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.

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.

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).

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.

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.

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.

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.

Generalized bioelectric impedance-based equations underestimate body fluids in athletes

The current study aimed: (i) to external validate total body water (TBW) and extracellular water (ECW) derived from athlete and non‐athlete predictive equations using radioisotope dilution techniques as a reference criterion in male and female athletes; (ii) in a larger sample, to determine the agreement between specific and generalized equations when estimating body fluids in male and female athletes practicing different sports. A total of 1371 athletes (men: n = 921, age 23.9 ± 1.4 y; women: n = 450, age 27.3 ± 6.8 y) participated in this study. All athletes underwent bioelectrical impedance analyses, while TBW and ECW were assessed with dilution techniques in a subgroup of 185 participants (men: n = 132, age 21.7 ± 5.1 y; women: n = 53, age 20.3 ± 4.5 y). Two specific and eight generalized predictive equations were tested. Compared to the criterion methods, no mean bias was observed using the athlete‐specific equations for TBW and ECW (−0.32 to 0.05, p > 0.05) and the coefficient of determination ranged from R² = 0.83 to 0.94. The majority of the generalized predictive equations underestimated TBW and ECW (p < 0.05); R² ranged from 0.66 to 0.89. In the larger sample, all the generalized equations showed lower TBW and ECW values (ranging from −6.58 to −0.19, p < 0.05) than specific predictive equations; except for TBW in female power/velocity (one equation) athletes and team sport (two equations). The use of generalized BIA‐based equations leads to an underestimation of TBW, and ECW compared to athlete‐specific predictive equations. Additionally, the larger sample indicates that generalized equations overall provided lower TBW and ECW compared to the athlete‐specific equations.

Determinants of dynamic inspiratory muscle strength in healthy trained elderly

BACKGROUND

The S-Index assessed by means of electronic devices is a measure of Inspiratory Muscle Strength (IMS) that highly correlates with the maximal inspiratory pressure (MIP). The variables involved when using regression models for the prediction of IMS/MIP depend on both the sample characteristics and the device or protocol used. In light of the scarce information on the influence of physical activity (PA) on IMS in healthy older adults (OA), together with the incorporation of new assessment devices, the objectives of this research are: 1) to determine which factors influence the IMS in a group of trained OA, using a portable electronic device; and 2) to propose a regression model to improve its assessment and training.

METHODS

One hundred and fifty-nine individuals were assessed for body composition, lung capacity, IMS, and PA. A total of 92 individuals (72.73 ± 4.99 years) were considered for the final sample.

RESULTS

Using age, sex, and weight as control variables, forced expiratory volume in the first second (FEV₁) is the variable which is most likely to be included in the model (80%), without physical fitness appearing to be decisive. In the absence of spirometric variables, cardiorespiratory fitness (6MWT) takes on this role in a predictive model (16%).

CONCLUSIONS

This is the first study proposing IMS predictive formulas considering spirometry and/or physical fitness results for a Spanish, healthy, and trained OAs population. A predictive formula including also the spirometric variables (mainly the FEV₁) might better predict the inspiratory muscle strength. In addition, physical and respiratory functions confirm to be different, so it is necessary for the inspiratory muscles to be trained in a specific way.

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

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

Current Predictive Resting Metabolic Rate Equations Are Not Sufficient to Determine Proper Resting Energy Expenditure in Olympic Young Adult National Team Athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.

Reference Interval for Pulse Oxygen Saturation in Neonates at Different Altitudes: A Systematic Review

Introduction: The reference interval for pulse oxygen saturation (SpO₂) in neonates born at high altitudes has not been defined to date. The purpose of this study was to systematically review published studies and determine the reference interval of SpO₂ in neonates at different altitudes. Methods: Databases of PubMed, Embase, Cochrane Library, Clinicaltrials.Gov, Chinese National Knowledge Infrastructure Database, Wanfang Database, Chinese Science Technology Journals Database, and Chinese Clinical Trial Registry were searched for studies reporting SpO₂ in healthy neonates at different altitudes. Retrieval time was from inception of the database to August 16, 2021. The Agency for Healthcare Research and Quality checklist was used to evaluate the quality of studies. Python v3.8 was used to analyze the data. This systematic review was drafted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: Seven cross-sectional studies, published between 1991 and 2020, were identified. They were from US, Mexico, Israel, Ecuador, and China. Three studies were rated as high quality and four as moderate quality. The mean SpO₂ (with standard deviation or standard error) of neonates born in 40 different altitudes (ranging from 25 meters to 3,100 meters) were obtained. The prediction equation for calculation of the lower limit of the reference interval was established, and the reference intervals for SpO₂ at different altitudes were determined. Conclusions: In healthy neonates, the lower limit of the reference interval of SpO₂ decreases with increase in altitude. High-quality prospective studies are need to confirm our findings.

Measured vs Predicted Energy Expenditure in Mechanically Ventilated Adults With Acute, Traumatic Spinal Cord Injuries

BACKGROUND

Research regarding the impact of acute spinal cord injury (aSCI) on energy expenditure is limited. Patients with aSCI are prone to complications of both over- and under-feeding, making appropriate nutrition support pivotal to patient care. The purpose of this study was to describe energy expenditure and assess the performance of predictive equations in mechanically ventilated adults with aSCI.

METHODS

Adult patients admitted to a single trauma center from March 2017 through June 2018 with aSCI and a documented indirect calorimetry (IC) within 6 weeks of injury were included for analysis. Predictive equations evaluated included Penn State 2003b (PS 2003b), the derived Weir equation, 25 kcal/kg and 30 kcal/kg. Sub-set analysis was performed for patients with and without obesity, isolated aSCI, and concomitant traumatic injuries.

RESULTS

On hundres fifteen IC studies in 51 patients were included for analysis. Median energy expenditure was 1747 kcal/day (interquartile range [IQR], 1492-2099 kcal/day), or 22.7 kcal/kg (IQR, 19.3-25.9 kcal/kg). When stratified by hospital day, energy expenditure ranged from 20 to 25 kcal/kg. PS 2003b and the derived Weir equation had similar correlation coefficients (r = 0.81 and 0.82, respectively). The 25 and 30 kcal/kg performed unacceptably (r = 0.61). PS 2003b predicted within 10% of measured energy expenditure most frequently. All equations were biased towards overfeeding, except for PS 2003b in the obese subset.

CONCLUSION

In the absence of IC, PS 2003b or the derived Weir equation may be acceptable predictive equations in this population. However, bedside clinicians should monitor carefully for signs and symptoms of overfeeding.

Validity of Bioimpedance Equations to Evaluate Fat-Free Mass and Muscle Mass in Severely Malnourished Anorectic Patients

BACKGROUND

Bioelectrical impedance analysis (BIA) is a simple and rapid technique to measure body composition (BC). Validity of BIA in patients with low body mass index (BMI) remains controversial. We assessed the validity of several BIA equations to evaluate fat-free mass (FFM), fat mass (FM) and muscle mass in patients with anorexia nervosa (AN) by using dual X ray absorptiometry (DXA) as reference.

METHODS

Sixteen BIA equations developed for FFM and appendicular lean mass (ALM) were applied on electrical data measured by BIA in AN patients with BMI <16 kg/m². BIA and DXA were done the same day after overnight fasting. Results were compared with the Bland-Altman method, Pearson correlation and a Lin concordance test.

RESULTS

Data from 115 female AN patients (14.6 ± 1.2 kg/m^²; 32.3 ± 14.5 years) were included. FM and FFM assessed by DXA were, respectively, 4.2 ± 2.4 kg and 35.5 ± 3.8 kg. The best results were obtained with Sun's equation: respectively for FM and FFM, Bland Altman bias at 0.548 and 0.706 kg, Pearson correlation r at 0.86 and 0.86 and Lin concordance coefficient at 0.81 and 0.84. However, confidence intervals (CI) at 95% were high (-2.73-3.83 kg for FM; -4.55-3.13 kg for FFM). Other equations also showed high 95% CI. Accuracy was acceptable for Sun and Bedogni equations for FFM (approximately 66%) but very low for FM prediction considering all equations (<15%). Concerning ALM evaluated at 14.88 ± 2.04 kg by DXA, only Scafoglieri and Yoshida equations showed acceptable values: bias (-0.2 and 2.8%), Pearson r (0.89 and 0.86), Lin concordance coefficient (0.82 and 0.82) and accuracy (83.5 and 82.6%). Confidence intervals at 95% were high for both equations (-2.1-2.0 for Scafoglieri equation and -1.6-2.4 for Yoshida equation).

CONCLUSION

In AN patients with BMI < 16 kg/m², no BIA equation tested was adapted to evaluate BC at the individual level.

Comparison of Equations to Predict Energy Requirements With Indirect Calorimetry in Hospitalized Patients

BACKGROUND

The accuracy of methods to determine resting energy expenditure (REE) contributes toward the adequate provision of nutrition support to hospitalized patients. Indirect calorimetry (IC) is considered the gold-standard method to determine REE. The aim of this study is to evaluate the degree of agreement between the REE measured by IC (REE-IC) and REE estimated by predictive equations in intensive care unit patients.

METHODS

The sample is made up of intensive care unit patients aged >18 years, both male and female, undergoing nutrition therapy. The predictive equations to estimate REE were the Harris Benedict (HB), Ireton Jones (IJ), and practical method (PM). Degree of agreement between REE-predictive equations and REE-IC was analyzed by the interclass correlation coefficient (ICC) and the Bland-Altman test.

RESULTS

Average energy obtained by IC was significantly different from HB and IJ equations (P < .001). The HB equation significantly underestimated the REE-IC for body mass index (BMI) classification. Significant concordance was observed between the REE-IC and all estimate equations (P < .05). The IJ equation showed the greatest degree of concordance for BMI classification of underweight (ICC = 0.674; P = .011) and presented the least difference between the averages of the energy when compared with REE-IC (107.8 kcal/d; P < .05).

CONCLUSION

The IJ equation showed better results with IC, with the greatest degree of concordance for BMI classification of underweight. Further research should develop others equations and validate tools to measure energy expenditure for accurate dietary recommendations for hospitalized patients undergoing nutrition therapy.

Comparison of Measured Energy Expenditure Using Indirect Calorimetry vs Predictive Equations for Liver Transplant Recipients

BACKGROUND

To assess the appropriate energy expenditure requirement for liver transplant (LT) recipients in South Korea, 4 commonly used predictive equations were compared with indirect calorimetry (IC).

METHODS

A prospective observational study was conducted in the surgical intensive care unit (ICU) of an academic tertiary hospital between December 2017 and September 2018. The study population comprised LT recipients expected to remain in the ICU >48 hours postoperatively. Resting energy expenditure (REE) was measured 48 hours after ICU admission using open-circuit IC. Theoretical REE was estimated using 4 predictive equations (simple weight-based equation [25 kcal/kg/day], Harris-Benedict, Ireton-Jones [ventilated], and Penn State 1988). Derived and measured REE values were compared using an intraclass correlation coefficient (ICC) and Bland-Altman plots.

RESULTS

Of 50 patients screened, 46 were enrolled, were measured, and completed the study. The Penn State equation showed 65.0% agreement with IC (ICC, 0.65); the simple weight-based (25 kcal/kg/day), Harris-Benedict, and Ireton-Jones equations showed 62.0%, 56.0% and 39.0% agreement, respectively. Bland-Altman analysis showed that all 4 predictive equations had fixed bias, although the simple weight-based equation (25 kcal/kg/day) showed the least.

CONCLUSION

Although predicted REE calculated using the Penn State method agreed with the measured REE, all 4 equations showed fixed bias and appeared to be inaccurate for predicting REE in LT recipients. Precise measurement using IC may be necessary when treating LT recipients to avoid underestimating or overestimating their metabolic needs.

Reference values for distal motor conduction of the tibial nerve: Effects of demographic and anthropometric measures

INTRODUCTION

In this study we collected reference values for the across-tarsal-tunnel conduction of the motor tibial nerve (mTN).

METHODS

The mTN compound muscle action potentials (CMAPs) from the abductor hallucis muscle were obtained by stimulating below/above the malleolus and the popliteal fossa. The effect of weight, height, body mass index (BMI), foot and leg length, sex, and age were evaluated using univariate and multivariate correlation analyses, and predictive equations for each mTN conduction parameter were developed.

RESULTS

On the basis of data from 185 subjects, there were differences between women and men in all anthropometric parameters and for some nerve conduction values. Through multivariate analysis, age, but not sex, was found to have a significant impact. Height affected both distal and proximal conduction velocity. BMI affected CMAP amplitude.

DISCUSSION

mTN conduction is influenced by various demographic and anthropometric factors. For all intrinsic factors, height demonstrated the greatest effect on mTN conduction across the tarsal tunnel.

Predictive energy equations for spinal muscular atrophy type I children

BACKGROUND

Knowledge on resting energy expenditure (REE) in spinal muscular atrophy type I (SMAI) is still limited. The lack of a population-specific REE equation has led to poor nutritional support and impairment of nutritional status.

OBJECTIVE

To identify the best predictors of measured REE (mREE) among simple bedside parameters, to include these predictors in population-specific equations, and to compare such models with the common predictive equations.

METHODS

Demographic, clinical, anthropometric, and treatment variables were examined as potential predictors of mREE by indirect calorimetry (IC) in 122 SMAI children consecutively enrolled in an ongoing longitudinal observational study. Parameters predicting REE were identified, and prespecified linear regression models adjusted for nusinersen treatment (discrete: 0 = no; 1 = yes) were used to develop predictive equations, separately in spontaneously breathing and mechanically ventilated patients.

RESULTS

In naïve patients, the median (25th, 75th percentile) mREE was 480 (412, 575) compared with 394 (281, 554) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P = 0.009).In nusinersen-treated patients, the median (25th, 75th percentile) mREE was 609 (592, 702) compared with 639 (479, 723) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P = 0.949).Both in spontaneously breathing and mechanically ventilated patients, the best prediction of REE was obtained from 3 models, all using as predictors: 1 body size related measurement and nusinersen treatment status. Nusinersen treatment was correlated with higher REE both in spontaneously breathing and mechanically ventilated patients. The population-specific equations showed a lower interindividual variability of the bias than the other equation tested, however, they showed a high root mean squared error.

CONCLUSIONS

We demonstrated that ventilatory status, nusinersen treatment, demographic, and anthropometric characteristics determine energy requirements in SMAI. Our SMAI-specific equations include variables available in clinical practice and were generally more accurate than previously published equations. At the individual level, however, IC is strongly recommended for assessing energy requirements. Further research is needed to externally validate these predictive equations.

Predictive Equations Based on Anthropometric Parameters

Accurate estimation of energy expenditure in a breastfeeding woman is crucial for maintaining the proper nutritional status of the woman and healthy development of the infant. The current literature does not contain data regarding resting energy expenditure (REE) in breastfeeding women. Using mathematical equations is the most common method of REE assessment. However, due to changes in metabolism and body composition during pregnancy and lactation, the mathematical equations used among the general population may not apply. The aim of this study was to evaluate the resting energy expenditure of exclusively breastfeeding women by using body composition analysis–estimated REE (eREE) and to provide the most appropriate predictive equations–predicted REE (pREE) based on anthropometric parameters to estimate it. This was a pilot study with 40 exclusively breastfeeding women. Height and weight were measured and body composition analysis was performed. We predicted REE using fourteen self-selected equations, based on anthropometric parameters and/or age, and/or sex. The median eREE was 1515.0 ± 68.4 kcal (95% Cl, 1477–1582 kcal) and the pREE ranged from 1149.7 kcal (95% Cl, 1088.7–1215.0) by Bernstein et al., to 1576.8 kcal (95% Cl, 1479.9–1683.4), by Müller et al. Significant differences between eREE and all pREE were observed (p < 0.001, except Korth et al. equations). The Müller et al. equation was the most accurate with the smallest individual variation. All predictive equations showed low agreement, and in most cases, the results were underestimated. These findings indicate the need for further studies to propose more suitable methods to determine the energy requirements for breastfeeding women.

Is Energy Delivery Guided by Indirect Calorimetry Associated With Improved Clinical Outcomes in Critically Ill Patients? A Systematic Review and Meta-analysis

Background:

Indirect calorimetry (IC) is recommended to guide energy delivery over predictive equations in critical illness due to its precision. However, the impact of using IC to measure energy expenditure on clinical outcomes is uncertain.

Objective:

To evaluate whether using IC to measure energy expenditure to inform energy delivery reduced hospital mortality and improved other important outcomes compared to using predictive equations in critically ill adults.

Methods:

A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline. Medline, Embase, CINAHL, and the Cochrane Library were searched for studies using IC to guide energy delivery compared to a predictive equation in adult critically ill patients with the primary outcome (hospital mortality) or any of the secondary outcomes reported (including but not limited to hospital and intensive care unit (ICU) length of stay (LOS) and duration mechanical ventilation (MV). Risk of bias within studies was assessed using the Cochrane “Risk of Bias” 1 tool. Random-effect meta-analyses were used when heterogeneity between studies existed (I² > 50%). Data are reported as median (interquartile range [IQR]), binomial outcomes as odds ratio (OR), 95% confidence interval (CI), and continuous outcomes as mean difference (MD).

Results:

Of 4060 articles, 4 randomized controlled trials were identified with 396 patients included in analysis. Three studies were considered low risk of bias and 1 as high risk. Two studies reported hospital mortality (n = 130 and 40 participants, respectively). When combined, no association between IC-guided energy delivery and hospital mortality was found (OR = 0.81, 95% CI = [0.25, 2.67], P = 0.73, I² = 52). No differences were reported with ICU mortality and hospital LOS between groups, but ICU LOS and duration of MV varied across all studies. According to the meta-analysis, no differences were observed in ICU LOS (MD = 1.39, 95% CI = [–5.01, 7.79], P = 0.67, I² = 81%), although the duration of MV was increased when energy delivery was guided by IC (MD = 2.01, 95% CI = [0.45, 3.57], P = 0.01, I² = 26%). In all 4 studies, prescribed energy targets were more closely met when energy delivery was informed by IC compared to a predictive equation. Three studies reported the percentage delivered versus the prescribed energy target, with the median (IQR) delta between the IC and predictive equation arms 19% (10%-32%).

Conclusion:

Limited data exist to assess the impact of using IC to inform energy delivery in comparison to predictive equations on hospital mortality. The association of IC use with other important outcomes, including duration of MV, needs to be further explored before definitive conclusions can be made.

New Predictive Equations for Estimating Resting Energy Expenditure in Adults With Crohn's Disease

BACKGROUND

Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's disease (CD). This study aimed to develop and validate new predictive equations for estimating REE in adults with CD.

METHODS

Adults, ages 18-65 years, with CD were recruited. Anthropometry, indirect calorimetry, and bioimpedance analysis were performed in all patients. Disease activity was assessed by Crohn's Disease Activity Index. The new predictive equations were generated using different regression models. Prediction accuracy of the new equations was assessed and compared with the most commonly used equations.

RESULTS

A total of 270 CD patients (159 males, 111 females) were included and randomly assigned to the calibration (n = 180) and validation groups (n = 90). REE was directly correlated with weight and bioimpedance index, whereas the relation with both age and disease activity was inverse. The new equations were suitable for estimating REE at population level (bias: -0.2 and -0.3, respectively). Individual accuracy was good in both models (≥80%, respectively), especially in females; and similar results were shown by some of the selected equations. But, when accuracy was set within ±5%, the new equations gave the highest prediction.

CONCLUSION

The new, disease-specific, equations for predicting REE in individuals with CD give a good prediction accuracy as far as those proposed in the literature for the general population. However, the new ones performed better at the individual level. Further studies are needed to verify the reliability and usefulness of these new equations.

Predictive Equations

Introduction: Predictive equations (PE) are used in lieu of indirect calorimetry (IC) due to cost and limited resources; however, these equations may not be as accurate as IC in estimating resting energy expenditure (REE) in critically ill patients, putting them at risk of malnutrition. The purpose of this study is to compare predicted and measured energy expenditure (MEE) in critically ill adults with acute brain injury.

Materials and Methods: This was a retrospective review of adult patients admitted to the Neurosciences ICU with acute brain injury between May 1st, 2014 and April 1st, 2016 who had IC performed. The Harris Benedict (HBE), Penn State University, and Mifflin St Jeor (MSJ) PE were used in comparison to IC results. Subgroup analyses stratified patients based on BMI and type of acute brain injury.

Results: One hundred and forty-four patients met inclusion criteria. Comparing predicted and MEE found no significant difference (p = 0.1). High degrees of interpatient variability were discovered, with standard deviations ranging from 17 to 29% of each PE. Pearson's correlations indicated weak associations when HBE, Penn State, and MSJ were individually compared to MEE (r = 0.372, 0.409, and 0.372, respectively). A significant difference was found between predicted and MEE in patients with a BMI < 30 kg/m² (p < 0.01) and in those with aneurysmal subarachnoid hemorrhage (p < 0.01).

Discussion: Due to interpatient variability that exists among REE of critically ill patients with acute brain injury, IC should be used when feasible.

Validation of predictive equations for resting energy expenditure in treatment-seeking adults with overweight and obesity: Measured versus estimated

The quantification of resting energy expenditure (REE) in patients with obesity is an important measure. We aimed to evaluate the validity of predictive equations in estimating REE compared with indirect calorimetry (IC) in treatment-seeking Arab adults with overweight or obesity. Twenty-three predictive equations were compared with REE values measured by IC (Vmax Encore 229) in 89 adult participants with overweight or obesity (mean age = 40.62 ± 15.96 years and mean body mass index [BMI] = 35.02 ± 4.60 kg/m²) referred to the Department of Nutrition and Dietetics of Beirut Arab University (Lebanon). The accuracy of the predictive equations was evaluated on the basis of whether the percentage prediction was within 10% of the measured REE, and the mean difference between predicted and measured values (bias). The Bland-Altman method was used to assess the agreement between the predicted and measured values. The equations that demonstrated the closest agreement with IC were the De La Cruz equation in males (accurate predictions: 68.2%; bias: -19.52 kcal/day) and the Mifflin equation in females (accurate prediction: 61.2%; bias: -36.43 kcal/day). In conclusion, we suggest that these two equations produce the least biased estimations for REE in this population.

Predictive Equations Based on Body Composition for Resting Energy Expenditure Estimation in Adults with Obesity

BACKGROUND AND AIM

An accurate estimation of Resting Energy Expenditure (REE) in patients with obesity is crucial. Therefore, our aim was to assess the validity of REE predictive equations based on body composition variables in treatment-seeking Arab adults with obesity.

METHODS

Body composition and REE were measured by Tanita BC-418 bioimpedance and Vmax Encore 229 IC, respectively, and predictive equations based on fat mass and fat-free mass were used in REE estimations among 87 adults of both genders, in the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). The mean differences between the measured and estimated REE values were calculated to assess the accuracy, and the Bland-Altman method was used to assess the level of agreement.

RESULTS

Ten predictive equations were included. In males, all the predictive equations gave significantly different estimates of REE when compared to that measured by IC. On the other hand, in females, the mean difference between the REE value estimated by Huang and Horie-Waitzberg equations and that measured using IC was not significant, and the agreement was confirmed using Bland-Altman plots.

CONCLUSION

Huang and Horie-Waitzberg equations are suggested for accurate REE estimation in females; however, new validated REE estimation equations for males in this population are still needed.

Predicting Equations and Resting Energy Expenditure Changes in Overweight Adults

Introduction

The aim of the study is to show the differences between the measured and estimated values of resting energy expenditure and any changes occurring after the 6-month weight loss intervention program.

Methods

We included 33 healthy adults aged 25–49 years with an average body mass index 29.1±2.7 kg/m 2 for female and 29.8±2.8 kg/m² for male. The measured resting energy expenditure was obtained by indirect calorimeter MedGem® Microlife and estimated resting energy expenditure by the Harris–Benedict equation, the Mifflin–St Jeor equation, the Owen equation, the Wright equation, and by the Tanita body composition analyser. All measurements and calculations were carried out before and after the 6-month intervention. Results were compared using paired t-tests. P value less than 0.05 was considered statistically significant.

Results

A comparison of the measured resting energy expenditure of female subjects with the estimated resting energy expenditure using the Harris–Benedict equation, the Mifflin–St Jeor equation and the Wright equation showed a statistically significant difference. A comparison of the measured resting energy expenditure of male subjects with the estimated resting energy expenditure using the Harris–Benedict equation and the Wright equation showed a statistically significant difference. There was a significant difference in the measured resting energy expenditure and estimated resting energy expenditure using Tanita.

Conclusions

We concluded that the most comparable equation for our sample was the Owen’s equation. After losing weight, the measured resting energy expenditure has decreased, which must be taken into account in further diet therapy.

The use of whole body calorimetry to compare measured versus predicted energy expenditure in postpartum women

BACKGROUND

Accurate assessment of energy expenditure may support weight-management recommendations. Measuring energy expenditure for each postpartum woman is unfeasible; therefore, accurate predictive equations are needed.

OBJECTIVES

This study compared measured with predicted resting energy expenditure (REE) and total energy expenditure (TEE) in postpartum women.

METHODS

This was a longitudinal observational study. REE was measured at 3 mo postpartum (n = 52) and 9 mo postpartum (n = 49), whereas TEE was measured once at 9 mo postpartum (n = 43) by whole body calorimetry (WBC). Measured REE (REEWBC) was compared with 17 predictive equations; measured TEE plus breast milk energy output (ERWBC) was compared with the estimated energy requirements/Dietary Reference Intakes equation (EERDRI). Fat and fat-free mass were measured by dual-energy X-ray absorptiometry. Group-level agreement was assessed by the Pearson correlation, paired t test, and Bland-Altman (bias) analyses. Individual-level accuracy was assessed with the use of Bland-Altman limits of agreement, and by the percentage of women with predicted energy expenditure within 10% of measured values ("accuracy").

RESULTS

The cohort was primarily Caucasian (90%). At a group level, the best equation predicting REEWBC was the DRI at 3 mo postpartum (-7 kcal, -0.1%; absolute and percentage bias, respectively), and the Harris-Benedict at 9 mo postpartum (-17 kcal, -0.5%). At an individual level, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) height and weight equation was the most accurate at 3 mo postpartum (100% accuracy) and 9 mo postpartum (98% accuracy), with the smallest limits of agreement. Equations including body composition variables were not more accurate. Compared with ERWBC, EERDRI bias was -36 kcal, with inaccurate predictions in 33% of women.

CONCLUSIONS

Many REE predictive equations were accurate for group assessment, with the FAO/WHO/UNU height and weight equation having the highest accuracy for individuals. EERDRI performed well at a group level, but inaccurately for 33% of women. A greater understanding of the physiology driving energy expenditure in the postpartum period is needed to better predict TEE and ultimately guide effective weight-management recommendations.

Are Predictive Energy Expenditure Equations Accurate in Cirrhosis?

Malnutrition is associated with significant morbidity and mortality in cirrhosis. An accurate nutrition prescription is an essential component of care, often estimated using time-efficient predictive equations. Our aim was to compare resting energy expenditure (REE) estimated using predictive equations (predicted REE, pREE) versus REE measured using gold-standard, indirect calorimetry (IC) (measured REE, mREE). We included full-text English language studies in adults with cirrhosis comparing pREE versus mREE. The mean differences across studies were pooled with RevMan 5.3 software. A total of 17 studies (1883 patients) were analyzed. The pooled cohort was comprised of 65% men with a mean age of 53 ± 7 years. Only 45% of predictive equations estimated energy requirements to within 90⁻110% of mREE using IC. Eighty-three percent of predictive equations underestimated and 28% overestimated energy needs by ±10%. When pooled, the mean difference between the mREE and pREE was lowest for the Harris⁻Benedict equation, with an underestimation of 54 (95% CI: 30⁻137) kcal/d. The pooled analysis was associated with significant heterogeneity (I² = 94%). In conclusion, predictive equations calculating REE have limited accuracy in patients with cirrhosis, most commonly underestimating energy requirements and are associated with wide variations in individual comparative data.

Impact of Feeding on Resting Metabolic Rate and Gas Exchange in Critically Ill Patients

BACKGROUND

Feeding is understood to alter gas exchange and resting metabolic rate (RMR) in critically ill patients, but there are limited data describing such changes.

METHODS

A large collection of RMR measurements and energy intake was assembled in mechanically ventilated and critically ill patients. The data were used to explore differences in RMR, respiratory quotient (RQ), and carbon dioxide production (V̇co₂ ) related to feeding state.

RESULTS

A total of 708 RMR measurements were available. No significant thermogenic effect of feeding could be appreciated. When controlled for body weight, the difference was 4.2% (1946 ± 400 kcal/d for unfed vs 2028 ± 341 kcal/d for fed patients; P = .299), and this small difference was fully eliminated when body temperature was also controlled for. RQ increased as carbohydrate and energy intake increased, but there was much variation in the relationship. Among 215 overfed patients, only 22 had RQ ≥1.0, and among 9 patients whose carbohydrate intake alone exceeded RMR, only 4 had an RQ ≥1.0. V̇co₂ was influenced mainly by the volume of oxygen consumed and to a lesser degree by carbohydrate intake.

CONCLUSIONS

No appreciable thermogenic effect of feeding occurs in continuously fed critically ill patients. This factor does not need to be considered in the assessment of resting energy needs. RQ is not a reliable indicator of overfeeding, and the ability of carbohydrate intake to alter V̇co₂ is small, although not absent.

Accuracy of Predictive Equations for Energy Expenditure in Mexicans Living With HIV/AIDS With and Without Antiretroviral Therapy

INTRODUCTION

Determination of the resting energy expenditure (REE) is essential for planning nutrition therapy in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) to help to improve their nutrition status. We aim to evaluate the agreement and accuracy of prediction equations that estimate the REE in a Mexican population with a diagnosis of HIV/AIDS with and without antiretroviral therapy (ART).

METHODS

A cross-sectional study in Mexican patients with HIV/AIDS with and without ART. Weight, height, and body composition measured with dual-energy x-ray absorptiometry were evaluated. The REE was determined with indirect calorimetry and estimated using the Mifflin-St Jeor (MSJ), Harris-Benedict (HB), Schofield 1 and 2, Cunningham, Melchior 91, Melchior 93, and Batterham equations. The Bland-Altman method assessed agreement between the real and estimated values, and the percent difference between these values was used to assess the prediction accuracy.

RESULTS

Sixty-five adults without ART and 102 adults with ART were included. The mean REE (kcal/kg) was 24.8 ± 2.4 and 23.8 ± 3.6 in patients without and with ART, respectively. Good agreement and reliability were observed in the HB (intraclass correlation coefficient [ICC], 0.75; P < .05), Batterham (ICC, 0.79; P < .05), Schofield 1 (ICC, 0.74; P < .05), and Schofield 2 (ICC, 0.78; P < .05) results in individuals without ART. In individuals with ART, good agreement and reliability were observed with the HB equation (ICC, 0.76; P < .05). The MSJ equation showed good agreement with poor reliability (ICC, 0.05; P < .05).

CONCLUSION

The equations with the best agreement and accuracy were Schofield 2, Batterham, and HB in individuals without ART and HB and MSJ in the population with ART.

Low validity of predictive equations for calculating resting energy expenditure in overweight and obese women with polycystic ovary syndrome

BACKGROUND

Predictive equations are the main clinical tools for determining resting energy expenditure (REE). However, their adequate use in overweight and obese individuals is unclear. Thus, we investigated the best predictive equations for estimating REE in overweight and obese women with polycystic ovary syndrome (PCOS).

METHODS

Eleven analyses were performed with prediction equations (pREE) based on anthropometric parameters in 30 overweight or obese women with PCOS without other chronic diseases. The measured REE (mREE) was calculated by indirect calorimetry. The validity of the equations was investigated by comparison, accuracy and agreement tests between pREE and mREE at both the individual and group level.

RESULTS

Four analyses were similar to those of mREE, and smallest mean differences were observed for the World Health Organization/Food and Agriculture Organization of the United Nations/United Nations University (WHO/FAO/UNU) considering weight (W) [0.07 (1.13) MJ (16 [270] kcal)]. Individual accuracy was greater than 50% for Harris and Benedict, Müller and Lazzer equations. The percentage of REE underestimation ranged between 16.7% and 73.3%, whereas higher rates of overestimation were observed in the De Luis (66.7%) and Ireton-Jones (43.3%) equations. Mean bias at the group level was lowest in the WHO/FAO/UNU W and WHO/FAO/UNU considering weight and height (WH), Müller and Lazzer equations (-2.8 to 0.5). The WHO/FAO/UNU W and WHO/FAO/UNU WH formulas were optimal in individual agreement (33.3%).

CONCLUSIONS

FAO/WHO/UNU W equations may estimate the REE in overweight and obese women with PCOS. However, the low individual accuracy and agreement in relation to mREE suggest caution regarding when to use the formula to perform an individual nutritional plan.

Estimation of Resting Energy Expenditure: Validation of Previous and New Predictive Equations in Obese Children and Adolescents

OBJECTIVE

Accurate estimation of resting energy expenditure (REE) in childrenand adolescents is important to establish estimated energy requirements. The aim of the present study was to measure REE in obese children and adolescents by indirect calorimetry method, compare these values with REE values estimated by equations, and develop the most appropriate equation for this group.

METHODS

One hundred and three obese children and adolescents (57 males, 46 females) between 7 and 17 years (10.6 ± 2.19 years) were recruited for the study. REE measurements of subjects were made with indirect calorimetry (COSMED, FitMatePro, Rome, Italy) and body compositions were analyzed.

RESULTS

In females, the percentage of accurate prediction varied from 32.6 (World Health Organization [WHO]) to 43.5 (Molnar and Lazzer). The bias for equations was -0.2% (Kim), 3.7% (Molnar), and 22.6% (Derumeaux-Burel). Kim's (266 kcal/d), Schmelzle's (267 kcal/d), and Henry's equations (268 kcal/d) had the lowest root mean square error (RMSE; respectively 266, 267, 268 kcal/d). The equation that has the highest RMSE values among female subjects was the Derumeaux-Burel equation (394 kcal/d). In males, when the Institute of Medicine (IOM) had the lowest accurate prediction value (12.3%), the highest values were found using Schmelzle's (42.1%), Henry's (43.9%), and Müller's equations (fat-free mass, FFM; 45.6%). When Kim and Müller had the smallest bias (-0.6%, 9.9%), Schmelzle's equation had the smallest RMSE (331 kcal/d). The new specific equation based on FFM was generated as follows: REE = 451.722 + (23.202 * FFM). According to Bland-Altman plots, it has been found out that the new equations are distributed randomly in both males and females.

CONCLUSION

Previously developed predictive equations mostly provided unaccurate and biased estimates of REE. However, the new predictive equations allow clinicians to estimate REE in an obese children and adolescents with sufficient and acceptable accuracy.

Resting Energy Expenditure of Children and Adolescents With Nonalcoholic Fatty Liver Disease

BACKGROUND

The mainstay of treatment for pediatric nonalcoholic fatty liver disease (NAFLD) is lifestyle modification, which includes dietary changes that lead to slow but sustained weight loss or weight stabilization in growing children. Accurate estimation of energy requirements is necessary to achieve this goal. The objective of this study was to assess the accuracy of the most commonly used equations in predicting the resting energy expenditure (REE) of children with NAFLD.

METHODS

This was a retrospective study performed in a single institution. The predictive accuracy of various equations was assessed by comparing their estimates against the measured REE obtained with indirect calorimetry. Accuracy was defined as an estimate within 10% of measured REE.

RESULTS

Fifty-six children (70% male; 52% white and 36% Asian) with a median age of 13 years were included. The median measured REE was 1829 kcal/d. Of the equations studied, the Schofield had the smallest average bias (-32 kcal/d; confidence interval, -121 to 56). The Schofield and Molnar equations were the most accurate, providing REE estimates within 10% of measured in 59% of cases. The remaining equations had lower and variable predictive accuracy. The use of adjusted body weight in predictive equations did not improve the predictive accuracy.

CONCLUSION

In a cohort of children and adolescents with NAFLD, the Schofield and Molnar equations performed best in predicting energy expenditure. However, predictive equations were often inaccurate, suggesting that clinicians should interpret their results with caution and consider using indirect calorimetry when available.

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets

Background: Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. Methods: A total of 396 males, Brazilian Army cadets, aged 17–24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student’s t-test (for paired sample), linear regression analysis, and Bland–Altman method were used to test the validity of the BIA equations. Results: Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05) and large limits of agreement by Bland–Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Conclusion: Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Energy Expenditure in Critically Ill Elderly Patients: Indirect Calorimetry vs Predictive Equations

BACKGROUND

Predictive equations (PEs) are used for estimating resting energy expenditure (REE) when the measurements obtained from indirect calorimetry (IC) are not available. This study evaluated the degree of agreement and the accuracy between the REE measured by IC (REE-IC) and REE estimated by PE (REE-PE) in mechanically ventilated elderly patients admitted to the intensive care unit (ICU).

METHODS

REE-IC of 97 critically ill elderly patients was compared with REE-PE by 6 PEs: Harris and Benedict (HB) multiplied by the correction factor of 1.2; European Society for Clinical Nutrition and Metabolism (ESPEN) using the minimum (ESPENmi), average (ESPENme), and maximum (ESPENma) values; Mifflin-St Jeor; Ireton-Jones (IJ); Fredrix; and Lührmann. Degree of agreement between REE-PE and REE-IC was analyzed by the interclass correlation coefficient and the Bland-Altman test. The accuracy was calculated by the percentage of male and/or female patients whose REE-PE values differ by up to ±10% in relation to REE-IC.

RESULTS

For both sexes, there was no difference for average REE-IC in kcal/kg when the values obtained with REE-PE by corrected HB and ESPENme were compared. A high level of agreement was demonstrated by corrected HB for both sexes, with greater accuracy for women. The best accuracy in the male group was obtained with the IJ equation but with a low level of agreement.

CONCLUSIONS

The effectiveness of PEs is limited for estimating REE of critically ill elderly patients. Nonetheless, HB multiplied by a correction factor of 1.2 can be used until a specific PE for this group of patients is developed.

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key pointsThe 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed.In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity.Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams.

Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents

BACKGROUND/OBJECTIVES

Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes.

SUBJECTS/METHODS

A total of 50 athletes (mean age of 16.6 ± 1.0 years, 30 males and 20 females) and 50 non-athletes (mean age of 16.5 ± 0.5 years, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis.

RESULTS

Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [RMR = 730.4 + 15 × fat-free mass] is paramount when examining athletes.

CONCLUSIONS

The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

Validation of the Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition Recommendations for Caloric Provision to Critically Ill Obese Patients: A Pilot Study

BACKGROUND

The Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (ASPEN) recommend that obese, critically ill patients receive 11-14 kcal/kg/d using actual body weight (ABW) or 22-25 kcal/kg/d using ideal body weight (IBW), because feeding these patients 50%-70% maintenance needs while administering high protein may improve outcomes. It is unknown whether these equations achieve this target when validated against indirect calorimetry, perform equally across all degrees of obesity, or compare well with other equations.

METHODS

Measured resting energy expenditure (MREE) was determined in obese (body mass index [BMI] ≥30 kg/m(2)), critically ill patients. Resting energy expenditure was predicted (PREE) using several equations: 12.5 kcal/kg ABW (ASPEN-Actual BW), 23.5 kcal/kg IBW (ASPEN-Ideal BW), Harris-Benedict (adjusted-weight and 1.5 stress-factor), and Ireton-Jones for obesity. Correlation of PREE to 65% MREE, predictive accuracy, precision, bias, and large error incidence were calculated.

RESULTS

All equations were significantly correlated with 65% MREE but had poor predictive accuracy, had excessive large error incidence, were imprecise, and were biased in the entire cohort (N = 31). In the obesity cohort (n = 20, BMI 30-50 kg/m(2)), ASPEN-Actual BW had acceptable predictive accuracy and large error incidence, was unbiased, and was nearly precise. In super obesity (n = 11, BMI >50 kg/m(2)), ASPEN-Ideal BW had acceptable predictive accuracy and large error incidence and was precise and unbiased.

CONCLUSIONS

SCCM/ASPEN-recommended body weight equations are reasonable predictors of 65% MREE depending on the equation and degree of obesity. Assuming that feeding 65% MREE is appropriate, this study suggests that patients with a BMI 30-50 kg/m(2) should receive 11-14 kcal/kg/d using ABW and those with a BMI >50 kg/m(2) should receive 22-25 kcal/kg/d using IBW.

Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry: A Systematic Literature Review

BACKGROUND

Underfeeding and overfeeding has been associated with adverse patient outcomes. Resting energy expenditure can be measured using indirect calorimetry. In its absence, predictive equations are used. A systematic literature review was conducted to determine the prevalence of underprescription and overprescription of energy needs in adult mechanically ventilated critically ill patients by comparing predictive equations to indirect calorimetry measurements.

METHODS

Ovid MEDLINE, CINAHL Plus, Scopus, and EMBASE databases were searched in May 2013 to identify studies that used both predictive equations and indirect calorimetry to determine energy expenditure. Reference lists of included publications were also searched. The number of predictive equations that underestimated or overestimated energy expenditure by ±10% when compared to indirect calorimetry measurements were noted at both an individual and group level.

RESULTS

In total, 2349 publications were retrieved, with 18 studies included. Of the 160 variations of 13 predictive equations reviewed at a group level, 38% underestimated and 12% overestimated energy expenditure by more than 10%. The remaining 50% of equations estimated energy expenditure to within ±10 of indirect calorimetry measurements. On an individual patient level, predictive equations underestimated and overestimated energy expenditure in 13-90% and 0-88% of patients, respectively. Differences of up to 43% below and 66% above indirect calorimetry values were observed.

CONCLUSIONS

Large discrepancies exist between predictive equation estimates and indirect calorimetry measurements in individuals and groups. Further research is needed to determine the influence of indirect calorimetry and predictive equation limitations in contributing to these observed differences.

Appendicular skeletal muscle in hospitalised hip-fracture patients: development and cross-validation of anthropometric prediction equations against dual-energy X-ray absorptiometry

BACKGROUND

accurate and practical assessment methods for assessing appendicular skeletal muscle (ASM) is of clinical importance for the diagnosis of geriatric syndromes associated with skeletal muscle wasting.

OBJECTIVES

the purpose of this study was to develop and cross-validate novel anthropometric prediction equations for the estimate of ASM in older adults post-surgical fixation for hip fracture, using dual-energy X-ray absorptiometry (DEXA) as the criterion measure.

SUBJECTS

community-dwelling older adults (aged ≥65 years) recently hospitalised for hip fracture.

SETTING

participants were recruited from hospital in the acute phase of recovery.

DESIGN

validation measurement study.

MEASUREMENTS

a total of 79 hip fracture patients were involved in the development of the regression models (MD group). A further 64 hip fracture patients also recruited in the early phase of recovery were used in the cross-validation of the regression models (CV group). Multiple linear regression analyses were undertaken in the MD group to identify the best performing prediction models. The linear coefficient of determination (R(2)) in addition to the standard error of the estimate (SEE) were calculated to determine the best performing model. Agreement between estimated ASM and ASMDEXA in the CV group was assessed using paired t-tests with the 95% limits of agreement (LOA) assessed using Bland-Altman analyses.

RESULTS

the mean age of all the participants was 82.1 ± 7.3 years. The best two prediction models are presented as follows: ASMPRED-EQUATION_1: 22.28 - (0.069 * age) + (0.407 * weight) - (0.807 * BMI) - (0.222 * MAC) (adjusted R(2): 0.76; SEE: 1.80 kg); ASMPRED-EQUATION_2: 16.77 - (0.036 * age) + (0.385 * weight) - (0.873 * BMI) (adjusted R(2): 0.73; SEE: 1.90 kg). The mean bias from the CV group between ASMDEXA and the predictive equations is as follows: ASMDEXA - ASMPRED-EQUATION_1: 0.29 ± 2.6 kg (LOA: -4.80, 5.40 kg); ASMDEXA - ASMPRED-EQUATION_2: 0.13 ± 2.5 kg (LOA: -4.77, 5.0 kg). No significant difference was observed between measured ASMDEXA and estimated ASM (ASMDEXA: 16.4 ± 3.9 kg; ASMPRED-EQUATION_1: 16.7 ± 3.2 kg (P = 0.379); ASMPRED-EQUATION_2: 16.6 ± 3.2 kg (P = 0.670)).

CONCLUSIONS

we have developed and cross-validated novel anthropometric prediction equations against DEXA for the estimate of ASM designed for application in older orthopaedic patients. Our equation may be of use as an alternative to DEXA in the diagnosis of skeletal muscle wasting syndromes. Further validation studies are required to determine the clinical utility of our equation across other settings, including hip fracture patients admitted from residential care, and also with a longer-term follow-up.

TT Mutant Homozygote of Kruppel-like Factor 5 Is a Key Factor for Increasing Basal Metabolic Rate and Resting Metabolic Rate in Korean Elementary School Children

We investigated the contribution of genetic variations of KLF5 to basal metabolic rate (BMR) and resting metabolic rate (RMR) and the inhibition of obesity in Korean children. A variation of KLF5 (rs3782933) was genotyped in 62 Korean children. Using multiple linear regression analysis, we developed a model to predict BMR in children. We divided them into several groups; normal versus overweight by body mass index (BMI) and low BMR versus high BMR by BMR. There were no differences in the distributions of alleles and genotypes between each group. The genetic variation of KLF5 gene showed a significant correlation with several clinical factors, such as BMR, muscle, low-density lipoprotein cholesterol, and insulin. Children with the TT had significantly higher BMR than those with CC (p = 0.030). The highest muscle was observed in the children with TT compared with CC (p = 0.032). The insulin and C-peptide values were higher in children with TT than those with CC (p= 0.029 vs. p = 0.004, respectively). In linear regression analysis, BMI and muscle mass were correlated with BMR, whereas insulin and C-peptide were not associated with BMR. In the high-BMR group, we observed that higher muscle, fat mass, and C-peptide affect the increase of BMR in children with TT (p < 0.001, p < 0.001, and p = 0.018, respectively), while Rohrer's index could explain the usual decrease in BMR (adjust r(2) = 1.000, p < 0.001, respectively). We identified a novel association between TT of KLF5 rs3782933 and BMR in Korean children. We could make better use of the variation within KLF5 in a future clinical intervention study of obesity.

Energy expenditure in acute posttraumatic amputation: comparison of four methods for assessment

BACKGROUND

Adequate energy intake is a component of successful recovery after injury, yet little is known about the energy requirements in the acute period following traumatic amputation. The purpose of this study was to compare the clinical applicability of resting energy expenditure (REE) measured by a handheld calorimeter and estimated by 3 different predictive equations to that measured by the gold standard, indirect calorimetry using a metabolic cart, during the acute postamputation period of inpatient hospitalization.

MATERIALS AND METHODS

Indirect calorimetry measured using both a metabolic cart and handheld calorimeter and predicted by 3 equations were used to assess energy needs of eligible subjects admitted to Brooke Army Medical Center with traumatic amputation(s). REE measured by the handheld calorimeter and estimated using 3 predictive equations (Mifflin St. Jeor, Ireton-Jones 1992, and the American College of Chest Physicians [ACCP]) were compared to the gold standard metabolic cart. Each measure was assessed for significant differences and level of clinical acceptability defined as ± 10% REE by metabolic cart.

RESULTS

Thirteen male service members with traumatic amputation(s) were included. The majority of subject's measurements using the handheld calorimeter (n = 9, 69%) and 3 predictive equations (Mifflin St. Jeor [n = 7, 54%], Ireton-Jones 1992 [n = 8, 62%], ACCP [n = 7, 54%]) fell outside of the ± 10% range of clinical acceptability.

CONCLUSION

Use of the metabolic cart for measuring energy needs remains optimal in this population.

Interactive Effects of Temperature, Initial pH, Sodium Chloride, and Sodium Pyrophosphate on the Growth Kinetics of Clostridium perfringens †

The interactive effects of temperature (12 to 42°C), initial pH (5.5 to 7), sodium chloride (0 to 3%) and sodium pyrophosphate concentrations (0 to 0.3%) on the growth in Trypticase-peptone-glucose-yeast extract broth of a three-strain mixture of Clostridium perfringens vegetative cells were determined. The number of viable C. perfringens cells was determined at appropriate intervals by plating on tryptose-sulfite-cycloserine agar. Growth data were analyzed by the Gompertz equation; the gompertz B and M parameters were then used to calculate lag-phase duration, exponential growth rate, generation time, and maximum population-density values. The data indicated that the growth kinetics of C. perfringens were dependent on the interaction of the four variables, particularly in regard to exponential growth rates and lag-phase durations. Cubic models based on the natural logarithm transformation of lag-phase duration and generation time were evaluated and appeared to adequately fit the data. The data suggest that sodium pyrophosphate can have significant bacteriostatic activity against C. perfringens and may provide processed meats with a degree of protection against this microorganism, particularly if employed in conjunction with a combination of acidic pH, high salt concentrations, and adequate refrigeration.