Estimation of resting energy expenditure considering effects of race and diabetes status

Prediction of resting energy expenditure in Italian older adults with severe obesity

Background

In the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity.

Methods

A retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE.

Results

All the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model.

Conclusion

Although the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice.

Factors related to hypermetabolism in individuals with type 2 diabetes mellitus and non-alcoholic fatty liver disease

Considering the progressive prevalence and co-occurrence of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), as well as the current evidence suggesting the elevated levels of basal metabolic rate (BMR) among these individuals, the present study aimed to identify factors determining hypermetabolism in such subjects. This cross sectional study was conducted in 30 to 53-year-old individuals with concurrent T2DM and NAFLD (controlled attenuation parameter score ≥ 260 dB/m). Resting energy expenditure (REE) was determined by an indirect calorimetry device. Hypermetabolism was defined as an elevated measured REE > 110% of the predicted REE. The multivariate logistic regression test was used for detecting factors associated with hypermetabolism. Between September, 2017, and March, 2018, a total of 95 eligible participants (64.40% male) with both T2DM and NAFLD were included, while 32.63% of them were classified as hypermetabolic. Overall, the mean recruitment age ± standard deviation and median (interquartile range) body mass index were 44.69 ± 5.47 years and 30.20 (27.80-33.30) kg/m², respectively. Demographic, anthropometric and biochemical variables did not vary significantly across two groups except for total body water, low-density lipoprotein cholesterol and dipeptidyl peptidase 4 (DPP-4) inhibitors (p < 0.05). According to the results of multivariable logistic regression analyses, hypermetabolism had a positive association with adiponectin (odds ratio [OR] 1.167, 95% confidence interval [CI] 1.015-1.342, p = 0.030), physical activity (OR 1.134, 95% CI 1.002-1.284, p = 0.046), alanine transaminase (OR 1.062, 95% CI 1.006-1.122, p = 0.031) and diastolic blood pressure (OR 1.067, 95% CI 1.010-1.127, p = 0.021). However, fat free mass was inversely related to hypermetabolism (OR 0.935, 95% CI 0.883-0.991, p = 0.023). Adiponectin, alanine transaminase, physical activity, diastolic blood pressure and fat free mass were independently associated with hypermetabolism in subjects with NAFLD and T2DM.

Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial

BACKGROUND & AIMS

Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention.

DESIGN

Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention.

RESULTS

A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM^0.66 × FM^0.066] was body size and sex independent (p = 0.91 and p = 0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r = -0.47 for female and r = -0.44 for male, both p < 0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p < 0.001) and between individuals who are overweight and obese (p < 0.001) while no significant differences were observed when indexed to REE/[LBM^0.66 × FM^0.066], p > 0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p = 0.614).

CONCLUSION

Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

Resting energy expenditure of a diverse group of South African men and women

BACKGROUND

In South Africa, overweight/obesity is a public health concern, disproportionally affecting Black females. A contributory role of a lower resting energy expenditure (REE) is suggested for African Americans. The present study assessed the REE of Black and White South African adults aiming to better understand the underlying predictors to overweight/obesity and transform this into locally appropriate recommendations.

METHODS

In 328 (63% female; 39% Black) healthy South African adults, REE was measured with indirect calorimetry and body composition with multifrequency bioelectrical impedance analysis. The REE was estimated with 30 sets of published equations. Black-White differences in REE, as measured and adjusted (analysis of covariance), were determined with quantile regression. Reliability/agreement of estimated (against measured) REE was determined with intra-class correlations (ICCs) and Bland-Altman analysis. A new equation was developed by median regression followed by preliminary validation.

RESULTS

Measured REE (adjusted for age along with fat-free mass [FFM], FFM index, FFM plus fat mass, FFM index plus fat mass index) in White subjects was significantly higher (p < 0.001) than in Black subjects for men and women alike, regardless of obesity class. None of the sets of estimation equations had good agreement with measured REE for Black, White, male and female subjects simultaneously. A new estimation equation, based on whole-body variables, had good reliability (ICC = 0.79) and agreement (mean difference: 27 kJ) and presents practical opportunities for groups at the local grass-roots level.

CONCLUSIONS

The REE in Black South African adults is lower than in White adults. Tailored REE equations may improve REE estimation of racially/ethnically diverse South African groups and contribute to improved obesity management.

Novel equation for estimating resting energy expenditure in patients with chronic kidney disease

BACKGROUND

In chronic kidney disease (CKD), determining energy expenditure is the precondition for recommending energy intake in nutrition management.

OBJECTIVES

We aimed to develop and validate a resting energy expenditure (REE) equation for patients with CKD.

METHODS

This cross-sectional study enrolled 300 patients with CKD (stages 3-5) according to inclusion and exclusion criteria. Stepwise linear regression analysis was used to derive a new REE equation (eREE-CKD) according to actual REE (aREE) measured using indirect calorimetry in the development dataset. The eREE-CKD value was then validated with aREE in the validation dataset and compared with values from existing equations obtained in general populations, namely, the Harris-Benedict, Mifflin, WHO, and Schofield equations in terms of bias, precision, and accuracy.

RESULTS

The eREE-CKD equation: eREE-CKD (kcal) = (1 if male; 0 if female) × 106.0 - [1 if diabetes mellitus (DM); 0 if non-DM] × 51.6 - 4.7 × age (y) + 13.1 × weight (kg) + 645.5 (R2 = 0.779).The bias, precision, and accuracy (percentage of estimates that differed >20% from the measured REE) of the eREE-CKD equation were -0.4 (IQR: -29.8, 23.8) kcal, 98.4 (IQR: 79.5, 116.6) kcal, and 5.4%, respectively with indirect calorimetry as the reference method. Both bias and precision of the eREE-CKD were significantly better than the Harris-Benedict, WHO, and Schofield equations (P < 0.001) and similar to the Mifflin equation (P = 0.125 for bias and 0.268 for precision). Accuracy of the eREE-CKD was significantly better than the Harris-Benedict, WHO, Mifflin, and Schofield equations (P < 0.001). Bias, precision, and accuracy of the eREE-CKD equation were consistent when applied to subgroups categorized according to high-sensitivity C-reactive protein concentrations and CKD stages, respectively.

CONCLUSIONS

The eREE-CKD equation using age, sex, weight, and DM data could serve as a reliable tool for estimating REE in patients with CKD. This trial was registered at clinicaltrials.gov as NCT03377413.

Comparison of Equations Estimating Resting Metabolic Rate in Older Adults with Type 2 Diabetes

Measuring resting metabolic rate (RMR) is time-consuming and expensive, and thus various equations for estimating RMR have been developed. This study's objective was to compare five equations in elderly people with type 2 diabetes (T2DM). RMR was measured in 90 older adults (≥65 years) with T2DM (mean body mass index (BMI) of 31.5 kg/m2), using indirect calorimetry. Results were compared to four frequently used equations (those of Cunningham, Harris and Benedict, and Gougeon developed for young adults with T2DM, and that of Lührmann, which was developed for the elderly), in addition to a new equation developed recently at the Academic College at Wingate (Nachmani) for overweight individuals. Estimation accuracy was defined as the percentage of subjects with calculated RMR within ±10% of measured RMR. Measured RMR was significantly underestimated by all equations. The equations of Nachmani and Lührmann had the best estimation accuracy: 71.4% in males and 50.9% in females. Skeletal muscle mass, fat mass, hemoglobin A1c (HbA1c), and the use of insulin explained 70.6% of the variability in measured RMR. RMR in elderly participants with T2DM was higher than that calculated using existing equations. The most accurate equations for this specific population were those developed for obesity or the elderly. Unbalanced T2DM may increase caloric demands in the elderly. It is recommended to adjust the RMR equations used for the target population.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m², and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REE_IC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REE_IC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REE_IC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REE_IC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

Revising Diabetes Programming for Black Men and Their Families

Type-2 diabetes has increased 160% for African American males in the United States. This two-part study's purpose was to apply social marketing theory to understand the Type-2 diabetes education needs of men in Iowa. Study One was a preference assessment of Type-2 diabetes education strategies. Four African American men participated in a series of four focus groups and indicated that they were interested in diabetes prevention programming with their families but not in having it labeled as diabetes education. Participating men would rather increase their physical activity as opposed to tracking their food intake. As a follow-up to this study, nine other African American males took part in Study Two, which used cooking demonstrations and recipe taste-testing with the men to examine their relationship to food in the context of managing their Type-2 diabetes. The findings of both studies, which provided insight into these African American men's lifestyle as related to their Type-2 diabetes, could be useful for nursing professionals who have a critical role in navigating cultural, gender, and family norms while developing care plans, offering patient education, and promoting quality of life.

Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?

BACKGROUND

Evaluation of the resting energy expenditure (REE) is essential to ensure an appropriate dietary prescription for patients with type 2 diabetes. The aim of this record was to evaluate the accuracy of predictive equations for REE estimation in patients with type 2 diabetes, considering indirect calorimetry (IC) as the reference method.

METHODS

A cross-sectional study was performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland-Altman plots, paired t-tests, and Pearson's correlation coefficients.

RESULTS

Sixty-two patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1-36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (- 1.8% difference) and Oxford in men (- 1.3% difference).

CONCLUSION

In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.

Association between Resting Energy Expenditure and Heat Pattern in Traditional Medicine

Many symptoms of heat pattern (HP) in traditional medicine are related to thermoregulation; however, research on the association between energy expenditure characteristics and HP is limited. We aimed to study the association between HP and resting energy expenditure (REE). A total of 109 participants were divided into the HP and non-HP groups based on a self-administered questionnaire and their REE was measured using an indirect calorimeter. Multiple logistic regression models were used to calculate the odds ratio (OR) of HP according to the level of REE. No significant differences in age, anthropometric, and body composition characteristics were observed between the HP and the non-HP groups. The likelihood of having an HP significantly increased with higher REE after adjustment for fat-free mass (OR 1.22 [95% CI 1.03-1.46]) and also after additional adjustment for sex and age (OR 1.21 [95% CI 1.01-1.46]). These results suggest that increased REE could be a biological characteristic of HP. Future studies are necessary to investigate the underlying mechanisms associated with the differing energy expenditure in HP.

Assessment of resting energy expenditure and body composition in Japanese pregnant women with diabetes

AIMS/INTRODUCTION

To measure longitudinal changes in resting energy expenditure and body composition of Japanese pregnant women with or without diabetes.

MATERIALS AND METHODS

The study population consisted of women who had delivered a live singleton neonate after 22 weeks' gestation at Okayama University Hospital from July 2013 to June 2017. Resting energy expenditure and body composition were measured in the first trimester, second trimester, third trimester and postpartum.

RESULTS

A total of 144 women participated in this study: 103 with normal glucose tolerance and 41 with diabetes. The resting energy expenditure (kcal/day) of pregnant women with normal glucose tolerance was significantly higher in the third trimester (1,644 ± 234) than in the first (1,461 ± 215) and second trimesters (1,491 ± 219), and postpartum (1,419 ± 254), whereas that of pregnant women with diabetes did not significantly change during all periods (1,568 ± 404, 1,710 ± 332, 1,716 ± 251, 1,567 ± 249). The resting energy expenditure of women with good glycemic control was lower than that of women with poor control. Fat-free mass was closely correlated with resting energy expenditure.

CONCLUSIONS

The resting energy expenditure of Japanese pregnant women with normal glucose tolerance was significantly increased in the third trimester. The resting energy expenditure of women with good glycemic control was lower than that of women with poor control. Resting energy expenditure and fat-free mass are potential indexes for medical nutrition therapy in pregnant women with diabetes.

Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects

OBJECTIVE

Resting energy expenditure (REE) is linked to obesity, insulin resistance and type 2 diabetes (T2DM). REE and T2DM are inherited traits. Therefore, we investigated the effect of parental T2DM on REE in normoglycemic subjects.

METHODS

Eighty-seven subjects with parental T2DM and 83 subjects without parental T2DM were matched in age, gender, race, BMI, weight and waist circumference. Subjects underwent a 75 g oral glucose tolerance test; REE was determined by indirect calorimetry and body composition was assessed by dual energy X-ray absorptiometry. Statistical analysis was performed using Student's t-test, analysis of variance and regression analysis.

RESULTS

The mean age was 38.8±11.3 years, 57% were females and 53% were African-Americans. The mean BMI was 28.5±6.1 kg/m², waist circumference 91.8±15.1 cm, weight 83.9±20.3 kg, fat mass 31.0%±10.0%, mean fat-free mass (FFM) 54.4±12.9 kg. REE was significantly lower in subjects with parental diabetes, normalized REE 1364.4±263.4Kcal/day vs 1489.4±323.2 Kcal/day, p=0.006 and 29.2±5.3Kcal/kg FFM/day vs 31.9±6.0 Kcal/kg FFM/day, p=0.002. African-Americans had a lower REE compared with Caucasians 28.6±5.4Kcal/kg FFM/day vs 32.6±5.5 Kcal/kg FFM/day, p<0.0001. In a multiple regression model, ethnicity (p<0.0001), parental history of T2DM (p=0.006) and FFM (p=0.021) were independent predictors of REE.

CONCLUSION

Compared with subjects without parental diabetes, offspring with parental T2DM had lower REE, which was more pronounced in African-Americans. This metabolic alteration could increase the risk of obesity, insulin resistance and dysglycemia.

Development and validation of new predictive equation for resting energy expenditure in adults with overweight and obesity

BACKGROUND & AIMS

Accurate predictive equations of resting energy expenditure (REE) are crucial in devising nutritional strategies to manage overweight/obesity, especially in countries where these are highly prevalent. REE is the most common measurement used to estimate energy requirements in the nutritional context; the most accurate method of measuring REE is indirect calorimetry (IC). However, this method is costly and often rarely feasible in many clinical settings. The objective of the present study was to develop and validate a new equation for predicting REE in adults with overweight and obesity.

METHODS

This was a cross-sectional study including 410 men and women with overweight and obesity (20-60 y). Participants were randomly assigned; the development group included 200 subjects and the validation group 210 subjects. The new predictive equation was derived using stepwise multiple linear regression analysis. The accuracy of the new equation was compared to several existing predictive equations (PEs). The accuracy rate was calculated as the percentage of subjects whose REE-PE was within ±10% of the REE-IC. REE was measured by IC and anthropometric measurements.

RESULTS

One predictive equation was developed (NEQ) in which weight was the strongest predictor of REE. Compared with others predicted equations already using, the new designed equation showed the less mean bias (Kj/day): NEQ: 25.7, Valencia:129, WHO/FAO/United Nations University: 270, Mifflin-St Jeor: 308, Owen: -808, Carrasco: -1097, Korth: -36.4, Johnstone: -375, Livingstone: -315, De Lorenzo: -28.3, Lazzer: -123, Muller: -145, Huang: -399 and Bernstein: -1335.

CONCLUSIONS

The present equation had the highest predictive accuracy in subjects with overweight or obesity compared with the previous equations derived from different populations. Thus, these new equation can be used to assist the nutritional management of these subjects.

Cold and Spleen-Qi Deficiency Patterns in Korean Medicine Are Associated with Low Resting Metabolic Rate

Background. Korean medicine (KM) patterns such as cold, heat, deficiency, and excess patterns have been associated with alterations of resting metabolic rate (RMR). However, the association of KM patterns with accurately measured body metabolic rate has not been investigated. Methods. Data on cold (CP), heat (HP), spleen-qi deficiency (SQDP), and kidney deficiency (KDP) patterns were extracted by a factor analysis of symptoms experienced by 954 participants. A multiple regression analysis was conducted to determine the association between KM patterns and RMR measured by an indirect calorimeter. Results. The CP and SQDP scores were higher and the HP score was lower in women. The HP and SQDP scores decreased with age, while KDP scores increased with age. A multiple regression analysis revealed that CP and SQDP scores were negatively associated with RMR independently of gender and age, and the CP remained significantly and negatively associated with RMR even after adjustment for fat-free mass. Conclusions. The underlying pathology of CP and SQDP might be associated with the body's metabolic rate. Further studies are needed to investigate the usefulness of RMR measurement in pattern identification and the association of CP and SQDP with metabolic disorders.

Predicting Energy Expenditure in Extremely Obese Women

BACKGROUND

The most common clinical method for resting energy expenditure (REE) assessment is prediction equations. The purpose of this study was to elucidate which prediction equation is most accurate for REE assessment in extremely obese women.

METHODS

Fourteen extremely obese women (mean +/- SD body mass index: 49.8 +/- 6.2 kg/m(2); age: 49 +/- 10 years) were measured for height and weight and REE via indirect calorimetry (IC) by a metabolic cart system. Predicted REE was evaluated by several equations, including Harris-Benedict with actual body weight, Harris-Benedict with several adjustments to body weight, Cunningham, Mifflin-St Jeor, Owen, World Health Organization (WHO), and Bernstein equations. Accuracy was determined by mean difference data (IC REE - equation REE; Student's paired t-test), correlation coefficients, and agreement between methods by Bland-Altman plots. Accuracy was also evaluated on an individual basis, defined by the percentage of individuals within +/-10% of IC REE.

RESULTS

The Mifflin-St Jeor, Harris-Benedict with actual body weight, and the WHO equations were the most accurate in terms of mean predicted REE. The mean predicted REE values by all other equations were different from the IC REE values (p < .1). According to the individual data, the Mifflin-St Jeor was most accurate (14% outside +/-10% IC REE). The Harris-Benedict with actual body weight and WHO equations were less accurate on individual terms, with 29% and 42% of the predicted REE values, respectively, falling outside +/-10% of IC REE.

CONCLUSIONS

The Mifflin-St Jeor equation was most accurate method for REE assessment in extremely obese women.

Energy Expenditure in People with Diabetes Mellitus: A Review

Physical activity (PA) is an important non-therapeutic tool in primary prevention and treatment of diabetes mellitus (DM). To improve activity-based health management, patients need to quantify activity-related energy expenditure and the other components of total daily energy expenditure. This review explores differences between the components of total energy expenditure in patients with DM and healthy people and presents various tools for assessing the energy expenditure in subjects with DM. From this review, it appears that patients with uncontrolled DM have a higher basal energy expenditure (BEE) than healthy people which must be considered in the establishment of new BEE estimate equations. Moreover, studies showed a lower activity energy expenditure in patients with DM than in healthy ones. This difference may be partially explained by patient with DMs poor compliance with exercise recommendations and their greater participation in lower intensity activities. These specificities of PA need to be taken into account in the development of adapted tools to assess activity energy expenditure and daily energy expenditure in people with DM. Few estimation tools are tested in subjects with DM and this results in a lack of accuracy especially for their particular patterns of activity. Thus, future studies should examine sensors coupling different technologies or method that is specifically designed to accurately assess energy expenditure in patients with diabetes in daily life.

Gender- and Age-Specific REE and REE/FFM Distributions in Healthy Chinese Adults

Basic data on the resting energy expenditure (REE) of healthy populations are currently rare, especially for developing countries. The aims of the present study were to describe gender- and age-specific REE distributions and to evaluate the relationships among glycolipid metabolism, eating behaviors, and REE in healthy Chinese adults. This cross-sectional survey included 540 subjects (343 women and 197 men, 20–79 years old). REE was measured by indirect calorimetry and expressed as kcal/day/kg total body weight. The data were presented as the means and percentiles for REE and the REE to fat-free mass (FFM) ratio; differences were described by gender and age. Partial correlation analysis was used to analyze the correlations between REE, tertiles of REE/FFM, and glycolipid metabolism and eating behaviors. In this study, we confirmed a decline in REE with age in women (p = 0.000) and men (p = 0.000), and we found that men have a higher REE (p = 0.000) and lower REE/FFM (p = 0.021) than women. Furthermore, we observed no associations among glycolipid metabolism, eating behaviors, and REE in healthy Chinese adults. In conclusion, the results presented here may be useful to clinicians and nutritionists for comparing healthy and ill subjects and identifying changes in REE that are related to aging, malnutrition, and chronic diseases.

Resting Energy Expenditure of Black Overweight Women in South Africa Is Lower than of White Women

BACKGROUND

Overweight affects 65% of black South African women. Effective weight management requires accurate measurement or estimation of energy expenditure.

AIMS

The study aimed to determine, among overweight women, whether measured resting energy expenditure (REE) differs between black and white participants, and the performance of REE estimation equations.

METHODS

The REE of 44 black (age 39.6 ± 9.7 years, body mass index (BMI) 35.1 ± 6.2 kg/m2) and 41 white (age 38.0 ± 11.6 years, BMI 33.9 ± 7.6 kg/m2) women was measured with indirect calorimetry and estimated with equations. Body composition was assessed with multi-frequency bioelectrical impedance analysis. Differences in REE were determined with t tests (Welch), and included adjustment for fat free mass (FFM) and BMI, and for FFM index (FFMI).

RESULTS

Measured REE was 585 kJ/day (95% CI 264-905; p = 0.0005) and 861 kJ/day (95% CI 499-1,221; p < 0.0001) lower in black than in white women when adjusted for FFM and BMI, and FFMI, respectively. Out of 14 equations, 13 underestimated REE (error range 2,261 ± 727 kJ/day (Bernstein equation, white women) to 8 ± 782 kJ/day (BMI equation, black women)).

CONCLUSIONS

Black overweight women have significantly lower REE than their white counterparts. No tested estimation equation provided satisfactory results across race/ethnicity. REE measurements or development of overweight- or race/ethnicity-specific estimation equations are recommended.

Cardiovascular risk in Gullah African Americans with high familial risk of type 2 diabetes mellitus: project SuGAR

OBJECTIVES

To determine the prevalence of cardiovascular disease, levels of cardiovascular risk factors, and extent of preventive care in Gullah African Americans with a high familial risk of type 2 diabetes mellitus.

METHODS

Between 1995 and 2003, 1321 Gullah African Americans with a high prevalence of diabetes mellitus from the South Carolina Sea Islands consented to and enrolled in the Sea Islands Genetic African American Registry (Project SuGAR). A cross-sectional analysis of cardiometabolic risk, preventive care, and self-reported cardiovascular disease was conducted.

RESULTS

Cardiometabolic risk factor levels were high and vascular disease was prevalent. Among the subjects with diabetes mellitus, the mean disease duration was 10.5 years; approximately one-third reported reduced vision or blindness; and >80% reported numbness, pain, or burning in their feet. Preventive diabetes care was limited, with <60%, <25%, and <40% seeing an ophthalmologist, podiatrist, and dentist, respectively, within the past year. Only 54.4% of women and 39.3% of men reported daily glucose monitoring.

CONCLUSIONS

As the largest existing study of Gullah individuals, our study offers insight into not only the level of cardiovascular risk in this population but also the pathophysiological mechanisms central to ancestral differences in cardiometabolic risk in the broader African American population.

Resting energy expenditure in type 2 diabetic patients and the effect of insulin bolus

AIMS

Resting energy expenditure (REE) plays a critical role in the regulation of body weight, with important implications in type 2 diabetes (T2D). However, the relationships between REE and T2D have not been extensively evaluated. We compared REE in persons with diabetes and in persons without diabetes. We also investigated the acute effect of insulin on REE and venous lactate, the latter an indirect measure of neoglucogenetic activity.

METHODS

REE was measured using indirect calorimetry in 14 newly diagnosed, untreated T2D adults and in 14 non-diabetic age-, gender- and body mass index-matched persons. The REE and lactate venous concentrations were also measured in a subgroup of 5 T2D patients in the hour following an IV insulin bolus.

RESULTS

The REE normalized for fat-free mass (FFM) was significantly higher in T2D patients than in the group without diabetes (mean ± SD: 27.6 ± 1.9 vs. 25.8 ± 1.9 kcal/kg-FFM·24h; P=0.02). REE normalized for FFM was correlated with fasting plasma glucose concentration (r=0.51; P=0.005). Following the insulin venous bolus REE (0': 2,048 ± 242; 10': 1,804 ± 228; 20': 1,684 ± 230; 30': 1,634 ± 212; 45': 1,594 ± 179; 60': 1,625 ± 197 kcal/24h; P<0.001) and both glucose (P<0.001) and lactate (P<0.001) concentrations progressively declined in the ensuing hour.

CONCLUSIONS

Patients with diabetes have a higher energy expenditure, likely a consequence of higher gluconeogenetic activity. This study may contribute to recognizing the nature of body weight reduction that occurs in concomitance with poorly controlled diabetes, and of body weight gain as commonly observed when hypoglycemic treatment is started.

Resting metabolic rate in obese diabetic and obese non-diabetic subjects and its relation to glycaemic control

Background

Both obesity and type II diabetes mellitus are associated with insulin resistance and abnormal metabolic reactions. This study was conducted to evaluate resting metabolic rate in obese diabetic patients and to assess its relation to glycaemic control.

Results

This is a case control study conducted in Gabir AbuEliz centre in Khartoum, Sudan. A random sample of 40 obese diabetic patients (cases) and 40 obese non-diabetic subjects (controls) were interviewed and examined clinically to exclude presence of acute or chronic medical illness. Haemoglobin A1c was measured for each participant using the “NycoCard Haemoglobin A1c test” (Axis -Shield/ Norway). Fasting blood sugar was measured using one touch(R) glucometer (LifeScan Canada Ltd). The PowerLab 8/35 with a gas analyzer (AD Instruments, Castle Hill Australia) was used for measurement of VO2, VCO2 and Respiratory exchange ratio (RER). Resting metabolic rate was calculated using the Weir equation. VO2 (mean+/-SD) ml/min was significantly higher among cases (209.9+/-42.7) compared to the controls (192.4+/-28.1), (P = 0.034). Similarly, VCO2 (mean+/-SD) ml/min was higher among cases (191.4+/-35.0) than controls (178.3+/-22.5), (P = 0.05). Resting metabolic rate “RMR” (mean+/-SD) kcal/day was higher in obese diabetic patients (1480.7 +/- 274.2) than obese non-diabetic subjects (1362.4+/- 184.8), (P = 0.027). Participants with high glycated haemoglobin had higher RMR than those with normal glycated haemoglobin (P = 0.016).

Conclusion

It is concluded that resting metabolic rate is significantly higher in obese diabetic patients compared to obese non-diabetics, especially in those with poor glycaemic control.

Thyroid hormone promotes postnatal rat pancreatic β-cell development and glucose-responsive insulin secretion through MAFA

Neonatal β cells do not secrete glucose-responsive insulin and are considered immature. We previously showed the transcription factor MAFA is key for the functional maturation of β cells, but the physiological regulators of this process are unknown. Here we show that postnatal rat β cells express thyroid hormone (TH) receptor isoforms and deiodinases in an age-dependent pattern as glucose responsiveness develops. In vivo neonatal triiodothyronine supplementation and TH inhibition, respectively, accelerated and delayed metabolic development. In vitro exposure of immature islets to triiodothyronine enhanced the expression of Mafa, the secretion of glucose-responsive insulin, and the proportion of responsive cells, all of which are effects that were abolished in the presence of dominant-negative Mafa. Using chromatin immunoprecipitation and electrophoretic mobility shift assay, we show that TH has a direct receptor-ligand interaction with the Mafa promoter and, using a luciferase reporter, that this interaction was functional. Thus, TH can be considered a physiological regulator of functional maturation of β cells via its induction of Mafa.

Impact of endogenous and exogenous insulin on basal energy expenditure in patients with type 2 diabetes under standard treatment

BACKGROUND

Factors that affect resting energy expenditure or basal energy expenditure (BEE) in patients with type 2 diabetes under standard treatment have not been evaluated in detail.

OBJECTIVE

We determined the clinical factors that affected BEE in addition to body composition in patients with type 2 diabetes under standard treatment.

DESIGN

BEE was measured by using indirect calorimetry under a strict basal condition in 58 Japanese patients with type 2 diabetes after >7 d as inpatients under management of diabetes with medical nutrition therapy and medications. Insulin secretion was measured with a glucagon test. Stepwise regression was applied to explore determinants of BEE.

RESULTS

In the stepwise estimation, insulin secretion (P = 0.015), insulin therapy (P = 0.012), and pulse rate (P = 0.011) were selected in addition to fat-free mass (FFM) (P < 0.001) and fat mass (P = 0.006) as significant independent determinants of BEE. Standardized partial regression coefficients of the additional 3 factors were -0.16, -0.15, and 0.15, respectively, whereas those for FFM and fat mass were 0.82 and 0.19, respectively. The additional 3 factors explained another 3.9% of the variability of BEE, and the adjusted coefficient of determination was 83.4%. Age, sex, other medications, and parameters of glycemic control were not significant determinants beyond the combined contribution of body composition, endogenous and exogenous insulin, and pulse rate.

CONCLUSION

Endogenous insulin secretion and exogenous insulin administered in treatment have significant independent effects in the lowering of BEE in patients with diabetes under standard management with medical nutrition therapy and medications.

Resting energy expenditure in patients with intermittent claudication and critical limb ischemia

OBJECTIVE

The primary aim of this study was to compare the resting energy expenditure of patients with intermittent claudication and critical limb ischemia. A secondary aim was to identify predictors of resting energy expenditure.

METHODS

One hundred patients limited by intermittent claudication and 40 patients with critical limb ischemia participated in this study. Patients were assessed on resting energy expenditure, body composition, ankle brachial index (ABI), and calf blood flow.

RESULTS

Patients with critical limb ischemia had a lower resting energy expenditure than patients with intermittent claudication (1429 +/- 190 kcal/day vs 1563 +/- 229 kcal/day; P = .004), and higher body fat percentage (34.8 +/- 7.8% vs 31.5 +/- 7.8%; P = .037), higher fat mass (30.0 +/- 9.3 kg vs 26.2 +/- 8.9 kg;P = .016), and lower ABI (0.31 +/- 0.11 vs 0.79 +/- 0.23; P < .001). Resting energy expenditure was predicted by fat free mass (P < .0001), age (P < .0001), ABI (P < .0001), ethnicity (P < .0001), calf blood flow (P = .005), and diabetes (P = .008). Resting energy expenditure remained lower in the patients with critical limb ischemia after adjusting for clinical characteristics plus fat free mass (1473 +/- 27.8 kcal/day [mean +/- SEM] vs 1527 +/- 19.3 kcal/day; P = .031), but it was no longer different between groups after further adjustment for ABI and calf blood flow (1494 +/- 25.2 kcal/day vs 1505 +/- 17.7 kcal/day; P = .269).

CONCLUSION

Resting energy expenditure is decreased with a progression in peripheral arterial disease (PAD) symptoms from intermittent claudication to critical limb ischemia. Furthermore, patients with critical limb ischemia who are most susceptible for decline in resting energy expenditure are older, African American patients with diabetes. The lower resting energy expenditure of patients with critical limb ischemia, combined with their sedentary lifestyle, suggests that they are at high risk for long-term positive energy balance and weight gain.

Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children

Energy needs are influenced by many factors, including ethnicity. Multiple studies have shown that the accuracy of an energy prediction equation varies with the ethnic background of the study population. Therefore, it is crucial to identify the most accurate energy prediction equation to use for a given population. This study compared measured resting energy expenditure to results from commonly-used energy prediction equations to identify the most accurate equation to use for Korean children. Based on previous literature showing wide variation in accuracy of energy prediction equations in different ethnic groups, we hypothesized that results from measured- vs. predicted energy needs would be significantly different in this population. Subjects were 92 South Korean children (38 boys, 54 girls) age 7.7 +/- 2.7 years (mean +/- SD). Measurements included: resting metabolic rate (TrueOne 2400 metabolic cart), weight/height (digital scale/stadiometer); body fat (BIA, Inbody720), blood pressure (sphingomanometer), triceps skinfold thickness (MD-500 skinfold calipers), muscle mass (Heymsfield's formula) and body surface area (Dubois formula) calculations. Resting energy needs were predicted using the Harris-Benedict, WHO/NAO/FAO, Altman and Dittmer, Maffeis, and Schofield-HW equations, and the Dietary Reference Intake recommendations. Measured and predicted energy needs were significantly correlated (P < .001 for all; range R(2) = 0.54-0.56), yet significantly different for all equations studied (P < .05) except the Maffeis and Schofield-HW equations. Differences (means +/- SD) between measured vs. predicted energy needs ranged from 9.5 +/- 123.2 (Schofield-HW) to 199.6 +/- 132.7 (WHO/NAO/FAO) kcal/day, where a value closer to zero indicates increased accuracy of the prediction equation to correspond to measured energy needs. Although results from equations studied were significantly correlated with measured resting energy needs, notable discrepancies existed which, over time, could produce undesirable weight changes in Korean children.

The effect of exercise training on resting metabolic rate in type 2 diabetes mellitus

PURPOSE

Exercise is a possible means to increase resting energy expenditure, which could offset age-related metabolic declines and facilitate weight management, both of which are particularly important for people who have type 2 diabetes mellitus. We sought to determine the effects of aerobic exercise training and resistance exercise training and the incremental effect of combined aerobic and resistance exercise training on resting metabolic rate (RMR) in previously sedentary individuals with type 2 diabetes.

METHODS

After a 4-wk run-in period, 103 participants (72 male, 31 female, 39-70 yr, mean +/- SD body mass index = 32.9 +/- 5.7 kg x m(-2)) were randomly assigned to four groups for 22 wk: aerobic training, resistance training, combined aerobic and resistance exercise training, or waiting-list control. Exercise training was performed three times per week at community-based gym facilities. RMR was measured by indirect calorimetry for 30 min after an overnight fast. Body composition was assessed using bioelectrical impedance. These measurements were taken at baseline, at 3 months, and at 6 months of the intervention.

RESULTS

RMR did not change significantly in any group after accounting for multiple comparisons despite significant improvements in peak oxygen consumption and muscular strength in the exercising groups. Adjusting RMR for age, sex, fat mass, and fat-free mass in various combinations did not alter these results.

CONCLUSION

These results suggest that RMR was not significantly changed after a 6-month exercise program, regardless of modality, in this sample of adults with type 2 diabetes.

Resting energy expenditure: a stronger marker than body weight for bone mineral density in white women but not men? The Rancho Bernardo study

OBJECTIVE

To investigate the association between resting energy expenditure (REE) and bone mineral density (BMD) in white adults.

DESIGN

Cross-sectional study.

SETTING

White, middle-class to upper middle-class community-dwelling adults.

PARTICIPANTS

Women (n = 996) and men (n = 686) aged 36 to 97 years.

ASSESSMENT OF RISK FACTORS

REE calculated using the Harris-Benedict equation.

MAIN OUTCOME MEASURES

BMD of the lumbar spine (L1 to L4), total hip, and total body measured using dual energy x-ray absorptiometry.

RESULTS

REE was lower in women than in men (1220 versus 1566 kcal/day, P < 0.0001); women also had lower BMI. After adjusting for lean body mass (LBM), REE was higher in women than men (1407 versus 1296 kcal/kg LBM/d, P < 0.0001). In stepwise multiple linear regression models, REE explained 13% of variance in spine BMD in women and 6% in men, 33% of variance in hip BMD in women and 22% in men, and 32% of variance in total body BMD in women and 22% in men. In women, weight explained 10% less of the hip BMD variance and 6% less of the total body BMD variance than REE. In men, weight explained 4% more of the spine BMD variance and 1% more of the total body BMD variance than REE.

CONCLUSIONS

REE explained more of the BMD variance than weight in women, and the reverse was true in men. These sex differences were largely explained by sex differences in LBM or weight.

Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study

BACKGROUND

A low metabolic rate may be inherited and predispose to obesity, whereas a higher metabolic rate in obesity may be acquired by obesity-associated cardiometabolic risk.

OBJECTIVE

We aimed to explain the interindividual variation in resting energy expenditure (REE) by assessing 1) the association between REE and body composition, thyroid hormones, and obesity-related cardiometabolic risk factors, and 2) the familial (genetic and environmental) contribution to REE.

DESIGN

REE and metabolic risk factors (ie, blood pressure and plasma insulin, glucose, and C-reactive protein concentrations) were assessed in 149 two- or three-generation families, including at least one overweight or obese member. Heritability of REE, respiratory quotient (RQ), thyroid hormones [thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4)], and body composition (fat-free mass and fat mass) were estimated by using variance components-based quantitative genetic models.

RESULTS

REE adjusted for body composition, sex, and age (REEadj) significantly correlated with systolic and diastolic blood pressure, plasma insulin and glucose concentrations, and the homeostasis model assessment (HOMA) (r = 0.14-0.31, P < 0.05). Thyroid hormones had a modest influence on REE variance only. Heritability was 0.30 +/- 0.07 for REEadj and 0.29 +/- 0.08 for REE after additional adjustment for thyroid hormones and metabolic risk. Furthermore, heritability was estimated to be 0.22 +/- 0.08 for RQ, 0.37 +/- 0.08 for TSH, 0.68 +/- 0.06 for FT4, and 0.69 +/- 0.05 for FT3 (all significantly larger than zero).

CONCLUSIONS

Obesity-related cardiometabolic risk factors contribute to interindividual variation in REE, with hypertension and insulin resistance being associated with a higher REE. REE was moderately heritable, independent of body composition, sex, age, thyroid function, and cardiometabolic risk.

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

The objective of this study was to assess the effects of weight history status and ethnicity on the ability of the Harris Benedict (HB) formula to: 1) predict measured resting energy expenditure (REE), and, 2) accurately estimate energy needs over a 2-week test period. Subjects were never-overweight (BMI </= 25 kg/m(2), n=47), overweight (BMI 27-30 kg/m(2), n=170), and weight-reduced (BMI </= 25 kg/m(2), n=51) healthy, adult African-American (AA) and Caucasian (C) women. Food was provided for 2 weeks at an energy level calculated using the HB formula multiplied by a 1.35 activity factor. After 2 weeks, weight, REE (by indirect calorimetry), and body composition (by dual-energy X-ray absorptiometry) were assessed. Data were analyzed using 2-way ANOVA at p<0.05 significance. The HB formula overestimated REE 1) in each weight history group (by 160 +/-125 kcals among never-overweight, 295 +/-189 kcal among overweight, and 105 +/-135 among weight-reduced) such that there was a group effect on overestimation (P<0.001) and 2) between ethnicities, with a greater overestimation in AA vs. C (P<0.001). There was a significant effect of weight history group on weight change (P<0.001) over 2-weeks, such that weight-reduced women gained more weight than the other two groups (P<0.05). In conclusion, the ability of the HB formula to estimate REE differed with weight history status and ethnicity. The accuracy of the HB formula to predict dietary energy needs was affected by weight history status. These results suggest that formulas used to calculate energy needs should take into account weight history and ethnicity.

Validation of dietary intake data in black women with type 2 diabetes

The validity of baseline dietary intake data in women participating in a culturally competent intervention study for black women with type 2 diabetes was assessed. The relationship of sociodemographic and physiologic factors with underreporting of dietary intake was determined. Criterion validity of dietary intake, which had been assessed using the Nutritionist Five Collection Form, a combination of a standard 2-day dietary recall and a modified, culturally appropriate food frequency questionnaire, was determined. Data were analyzed using First Data Bank Nutritionist Five (version 2.3, 2000, First Data Bank, San Bruno, CA) software. Validation of baseline dietary data in 109 women was performed by calculating the ratio of energy intake to resting metabolic rate. Chi(2) and t tests were used to assess relationships between underreporting and sociodemographic and physiologic factors. Mean ratio of energy intake to resting metabolic rate was 1.46 (+/-0.4). Using a lower limit of 1.35, the prevalence of underreporting was 46.8%. Underreporting was significantly associated with body mass index (P< or =0.001) and waist circumference (P<0.001). Use of this combined dietary recall and modified food frequency questionnaire might, therefore, provide more accurate dietary assessment in this population. Additional modification and validity testing is warranted in this and other populations.

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

Resting energy expenditure (REE) is believed to be increased in type 2 diabetes, an increase that is associated with deteriorating glucose tolerance during its development. Meanwhile, insulin resistance, a state linked to obesity and observed in all type 2 diabetic patients, is associated with reduced REE. Our aim was to compare REE in obese patients with and without diabetes. REE, body composition (total body water, density, percentage fat and fat-free mass: 3-compartment model) and metabolic control were assessed in fifty obese Caucasian patients with diabetes (glycated haemoglobin level 7.6 (SD 1.5) %) and fifty obese patients who were non-diabetic. Despite being more overweight and younger, obese non-diabetic patients had an absolute REE (7.73 (SD 1.44) v. 8.12 (SD 1.37) MJ; P=0.17) and percentage fat-free mass similar to those of obese diabetic patients. Even when adjusted for differences in body composition, REE remained similar in both groups. Furthermore, REE (absolute and adjusted) was unaffected by both glucose level and control (glycated haemoglobin), with fat-free mass being the only determinant of REE. We conclude that REE is not necessarily increased by the presence of diabetes in obese people.

Resting energy expenditure, cardiovascular risk factors and insulin resistance in obese patients

OBJECTIVE

The aim of our study was to determine whether energy expenditure modified by increasing body mass over the wide range of body mass index (BMI) was related to insulin resistance, cardiovascular risk factors and dietary intakes.

SUBJECTS AND METHODS

A population of 87 obese non-diabetic outpatients was analyzed prospectively. Indirect calorimetry, tetrapolar electrical bioimpedance, serial assessment of nutritional intake using written 3-day food records and biochemical analyses were performed.

RESULTS

The mean age was 45.1 +/- 16.7 years and the mean BMI was 35.2 +/- 5.2. Indirect calorimetry showed a resting metabolic rate (RMR) of 1,732.2 +/- 406.6 kcal/day and oxygen consumption of 266.2 +/- 63.3 ml/min. RMR corrected by fat-free mass was 36.8 +/- 14.1 kcal/day/kg. Serial assessment of nutritional intake using written 3-day food records showed a calorie intake of 1,660 +/- 551.7 cal/day, a carbohydrate intake of 168.63 +/- 76.6 g/day, a fat intake of 72.1 +/- 26.42 g/day and a protein intake of 81.1 +/- 23.3 g/day. Insulin, HOMA, systolic blood pressure, diastolic blood pressure, waist circumference, fat mass, waist-to-hip ratio were higher in the third BMI tertile than in the first and second tertiles. No differences were detected in calorie intake, carbohydrate intake, fat intake, protein intake, drinking and alcoholic habit among BMI tertiles. RMR was similar in the different tertiles and the corrected RMR by fat-free mass was higher in first tertile than in the second and third tertiles (44.2 +/- 20.7 vs. 34.5 +/- 9.1 vs. 33.1 +/- 8.5 kcal/kg/day; p < 0.05). In multivariate analysis with a dependent variable (RMR), the fat-free mass remained in the model (F = 7.8; p < 0.05), with an increase of 10.1 (95% CI 3.6-17.5) kcal/day with each 1 kg of fat-free mass adjusted by age and sex.

CONCLUSION

Resting energy expenditure in obese patients is not related to the BMI, insulin resistance and dietary intake. RMR is related to fat-free mass in a multivariant model.