Predictive Equations For Resting Energy Expenditure Research Articles

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

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

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. 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. 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. 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.

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. 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. Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference = - 39.71kcal [95%CI = - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference = - 54.60kcal [95%CI = - 87.92; - 21.28]). The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation

ObjectivesAccurate resting energy expenditure (REE) prediction is needed to prevent over- or underfeeding in older hospitalized patients. However, few validated REE prediction Equations are known for such patients. Therefore, this study aimed to develop new REE prediction Equations and evaluate their validity. MethodsThis single-center, cross-sectional study enrolled 134 patients ages ≥70 y. For holdout validation, patients were randomized in a 3:1 ratio; for the development data set, a new Equation was developed according to the measured REE using indirect calorimetry. The new and existing Equations were compared using the validation data set. ResultsMean patient age was 87.4 ± 6.9 y, and 34.3% were male. Two Equations were developed in multivariable regression models: Equation 1: REE (kcal/day) = 313.582 + Height (cm) × 3.973 + Body weight (kg) × 5.332 - Age (y) × 5.474 - (0 if male; 1 if female) × 20.012 + Calf circumference (cm) × 12.174; and Equation 2: REE (kcal/day) = 594.819 + Height (cm) × 3.760 + Body weight (kg) × 8.888 − Age (y) × 6.298 − (0 if male; 1 if female) × 16.396. The mean relative bias (95% CI) with measured REE as a reference had a small bias for Equations 1 and 2 (−0.1 [−4.1 to 3.9]% and −0.2 [−4.4 to 4.1]%, respectively); however, the Harris-Benedict, Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University, Ganpule, and body weight × 20 Equations had larger biases (−6.2 [−10.3 to −2.0]%; 5.3 [1.3 to 9.3]%; −13.9 [−18.6 to −9.3]%; and −11.6 [−16.1 to −7.1]%, respectively). ConclusionsNew prediction Equations using height, body weight, age, sex, and calf circumference improve REE prediction accuracy in older hospitalized patients.

Resting energy expenditure in Korean type 2 diabetes patients: comparison between measured and predicted values.

Estimation of energy demand using resting energy expenditure (REE) is a reasonable approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare REE predictions and objective measurements in patients with T2DM in Korea. This study enrolled 36 participants with T2DM (age range, 20-60 years). Anthropometric variables including height, weight, waist-hip ratio, blood pressure, body fat, body fat percentage, and total body weight were measured using bioimpedance. REE was evaluated using indirect calorimetry. The measured REE values were compared to values estimated using five predictive equations: the Harris-Benedict, Mifflin, Owen, Food and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO), and Schofield equations. This study evaluated the associations between measured REE values and anthropometric/clinical data, including height, weight, and age, using multivariate linear regression. The mean measured REE value was 1891.79 ± 288.03 kcal/day (male), 1,502.00 ± 202.96 kcal/day (female). REE estimates generated from the Mifflin equation showed the largest differences from measured REE values, whereas estimates derived from the FAO/WHO equation were the closest to the measured REE values. This study also identified associations between measured REE values and various anthropometric/clinical variables. The accuracy of REE prediction equations is critically important in promoting the efficacy of dietary counseling and the effective treatment of diabetes. Our results indicate the need for additional studies informing more suitable methods for determining the energy requirements of Korean patients with T2DM.

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements.

Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70−89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.

A longitudinal analysis of resting energy expenditure and body composition in people with spinal cord injury undergoing surgical repair of pressure injuries: a pilot study.

Data informing energy needs of people with spinal cord injury (SCI) and pressure injuries are scarce, the impact of surgical repair unknown, and the role of body composition in healing unexplored. The study aims were to investigate resting energy expenditure (REE) over the course of pressure injury surgical repair, compare with available energy prediction equations, and explore associations between body composition and wound healing. Indirect calorimetry measured REE pre-surgery, post-surgery, at suture removal and hospital discharge. A clinically significant change was defined as +/-10% difference from pre-surgery. Eight SCI-specific energy prediction equations were compared to pre-surgery REE. Wound breakdown (Yes/No), weight, waist circumference (WC), and body composition (fat mass [FM], fat-free mass [FFM], bioimpedance spectroscopy) were measured. Twenty people underwent pressure injury surgical repair (95% male, mean age 56 ± 12 years, 70% paraplegia). Between pre-surgery and discharge, mean REE increased (+118 kcal/d, p = 0.005), but with <10% change at any timepoint. An energy prediction equation incorporating FFM showed greatest agreement (rc = 0.779, 95% CI: 0.437, 0.924). Those with wound breakdown (65%) had a higher weight (12.7 kg, 95% CI: -4.0, 29.3), WC (17.8 cm, 95% CI: -5.1, 40.7), and FM % (36.0% [IQR 31.8, 40.2] vs 26.0% [IQR 15.6, 41.3]) than those without wound breakdown, although statistical significance was not reached. The presence of pressure injuries and subsequent surgical repair did not impact REE and energy prediction equations incorporating FFM performed best. While not statistically significant, clinically important differences in body composition were observed in those with wound breakdown.

A comparative study of indirect calorimetry and prediction equations in overweight and obese Chinese adults

ObjectivesTo determine the resting energy expenditure (REE) in Chinese adults using indirect calorimetry (IC), and evaluate the validity of published predictive equations for estimating REE in overweight and obese subjects comparing to IC. Material and methods181 subjects with different body mass index (BMI) were recruited. Body composition was determined by bio-electrical impedance analysis (BIA). REE was measured by IC and estimated by 29 REE prediction equations to analyze the accuracy of REE prediction equations. ResultsFor all participants combined, the Bernstein et al. and Bernestin et al. (BC) equations similarly predicted REE values between -4% and 8% (P>0.05) of measured REE. When participant data were stratified by sex, BMI, the accuracy of each regression equation is different. In male overweight and obese group, the highest accuracy of the Bernestin et al. (BC) was 65.5% and 70.4%. In female overweight and obese group, the highest accuracy of the Bernestin et al. (BC) and Bernestin et al. was 79.6% and 69.4%. ConclusionThe Bernestin et al. (BC) was closest to IC in male overweight/obese and female overweight, and the Bernestin et al was closest to IC in female obese. However, because those equations have shown considerable variability, they should be used cautiously when estimating the individual REE. When the IC method cannot be used, the Bernestin et al. (BC) and Bernestin et al are more accurate.

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.

Resting Energy Expenditure Early after Cardiac Surgery and Validity of Predictive Equations: A Prospective Observational Study

Objective: Several predictive equations have been used to estimate patients’ energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations. Methods: This was a prospective observational study. Patients aged 18–80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson’s correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA). Results: mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable. Conclusions: The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention.

Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention. Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial. All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01). Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups.

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.

Resting Energy Expenditure in CrossFit® Participants: Predictive Equations versus Indirect Calorimetry

Background: CrossFit® involves high-intensity functional movements and research has shown that the program increases metabolic rates in participants. Objective: To measure resting energy expenditure (REE) in CrossFit® participants using indirect calorimetry (IC) and to verify the most appropriate predictive equation to estimate REE. Methods: Overall, 142 CrossFit® participants (18–59 years; 91 [64.1%], women) underwent weight, height, waist circumference, and body mass index (BMI) measurements. Body composition was evaluated using a portable ultrasound system (BodyMetrix®). REEs were measured (mREE) by IC and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980 and 1991), and Mifflin–St. Jeor. Results: The mean age was 33.0 (6.3) years, with no significant difference between men and women; mean mREE, 1583.2(404.4) kcal/d; and pREE, 1455.5(230.9) to 1711.3(285.5) kcal/d. The best REE predictive equations for this population were Cunningham (1991) (P=0.338), WHO (P=0.494), and Harris-Benedict (P=0.705) equations. The Harris-Benedict equation presented a smaller difference compared with IC [12.9(307.6) kcal], the Cunningham (1991) equation showed improved adequacy (102.5%), and the WHO equation presented highest accuracy (59.9%). The equations that were closest to the mREE were the Harris-Benedict for women and the WHO equation for men. Conclusion: Therefore, for CrossFit® participants, the REE can accurately be predicted with the Cunningham (1991), WHO, and Harris-Benedict equations.

The nutritional management of people living with amyotrophic lateral sclerosis: A national survey of dietitians.

People living with amyotrophic lateral sclerosis (ALS) face many challenges with respect to taking adequate nutrition. Growing evidence links weight loss with negative prognostic outcomes. We aimed to explore the practice of dietitians in the UK with regard to the nutritional management of ALS. A national online survey was disseminated via professional groups, social media and newsletters to UK healthcare professionals between September and November 2018. The survey examined the nutritional management of ALS. Dietitian responses are reported in the present study. In total, 130 dietitians responded to the survey. Two-thirds reported that ALS comprised less than 20% of their total patient caseload. Forty-two percent reported that nutritional screening took place in their organisation. One-half of dietitians reported that patients were referred for dietetic assessment at 'about the right time', although 44% reported referrals were made too late. The majority (83%) of dietitians used resting energy expenditure predictive equations not validated in ALS. When setting weight goals, dietitians reported most frequently recommending weight maintenance if the patient's body mass index was 18.5-25kgm-2 (72%), 25-30kgm-2 (98%), and >30kgm-2 (79%). In addition, 43% reported that people with ALS were not weighed sufficiently frequently. Although the importance of early nutritional assessment is recognised, the timeliness of dietetic input and on-going monitoring of nutritional status in ALS care might not currently be ideal. Dietitians report using energy requirement predictive equations and setting weight goals that may not promote positive outcomes. Further research is required to understand the optimal nutritional management of ALS.

Resting Energy Expenditure Is Elevated in Asthma.

Background: Asthma physiology affects respiratory function and inflammation, factors that may contribute to elevated resting energy expenditure (REE) and altered body composition. Objective: We hypothesized that asthma would present with elevated REE compared to weight-matched healthy controls. Methods: Adults with asthma (n = 41) and healthy controls (n = 20) underwent indirect calorimetry to measure REE, dual-energy X-ray absorptiometry (DEXA) to measure body composition, and 3-day diet records. Clinical assessments included spirometry, fractional exhaled nitric oxide (FENO), and a complete blood count. Results: Asthmatics had greater REE than controls amounting to an increase of ~100 kcals/day, even though body mass index (BMI) and body composition were similar between groups. Inclusion of asthma status and FENO in validated REE prediction equations led to improved estimates. Further, asthmatics had higher white blood cell (control vs. asthma (mean ± SD): 4.7 ± 1.1 vs. 5.9 ± 1.6, p < 0.01) and neutrophil (2.8 ± 0.9 vs. 3.6 ± 1.4, p = 0.02) counts that correlated with REE (both p < 0.01). Interestingly, despite higher REE, asthmatics reported consuming fewer calories (25.1 ± 7.5 vs. 20.3 ± 6.0 kcals/kg/day, p < 0.01) and carbohydrates than controls. Conclusion: REE is elevated in adults with mild asthma, suggesting there is an association between REE and the pathophysiology of asthma.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women.

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m2, and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REEIC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REEIC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REEIC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REEIC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

The agreement between resting energy expenditure predictive equations in patients with breast cancer

The agreement between resting energy expenditure predictive equations in patients with breast cancer

DEVELOPMENT OF NEW PREDICTIVE EQUATIONS FOR ESTIMATING RESTING ENERGY EXPENDITURE IN ADULTS WITH CROHN'S DISEASE

Increased resting energy expenditure (REE) has been hypothesized to be a potential cause of weight loss in individuals with Crohn's Disease (CD), mainly due to inflammatory response. This study aimed to develop and validate new predictive equations for REE in adults with CD. Adult patients aged between 18-65 years and with CD were recruited. Anthropometry, indirect calorimetry and bio-impedance analysis (BIA) were performed in all patients. New predictive equations were generated using two models: Model 1 with age, weight, height and CDAI as predictors, and Model 2 in which both bioimpedance-index (BI-index) and phase angle were added. Finally, accuracy prediction within ±10% was assessed and then compared with several published equations. A total of 270 CD patients (159 males, 111 females) were included and assigned to the calibration (n=180; age: 37.9±13years; BMI: 22.2±3.4kg/m2) and the validation groups (n=90; age: 38.5±13years; BMI: 22.7±3.7kg/m2). REE was directly correlated with weight (r=0.733, p=0.000) and BI-index (r=0.762, p=0.000). Models were both suitable for estimating REE at population level (bias: -0.2, bias: -0.3; respectively). Individual accuracy was high in both models (∼80%; 83%;respectively), unrelated to gender. Similarly, the Harris and Benedict, FAO and Schofield equations showed a good accuracy in males and females. Therefore, new equations specifically derived from CD patients provided a very good prediction of REE at population level. Interestingly, the formula based on raw-BIA variables was as accurate as those based on anthropometry, unrelated to gender. However, further studies are required to verify the application of those formulas and the role of raw-BIA variables in REE prediction.

Resting Energy Expenditure In Crossfit® Practitioners: Indirect Calorimetry Versus Predictive Equations

BACKGROUND: Indirect calorimetry (IC) is the gold standard method to assess individual resting energy expenditure (REE). However, due to its high cost and time demand, predictive equations are largely used to estimate energy requirements, which may vary according to different body compositions and health status. Crossfit® is a training program created by Greg Glessman in 1995 and consists of performing high intensity functional movements. Some studies have already reported increase of metabolic rates in this population. PURPOSE: To measure REE in Crossfit® practitioners, using IC, and verify the most appropriate predictive equation to estimate this variable. METHODS: 142 Crossfit® practitioners, 91 female (64.1%), aged between 16-59 years, underwent nutritional assessment, including weight, height, waist circumference (WC) and body mass index (BMI). Body composition was measured by a portable ultrasound. REE was measured by IC (mREE) and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980), Cunningham (1991), and Mifflin-St.Jeor. Statistical analysis were carried out by Kolmogorov-Smirnov Test, Student’s t test and Bland and Altman plots. RESULTS: The mean age was 33.0 ± 6.3 years of age, without difference between men and women. The mean BMI was 24.7 ± 3.5 kg/m2. The mean mREE was 1583.2 ± 404.4 kcal, and the pREE ranged from 1455.5 ± 230.9 kcal to 1711.3 ± 285.5. The best REE predictive equations for Crossfit® practitioners were the Cunningham (1991) (P=0.338), WHO equation (P=0.494) and Harris-Benedict (P=0.705). Harris-Benedict presented the smaller difference comparing with IC (12.9 ± 307.6 kcal), Cunningham (1991) showed better adequacy (102.5%) and WHO equation presented greater percentage of accuracy (59.9%). The same equations remained adequate when data were stratified by gender. CONCLUSIONS: This study showed that Cunningham (1991), WHO (1985) and Harris-Benedict (1919) equations were the most appropriated REE equations for Crossfit® practitioners. Further studies should investigate more suitable methods to determine the energy requirements in Crossfit® and should, perhaps, create and propose a specific equation for this population.

RESTING ENERGY EXPENDITURE IN PARKINSON'S DISEASE PATIENTS UNDER DOPAMINERGIC TREATMENT

Weight homeostasis is complex in Parkinson's disease (PD) and body weight changes substantially throughout the course of the disease. We designed a case-control study to i) investigate whether PD is associated with changes in resting energy expenditure (REE), ii) to assess the accuracy of REE predictive equations for healthy people in PD and iii) to eventually construct a new formula. Measured REE (mREE) was compared between 122 PD patients and 122 gender and body mass index (BMI)-matched controls. The accuracy of estimated REE by 5 common equations (Harris/Benedict-1919, Harris/Benedict-1984, Mifflin St. Jeor, WHO/FAO and aggregate formula) was investigated in PD using Bland-Altman analysis and reported as the frequency of accurate predictions (±10%). Concordance correlation coefficients (CCC) were also calculated. Then, we regressed a new REE equation - using gender, age, weight, height and Hoehn-Yahr stage - and validated it in an independent sample (N=100). No significant difference in mREE was recorded between the whole PD sample and healthy controls. However, mREE was increased in patients with BMI≥30 kg/m2 and Hoehn-Yahr stage≥3. Available REE equations showed limited accuracy (accurate prediction [±10%] frequency, <60% for all). For the new equation the proportion of accurate prediction was 67.0% (overestimation, 24.0%) and CCC was 0.77. PD patients are not commonly characterized by an increase in REE. This is limited to patients suffering from obesity and more severe disease. Common REE equations appear to be inaccurate. The new formula provided better REE estimates and its use can be proposed.