometric, and clinical parameters were assessed in 166 adults of varying weights. Subjects were categorized by race (white versus black) and into three subgroups based on glucose tolerance (normoglycemia, impaired glucose tolerance, and type 2 diabetes), termed the diabetes status index (DSI). REE was measured by indirect calorimetry. A multiple regression model was established for optimal prediction of REE based on covariables. RESULTS: An average decrease in REE of 135 kcal/day independent of all other variables was observed in blacks (P < 0.001). DSI was found to be a significant covariable (P = 0.002) in predicting REE, which was observed to be higher in diabetic women. Therefore, race and DSI entered the multiple regression equation to predict REE as significant independent variables, together with lean body mass (LBM) and age x BMI interaction (P < 0.001). Overall, REE prediction resulted in an R(2) of 0.79 and a root mean square error of 136 kcal/day. These values indicate that the resultant equations could offer advantages over other key published prediction equations. The equations are: 1) REE(female) = 803.8 + 0.3505 x age x (BMI - 34.524) - 135.0 x race + 15.866 x LBM + 50.90 x DSI; and 2) REE(male) = 909.4 + 0.3505 x age x (BMI -34.524) -135.0 x race + 15.866 x LBM -9.10 x DSI. The predictive value of the equations did not diminish substantially when fat-free mass estimated by skinfold calipers was substituted for dual-energy X-ray absorptiometry scan measurements of LBM. CONCLUSIONS: Race and diabetes status are important when predicting REE, coupled with LBM, age, BMI, and sex. Race and DSI have not been considered in equations commonly used to predict REE. Their inclusion could improve individualization of dietary prescriptions for type 2 diabetic subjects and heterogeneous populations.
OBJECTIVE: To evaluate the impact of diabetes status and race, in addition to other covariables, on the estimation of resting energy expenditure (REE). RESEARCH DESIGN AND METHODS: Demographic, anthropometric, and clinical parameters were assessed in 166 adults of varying weights. Subjects were categorized by race (white versus black) and into three subgroups based on glucose tolerance (normoglycemia, impaired glucose tolerance, and type 2 diabetes), termed the diabetes status index (DSI). REE was measured by indirect calorimetry. A multiple regression model was established for optimal prediction of REE based on covariables. RESULTS: An average decrease in REE of 135 kcal/day independent of all other variables was observed in blacks (P < 0.001). DSI was found to be a significant covariable (P = 0.002) in predicting REE, which was observed to be higher in diabeticwomen. Therefore, race and DSI entered the multiple regression equation to predict REE as significant independent variables, together with lean body mass (LBM) and age x BMI interaction (P < 0.001). Overall, REE prediction resulted in an R(2) of 0.79 and a root mean square error of 136 kcal/day. These values indicate that the resultant equations could offer advantages over other key published prediction equations. The equations are: 1) REE(female) = 803.8 + 0.3505 x age x (BMI - 34.524) - 135.0 x race + 15.866 x LBM + 50.90 x DSI; and 2) REE(male) = 909.4 + 0.3505 x age x (BMI -34.524) -135.0 x race + 15.866 x LBM -9.10 x DSI. The predictive value of the equations did not diminish substantially when fat-free mass estimated by skinfold calipers was substituted for dual-energy X-ray absorptiometry scan measurements of LBM. CONCLUSIONS: Race and diabetes status are important when predicting REE, coupled with LBM, age, BMI, and sex. Race and DSI have not been considered in equations commonly used to predict REE. Their inclusion could improve individualization of dietary prescriptions for type 2 diabetic subjects and heterogeneous populations.
Authors: Crystal C Douglas; Jeannine C Lawrence; Nikki C Bush; Robert A Oster; Barbara A Gower; Betty E Darnell Journal: Nutr Res Date: 2007-04 Impact factor: 3.315
Authors: Kelly J Hunt; Emily Kistner-Griffin; Ida Spruill; Abeba A Teklehaimanot; W Timothy Garvey; Michèle Sale; Jyotika Fernandes Journal: South Med J Date: 2014-10 Impact factor: 0.954
Authors: Cristina Aguayo-Mazzucato; Ann Marie Zavacki; Alejandra Marinelarena; Jennifer Hollister-Lock; Ilham El Khattabi; Alessandro Marsili; Gordon C Weir; Arun Sharma; P Reed Larsen; Susan Bonner-Weir Journal: Diabetes Date: 2013-01-10 Impact factor: 9.461