Validity of predictive equations for total energy expenditure against doubly labeled water.

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Evaluation of Effective Energy Values of Six Protein Ingredients Fed to Beagles and Predictive Energy Equations for Protein Feedstuff.

This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results showed that the apparent total tract digestibility of gross energy (GE) and organic matter (OM) in beagles fed the fish meal (FM) and corn gluten meal (CGM) diets was higher than for those fed the meat and bone meal (MBM), soybean meal (SBM), mealworm meal (MM), and yeast extract (YE) diets (p < 0.05). The digestible energy (DE), metabolizable energy (ME), and net energy (NE) of the MM diet were greater than the other diets, and MBM was the lowest (p < 0.05). The ME of protein ingredients was positively correlated with organic matter and negatively correlated with the ash content. The NE of protein ingredients was positively correlated with the crude protein content and negatively correlated with the ash content. The study resulted in predictive energy equations for protein ingredients that were more accurate than the NRC's predictive equation of ME when the ash content of the ingredient was more than 30% DM. In conclusion, the nutrient digestibility and energy value of corn gluten meal were similar to those of fish meal and those of soybean meal were similar to yeast extract. All predictive energy equations for six protein feedstuffs had slight differences with measured energy values.

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis.

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

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

Background: In the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity. Methods: A retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE. Results: All the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model. Conclusion: Although the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice.

Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations for Individuals After Bariatric Surgery: a Systematic Review with Meta-analysis.

PURPOSE: Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS. METHODS: MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist. RESULTS: Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference = - 39.71 kcal [95%CI = - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference = - 54.60 kcal [95%CI = - 87.92; - 21.28]). CONCLUSION: The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

Adequacy of the "pocket formulas" for measuring total energy expenditure in healthy women compared with the new DRI equation: A doubly labeled water study.

OBJECTIVES: "Pocket formulas" are practical alternatives for calculating an individual's total energy expenditure (TEE). Typically, more sophisticated predictive equations are used, such as the new equations proposed in the Dietary Reference Intakes (DRI). Nevertheless, these new equations necessitate estimating physical activity levels (PALs). The aim of this study was to compare the use of pocket formulas (kcal/kg of body weight) with the new predictive equations for energy expenditure proposed by the DRI (2023) in healthy women and with the doubly labeled water (DLW) method to predict TEE. METHODS: The TEEs of healthy adult women were measured by DLW and calculated using the pocket formulas (× 20, × 25, × 30, and × 35 kcal/kg of body weight) and the new DRI equation. PALs by triaxial accelerometers were also collected. RESULTS: The study included 55 women. For the entire sample, the × 30 pocket formula had the lowest bias (-6%; limits of agreement [LOAs]: -39.8; 27.5; root mean square error: 373.4) and the highest precision (42%). The pocket formulas showed reasonable agreement in the different body mass index categories compared with the results found by the 2023 DRI proposal. For individuals with normal weight, the agreement was × 35 kcal/kg: bias (%) = -4.8; LoA = -41.5; 31.8, with overweight, it was × 30 kcal/kg: bias (%) = -2.2; LoA = -25.1; 20.6, and with obesity, it was × 30 kcal/kg: bias (%) = 4.2; LoA = -21.1; 29.4. CONCLUSION: Pocket formulas provide a reasonable agreement with TEE in healthy, sedentary, or low-active adult women, which may be a more simplistic strategy when there is no PAL data for calculating the DRI equations.

Comparison of predictive equations and indirect calorimetry in critical care: Does the accuracy differ by body mass index classification?

BACKGROUND: Nutrition support professionals are tasked with estimating energy requirements for critically ill patients. Estimating energy leads to suboptimal feeding practices and adverse outcomes. Indirect calorimetry (IC) is the gold standard for determining energy expenditure. However, access is limited, so clinicians must rely on predictive equations. METHODS: A retrospective chart review of critically ill patients who underwent IC in 2019 was conducted. The Mifflin-St Jeor equation (MSJ), Penn State University equation (PSU), and weight-based nomograms were calculated using admission weights. Demographic, anthropometric, and IC data were extracted from the medical record. Data were stratified by body mass index (BMI) classifications, and relationships between estimated energy requirements and IC were compared. RESULTS: Participants (N = 326) were included. Median age was 59.2 years, and BMI was 30.1. The MSJ and PSU were positively correlated with IC in all BMI classes (all P < 0.001). Median measured energy expenditure was 2004 kcal/day, which was 1.1-fold greater than PSU, 1.2-fold greater than MSJ, and 1.3-fold greater than weight-based nomograms (all P < 0.001). CONCLUSION: Despite the significant relationships between measured and estimated energy requirements, the significant fold-differences suggest that using predictive equations leads to significant underfeeding, which may result in poor clinical outcomes. Clinicians should rely on IC when available, and increased training in the interpretation of IC is warranted. In the absence of IC, the use of admission weight in weight-based nomograms could serve as a surrogate, as these calculations provided the closest estimate to IC in participants with normal weight and overweight, but not obesity.

Predictive Equations Overestimate Resting Metabolic Rate in Young Chilean Women with Excess Body Fat.

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18-28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations. The percentage of adequacy (90-110%), overestimation (>110%), and underestimation (<90%) were evaluated for RMR IC. Data were described by percentiles because of non-normal distribution according to the Shapiro-Wilk test. The Kruskal-Wallis test and Bland-Altman analysis were applied at a significance level of α < 0.05. The RMR IC was 1192 and 1183 calories/day (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris-Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin-St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy.

Revised Harris-Benedict Equation: New Human Resting Metabolic Rate Equation.

This paper contains a revision of the Harris-Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris-Benedict, Mifflin-St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) - (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) - (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) - (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) - (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease).

Methods over Materials: The Need for Sport-Specific Equations to Accurately Predict Fat Mass Using Bioimpedance Analysis or Anthropometry.

Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.

Body composition affects the accuracy of predictive equations to estimate resting energy expenditure in older adults: An exploratory study.

BACKGROUND: To investigate the accuracy of ten different predictive equations to estimate resting energy expenditure (REE) in a sample of Brazilian older adults and develop a predictive equation for estimating REE based on body composition data. METHODS: A cross-sectional study with thirty-eight Brazilian older adults aged 60-84 years, who had their REE measured by indirect calorimetry (IC) and BC assessed by dual-energy x-ray absorptiometry (DXA). REE was compared to the estimation of ten predictive equations, and the differences between BC and anthropometric-based equations were investigated using Bland-Altman plots and Lin's concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured REE. RESULTS: The sample was composed of 57.9% men, with a mean age of 68.1 (5.8) years, and a mean REE by IC of 1528 (451) kcal. The highest accuracy was 47.4% obtained by Luhrmann and Fredrix equations, and the lowest accuracy was 13.2% reached by Weigle equation. In general, the proportion of underestimation was higher than overestimation. All anthropometric-based equations presented a good agreement with REE from IC. For those equations derived from BC, however, three of them reached only a moderate agreement. In terms of accuracy, all equations presented lower than 50% of accurate prediction of REE. CONCLUSIONS: In this sample of older adults, previous predictive equations to estimate REE did not show good accuracy, and those based on BC presented even worse results, showing that changes in BC related to aging could impact the accuracy of these equations.

Using static postures to estimate spinal loading during dynamic lifts with participant-specific thoracolumbar musculoskeletal models.

Static biomechanical simulations are sometimes used to estimate in vivo kinetic demands because they can be solved efficiently, but this ignores any potential inertial effects. To date, comparisons between static and dynamic analyses of spinal demands have been limited to lumbar joint differences in young males performing sagittal lifts. Here we compare static and dynamic vertebral compressive and shear force estimates during axial, lateral, and sagittal lifting tasks across all thoracic and lumbar vertebrae in older men and women. Participant-specific thoracolumbar full-body musculoskeletal models estimated vertebral forces from recorded kinematics both with and without consideration of dynamic effects, at an identified frame of peak vertebral loading. Static analyses under-predicted dynamic compressive and resultant shear forces, by an average of about 16% for all three lifts across the thoracic and lumbar spine but were highly correlated with dynamic forces (average r2 > .95). The study outcomes have the potential to enable standard clinical and occupational estimates using static analyses.

Comparative characteristics of some methods for estimating energy expenditure in critically ill mechanically ventilated patients.

AIM: To compare the energy expenditure (EE) assessed by ventilator-derived carbon dioxide production (EE-VCO2-ventilator) and the energy expenditure calculated from six predictive equations with the gold standard energy expenditure measured with indirect calorimetry (IC) in mechanically ventilated patients.

External validation of BIA equations to estimate appendicular skeletal muscle mass in older adults: Importance of the bias analysis and derivation of correction factors to achieve agreement.

There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASMDXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:-5.86--5.45, Toselli's:-0.51--0.15, Kyle's: 1.43-1.84, Rangel-Peniche's: 0.32-0.74, Sergi's: 0.83-1.23, and Yoshida's: 4.16-4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women.

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris−Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro−Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women.

Comparison of generalized and athletic bioimpedance-based predictive equations for estimating fat-free mass in resistance-trained exercisers.

OBJECTIVES: This study aimed to test whether athlete-specific, bioelectrical, impedance-based equations to estimate fat-free mass (FFM) could be more accurate than generalized equations when testing resistance-trained exercisers. METHODS: A total of 50 resistance-trained men (age 30.9 ± 7.4 y; body mass index: 25.3 ± 2.2 kg/m2) and 20 men from the general population (age 29.9 ± 9.1 y; body mass index: 22.8 ± 2.4 kg/m2) underwent bioelectrical impedance and dual-energy x-ray absorptiometry (DXA) evaluations. FFM was derived by one bioelectrical impedance-based equation specific for athletes and three generalized equations, all developed with foot-to-hand bioimpedance technologies at a 50 kHz frequency. DXA was the reference method for the FFM assessment. RESULTS: Compared with DXA, when assessing the resistance-trained participants, the athletic-specific equation had neither mean (-0.89 kg; P = 0.789) or proportional bias (r = -0.104; P = 0.474) with a coefficient of determination equal to R2 = 0.91. In contrast, the three generalized predictive equations overestimated FFM (range, 4.11-5.37 kg; P < 0.05) with R2 ranging from 0.84 to 0.90. The athletic-specific equation underestimated FFM in the general population participants (-2.93 kg; P < 0.05). CONCLUSIONS: When assessing body composition in resistance-trained exercisers, specific equations for athletes should be preferred to generalized ones to avoid an overestimation in FFM. Furthermore, athlete-specific and generalized formulas cannot be used interchangeably, even when assessing body composition in the general population.

What Mathematical Models Are Accurate for Prescribing Aerobic Exercise in Women with Fibromyalgia?

OBJECTIVES: This article aims to verify the agreement between the standard method to determine the heart rate achieved in the ventilatory threshold 1 in the cardiopulmonary exercise testing (VT1) and the mathematical models with exercise intensities suggested by the literature in order to check the most precise for fibromyalgia (FM) patients. METHODS: Seventeen women with FM were included in this study. The VT1 was used as the standard method to compare four mathematical models applied in the literature to calculate the exercise intensity in FM patients: the well-known "220 - age" at 76%, Tanaka predictive equation "208 - 0.7 × age" at 76%, the FM model HRMax "209 - 0.85 × age" at 76%, and Karvonen Formula at 60%. Bland-Altman analysis and correlation analyses were used to explore agreement and correlation between the standard method and the mathematical models. RESULTS: Significant correlations between the heart rate at the VT1 and the four mathematical estimation models were observed. However, the Bland-Altman analysis only showed agreement between VT1 and "220 - age" (bias = -114.83 + 0.868 × x; 95% LOA = -114.83 + 0.868 × x + 1.96 × 7.46 to -114.83 + 0.868 × x - 1.96 × 7.46, where x is the average between the heart rate obtained in the CPET at VT1 and "220 - age", in this case 129.15; p = 0.519) and "209 - 0.85 × age"(bias = -129.58 + 1.024 × x; 95% LOA = -129.58 + 1.024 × x + 1.96 × 6.619 to -129.58 + 1.024 × x - 1.96 × 6.619, where x is the average between the heart rate obtained in the CPET at VT1 and "209 - 0.85 × age", in this case 127.30; p = 0.403). CONCLUSIONS: The well-known predictive equation "220 - age" and the FM model HRMax ("209 - 0.85 × age") showed agreement with the standard method (VT1), revealing that it is a precise model to calculate the exercise intensity in sedentary FM patients. However, proportional bias has been detected in all the mathematical models, with a higher heart rate obtained in CPET than obtained in the mathematical model. The chronotropic incompetence observed in people with FM (inability to increase heart rate with increasing exercise intensities) could explain why methods that tend to underestimate the HRmax in the general population fit better in this population.

Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: a systematic review with meta-analysis.

CONTEXT: Energy expenditure predictive equations can generate inaccurate estimates for overweight or obese individuals. OBJECTIVE: The objective of this review was to determine which predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) have the lowest bias and the highest precision in adults with overweight and obesity. DATA SOURCES: Searches were performed in January 2022 in MEDLINE, Web of Science, Scopus, CENTRAL, and the gray literature databases. DATA EXTRACTION: Meta-analyses were performed with equations included in more than 1 study. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The Egger test was performed to assess potential publication biases, and metaregressions were conducted to explore the heterogeneity. Findings were presented separated by participants' body mass index classification (overweight and obesity). DATA ANALYSIS: Sixty-one studies were included. The FAO/WHO/UNU (1985) equation, which uses only body weight in its formula, showed the lowest bias in estimating REE (mean difference [MD] = 8.97 kcal; 95% CI = -26.99; 44.94). In the subgroup analysis for individuals with obesity, the Lazzer (2007) equation showed the lowest bias (MD = 4.70 kcal; 95% CI = -95.45; 104.86). The Harris-Benedict equation (1919) showed the highest precision values for individuals with overweight (60.65%) and for individuals with obesity (62.54%). Equations with body composition data showed the highest biases. The equation proposed by the Institute of Medicine (2005) showed the lowest bias (MD = -2.52 kcal; 95% CI = -125.94; 120.90) in estimating the TEE. Most analyses showed high heterogeneity (I2 > 90%). There was no evidence of publication bias. CONCLUSION: For individuals with overweight, the FAO/WHO/UNU (1985) and the Harris-Benedict equations (1919) showed the lowest bias and the highest precision in predicting the REE, respectively. For individuals with obesity, the Harris-Benedict equation (1919) showed the highest precision and the Lazzer equation (2007) showed the lowest bias. More studies are needed on predictive equations to estimate the TEE. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021262969.

Poor performance of predictive equations to estimate resting energy expenditure in patients with Crohn's disease.

Studies exploring the accuracy of equations calculating Resting Energy Expenditure (REE) in patients with Crohn's disease are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in Crohn's disease patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. 186 Crohn's disease outpatients (102 males) with mean age 41.3±14.1 years and 37.6% with active disease were evaluated. Mean mREE in the total sample was 1734±443 kcal/day. All equations under-predicted REE and showed moderate correlations with mREE (Pearson's r or Spearman's rho 0.600-0.680 for current weight, all p-values<0.001). Accuracy was low for all equations at the individual level (28-42% and 25-40% for current and adjusted body weight, respectively, 19-33% for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, body mass index and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with Crohn's disease.

Development of new predictive equations for basal metabolic rate in Iranian healthy adults: negligible effect of sex.

Some studies have reported inaccuracy of predicting basal metabolic rate (BMR) by using common equations for Asian people. Thus, this study was undertaken to develop new predictive equations for the Iranian community and also to compare their accuracy with the commonly used formulas. Anthropometric measures and thyroid function were evaluated for 267 healthy subjects (18-60 y). Indirect calorimetry (InCal) was performed only for those participants with normal thyroid function tests (n = 252). Comparison of predicted RMR (both kcal/d and kcal.kg.wt-1.d-1) using current predictive formulas and measured RMR revealed that Harris-Benedict and FAO/WHO/UNU significantly over-estimated and Mifflin-St. Jeor significantly under-estimated RMR as compared to InCal measurements. In stepwise regression analysis for developing new equations, the highest r2 (=0.89) was from a model comprising sex, height and weight. However, further analyses revealed that unlike the subjects under 30 y, the association between age and the measured RMR in subjects 30 y and plus was negative (r = -0.241, p = 0.001). As a result, two separate equations were developed for these two age groups. Over 80 percent of variations were covered by the new equations. In conclusion, there were statistical significant under- and over-estimation of RMR using common predictive equations in our subjects. Using the new equations, the accuracy of the calculated RMR increased remarkably.