Estimation Of Basal Metabolic Rate Research Articles

How much do large whales eat?

Estimates of the amount of prey consumed by cetaceans have been used in ecological models and also directly compared to human fisheries yields. Most of these estimates have been based on assumptions about energy requirements. However, the lack of direct measurements for large whales has necessitated extrapolation beyond the data points available from smaller species. A number of different parameterisations of general regressions in which energy requirements or consumption are estimated as proportional to body mass raised to some power B, were compared with estimates of Basal Metabolic Rate (BMR) from the widely used Kleiber equation. The choice of values has a large impact on estimates, which can differ by an order of magnitude, but modellers are frequently forced to make rather arbitrary decisions due to lack of data. Nevertheless, neither data nor theory appear to support values of B >0.75. Although some parameter values have obtained status through common usage, these have not always been based on actual data and estimates of consumption by whales need to reflect this uncertainty. Comparison of generalised relationships with data from other sources, including rates of filter feeding, oxygen consumption and seasonal changes in energy stores, suggest upper bounds on average daily metabolic rate of large whales. Estimates based on stomach contents also indicated average daily metabolic rates of less than four times the BMR from the Kleiber formula, but these are critically dependent on estimates of digestion time for which there appear to be little data. Estimates of stored energy suggest that large whales that migrate to seasonally productive feeding areas either have relatively low energy requirements for their size or need to meet a considerable proportion of their annual energy requirements outside of the feeding grounds.

Estimated basal metabolic rate and maintenance fluid volume in children: A proposal for a new equation.

The basal metabolic rate (BMR) of children aged <2years is proportional to their body weight (W; kg). However, no simple mathematical model for the estimation of BMR in children aged ≥2years has been established. Based on Japanese studies on childhood BMR, conducted until the 1960s, we noted that childhood BMR (after infancy) is proportional to body weight to the power of 1/2 (W1/2 ). Moreover, we confirmed that the two previously reported equations for calculating BMR (Schofield's equation and Oxford University's equations) are proportional to W1/2 . Based on these facts, we propose a new equation for the maintenance fluid volume for hospitalized children. Our equation (300×W1/2 mL/day) gives values almost equal to the maintenance fluid volume calculated by the most commonly used equation of Holliday and Segar in children aged 2-18years. Our equation will be useful for pediatricians to calculate the maintenance fluid volume for children in daily clinical settings.

Basal metabolic rate in pregnant adolescents

In most clinical settings basal metabolic rate (BMR) is estimated by predictive equations (PE) but there is no specific algorithm for pregnant women. To this end, this study aimed tomeasure BMR (BMRm) in single-fetus primigravida 63 adolescent pregnant women recruited from primary health care units from a tropical municipality in Brazil. Additionally, BMRm was compared with estimated BMR from internationally recommended PE for nonpregnant adolescents. BMR was measured by indirect calorimetry early in the morning in the fasting pregnantadolescents in a quiet, noise and luminosity controlled room. Anthropometry was measured byconventional research protocols and pre-pregnancy anthropometry was obtained from a questionnaire. Mean BMRm was 5447.3 (SD, 917.3) kJ/day. The PE provided biased (overestimation of 747.2±774.1kJ/day representing 15.7±17.6%) and inaccurate (approximately 40% fell within ±10% of BMRm). It is necessary to develop accurate estimates of BMR and energy requirements during pregnancy especially in adolescents who are still growing but already nurture a new human being.

Difference between Methods for Estimation of Basal Metabolic Rate and Body Composition in Pediatric Patients with Osteogenesis Imperfecta

Background/Aims: Osteogenesis Imperfecta (OI) is a bone disease characterized by bone fragility, deformities, and multiple fractures. The aim of this study was to compare the different methods of measuring the basal metabolic rate (BMR) and body composition (BC) in pediatric patients with OI. Methods: This cross-sectional study included 52 individuals with a median age of 9 (5.25–12.7) years. BMR was calculated by bioelectrical impedance analyses (BIA), predictive values according to age from the World Health Organization (WHO), a kcal/cm formula, and indirect calorimetry (IC). BC was assessed using the anthropometric calculation of percentage body fat (%BF) and lean mass (kg), BIA, and dual-energy X-ray absorptiometry (DEXA). Agreement among the methods was assessed using the Bland-Altman technique. Results: IC estimates of BMR were greater than BIA and lower than values obtained using the WHO and kcal/cm methods. Better agreement was observed using the WHO values for mild forms of OI and the kcal/cm formula for moderate-to-severe forms. For BC, DEXA estimates of %BF were higher and the lean mass was lower than the values obtained using BIA and anthropometry. Neither method agreed with the DEXA method results. Conclusions: Significant differences exist among the various methods used for measuring BMR and BC with regard to phenotypic differences between OI types.

Accuracy of basal metabolic rate estimated by predictive equations in Japanese with type 2 diabetes.

Estimation of energy demand using basal metabolic rate (BMR) is a rational approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). Here, we assessed the accuracy of predictive equations in estimating BMR in Japanese patients with T2DM. BMR was measured indirectly (BMRm) with a portable gas analyzer in the fasting state in 69 Japanese patients with T2DM. BMR was estimated using the Harris-Benedict equation (BMRhb) and Ganpule equation (BMRg). An original predictive equation (BMRdm) was formulated by stepwise multiple regression analysis using subject age, lean soft tissue mass, fat mass and bone mineral content. Mean differences and 95% limits of agreement between measured and three estimated BMRs were evaluated by Bland-Altman plots. In addition, subjects were divided into three BMI groups (normal, BMI <25; overweight, BMI >=25; obese, BMI >=30), and the influence of BMI on the error size between measured and estimated BMRs was assessed. Between BMRm and the three estimated BMRs (BMRhb, BMRg, and BMRdm), there were small systematic errors with large random errors (mean difference±2SD ; -32±365 kcal，26±405 kcal, and -1.6±349 kcal, respectively) and significant proportional errors (r=0.42, 0.44, and 0.30, respectively). BMI subgroup analysis revealed that the obese group showed larger random errors and significant proportional errors compared to the overweight and normal weight groups. Predictive equations provide unacceptably inaccurate estimates of BMR in Japanese patients with T2DM, particularly in obese individuals.

Novel approaches to the calculation and comparison of thermoregulatory parameters: Non-linear regression of metabolic rate and evaporative water loss in Australian rodents

The calculation and comparison of physiological characteristics of thermoregulation has provided insight into patterns of ecology and evolution for over half a century. Thermoregulation has typically been explored using linear techniques; I explore the application of non-linear scaling to more accurately calculate and compare characteristics and thresholds of thermoregulation, including the basal metabolic rate (BMR), peak metabolic rate (PMR) and the lower (Tlc) and upper (Tuc) critical limits to the thermo-neutral zone (TNZ) for Australian rodents. An exponentially-modified logistic function accurately characterised the response of metabolic rate to ambient temperature, while evaporative water loss was accurately characterised by a Michaelis-Menten function. When these functions were used to resolve unique parameters for the nine species studied here, the estimates of BMR and TNZ were consistent with the previously published estimates. The approach resolved differences in rates of metabolism and water loss between subfamilies of Australian rodents that haven’t been quantified before. I suggest that non-linear scaling is not only more effective than the established segmented linear techniques, but also is more objective. This approach may allow broader and more flexible comparison of characteristics of thermoregulation, but it needs testing with a broader array of taxa than those used here.

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

BackgroundMeasurement 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.MethodsSubjects were 121 men and 111 women (age: 21–67 years, BMI: 16–41 kg/m2). 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/m2).ResultsThe equation developed from our data was: BMR (kJ/d) = 52.6 x weight (kg) + 828 x gender + 1960 (women = 0, men = 1; R2 = 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 %.ConclusionsTo 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.

How do measurement duration and timing interact to influence estimation of basal physiological variables of a nocturnal rodent?

Metabolic rate and evaporative water loss are two commonly measured physiological variables. It is therefore important, especially for comparative studies, that these variables (and others) are measured under standardised conditions, of which a resting state during the inactive phase is part of the accepted criteria. Here we show how measurement duration and timing affect these criteria and impact on the estimation of basal metabolic rate (oxygen consumption and carbon dioxide production) and standard evaporative water loss of a small nocturnal rodent. Oxygen consumption, carbon dioxide production and evaporative water loss all decreased over the duration of an experiment. Random assortment of hourly values indicated that this was an animal rather than a random effect for up to 11h. Experimental start time also had a significant effect on measurement of physiological variables. A longer time period was required to achieve minimal carbon dioxide consumption and evaporative water loss when experiments commenced earlier in the day; however, experiments with earlier start times had a lower overall estimates of minimal oxygen consumption and carbon dioxide production. For this species, measurement duration of at least 8h, ideally commencing between before the inactive phase at 03:00h and 05:00h, is required to obtain minimal standard values for physiological variables. Up to 80% of recently published studies measuring basal metabolic rate and/or evaporative water loss of small nocturnal mammals may overestimate basal values due to insufficiently long measurement duration.

Variability of Resting Energy Expenditure in Infants and Young Children With Intestinal Failure–Associated Liver Disease

The aim of the study was to determine, in a cohort of young children with intestinal failure (IF), whether the estimates of basal metabolic rate (BMR) by standard equations can approximate measured resting energy expenditure (REE) by indirect calorimetry (IC). IC was performed using the dilutional canopy technique. REE measurements were compared with standard, age-based estimation equations (World Health Organization) for BMR. Subjects were classified as hypermetabolic (REE > 110% BMR), hypometabolic (REE < 90% BMR), or normal (REE = 90%-110% BMR). Twenty-eight IF patients (11 girls, 17 boys) had an underlying diagnosis of necrotizing enterocolitis (n = 10) or a congenital gastrointestinal defect (n = 18). Median age was 5.3 months. Median interquartile range (IQR) REE was 46 (42-58) kcal · kg · day. Median (IQR) total energy intake provided 209% (172%-257%) of REE, with parenteral nutrition providing 76% (23%) of total energy intake. REE was variable, with 39% (n = 11) of measurements hypermetabolic, 39% (n = 11) hypometabolic, and the remaining 21% (n = 6) normal. Although REE was well correlated with estimated BMR (r = 0.82, P < 0.0001), estimated BMR was not consistently an adequate predictor of REE. BMR over- or underestimated REE by >10 kcal · kg · day in 15 of 28 (54%) patients. REE was not significantly correlated with severity of liver disease, nutritional status, total energy intake, or gestational age. Energy expenditure is variable among children with IF and IF-associated liver disease, with approximately 80% of our cohort exhibiting either hypo- or hypermetabolism. Standard estimation equations frequently do not correctly predict individual REE. Longitudinal studies of energy expenditure and body composition may be needed to guide provision of nutrition regimens.

Prediction of basal metabolic rate in obese children and adolescents considering pubertal stages and anthropometric characteristics or body composition

To develop and crossvalidate new equations for predicting basal metabolic rate (BMR) in obese children and adolescents in relation to pubertal stages, anthropometric characteristics or body composition. A total of 1696 obese Caucasian children and adolescents (mean body mass index z-score: 3.5±0.8) participated in this study. BMR was determined by indirect calorimetry and fat-free mass (FFM) and fat mass (FM) by bioelectrical impedance analysis. Equations were derived by stepwise multiple regression analysis using a calibration cohort of 848 subjects, and the equations were crossvalidated with a Bland and Altman method in the remaining 848 subjects. Two new specific equations based on gender (1: males; 0: females), pubertal stages (from 1 to 5, assessed according Marshall & Tanner methods) and body weight (BW, kg), stature (m) or body composition (kg) were generated as follows: (1) BMR=(BW × 0.044)+(stature × 2.836)-(pubertal stage × 0.148)+(gender × 0.781)-0.551 (adjusted coefficient of determination (R(2)adj)= 0.69 and root mean squared error (RMSE)=0.954 MJ); (2) BMR=(FFM × 0.082)+(FM × 0.037)-(pubertal stage × 0.125)+(gender × 0.706)+2.528 (R(2)adj= 0.70 and RMSE=0.943 MJ). In the crossvalidation group, mean-predicted BMR was not significantly different from the mean-measured BMR (MBMR) for all children and adolescents, as well as for boys and girls (difference<2 %), and the limits of agreement (±2 s.d.) were +1.95 and -1.98 MJ/d, (P=NS). BMR was predicted accurately (90-110% of MBMR) in 67% of subjects. The new prediction equations considering the pubertal stages allow an accurate and more appropriate (vs equations using chronological age) estimation of BMR in obese children and adolescents.

Development of new equations for basal metabolic rate for adolescent student Indian population

Calculation of daily calorie needs is extremely essential in several aspects of public health nutrition. To check the applicability of the existing equations for the prediction of basal metabolic rate (BMR) for Indian adolescent population and to develop an appropriate equation for the estimation of BMR for Indian adolescent population. BMR was assessed in 152 healthy, adolescent student aged between 18 and 20 years. BMR is calculated from the measured skinfold parameters. Body density was determined by the equation suggested by Durnin and Wormley using the skinfold parameters (triceps, subscapula, biceps, and SIM). Siri's equation is employed for calculating the percentage of body fat from the body density. Eventually, the BMR is calculated using Cunningham's equation. The actual BMR's were compared with values obtained from published prediction equations that used solely, or in various combinations, measures of height, weight, and age. The equations suggested in the literature (Henry, Schofield, and Cole) are not able to predict the BMRs for Indian adolescent population. Hence, a new equation involving weight of an individual is suggested for Indian adolescent population. There is a need for generation of appropriate BMR prediction equations for Indian population for various age groups.

Effect of Sampling Regime on Estimation of Basal Metabolic Rate and Standard Evaporative Water Loss Using Flow‐Through Respirometry

Strict criteria have been established for measurement of basal metabolic rate and standard evaporative water loss to ensure that data can be compared intra- and interspecifically. However, data-sampling regimes vary, from essentially continuous sampling to interrupted (switching) systems with data recorded periodically at more widely spaced intervals. Here we compare one continuous and three interrupted sampling regimes to determine whether sampling regime has a significant effect on estimation of basal metabolic rate or standard evaporative water loss. Compared to continuous 20-s sampling averaged over 20 min, sampling every 6 min and averaging over 60 min overestimated basal metabolic rate and evaporative water loss, sampling every 3 min and averaging over 21 min underestimated basal metabolic rate, and sampling every 12 min and averaging over 36 min showed no difference in estimates. Increasing the period over which the minimum mean was calculated significantly increased estimates of physiological variables. Reducing the frequency of sampling from 20 s to a longer interval of 3, 6, or 12 min underestimated basal metabolic rate but not evaporative water loss. This indicates that sampling frequency per se influences estimates of basal metabolic rate and that differences are not just an artifact of differences in the period over which the mean is calculated. Sampling regime can have a highly significant influence on estimation of standard physiological variables, although the actual differences between sampling regimes were generally small (usually <5%). Although continuous sampling is the preferred sampling regime for open-flow respirometry studies, if time and cost are prohibitive, then use of an appropriate switching system will result in smaller errors than measuring individuals continuously for shorter periods.

Climate variables as predictors of basal metabolic rate: New equations

Estimation of basal metabolic rate (BMR) and daily energy expenditure (DEE) in living humans and in fossil hominins can be used to understand the way populations adapt to different environmental and nutritional circumstances. One variable that should be considered in such estimates is climate, which may influence between-population variation in BMR. Overall, populations living in warmer climates tend to have lower BMR than those living in colder climates, even after controlling for body size and composition. Current methods of estimating BMR ignore climate, or deal with its effects in an insufficient manner. This may affect studies that use the factorial method to estimate DEE from BMR, when BMR is not measured but predicted using an equation. The present meta-analysis of published BMR uses stepwise regression to investigate whether the inclusion of climate variables can produce a generally applicable model for human BMR. Regression results show that mean annual temperature and high heat index temperature have a significant effect on BMR, along with body size, age and sex. Based on the regression analysis, equations predicting BMR from body size and climate variables were derived and compared with existing equations. The new equations are generally more accurate and more consistent across climates than the older ones. Estimates of DEE in living and fossil humans using the new equations are compared with estimates using previously published equations, illustrating the utility of including climate variables in estimates of BMR. The new equations derived here may prove useful for future studies of human energy expenditure.

Tropical birds have a slow pace of life.

Tropical birds are relatively long-lived and produce few offspring, which develop slowly and mature relatively late in life, the slow end of the life-history axis, whereas temperate birds lie at the opposite end of this continuum. We tested the hypothesis that tropical birds have evolved a reduced basal metabolic rate (BMR). We measured BMR of 69 species of tropical birds, the largest data set amassed on metabolic rates of tropical birds, and compared these measurements with 59 estimates of BMR for temperate birds. Our analyses included conventional least squares regression, regressions based on phylogenetic independent contrasts, and a comparison of BMR of 13 phylogenetically matched pairs, one species from the tropics and one from northerly temperate areas. Our triptych showed that tropical birds had a reduced BMR, compelling evidence for a connection between the life history of tropical birds and a slow pace of life. Further, tropical migrants breeding in temperate habitats had a lower BMR than did temperate residents, suggesting that these migrants have physiological traits consistent with a slow pace of life. In addition, we determined that tropical birds had a lower cold-induced peak metabolic rate and thermogenic metabolic scope than temperate species, a finding that is consistent with the hypothesis that their environment has not selected for high levels of thermogenesis, or alternatively, that a slow pace of life may be incompatible with high thermogenic capacity. We conclude that physiological function correlates with the suite of life-history traits.

Total energy expenditure in underweight patients with severe chronic obstructive pulmonary disease living at home

Aim: The aim of the study was to assess total daily energy expenditure (TDE), as measured by doubly labelled water (DLW), and describe its components in home-living underweight patients with severe chronic obstructive pulmonary disease (COPD). Methods: Basal metabolic rate (BMR) was measured prior to the study. Ten patients received DLW, followed by urine analysis by isotope ratio mass spectrometry from 10 standardised occasions during 15 days. Dietary intake was registered by each patient the first 7 days of the study. The patients were also interviewed about their physical activity pattern. Results: Measured BMR was higher than predicted in five of the 10 patients using equations from WHO. Using disease-specific equations, estimated BMR was higher for male, but not for female COPD patients. The best estimation of BMR resulted from prediction including fat-free mass. TDE varied considerably between 5200 and 11,100 kJ. Physical activity level (PAL) ranged from 1.15 to 1.80. Energy intake varied between 4500 and 9100 kJ. In underweight patients with severe COPD, TDE is highly variable, ranging from 110 to 200 kJ/kg body weight. Conclusions: This is the first study assessing and describing total energy expenditure in underweight patients with severe COPD living at home. Energy requirement in the patient group cannot solely be calculated from prediction equations. BMR should be measured and physical activity level assessed.

Sociodemographic and nutritional determinants of obesity in Belgium.

To examine associations between sociodemographic, dietary factors and physical activity and the prevalence of obesity in the Belgian general population. Base-line data from the Belgian Interuniversity Research on Nutrition and Health (BIRNH) study (1979-1984). A total of 5837 men and 5243 women aged 25-74y were included in the analysis. The sample was considered representative of the Belgian population. Using the body mass index (BMI) as the criterion, obesity was defined as a BMI> or =30 kg/m2. Nutritional data were assessed by a 24 h food record. Physical activity level (PAL) was calculated by dividing total caloric intake by an estimation of basal metabolic rate (BMR, predicted from gender, weight and age). Age-adjusted odds ratios (OR) of the prevalence of obesity were estimated by multilogistic regression analysis. Prevalence of obesity was 12.1% in men and 18.4% in women. In both sexes, prevalence of obesity increased gradually in each 10-year age category (P<0.0001) and steeply decreased with level of education. Comparing lowest (Q1) to highest quartile (Q4), after adjustment for age, prevalence of obesity decreased with total carbohydrate intake, as well as total sugar intake in both sexes, and increased with total fat intake only in men. Obesity increased with a high fat to sugar ratio (men: OR(Q4/Q1) = 1.56; confidence interval (CI): 1.25-1.93; women: OR(Q4/Q1) = 1.45; CI: 1.17-1.80). PAL was inversely and very strongly associated with obesity (men: OR(Q4/Q1) = 0.20; CI: 0.15-0.26; women: OR(Q4/Q1) = 0.18; CI: 0.14-0.23). The same associations were observed with the mean BMI. This study indicates that prevalence of obesity is particularly high in Belgium. Low level of education and reduced physical activity, increased fat intake and especially elevated fat to sugar ratio appear to be powerful determinants of obesity in this Belgian population.

New equations to estimate basal metabolic rate in children aged 10-15 years.

To develop new equations for the estimation of basal metabolic rate in children aged 10-15 years, and to evaluate the impact of including pubertal stage into the equations. Mixed longitudinal. The children were recruited from schools in Oxford, and the measurements were made in the schools. 195 school children, aged 10-15 years, were recruited in three cohorts. The gender distribution of the subjects was 40% boys and 60% girls. Basal metabolic rate (BMR) was measured, by indirect calorimetry, at 6-monthly intervals for 3 years. Anthropometric data, height, weight, body breadths and skinfold measurements (biceps, triceps, subscapular, suprailiac and medial calf) were collected on each occasion. Fat and fat-free mass was calculated from the skinfold measurements. Pubertal development was also assessed on annually by paediatricians. Pubic hair (PH) and gonad (G) development was assessed in boys and breast (B) development in girls. The girls were questioned about menarche. Stepwise multiple regression analysis was used to develop and assess new formulae for BMR that also incorporate pubertal development. The mean BMR measured was 5.754 (s.d. 0.933) MJ/day (138 (s.d. 22) kJ/kg body wt/day) in the boys (n = 351) and 5.476 (s.d. 0.725) MJ/day (121 (s.d. 20) kJ/kg body wt/day) in the girls (n = 554). Weight was the most important factor in developing the regression equations for the calculation of BMR in both sexes (R2 = 0.61 and 0.52 for boys and girls, respectively). Stepwise multiple regression analyses, with independent variables such as gender, weight, height, puberty stage and skinfolds, allowed several BMR regression equations to be developed. The inclusion of the menarche status in the regression equations significantly (P < 0.05) improved BMR estimation in the pre-menarche girls. Boys, pubertal stage as assessed by Pubic Hair (PH) and Gonadal Stage (G) did not contribute to a significant improvement in BMR estimation, except for 11-year-olds. The inclusion of pubertal stage afforded only minor improvements in the derivation of regression equations for the estimation of BMR of children aged between 10 and 15 years.

An assessment of the use of simple methods to predict individual energy intakes for intervention studies.

To investigate to what extent individual energy intakes can be predicted by rapid easily available low-cost estimation methods. Data were obtained from a controlled dietary intervention study period of nine weeks in which the subjects should be weight stable. Thirty-one male students in domestic and kitchen management aged 29 +/- 6 y. (1) energy intake calculated from a quantitative food frequency questionnaire (FFQEI); (2) energy expenditure derived from estimates of basal metabolic rate (BMR) (FAO/WHO/UNU, 1985) based on weight, gender, age and low (1.55 x BMR), medium (1.78 x BMR) or high (2.10 x BMR) level of activity. Level of activity was determined by questions concerning habitual activities lasting more than 20 min (WHOEE); (3) energy expenditure derived from individual recording in a specially prepared activity diary (ADEE). During the intervention, the subjects were to be fed test diets which should provide them with enough energy to keep them weight stable. The energy levels were established after taking both the FFQEIs, WHOEEs and ADEEs into consideration, and 10 MJ, 13 MJ, 15 MJ and 17 MJ per day were chosen because these levels were estimated to closely match the energy requirements of most of the subjects. The levels of energy were changed during the intervention period if the weight of the subjects fluctuated. The served level of energy at the last day of the intervention was denoted the weight maintenance energy intake (WMEI). WMEI was compared to FFQEI, WHOEE and ADEE in order to evaluate if one estimation method predicted WMEI better than the two others. None of the three methods provided accurate estimates of WMEI of 13.3 +/- 1.8 MJ. However, WHOEE, gave the best estimate as demonstrated by the limits of agreement: -8.7 MJ to +8.9 MJ for FFQEI, -5.4 MJ to +3.9 MJ for WHOEE and -7.2 MJ to +5.2 MJ for ADEE. The coefficients of correlation between the differences and the means of WMEI and FFQEI, WHOEE and ADEE were -0.8 (P < or = 0.001), 0.1 (P = 0.6, NS) and -0.5 (P < or = 0.01), respectively. The coefficients of variation were 34.6% for FFQEI, 11.3% for WHOEE, and 21.0% for ADEE. Although not precise, WHOEE showed the best agreement with the WMEI. These results demonstrate that a rapid and simple low-cost method predicted WMEI closely enough to avoid major weight fluctuations among these men during the intervention period.

Are basal metabolic rate prediction equations appropriate for female children and adolescents?

The basal metabolic rate (BMR), which accounts for 50-70% of total energy expenditure, is essential for estimation of patient and population energy needs. Numerous equations have been formulated for prediction of human BMR. Most equations in current use are based on measurements of Caucasians performed more than four decades ago. We evaluated 10 prediction equations commonly used for estimation of BMR in 76 Caucasian and 42 African-American girls between 8 and 17 yr of age against BMR measured by whole-body calorimetry. The majority of the prediction equations (9 of 10) overestimated BMR by 60 +/- 46 kcal/day (range, 15-176 kcal/day). This overestimation was found to be significantly greater (P < 0.05) for African-American (77 +/- 17 kcal/day) than for Caucasians (25 +/- 17 kcal/day) in six equations, controlling for age, weight, and sexual maturity. We conclude that ethnicity is an important factor in estimation of the BMR and that the current prediction equations are not appropriate for accurate estimation of the BMR of individual female children and adolescents.

Energy expenditures of urban Colombian girls and women.

Anthropometry, basal metabolic rate (BMR), and resting energy expenditure (RMR) measured by indirect calorimetry and total daily energy expenditure (TDEE) by the minute-by-minute heart rate (HR) method have been studied in 52 school-aged girls 6-16 years and 46 nonpregnant, nonlactating women 19-43 years of age. BMR, RMR, and TDEE increased with age in children, reaching approximate adult values by 15 years. TDEE averaged 9.29 ± 2.32 MJ/d in women 19-43 years. The ratio RMR/BMR averaged 1.27 ± 0.29 across all age groups. TDEE/BMR varied from 1.41 ± 0.28 to 1.62 ± 0.37 in the girls and averaged 1.78 ± 0.43 in adults. Application of empirical equations to estimates of BMR showed that those of Schofield gave values of +3.1% (P = 0.03) and of Henry and Rees -3.8% (P = 0.052) of measured BMR (Schofield [1985] Hum Nutr Clin Nutr 39C [Suppl 1]:5-41; Henry and Rees [1991] Eur J Clin Nutr 45:177-185). The pattern of daily energy expenditure is highly variable across age groups during the awake portion of the day (0700-2000 hours). In the averaged data of women at home, there is a gradual increase in the morning, a decline at noon, followed by some increase in the early afternoon and decline in late afternoon and early evening. © 1996 Wiley-Liss, Inc.