Estimation of Resting Energy Expenditure Considering Effects of Race and Diabetes Status

Abstract

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 Ͻ 0.001). DSI was found to be a significant covariable (P ϭ 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 ϫ BMI interaction (P Ͻ 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 ϭ 803.8 ϩ 0.3505 ϫ age ϫ (BMI -34.524) -135.0 ϫ race ϩ 15.866 ϫ LBM ϩ 50.90 ϫ DSI; and 2) REE male ϭ 909.4 ϩ 0.3505 ϫ age ϫ (BMI -34.524) -135.0 ϫ race ϩ 15.866 ϫ LBM -9.10 ϫ 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. Diabetes Care 27:1405-1411, 2004T ype 2 diabetes and obesity have emerged as leading public health challenges in western societies and constitute an increasing health burden in developing countries. Obesity increases risk for diabetes in the context of the insulin resistance syndrome, a trait cluster consisting of insulin resistance, obesity, glucose intolerance, upper body fat distribution, hypertension, dyslipidemia, and dysfibrinolysis (1-4). The insulin resistance syndrome is a major factor conferring increased risk for cardiovascular disease (1-4). Therefore, type 2 diabetes, obesity, and the insulin resistance syndrome are important targets for dietetic intervention. Often, nutrition professionals must estimate energy expenditure when measurements of metabolic rate are unavailable and must consider the potential influence of obesity, glucose intolerance, and diabetes, which could alter energy metabolism. As current dietary recommendations in the treatment of diabetes include care plans based on lifestyle factors and diab...

Resting energy expenditure is not increased in mildly hyperglycaemic obese diabetic patients

“…Furthermore, REE was correctly predicted by the equations of the WHO (World Health Organisation, 1985) and Martin et al (2004), and differed by only 3·9 % from the value given by Huang et al (2004).…”

“…A number of studies have cited a relationship between glycaemic level or tolerance and REE (Gougeon et al 2002;Huang et al 2004;Martin et al 2004). These studies concern, first, values adjusted for differences in body composition, age, sex and race, and second, patients with uncontrolled diabetes (HbA 1c $ 8 %).…”

“…The WHO formula (World Health Organisation, 1985) correctly predicted REE (paired difference ¼ 0·155 MJ/d, P¼0·17). Compared with equations designed to evaluate REE in obese diabetic and non-diabetic patients, combined REE differed by 0·04 MJ/d, P¼0·67 (Martin et al 2004) …”

Ability of the Harris-Benedict formula to predict energy requirements differs with weight history and ethnicity

“…This result is not unexpected because numerous studies have shown that REE among AA is lower than among C [9,19,20]. Furthermore, the validity of the Harris Benedict formula was determined in a population composed largely of C subjects.…”

“…REE has been shown to be lower among African American (AA) vs Caucasian (C) women [6,7], in part due to a lower amount of highly metabolically-active, trunk lean tissue mass [3,8]. However, REE among AA remained lower than among C by an average of 135 kilocalorie (kcal/d) even after adjusting for differences in lean body mass [9], suggesting that ethnicity is associated with an unidentified factor that affects REE. These findings suggest that the HB formula, which was validated in a study population largely comprised of C individuals, may overestimate REE in AA.…”