Development and cross-validation of a prediction equation for estimating resting energy expenditure in healthy African-American and European-American women

Resting Energy Expenditure of Master Athletes: Accuracy of Predictive Equations and Primary Determinants

Resting energy expenditure (REE) is determined mainly by fat-free mass (FFM). FFM depends also on daily physical activity. REE normally decreases with increased age due to decreases in FFM and physical activity. Measuring REE is essential for estimating total energy expenditure. As such, there are a number of different equations in use to predict REE. In recent years, an increasing number of older adults continue to participate in competitive sports creating the surge of master athletes. It is currently unclear if these equations developed primarily for the general population are also valid for highly active, older master athletes. Therefore, we tested the validity of six commonly-used equations for predicting REE in master athletes. In conjunction with the World Masters Athletic Championship in Malaga, Spain, we measured REE in 113 master athletes by indirect calorimetry. The most commonly used equations to predict REE [Harris & Benedict (H&B), World Health Organization (WHO), Müller (MÜL), Müller-FFM (MÜL-FFM), Cunningham (CUN), and De Lorenzo (LOR)] were tested for their accuracies. The influences of age, sex, height, body weight, FFM, training hours per week, phase angle, ambient temperature, and athletic specialization on REE were determined. All estimated REEs for the general population differed significantly from the measured ones (H&B, WHO, MÜL, MÜL-FFM, CUN, all p < 0.005). The equation put forward by De Lorenzo provided the most accurate prediction of REE for master athletes, closely followed by FFM-based Cunningham’s equation. The accuracy of the remaining commonly-used prediction equations to estimate REE in master athletes are less accurate. Body weight (p < 0.001), FFM (p < 0.001), FM (p = 0.007), sex (p = 0.045) and interestingly temperature (p = 0.004) are the significant predictors of REE. We conclude that REE in master athletes is primarily determined by body composition and ambient temperature. Our study provides a first estimate of energy requirements for master athletes in order to cover adequately athletes’ energy and nutrient requirements to maintain their health status and physical performance.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

New Predictive Equations for Resting Energy Expenditure in Normal to Overweight and Obese Population

BACKGROUND AND AIMS

The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population.

METHODS

A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18-57 ± 14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men = 50%).

RESULTS

Using a number of parameters (bias, underprediction, overprediction, and % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (∼55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as the Almajwal-Abulmeaty (AA) equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE = 3832.955 + AdjWt (kg) × 48.037 - Ht (cm) × 30.642 + gender × 141.268 - age (years) × 4.525 [AdjWt is Adjusted body weight = (Wt - IBW)/4 + IBW. IBW is Ideal body weight; for men IBW = (Ht(cm) - 152.4) × 1.0714) + 45.36 and for women IBW = (Ht(cm)-152.4) × 0.8928) + 45.36]. The regression model accounted for approximately 70% of the variance in REEm (R 2 = 0.702).

CONCLUSION

Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations.

Do we need race-specific resting metabolic rate prediction equations?

Background

Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR.

Methods

We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR.

Results

Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m², and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively.

Conclusion

We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.

Congruent Validity of Resting Energy Expenditure Predictive Equations in Young Adults

Having valid and reliable resting energy expenditure (REE) estimations is crucial to establish reachable goals for dietary and exercise interventions. However, most of the REE predictive equations were developed some time ago and, as the body composition of the current population has changed, it is highly relevant to assess the validity of REE predictive equations in contemporary young adults. In addition, little is known about the role of sex and weight status on the validity of these predictive equations. Therefore, this study aimed to investigate the role of sex and weight status in congruent validity of REE predictive equations in young adults. A total of 132 young healthy adults (67.4% women, 18⁻26 years old) participated in the study. We measured REE by indirect calorimetry strictly following the standard procedures, and we compared it to 45 predictive equations. The most accurate equations were the following: (i) the Schofield and the "Food and Agriculture Organization of the United Nations/World Health Organization/United Nations" (FAO/WHO/UNU) equations in normal weight men; (ii) the Mifflin and FAO/WHO/UNU equations in normal weight women; (iii) the Livingston and Korth equations in overweight men; (iv) the Johnstone and Frankenfield equations in overweight women; (v) the Owen and Bernstein equations in obese men; and (vi) the Owen equation in obese women. In conclusion, the results of this study show that the best equation to estimate REE depends on sex and weight status in young healthy adults.

Validity of predictive equations for resting metabolic rate in healthy humans

Background: There are several predictive equations for estimating resting metabolic rate (RMR) in healthy humans. Concordance of these equations against measured RMR is variable, and often dependent on the extent of RMR. Part of the discrepancy may be due to an insufficient accuracy of metabolic carts, but this accuracy can be improved via a correction procedure. Objective: To determine the validity of predictive RMR equations by comparing them against measured and corrected (i.e. the reference) RMR. Methods: RMR was measured, in 69 healthy volunteers (29 males/40 females; 32±8 years old; BMI 25.5±3.8 kg/m2) and then corrected by simulating gas exchange through pure gases and high-precision mass-flow regulators. RMR was predicted using 13 published equations. Bland–Altman analyses compared predicted vs. reference RMRs. Results: All equations correlated well with the reference RMR (r>0.67; P<0.0001), but on average, over-predicted the reference RMR (89–312 kcal/d; P<0.05). Based on Bland–Altman analyses, 12 equations showed a constant bias across RMR, but the bias was not different from zero for nine of them. Three equations stood out because the absolute difference between predicted and reference RMR was equal or lower than 200 kcal/d for >60% of individuals (the Mifflin, Oxford and Müller equations). From them, only the Oxford equations performed better in both males and females separately. Conclusion: The Oxford equations are a valid alternative to predict RMR in healthy adult humans. Gas-exchange correction appears to be a good practice for the reliable assessment of RMR.

Estimation of basal metabolic rate in Chinese: are the current prediction equations applicable?

BACKGROUND

Measurement of basal metabolic rate (BMR) is suggested as a tool to estimate energy requirements. Therefore, BMR prediction equations have been developed in multiple populations because indirect calorimetry is not always feasible. However, there is a paucity of data on BMR measured in overweight and obese adults living in Asia and equations developed for this group of interest. The aim of this study was to develop a new BMR prediction equation for Chinese adults applicable for a large BMI range and compare it with commonly used prediction equations.

METHODS

Subjects were 121 men and 111 women (age: 21-67 years, BMI: 16-41 kg/m(2)). Height, weight, and BMR were measured. Continuous open-circuit indirect calorimetry using a ventilated hood system for 30 min was used to measure BMR. A regression equation was derived using stepwise regression and accuracy was compared to 6 existing equations (Harris-Benedict, Henry, Liu, Yang, Owen and Mifflin). Additionally, the newly derived equation was cross-validated in a separate group of 70 Chinese subjects (26 men and 44 women, age: 21-69 years, BMI: 17-39 kg/m(2)).

RESULTS

The equation developed from our data was: BMR (kJ/d) = 52.6 x weight (kg) + 828 x gender + 1960 (women = 0, men = 1; R(2) = 0.81). The accuracy rate (within 10 % accurate) was 78 % which compared well to Owen (70 %), Henry (67 %), Mifflin (67 %), Liu (58 %), Harris-Benedict (45 %) and Yang (37 %) for the whole range of BMI. For a BMI greater than 23, the Singapore equation reached an accuracy rate of 76 %. Cross-validation proved an accuracy rate of 80 %.

CONCLUSIONS

To date, the newly developed Singapore equation is the most accurate BMR prediction equation in Chinese and is applicable for use in a large BMI range including those overweight and obese.

Predicting resting energy expenditure in young adults

PURPOSE

To develop and validate a REE prediction equation for young adults.

METHODS

Baseline data from two studies were pooled (N=318; women=52%) and randomly divided into development (n=159) and validation samples (n=159). REE was measured by indirect calorimetry. Stepwise regression was used to develop an equation to predict REE (University of Kansas (KU) equation). The KU equation and 5 additional REE prediction equations used in clinical practice (Mifflin-St. Jeor, Harris-Benedict, Owens, Frankenfield (2 equations)) were evaluated in the validation sample.

RESULTS

There were no significant differences between predicted and measured REE using the KU equation for either men or women. The Mifflin-St. Jeor equation showed a non-significant mean bias in men; however, mean bias was statistically significant in women. The Harris-Benedict equation significantly over-predicted REE in both men and women. The Owens equation showed a significant mean bias in both men and women. Frankenfield equations #1 and #2 both significantly over-predicted REE in non-obese men and women. We found no significant differences between measured REE and REE predicted by the Frankenfield #2 equations in obese men and women.

CONCLUSION

The KU equation, which uses easily assessed characteristics (age, sex, weight) may offer better estimates of REE in young adults compared with the 5 other equations. The KU equation demonstrated adequate prediction accuracy, with approximately equal rates of over and under-prediction. However, enthusiasm for recommending any REE prediction equations evaluated for use in clinical weight management is damped by the highly variable individual prediction error evident with all these equations.

Predictive equation of resting energy expenditure in obese adult Taiwanese

SUMMARY

OBJECTIVE

To examine the accuracy of present resting energy expenditure (REE) predictive equations among obese adult Taiwanese, and obtain new predictive equations for this population.

METHOD

A total of 80 participants (men = 42, women = 38) aged 18-64 years (mean 41.5 ± 13.1 years) with body mass index (BMI) ≥30 kg/m(2) were recruited from obesity clinic of a tertiary hospital. Anthropometric data including weight, height, waist circumference (WC), hip circumference (HipC) were collected. Body fat and fat free mass (FFM) were measured using bioelectric impedance analysis. Indirect calorimeter (Vmax 29n) was used for measurement of REE. The commonly used predictive equations (Harris-Benedict equation, Bernstein equation, Mifflin equation, Owen equation, Schofield equation, Cunningham equation, Wang equation, and Liu equation) were tested for group means and individual prediction accuracy for our target population. New predictive equations were formulated using multiple linear regression analyses.

RESULT

The mean BMI was 34.3 ± 4.6 kg/m(2). All of the commonly used predictive equations showed statistically significant differences with the measured REE values. As for individual prediction accuracy, these predictive equations showed poor performance and only Mifflin equation provided an individual accuracy over 40%. The new predictive equation recommended for obese adult Taiwanese is below: REE (kcal/d) = 11.6 × weight (kg) - 204.84 × gender - 4.8 × height (cm) + 1594.6 (gender: men = 1; women = 2).

CONCLUSION

The previous commonly used predictive equations were less accurate in obese Taiwanese. For this specific population, the newly developed equation should be applied for better prediction of REE.

Common Prediction Equations Overestimate Measured Resting Metabolic Rate in Young Hispanic Women

The accuracy of 6 resting metabolic rate (RMR) prediction equations to indirect calorimetry was compared in 38 Hispanic women (age = 30 ± 7 years; body mass index = 28.9 ± 7.2 kg/m2; body fat = 42% ± 8%). Paired t tests examined differences between predicted and measured RMR; significance defined as P < 0.05. Bias and agreement were displayed using Bland-Altman plots. Accuracy was defined when the predicted RMR was ± 10% of the measured RMR. Data were analyzed with SPSS (version 19). Only the equation of Owen et al was not significantly different from the measured RMR (1336 ± 142 and 1322 ± 203 kcal/d, respectively). The equation of Owen et al was accurate for 84.2% of women; RMR prediction equations had limited applicability for young Hispanic women.

Do African American women require fewer calories to maintain weight?: Results from a controlled feeding trial

BACKGROUND

The high prevalence of obesity in African American (AA) women may result, in part, from a lower resting metabolic rate (RMR) than non-AA women. If true, AA women should require fewer calories than non-AA women to maintain weight. Our objective was to determine in the setting of a controlled feeding study, if AA women required fewer calories than non-AA women to maintain weight.

MATERIALS AND METHODS

This analysis includes 206 women (73% AA), aged 22-75 years, who participated in the Dietary Approaches to Stop Hypertension (DASH) trial-a multicenter, randomized, controlled, feeding study comparing the effects of 3 dietary patterns on blood pressure in individuals with prehypertension or stage 1 hypertension. After a 3-week run-in, participants were randomized to 1 of 3 dietary patterns for 8 weeks. Calorie intake was adjusted during feeding to maintain stable weight. The primary outcome of this analysis was average daily calorie (kcal) intake during feeding.

RESULTS

AA women had higher baseline weight and body mass index than non-AA women (78.4 vs 72.4 kg, P < .01; 29.0 vs 27.6 kg/m(2), P < .05, respectively). During intervention feeding, mean (SD) kcal was 2168 (293) in AA women and 2073 (284) in non-AA women. Mean intake was 94.7 kcal higher in AA women than in non-AA women (P < .05). After adjustment for potential confounders, there was no difference in caloric intake between AA and non-AA women (Δ = -2.8 kcal, P = .95).

CONCLUSION

These results do not support the view that AA women are at greater risk for obesity because they require fewer calories to maintain weight.

Validity of resting energy expenditure predictive equations before and after an energy-restricted diet intervention in obese women

BACKGROUND

We investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m(2)>BMI<40 kg/m(2)) women.

METHODS

We measured REE (indirect calorimetry), body weight, height, and fat mass (FM) and fat free mass (FFM, dual X-ray absorptiometry) in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women) the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM.

RESULTS

At baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241-247) when using weight (bias:-0.2%, P = 0.982), 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction). After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1-19) equation when using weight (bias:-1.7%, P = 0.044), 81% accurate prediction, yet it provided 53% accurate predictions at baseline.

CONCLUSIONS

There is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.

[Characterization of metabolic resting rate and proposal of a new equation for a female Brazilian population]

OBJECTIVES

The goals of this study were to standardize resting metabolic rate (RMR) in the Brazilian female population and evaluate the suitability of the HB equation.

SUBJECTS AND METHODS

In order to do so, 760 indirect calorimetry (CI) measurements performed in Brazilian female patients between 18 and 65 years old, were used. The results were tabulated, the population distributed according to the quintiles of weight, and the statistical analyses applied.

RESULTS

The average RMR varied from 1,226 to 1,775 calories/day. The HB equation overestimated the RMR by about 7%. From the correlation between the variables, we conclude that weight has a positive correlation with RMR and age a negative correlation.

CONCLUSION

The RMR and respiratory quotient (QR) can be used as obesity predictors. Two new equations were proposed in our study, one for the population with body mass index (BMI) > 35 kg/m(2) and another for the population with BMI < 35 kg/m(2).

Sociodemographic, lifestyle, mental health and dietary factors associated with direction of misreporting of energy intake

OBJECTIVE

To estimate the extent of under- and over-reporting, to examine associations with misreporting and sociodemographic and lifestyle characteristics and mental health status and to identify differential reporting in micro- and macronutrient intake and quality of diet.

DESIGN

A health and lifestyle questionnaire and a semi-quantitative FFQ were completed as part of the 2007 Survey of Lifestyle, Attitudes and Nutrition. Energy intake (EI) and intake of micro- and macronutrients were determined by applying locally adapted conversion software. A dietary score was constructed to identify healthier diets. Accuracy of reported EI was estimated using the Goldberg method. ANOVA, χ2 tests and logistic regression were used to examine associations.

SETTING

Residential households in Ireland.

SUBJECTS

A nationally representative sample of 7521 adults aged 18 years or older.

RESULTS

Overall, 33·2 % of participants were under-reporters while 11·9 % were over-reporters. After adjustment, there was an increased odds of under-reporting among obese men (OR = 2·01, 95 % CI 1·46, 2·77) and women (OR = 1·68, 95 % CI 1·23, 2·30) compared to participants with a healthy BMI. Older age, low socio-economic status and overweight/obesity reduced the odds of over-reporting. Among under-reporters, the percentage of EI from fat was lower and overall diet was healthier compared to accurate and over-reporters. The reported usage of salt, fried food consumption and snacking varied significantly by levels of misreporting.

CONCLUSIONS

Patterns in differential reporting were evident across sociodemographic, lifestyle and mental health factors and diet quality. Consideration should be given to how misreporting affects nutrient analysis to ensure sound nutritional policy.

Resting energy expenditure changes with weight loss: racial differences

It is controversial whether weight loss reduces resting energy expenditure (REE) to a different magnitude in black and white women. This aim of this study was to determine whether changes in REE with weight loss were different between black and white postmenopausal women, and whether changes in body composition (including regional lean and fat mass) were associated with REE changes within each race. Black (n = 26) and white (n = 65) women (age = 58.2 +/- 5.4 years, 25 < BMI < 40 kg/m(2)) completed a 20-week weight-loss intervention. Body weight, lean and fat mass (total body, limb, and trunk) via dual-energy X-ray absorptiometry, and REE via indirect calorimetry were measured before and after the intervention. We found that baseline REE positively correlated with body weight, lean and fat mass (total, limb, and trunk) in white women only (P < 0.05 for all). The intervention decreased absolute REE in both races similarly (1,279 +/- 162 to 1,204 +/- 169 kcal/day in blacks; 1,315 +/- 200 to 1,209 +/- 185 kcal/day in whites). REE remained decreased after adjusting for changes in total or limb lean mass in black (1,302-1,182 kcal/day, P = 0.043; 1,298-1,144 kcal/day, P = 0.006, respectively), but not in white, women. Changes in REE correlated with changes in body weight (partial r = 0.277) and fat mass (partial r = 0.295, 0.275, and 0.254 for total, limb, and trunk, respectively; P < 0.05) independent of baseline REE in white women. Therefore, with weight loss, REE decreased in proportion to the amount of fat and lean mass lost in white, but not black, women.

Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women

BACKGROUND & AIMS

Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.

METHODS

Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.

RESULTS

Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18-71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris-Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias -1.7, -0.5, +1.1, +2.2, and -1.8%, RMSE 168, 170, 163, 167, and 173kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45kg/m(2), the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.

CONCLUSIONS

In Belgian women, the original Harris-Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5-50). Estimations for the BMI range between 30 and 40kg/m(2), however, should be improved.

Validity of resting energy expenditure estimated by an activity monitor compared to indirect calorimetry

The use of activity monitors (triaxial accelerometers) to estimate total energy expenditure in kilocalories is dependent on the estimation of resting energy expenditure (REE). However, the REE estimated by activity monitors has not been validated against more precise techniques, such as indirect calorimetry (IC). Therefore, the objective of the present study was to compare REE estimated by the Actical activity monitor (ActMon) to that measured by IC and standard prediction equations of REE. Fifty healthy adults between 18 and 43 years of age were measured for weight and percentage of body fat using a digital scale and bioelectrical impedance. The REE estimated by the ActMon was only 129 kJ/d higher, but not statistically different (P>0·05), than the REE measured with IC. Using multiple linear regression, there was a positive relationship for men, but not for women, between fat mass (kg) and percentage of body fat and the difference in REE estimated by the ActMon compared to IC (P < 0·001). Therefore, in the cohort studied, the use of an activity monitor to estimate REE is valid when compared to IC, but not to a standard prediction equation of REE.

Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18-65 y

BACKGROUND

Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry.

OBJECTIVE

The objective was to analyze which resting energy expenditure (REE) predictive equation was the best alternative to indirect calorimetry in US and Dutch adults aged 18-65 y with a body mass index (in kg/m(2)) of 25 to 40.

DESIGN

Predictive equations based on weight, height, sex, age, fat-free mass, and fat mass were tested. REE in Dutch adults was measured with indirect calorimetry, and published data from the Institute of Medicine were used for US adults. The accuracy of the equations was evaluated on the basis of the percentage of subjects predicted within 10% of the REE measured, the root mean squared prediction error (RMSE), and the mean percentage difference (bias) between predicted and measured REE.

RESULTS

Twenty-seven predictive equations (9 of which were based on FFM) were included. Validation was based on 180 women and 158 men from the United States and on 154 women and 54 men from the Netherlands aged <65 y with a body mass index (in kg/m(2)) of 25 to 40. Most accurate and precise for the US adults was the Mifflin equation (prediction accuracy: 79%; bias: -1.0%; RMSE: 136 kcal/d), for overweight Dutch adults was the FAO/WHO/UNU weight equation (prediction accuracy: 68%; bias: -2.5%; RMSE: 178), and for obese Dutch adults was the Lazzer equation (prediction accuracy: 69%; bias: -3.0%; RMSE: 215 kcal/d).

CONCLUSIONS

For US adults aged 18-65 y with a body mass index of 25 to 40, the REE can best be estimated with the Mifflin equation. For overweight and obese Dutch adults, there appears to be no accurate equation.

Ethnicity, energy expenditure and obesity: are the observed black/white differences meaningful?

PURPOSE OF REVIEW

Recent studies have suggested that black individuals have lower energy expenditure than whites. Many investigators hypothesized that this is why black women experience higher rates of obesity than white women. These findings initiated much research on race as a primary biological determinant of obesity and energy expenditure as a potential pathway. Race is a difficult construct to use in biomedical research.

RECENT FINDINGS

Recent findings have included: an explanation for the lower resting energy expenditure observed among black adults, data showing that relative resting energy expenditure may not be a significant predictor of weight change in African-origin populations, and inconsistent data on the role of activity energy expenditure as a determinant of children's weight change.

SUMMARY

The data suggest that black individuals have lower resting energy expenditure and possibly activity energy expenditure than white individuals. The lower resting energy expenditure is probably caused by a smaller mass of high metabolically active organs. It is unlikely that increased weight gain is associated with lower resting energy expenditure or activity among blacks, because no association has been found within populations. Clinically, it is important to focus on personal modifiable risk factors, e.g., energy intake and physical activity levels.

Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice?

PURPOSE OF REVIEW

To recognize the fundamental factors that alter energy expenditure on a daily basis and the impact they have on the measurement of caloric consumption by the human body, through respiratory indirect calorimetry, and thus to try to determine which predictive equation best correlates with total energy expenditure generated from energy measurements.

RECENT FINDINGS

The most important compartment of the body, for its metabolic activity and influence upon resting metabolic rate, is fat-free mass. Other variables affecting energy expenditure are sex, weight, height, age, body surface area, fat mass and ethnicity. Metabolic and activity factors such as the thermic effect of nutrients, facultative thermogenesis, anabolism/growth and physical activity, also contribute, comprising total daily energy expenditure. Following the pioneering work of Harris and Benedict for the estimation of energy expenditure, several authors turned their experimental interest to this area, and various recent predictive formulae were derived. These are useful and easy to apply in daily clinical nutrition practice. However, because of the cited variables upon energy expenditure, the final daily caloric estimates show inherent errors ranging from -23.5 to +22.5% upon measured caloric expenditure. These are particularly remarkable in critically ill patients who are exposed to medical and surgical interventions.

SUMMARY

One has to be careful in choosing, understanding and clinically applying the results from predictive equations, bearing in mind that the original population from which the equation was derived does not always correspond to that currently being evaluated.