Prediction Of Basal Metabolic Rate Research Articles

Resting energy expenditure during spinal cord injury rehabilitation and utility of fat-free mass-based energy prediction equations: a pilot study

Study designLongitudinal observational study. Measurements were undertaken between weeks 4–6 post-spinal cord injury (SCI), repeated at week 8 and every 4 weeks thereafter until week 20 or rehabilitation discharge, whichever occurred first.ObjectivesObserve variation in measured resting energy expenditure (REE) and body composition in males undergoing SCI rehabilitation, compare REE with SCI-specific prediction equations incorporating fat-free mass (FFM), and explore the prevalence of clinical factors that may influence individual REE.SettingSpinal Injuries Unit, Brisbane, Queensland, Australia.MethodsIndirect calorimetry was used to measure REE and bioimpedance spectroscopy to assess body composition. Four SCI-specific FFM-based REE and basal metabolic rate (BMR) prediction equations were compared to measured REE. A clinically significant change in REE was defined as +/− 10% difference from the week 4–6 measurement. Clinical factors that may affect REE variations were collected including infection, pressure injuries, autonomic dysreflexia, spasticity, and medications.ResultsFifteen people participated (mean age 35 ± 13 years, 67% paraplegic). There was no statistically significant change in mean REE, weight, or body composition, and the Chun and Nightingale BMR prediction equations performed best (rc > 0.8 at all time points). One-third of participants had >10% change in REE on 11 occasions, with clinical factors not consistently associated with the observed changes.ConclusionDuring SCI rehabilitation, mean REE, weight, and body composition remain unchanged, and FFM-based BMR prediction equations may be an acceptable alternative to indirect calorimetry. Future research designs should avoid single indirect calorimetry measures as snapshot data may not represent typical REE in this population.

Predictive Equation for Basal Metabolic Rate in Normal-Weight Chinese Adults.

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland-Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland-Altman plots were -514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

Basal metabolic rate for high-performance female karate athletes.

Introduction: karate is a millennial martial art, currently inserted in the context of Olympic Combat Sports. However, important scientific gaps still persist in monitoring high-performance athletes, including the basal metabolism measurement of female karate athletes. Aim: to contribute to understanding the applicability of equations for predicting basal metabolic rate in this population. Methods: this is a cross-sectional study with a retro-analytical component, in which data were obtained from the medical records of seven athletes participating in the project "Karate São Paulo Olímpico" (São Paulo Olympic Karate) (KSPO) during their nutrition counseling, including body composition and indirect calorimetry testing, with the aim of comparing these data to basal metabolic rate prediction equations. Results: only one out of the five evaluated equations did not have a significant statistical difference relative to the value obtained by open-circuit indirect calorimetry. Conclusion: in case basal metabolism cannot be measured through standard methodology (calorimetry), Cunningham's prediction equation (1980) would be appropriate to obtain total energy expenditure for high-performance female karate athletes.

Evaluating The Accuracy Of Basal Metabolic Rate Prediction Equations For Masters Athletes

Basal metabolic rate (BMR) is the energy required to perform basic metabolic functions at rest. Indirect calorimetry is one method of measuring BMR, but it can also be predicted using BMR prediction equations. Depending on the equation used, a prediction can take into account a combination of different factors (e.g., age, body weight, height, lean body mass, fat mass). These equations tend to overestimate BMR. However, most of the researchers relied on small sample sizes or specific populations. PURPOSE: To determine the accuracy of BMR prediction equations for Masters Athletes. METHODS: Two hundred and eighty-five athletes (157 women, 128 men; ≥ 26 years of age) representing a number of sports and physical activities, who exercised at least twice a week and were non-smokers, participated in our cross-sectional study. Resting metabolic rate (RMR) was measured using indirect calorimetry. Prior to measuring RMR, participants fasted and refrained from caffeine for 12 hours, avoided alcohol and exercise for 24 hours, and rested in a recliner for 15 minutes prior to data collection. Body composition was determined using dual-energy X-ray absorptiometry (DXA). We compared the Mifflin-St. Jeor, Harris-Benedict, Cunningham, and Owen to indirect calorimetry using one-way analysis of variance (ANOVA) with repeated measures. RESULTS: We found that indirect calorimetry measured significantly lower (p < 0.001) kilocalories (kcal) compared to any of the four prediction equations. The Mifflin-St. Jeor equation was more closely aligned with indirect calorimetry for all participants combined and women (mean difference, percent accuracy: 115, 54.8%; 95, 57.2%, respectively) compared to indirect calorimetry. However, the Owen equation was more accurate for men (98, 61.3%). The Cunningham equation generated the highest mean difference and lowest percent accuracy for all participants combined, women, and men (234, 29.1%; 221, 23.6%; 250, 35.9%, respectively). CONCLUSIONS: We found that the four BMR prediction equations analyzed significantly over-predicted BMR compared to indirect calorimetry. Our results emphasize the need for a more appropriate BMR prediction equation for athletes. This study was not funded

Obesity in adolescence-from etiological variability to interventional efficacy in the school context

Introduction: Our paper concern two fundamental pillars to face obesity in adolescence- nutrition education and the phenomenon of human variability, namely in the biological and behavior dimension. Objectives: Assessment of the impact of an innovate school-based nutrition education program (OBESIDATA); the multivariate study of the anthropometric profile and physical activity pattern of adolescents in Portugal; comparison between energy consumption and their energy needs; Evaluation of the accuracy of four commonly used basal metabolic rate prediction equations. Results: After a two weeks period of open interactive digital platform, 85.8% of students increased their nutritional knowledge; the overall prevalence of overweight and obesity in adolescents was 16.5% and 5.9%, respectively; just 38% of participants reported a level of physical activity in line with WHO recommendations; the RMR mean differences of six identified anthropometrics clusters vary from 1349 Kcalories to 1955 Kcalories; the mean reported energy intake was lower than the estimated energy requirements; from all predictive equations estimating energy requirements the relative accuracy, using indirect calorimetric evaluations, is just about 17%. Conclusions: School-based nutrition using technology may provide a practical, attractive and cost- effective strategy to improve nutrition knowledge and eating behaviors; the adolescent’s prevalence of overweight/obesity remains relatively high and physical activity was clear below the WHO recommendations; at average level adolescents REI seems to be lower than their EER, but REI reveals a great individual variability; the accuracy of RMR using predictive equations may have limited applicability; a new paradigma to prevent obesity is coming where genetic will have soon a practical central role.1,2

Simple Skeletal Muscle Mass Estimation Formulas: What We Can Learn From Them.

One century ago Harris and Benedict published a short report critically examining the relations between body size, body shape, age, and basal metabolic rate. At the time, basal metabolic rate was a vital measurement in diagnosing diseases such as hypothyroidism. Their conclusions and basal metabolic rate prediction formulas still resonate today. Using the Harris-Benedict approach as a template, we systematically examined the relations between body size, body shape, age, and skeletal muscle mass (SM), the main anatomic feature of sarcopenia. The sample consisted of 12,330 non-Hispanic (NH) white and NH black participants in the US National Health and Nutrition Survey who had complete weight, height, waist circumference, age, and dual-energy X-ray (DXA) absorptiometry data. A conversion formula was used to derive SM from DXA-measured appendicular lean soft tissue mass. Weight, height, waist circumference, and age alone and in combination were significantly correlated with SM (all, p < 0.001). Advancing analyses through the aforementioned sequence of predictor variables allowed us to establish how at the anatomic level these body size, body shape, and age measures relate to SM much in the same way the Harris-Benedict equations provide insights into the structural origins of basal heat production. Our composite series of SM prediction equations should prove useful in modeling efforts and in generating hypotheses aimed at understanding how SM relates to body size and shape across the adult lifespan.

Validity of Predictive Equations for Metabolic Basal Rate in Brazilian Patients with Type 2 Diabetes (P12-039-19)

ObjectivesTo evaluate in Brazilian patients with type 2 diabetes which of the seven predictive equations selected to estimate basal metabolic rate (BMR) is the best alternative considering calorimetry indirect (CI) as a reference method. MethodsA cross-sectional study was conducted with 62 patients (31 men and 31 women) with type 2 diabetes. Clinical and laboratorial variables were evaluated as well as body composition by electrical bioimpedance. The BMR was measured by IC (Analisador MedGraphics Cardiorespiratory Diagnostic Systems, model CCM Express®) and estimated by prediction equations. Dietary intake was evaluated by a food frequency questionnaire (FFQ). Data were analyzed using Bland–Altman plots, paired t-tests, and Pearson’s correlation coefficients. ResultsOur patients were aged 63.1 ± 5.2 years, had diabetes duration of 11 (1–36) years and A1C test 7.6 ± 1.2%. The body composition contained a fat free mass of 35.2 ± 11.8 kg and a fat mass of 29.1 ± 8.8 kg. The energy intake by FFQ was 1826.9 ± 628.1 Kcal/day and the BMR by IC was 1644.6 ± 310.6 kcal/day. There was a wide variation in the accuracy of BMR values predicted by equations when compared to IC BMR measurement. FAO/WHO/UNO and Oxford equations produced the smallest differences to IC. For women, the FAO/WHO/UNO equation provided the best BMR prediction in comparison to measured BMR (–1.8% of the difference). For men, the equation of the Oxford was the one closest to the BMR values as measured by IC (–1.3% of the difference). ConclusionsIn Brazilian patients with type 2 diabetes, the predictive equations by FAO/WHO/UNO (for women) and Oxford (for men) were the best to estimate BMR. Funding SourcesFundação do Incentivo à Pesquisa (FIPE) of Hospital de Clinicas de Porto Alegre (#protocol 15.0625) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

Validity of basal metabolic rate prediction equations in elderly women living in an urban tropical city of Brazil

To measure basal metabolic rate (BMR) and to compare it with the values obtained from predictive equations in a sample of elderly (≥60y) women. Cross-sectional study. Seventy-nine women living in Niterói, Rio de Janeiro, Brazil enrolled in physical activity programs open to the community. Anthropometric measures were obtained using standard procedures. Percent body fat (PBF) was assessed by DXA. BMR was measured (BMRm) by indirect calorimetry under standardized conditions and compared with BMR estimated by 14 predictive equations that included elderly individuals in their development. Mean (±SD) age, BMI and PBF were 69.7±6.5y, 27.2±4.6 kg/m2 and 42.1±5.9%. BMRm (4188.3±707.2kJ/day) was significantly lower than estimated BMR by all predictive equations, including the equation developed for the Niteroian adult population (4565.6±607.9kJ/day). This population-specific equation provided the largest number of results within ±10% of BMRm and the lowest overestimation (10.6±15.4%), much lower than the results from the internationally recommended Schofield equation (27.2±17.6%). Regression of calf circumference (CC), age and body mass on BMRm provided similar estimates in comparison to models with fat-free mass (FFM). All predictive equations provided biased, inaccurate estimates of BMR values in comparison to BMRm. Anthropometry and body composition explained only approximately 50% of the variability of BMRm. New equations should account for the variability of organ-metabolic rates and underlying undetected health conditions in older individuals living in tropical regions.

Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetes or Type 2 Diabetes Mellitus.

ObjectivesTo compare mean basal metabolic rate (BMR) estimated using Harris-Benedict equation (HB) and Bioelectrical Impedance Analysis (BIA) and the BMR measured using Indirect Calorimetry (IC) among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus (T2DM).MethodologyThis was a multi-center, cross-sectional study based on review of outpatient medical records of adult, obese Filipino patients with pre-diabetes or type 2 diabetes mellitus who were seen prior to weight loss intervention at the Outpatient Clinic of St. Luke’s Medical Center-Quezon City and the Metabolic and Diabetes Center of Providence Hospital from August 2017 to January 2018. BMR was derived using three methods: Harris-Benedict equation, Bioelectrical Impedance Analysis and Indirect Calorimetry.ResultsA total of 153 subjects were included in the study. Eighty subjects (52%) have pre-diabetes while 73 subjects (48%) were diagnosed with T2DM. The mean BMR measured using IC is 1299±252 kcal/day while estimated mean BMR predicted using HB equation and BIA were 1628±251 kcal/day and 1635±260 kcal/day, respectively. Compared to measurement by IC, HBE and BIA significantly overestimated the mean BMR by 329 and 336 kcal/day, respectively (p-value=<0.0001). IC measured BMR showed strong positive correlation with weight and moderate positive correlation with height. Multiple stepwise regression analysis yielded the BMR prediction equation: BMR (kcal/day) = -780.806 + (11.108 x weight in kg) + (7.164 x height in cm).ConclusionAmong obese Filipinos with T2DM or prediabetes, HB equation and BIA tend to overestimate the BMR measured using IC.

Predicting Basal Metabolic Rate in Men with Motor Complete Spinal Cord Injury

This study aimed to assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (>1 yr) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition, and/or demographic variables that are strongly associated with BMR. Thirty men with chronic SCI (paraplegic, n = 21, tetraplegic, n = 9) 35 ± 11 yr old (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual-energy x-ray absorptiometry) and anthropometric measurements (circumferences and diameters) were also taken. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. Criterion BMR values were compared with values estimated from six existing and four developed prediction equations. Existing equations that use information on stature, weight, and/or age significantly (P < 0.001) overpredicted measured BMR by a mean of 14%-17% (187-234 kcal·d). Equations that used fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height, and calf circumference) to FFM (model 3; r = 0.77), explained 8% more of the variance in BMR than FFM alone (model 1; r = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (model 4; r = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤6%. BMR can be more accurately estimated when dual-energy x-ray absorptiometry-derived FFM is incorporated into prediction equations. Using anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.

Predicting Basal Metabolic Rate After Spinal Cord Injury

PURPOSE: To assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (> 1 year) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition and/or demographic variables that are strongly associated with BMR. METHODS: Thirty men with chronic SCI (Paraplegic; n = 21, Tetraplegic; n = 9), aged 35 ± 11 years (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual energy X-ray absorptiometry; DXA) and anthropometric measurements (circumferences and diameters) were also taken. Criterion BMR values were compared to values estimated from six commonly used prediction equations. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. RESULTS: Existing equations that use information on stature, weight and/or age, significantly (P < 0.001) over-predicted measured BMR by a mean of 14-17% (187-234 kcal/day). Equations that utilised fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height and calf circumference) to FFM (Model 3; r2 = 0.77), explained 8% more of the variance in BMR than FFM alone (Model 1; r2 = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (Model 4; r2 = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤ 6%. CONCLUSIONS: BMR can be more accurately estimated when DXA derived FFM is incorporated into prediction equations. Utilising anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.

Estimating the Basal metabolic rate from fat free mass in individuals with motor complete spinal cord injury.

Cross-sectional study. This study aimed to validate the existing basal metabolic rate (BMR) predictive equations that include fat free mass (FFM) as an independent variable and, based on the FFM assessment, to develop a new SCI population-specific equation. Outpatient clinic in a general hospital. Our study group was formed of 50 individuals with chronic motor complete SCI: 27 patients with tetraplegia and 23 with paraplegia. Both BMR and FFM values were measured by indirect calorimetry (IC) and the whole-body dual energy X-ray absorptiometry, respectively. The BMR values measured by IC were compared with the values estimated from the Cunningham equation. Multiple linear regression analysis was performed to develop a new FFM-based, BMR predictive equation. The mean value of BMR measured by IC was 1274.8 (s.d.=235.2) kcal day-1. The intra-class correlation coefficient (ICC) between values measured by IC and estimated from the Cunningham equation was 0.845 and the limits of agreement ranged from -30.6 to 241.3 kcal. SCI population specific BMR predictive equation was developed; BMR (kcal day-1)=24.5 × FFM (kg)+244.4. The newly developed equation showed ICC of 0.866 with the limits of agreement from -229.0 to 233.1 kcal day-1. A considerable bias from the BMR values measured by IC was still observed, which warrants clinical consideration when applying FFM-based BMR prediction equations to individuals with SCI.

Can We Rely on Predicted Basal Metabolic Rate in Patients With Intestinal Failure on Home Parenteral Nutrition?

Intestinal failure (IF) is a serious and common complication of short bowel syndrome with patients depending on parenteral nutrition (PN) support. Effective nutrition management requires an accurate estimation of the patient's basal metabolic rate (BMR) to avoid underfeeding or overfeeding. However, indirect calorimetry, considered the gold standard for BMR assessment, is a time- and resource-consuming procedure. Consequently, several equations for prediction of BMR have been developed in different settings, but their accuracy in patients with IF are yet to be investigated. We evaluated the accuracy of predicted BMR in clinically stable patients with IF dependent on home parenteral nutrition (HPN). In total, 103 patients with IF were included. We used indirect calorimetry for assessment of BMR and calculated predicted BMR using different equations based on anthropometric and/or bioelectrical impedance parameters. The accuracy of predicted BMR was evaluated using Bland-Altman analysis with measured BMR as the gold standard. The average measured BMR was 1272 ± 245 kcal/d. The most accurate estimations of BMR were obtained using the Harris-Benedict equation (mean bias, 14 kcal/d [ P = .28]; limits of agreement [LoA], -238 to 266 kcal/d) and the Johnstone equation (mean bias, -16 kcal/d [ P = .24]; LoA, -285 to 253 kcal/d). For both equations, 67% of patients had a predicted BMR from 90%-110% All other equations demonstrated a statistically and clinically significant difference between measured and predicted BMR. The Harris-Benedict and Johnstone equations reliably predict BMR in two-thirds of clinically stable patients with IF on HPN.

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.

Prediction of basal metabolic rate in patients with Prader-Willi syndrome.

The objective of this study was to develop new equations for predicting basal metabolic rate (BMR) in Prader-Willi syndrome (PWS) subjects and to compare their accuracy with commonly used equations developed by Lazzer (2007), Livingston (2005), Huang (2004), Nelson (1992), Mifflin (1990), Owen (1987), WHO (1985), Bernstein (1983) and Harris-Benedict (1919), using the Bland-Altman method. BMR was measured by indirect calorimetry and fat-free mass (FFM) and fat mass (FM) by a tetrapolar impedancemeter in 80 Caucasian PWS patients (mean body mass index: 39.1 kg/m(2); 17-50 years). Equations were derived by stepwise multiple regression analysis using a calibration group (n:50) and tested against the validation group (n:30). Two new equations, based on anthropometric (BMR=body mass × 0.052+sex × 0.778-age × 0.033+2.839 (R(2)adj=0.61, s.e.=0.89 MJ per day)) or body composition (BMR=FFMx0.074+FMx0.042+sexx0.636-agex0.037+2.515 (R(2)adj=0.69, s.e.=0.82 MJ per day)), were generated. Predicted BMR (PBMR) was not significantly different from the measured BMR (<3.3%), and was accurate in 59% and 62% of patients, respectively. Nevertheless, significant magnitude bias was found for both equations (P<0.001, R(2)=0.36). The Owen (1987), Mifflin (1990), Huang (2004) and Lazzer (2007) equations showed mean differences <5% and PBMR was accurate in ~50% of patients. The Livingston (2005), WHO (1985) and Harris-Benedict (1919) equations showed a PBMR overestimation >7% and were accurate in <50% of patients. The Nelson (1992) and Bernstein (1983) equations showed a greater PBMR underestimation in >60% of subjects. The new prediction equations showed significantly higher accuracy compared with equations tested, with exception of Lazzer (2007) and Livingston (2005) equations, and result in lower mean differences and lower limits of agreement compared with the equations tested.

Prediction of basal metabolic rate in overweight/obese and non-obese subjects and its relation to pulmonary function tests.

BackgroundFew studies investigated the association between basal metabolic rate (BMR) and indicators of pulmonary function. This study was conducted to estimate BMR in overweight/obese and non-obese healthy subjects using four commonly used predictive equations and to investigate its relation to the indicators of lung function tests (LFT). A cross sectional study was conducted in Tabuk University, Tabuk, Saudi Arabia. A total of 201 students (98 males and 103 females) participated in the study. Four different values of BMR were calculated for each participant using four different predictive equations (Harris-Benedict, Mifflin, FAO/WHO/UNU and Henry-Rees). A portable All-flow spirometer (Clement Clarke International, Harlow, UK) was used for measurements of LFT.ResultsSignificantly higher values of spirometric indicators (p < 0.05) were found in males compared to females, except for FEF75 and FEF75-85. Mean BMR values predicted with the four equations were significantly higher in the males compared to the females and among the overweight/obese compared to the non-obese subjects (p < 0.05). The relation between mean BMR values and the indicators of LFT was statistically insignificant (p > 0.05).ConclusionMean values of LFT indicators are not related to the estimated values of BMR. A practical calculation of BMR based on direct measurement of oxygen consumption is recommended to confirm the absence of this association.

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.

Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations.

Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight.

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.

Basal Metabolic Rate and Body Composition in Patients with Post-Polio Syndrome

The aim of this study was to compare basal metabolic rate (BMR) of post-polio syndrome (PPS) patients with healthy individuals and to determine its correlation to body composition. BMR (kcal/day) was determined by indirect calorimetry and body composition by dual energy X-ray absorptiometry. BMR was lower in the PPS patient group than in the control group, although it was similar in both groups when adjusted for body surface area, total body mass (TBM), lean body mass (LBM) and fat-free mass (FFM). PPS patients also showed reduced TBM, LBM and FFM in relation to controls. As muscle energy expenditure while at rest contributes only 20% to the BMR, a proportional reduction in BMR and FFM or LBM could suggest that muscle mass or other factors may interfere more than predicted. It was concluded that the prediction of BMR from the Harris-Benedict equation in PPS patients must be carefully reviewed.