Detection of resting energy expenditure in prostate cancer: Assessment of energy prediction equations

Detection of resting energy expenditure in prostate cancer: Assessment of energy prediction equations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detection of resting energy expenditure in prostate cancer: Assessment of energy prediction equations Tevfik Koçak, Nilüfer Acar Tek, Süleyman YEŞİL, Tevfik Sinan SÖZEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4711548/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2025 Read the published version in BMC Urology → Version 1 posted 14 You are reading this latest preprint version Abstract Background An accurate calculation of energy expenditure (REE) is necessary for estimating energy needs in prostate cancer. The purpose of this research was to evaluate the accuracy of the established new equation for predicting REE in malign and benign prostate patients versus the accuracy of the previously used predictive equations based on REE measured by indirect calorimetry. Methods Subjects with 41 malign prostate and 42 benign prostate subtects were both over the age of 40 (65.3 ± 6.30 years) and recruited for the study. Cosmed-FitMate GS Indirect Calorimetry with Canopy-hood (Rome, Italy) was used to measure REE. A full body composition analysis and anthropometric measurements were taken. Results Malign prostate group PSA Total and measured REE values (4.93±5.44 ng/ml, 1722.9±272.69kcal/d respectively) were statisticaly significantly higher than benign group (1.76±0.73ng/ml, 1670.5±266.76 kcal/d respectively) (p < 0.05). Malign (MPG) and benign prostate groups (BPG) have the highest percentage of the accurate-prediction value of equations 80.9% (New EquationMPG) and 64.2% (New EquationBPG). The bias of the equations varied from-36.5% (Barcellos II Equation) to 19.2% (Mifflin-St. Jeor equation) for malign prostate group and varied from − 41.1% (Barcellos II Equation) to 17.7% (Mifflin-St.Jeor equation) in benign prostate group. The smallest RMSE values in the malign and benign prostate group were New EquationMPG (149 kcal/d) and New EquationBPG (202 kcal/d). The new specific equation for malign prostate cancer: REE = 3192,258+(208,326* body weight(WT)) - (20,285* height(HT)) - (187,549* Fat Free Mass(FFM)) - (203,214* Fat Mass(FM)) + (4,194* Prostate Specific Antigen Total(PSAT)). The new specific equation for benign prostate group: REE = 615,922+ (13,094* WT). Bland-Altman plots reveal an equally random distribution of new equations in malign and benign prostate group. Conclusions The majority of the previously developed predictive equations for REE were inaccurate and biased. The new specific equation for malign prostate cancer that we created enabled us to develop prostate cancer-specific energy prediction equations with the PSAT parameter. In any case, the new predictive equations enable clinicians to estimate REE in people with malign and benign prostate groups with sufficient and most acceptable accuracy. Prostate cancer resting energy expenditure indirect calorimetry predictive equations Figures Figure 1 Figure 2 Background An increased risk of prostate cancer is discussed in relation to an excessive energy intake in comparison to energy expenditure [ 1 ]. Energy is essential for development, reproduction, structure maintenance, and environmental adaptation in all cells [ 2 ]. Total energy expenditure is the sum of all energy consumed by an organism over the course of a day [ 3 ]. The majority of energy needs are met by basal/resting metabolic rate (BMR/REE), which accounts for roughly 60–70% of total energy expenditure [ 4 – 6 ]. Both the resting energy expenditure and the amount of physical activity contribute significantly to total energy expenditure, and both were modifiable in cancer patients [ 7 ]. Prostate cancer patients may experience metabolic changes that alter their overall energy expenditure depending on the stage and type of the disease. The significance of resting energy expenditure in an individual's total energy expenditure is increased by the advanced age and declining physical activity of cancer patients [ 7 ]. Reduced physical performance and metabolic response, fatigue, incrased inflammatory, weight loss, malnutrition, and even obesity have all been linked to inaccurate estimates of total energy requirements [ 8 ]. On the other hand, to prevent increases in fat free mass and aggravating comorbidities, energy recommendations for overweight or obese patients shouldn't be exaggerated [ 9 ]. To avoid undernutrition and obesity in patients, it is essential to accurately determine changes in energy expenditure and estimate each person's specific energy needs [ 10 ]. For the patients' survival and mobilization, it was crucial to receive adequate and balanced nutrition tailored to their particular cancer. The accuracy of using predictive equations to estimate the energy requirements of patients with various cancer types may be low due to the fluctuating energy metabolism and metabolic variations of cancer patients. As a result the primary strategy should, whenever possible, be to evaluate each patient individually while measuring energy expenditure via indirect calorimetry. It will be more accurate to plan a unique nutrition therapy program by measuring REE with indirect calorimetry for the patient's treatment and essential comfort in order to prevent negative energy balance in those with prostate cancer [ 11 ]. Measuring energy expenditure via pulmonary gas exchanges with indirect calorimetry (IC), is widely regarded as the gold standard. It's a non-invasive method that improves clinical outcomes by tailoring nutritional support prescriptions to patients' individual metabolic requirements. This method is accurate however necessity of equipment, expensive, time-consuming and requiring expert personal [ 12 ]. Therefore, equations have been developed to predict REE across all age groups since the 1980’s. A number of REE prediction equations have been developed healthy and clinical usage; these include the HarriseBenedict (1919), Schofield (1985), IretoneJones (2002), Mifflin-St.Jeor (1990), Barcellos Equation II (2020), Cunningham (1991), Owen (1986), Wang (2000), IOM (2004), Henry (2004), and 21 kcal/kg/d Silver (2013) models which are based on weight, age, BMI, height or gender and mid-upper arm circumference. Many studies have evaluate the accuracy of the resting energy expenditure prediction equations in diffent patients groups [ 13 , 14 ]. The use of previous predictive equations shown in Table 1 in the malign and benign prostate group was discussed in this study. Table 1 Predictive Equations for REE in malign and benign prostate group used in the present study Author and Year Gender Age (years) Predictive Equation for REE Note HarriseBenedict, 1919 Male - kcal/d: 66.4730 + (13.7516WT) + (5.0033 HT) - (6.7550 AGE) WT:kg, HT:cm, AGE:years Schofield, 1985 Male 30–60 kcal/d: (( 0.048 *WT + 3.653) * 239) WT:kg, IretoneJones, 2002 All - kcal/d: 1784 - (11 AGE ) + (5 WT) + (244 SEX) + (239T) + (804B) WT:kg AGE:years SEX: Male:0, Female:1 T: trauma (absence : 0 and presence : 1) B : burnt (absence : 0 and presence : 1) Mifflin-St.Jeor, 1990 Male - kcal/d: (10*WT) + (6.25*HT) – (5*AGE) + 5 WT:kg, HT:cm, AGE:years Barcellos Equation II, 2020 All - kcal/d: 58(MUAC) + 621.4( SEX ) − 425(O) − 19 MUAC: mid-upper arm circumference SEX: Male:0, Female:1 O: obesity (0: BMI < 30 and 1: BMI ≥ 30 kg/m2). Cunningham1, 1991 All 18–65 kcal/d: 370 + 21.6*FM FM:kg Owen, 1986 Male 18–65 kcal/d: 879 + 10.2*WT WT:kg, Wang, 2000 All 18–65 kcal/d: 24.6*FFM + 175 FFM:kg IOM, 2004 Male - kcal/d: 293 - (3.8*AGE) + (456.4 *HT) + (10.12*WT) WT:kg, HT:cm, AGE:years Henry, 2004 height and weight Male 30–60 > 60 kcal/d: Men age 30–60 years: 11.4 × WT + 541 × HT − 137 kcal/d: Men age > 60 years: 11.4 × WT + 541 × HT − 256 HT:meters 21 kcal/kg/d Silver, 2013 All - kcal/d: 21 × WT WT:kg WT: Weight, HT: Height, FM: Fat mass, When directly measuring an individual's REEs is not possible, predictive equations may be used in practice instead. Thus, an accurate prediction of REE value is necessary for an accurate prediction of TEE [ 15 , 16 ].Since the invention of compact indirect calorimeters, routine REE testing has become feasible. Indirect calorimetry, which involves the measurement of oxygen consumption, allows for a quick and precise estimate of REE. It has recently been accepted as a valid surrogate for the gold standard of direct calorimetry [ 17 , 18 ]. There were both advantages and disadvantages to using this method. Indirect calorimetric methods are more challenging to implement on a large scale due to the higher cost of measurement equipment and the need for trained personnel [ 19 , 20 ]. Other indirect measuring devices were challenging to use in cancer patients due to their mouthpiece and nasal designs. When it was come to comfort and ease of measurement, however, portable indirect measurement tools or those with a canopy-hood rather than a face mask are ideal [ 21 ]. Because of limitations imposed by clinical applications, scientists developed body composition parameters and anthropometric measurement systems predicated on REEs for adults and older patients. The use of predictive equations has become widely for field of REE predictions [ 14 , 22 ]. Indirect calorimetric measurement values were the dependent variable, while demographics (age, gender, ethnicity), anthropometrics (weight, height), and body composition are the independent variables in these equations (adipose tissue, lean tissue). Similar to the regression analysis-based equations, these equations were developed using data collected from the population of healthy people at large to provide fast and easy solutions [ 23 ]. A variety of clinically applicable predictive equations have been developed, and these were utilized to estimate REE [ 24 – 29 ]. There were a number of equations developed in healthy cancer populations and that can be used to determine REE for people who have cancer. But there was still no agreement on what the best equation was for determining REE for different types of cancer [ 10 , 13 , 14 ]. To date, some studies examining the accuracy of REE prediction equations have used in conclusion current equations were not providing valid and accurate predictions in cancer patients [ 13 , 30 , 31 ]. The aim of the present study was to compare measured resting energy expenditure (REE) by an indirect calorimetry model (FitMate GS with canopy-hood) and established new equation to calculated by previous predictive equations in individuals with malign and benign prostate patients. Methods Study design and participants The study was conducted between December 2020 and May 2021 and 40 individuals over the age of 40 who applied to the Urology Clinic of Gazi University Faculty of Medicine, with malign prostate cancer according to the pathology result and benign prostate prostate cancer in the pathology result. In order to measure REE in individuals with malign and benign prostate cancer by indirect calorimetry method, compare with REE values estimated by equations and develop the most appropriate equation for this group. Total 83 (41 malign prostate cancer and 42 benign prostate canceraged over 40 (65.3 ± 6.30 years) volunteers were recruited in the study. The inclusion criteria were included being over the age of 40, male, having pathology results indicating either prostate cancer (malign prostate tissue) or benign prostate tissue (without prostate cancer), and not having undergone surgical intervention. The exclusion criteria were; having endocrine and metabolic disorders, chronic kidney and liver disease, sleep disorders, psychiatric and cognitive disorder, heart failure, respiratory diseases such as asthma, influenza, colds, regular medication and have received chemotherapy and radiotherapy during the all measurements.. Ethics committee approval of the study was obtained from the Gazi University Faculty of Medicine Clinical Research Ethics Committee with decision number 4 on 7 April 2020. Signed informed consent was obtained from all patients, and this study was conducted by the Declaration of Helsinki. Anthropometric Measurements and Body Composition Analysis Anthropometric measurements and body composition analysis were performed by the researcher, who is a specialist dietitian, in the early morning hours after at least 8 hours of night fasting. Body weight measurement and body composition analysis (fat mass, percent of fat, fat free mass (FFM) were made by using the TANITA BC 601. The scale's specifications include a frequency measurement range of 50 kHz, a current measurement range of 100 µA, and a voltage measurement range of 150–1200 Ω. Height was measured (cm) with feet close together and the head was at Frankfort plane with a 0.1 cm sensitive portable stadiometer. An individual's waist circumference (in centimeters) was recorded to the closest 0.1 centimeter by putting a nonelastic tape measure midway between the lowest rib border margin and the iliac crest at the end of a normal expiration without the tape compressing the skin. Resting Energy Expenditure (REE) REE measurements were made by using the Cosmed- FitMate GS Indirect Calorimetry with canopy-hood (Rome, Italy). FitMate is a reliable and valid system for measuring oxygen - FitMate GS Indirect Calorimetry with canopy-hood consumption and RMR in many studies [ 32 , 33 ]. The Fitmate GS measures VO 2 consumption and calculates the RMR by estimating VCO 2 production from a fixed RQ of 0.85 based on the abbreviated Weir equation as it does not contain a VCO 2 sensor [ 34 ]. In healthy adults and outpatients, Fitmate GS with canopy-hood has been validated against the gold standard device DELTATRAC and Douglas bag. The Canopy-hood System is a convenient method of performing resting energy expenditure testing. It is ideal for subjects who may experience discomfort with mouthpieces or masks. It is an advanced metabolic measurement system option [ 35 – 37 ]. Routine procedures for measuring RMR were followed. Calibration was carried out before each measurement in accordance with the manufacturer's instructions. A canopy-hood was then placed over the patient’s head. When the VO 2 coefficient of variation was less than 10%, steady state was achieved. After the instrument stabilized for 15 minutes, RMR readings were taken. During the measurement, outpatients were lying in a supine position and instructed to limit movement, talking and to avoid sleeping [ 38 ]. Before the canopy-hood measurement, the malign and benign prostate group individuals were instructed to remain in the supine position for 15 minutes. Then, Fitmate GS RMR measurement was made with the canopy-hood [ 39 ].The measurements were made during the early morning period between 8.00am and 10.00pm in the morning after at least 8 hours of night hunger. The participant did not engage in heavy exercise and consumed their usual diet in the day leading up to the REE measurement. Specifically, the method developed by Compher et al. (2006) [ 40 ]. As a result; For FitMate GS with canopy-hood REE (kcal/day), VO 2 (ml/min), Vp (l/min) and FeO 2 (%) measurements were evaluated. REE Prediction Equations Eleven different REE calculation equations were used to evaluate the accuracy of the measured and calculated REE values. The study included these equations; HarriseBenedict-1919, Schofield-1985, IretoneJones-2002, Mifflin-St.Jeor-1990, Barcellos Equation II-2020, Cunningham-1991, Owen-1986, Wang-2000, Institutes of Medicine (IOM)-2004, Henry-2004 and 21 kcal/kg/day (Table 1 ). Statistical Analysis All statistical analyses were performed using SPSS (The Statistical Package for Social Sciences) Version 22.0 (SPSS Inc., Chicago, IL, USA). Data were presented mean and standard deviation (SD). The normality of data distribution was evaluated by using Shapiro-Wilk or Kolmogorov-Smirnov tests. The two-tailed Student’s t-test was used to compare differences in the mean values of normally distributed variables between malign and benign prostate group. Mann-Whitney U test was used to compare malign and benign prostate group for not normally distributed. The aim of this research was to establish and validate new equations in men diagnosed with malign and benign prostate cancer. Measured REE-related variables were analyzed statistically with simple linear regression. Measured REE was used in a stepwise multiple regression analysis (backward selection technique), which included integrating all factors with a p value in the simple linear regression analysis of less than 0.20. The predictive equations' accuracy was determined both for individuals and for the entire population. Accuracy at the group level was determined by calculating the average percentage difference between the predicted and measured REE. Individual accuracy was evaluated by the proportion of patients who’s predicted REE was within ±10% of their measured REE. Accurate predictions were determined to be between 90% and 110% of the measured REE, with values below 90% being labeled as an under-prediction and values above 110% as an over-prediction. A more accurate representation of the prediction made by this model in our data set was found by calculating its root mean squared error (RMSE). Furthermore, the bias (mean difference and standard deviation of the differences) and the 95% confidence intervals for the bias were calculated to evaluate the degree of agreement between indirect calorimetry and the 11 equations of interest. In this study, we compared malign and benign prostate cancer patients whose REE was measured with an indirect calorimeter to those whose REE was calculated using different predictive equations, and we did so use a Bland-Altman plot and analysis. Both the mean difference and the limits of agreement, which were calculated as the mean difference plus or minus 2 times the standard deviation of the differences, were plotted as horizontal lines in Bland-Altman plots of individuals with malign and benign prostate group. In all cases, significance was determined using a two-tailed p value of < 0.05. Insert Table 1 Here Results The primary characteristics of the prostate subjects are shown in Table 2 . The mean age body weight, BMI, fat percentage, body fat mass, FFM were not significant different between two groups (p > 0.05). Malign prostate patient group PSA Total (PSAT) and measured REE values (4.93±5.44 ng/ml, 1722.9±272.69kcal/d respectively) were statisticaly significant higher than benign group (1.76±0.73ng/ml, 1670.5±266.76 kcal/d respectively) (p < 0.05). In the malign prostate patients, the highest density was found in the tumor grade group as group four 13(31.7%), and D'Amico risk classification as (high risk) 17(41.5%) in Table 2 . Table 2 The Main Characteristics of Subjects Malign prostate patient group (n:41) Benign prostate patient group (n:42) Variables Mean ± SD Mean ± SD Age (year) 66.0±6.64 64.5±5.94 Weight (kg) 82.0±12.50 80.5±12.72 Height (cm) 170.0±5.12 168.2±5.24 BMI (kg/m 2 ) 28.3±3.99 28.5±4.44 Percent of body fat (%) 26.8±4.97 26.7±6.27 FM (kg) 22.3±6.80 22.0±7.86 FFM (kg) 56.8±6.67 55.6±6.80 PSA Total (ng/ml)** 4.93±5.44 1.76±0.73 Measured REE (kcal/day) ** 1722.9±272.65 1670.7±266.76 VO 2 (ml/min) 250.2±40.70 240.4±40.92 Vp (l/min) 32.5±5.37 33.0±6.50 FeO 2 (%) 19.8±0.13 19.9±0.13 Tumor Grade (%) 1 9 (22,0) - 2 6 (14,5) - 3 9 (22,0) - 4 13 (31,7) - 5 4 (9,8) - D’Amico risk classification (%) Low Risk 10 (24,4) - Medium Risk 14 (34,1) - High Risk 17 (41,5) - ** The difference between male and female groups were found statistically significant (p < 0.05). BMI: Body Mass Index, FM: Fat Mass, FFM: Fat Free Mass, PSA: Prostate Specific Antigen, REE: Resting Energy Expenditure Insert Table 2 Here Table 3 shows Measured and calculated REE values of malign and benign prostate groups are compared in Table 3 . The malign prostate cancer group predicted-measured (kcal/d) differences ranged from − 630 (Barcellos II equation) to 330 (Mifflin-St.Jeor equation) after the comparison. This measured for benign prostate group ranged from − 687 (Barcellos II equation) to 297 (Mifflin-St.Jeor equation) after the comparison. The bias of the equations varied from − 36.5% (Barcellos II Equation) to 0.0% (New Equation-MPG) in maling group. This measured for benign group ranged from − 41.1% (Barcellos II Equation) to 0.0% (New Equation BPG). The highest RMSE value was Barcellos II Eq. (691 kcal/d) and also smallest RMSE value was the New Equation MP (149 kcal/d) in malign prostate patients. Similarly in benign group New Equation-BPG was the smallest RMSE (202 kcal/d) and the Barcellos II equation was the highest RMSE (734 kcal/d) in Table 3 . In malign prostate patients, the percentage of accurate-prediction value of equations varied from 7.3 (Barcellos II) to 80.9 (New Equation MPG). This measured for benign prostate patients accurate-prediction value of equations varied from 2.4 (Barcellos II) to 64.2 (New Equation BPG). The under-prediction values varied from 0% (Barcellos II and Mifflin-St.Jeor equation) to 39.0% (Cunningham and Henry equation) in addition the highest over-prediction value was found in the Barcellos II Eq. (92.7%). The benign prostate group under-prediction value varies from 0% (Barcellos II equation) to 40.5% (Cunningham equation). The highest over-prediction value was found in the Barcellos II Eq. (97.6%). In addition, the maximum negative error values in malign group varied from-0.3% (Mifflin-St.Jeor equation) to -103.2% (Barcellos II equation). The maximum positive error value was the highest for the Mifflin-St.Jeor equation at 33.0% and lowest for the Barcellos II equation at 4.4%. The benign prostate group maximum negative error varied from-18.2% (Mifflin-St.Jeor equation) to -131.7% (Barcellos II equation). In benign prostate cancer group the maximum positive error value was the highest for the Mifflin-St.Jeor equation at 35.4% and lowest for the Barcellos II equation at 3.1%. (Table 3 ). Table 3 Evaluation of measured REE with different predictive equations malign and benign prostate group based on differences predicted-measured, SD of predictive REE, percentage of accuracy, under-prediction, over-prediction, bias, maximum negative error, maximum positive error, and RMSE REE predictive equations Difference predicted-measured REE kcal/d SD Accurate-prediction % Under-prediction % Over-prediction % BIAS % Maximum negative Error % Maximum positive Error % RMSE* Malign prostate group (MPG) Harris Benedict 123 192 43.9 36.3 9.8 7.1 -16.6 22.3 226 Schofield -91 194 60.9 2.4 36.7 -5.3 -43.0 13.8 212 IretoneJones 11 235 48.8 26.8 24.4 -0.6 -47.4 21.0 233 Mifflin-St.Jeor 330 198 26.8 - 73.2 19.2 -0.3 33.6 384 Barcellos Equation II -630 288 7.3 - 92.7 -36.5 -103.2 4.4 691 Cunningham 125 196 56.1 39.0 4.9 7.2 -26.3 21.9 231 Owen, 11 199 58.5 19.5 22.0 -0.6 -36.8 18.9 197 Wang, 93 196 56.1 31.7 12.2 5.4 -29.5 20.6 215 IOM 74 197 53.7 31.7 14.6 4.3 -23.6 22.0 208 Henry 100 213 43.9 39.0 17,1 5.8 -21.5 23.8 234 21 kcal/kg/d 0.9 196 58.5 17.1 24.4 0.0 -28.2 15.5 194 New Equation MPG 0.6 151 80.9 7.0 12.1 0.0 -28.0 11.7 149 Benign prostate group (BPG) Harris Benedict 90 214 50.0 33.3 16.7 5.4 -31.7 27.2 230 Schofield -126 209 57.1 4.8 38.1 -7.5 -66.8 12.2 242 IretoneJones -49 234 54.8 14.2 31.0 -2.9 -66.6 15.2 237 Mifflin-St.Jeor 297 213 14.2 4.8 81.0 17.7 -18.2 35.4 364 Barcellos Equation II -687 261 2.4 - 97.6 -41.1 -131.7 3.1 734 Cunningham 99 212 42.9 40.5 16.7 5.9 -45.5 20.7 231 Owen, -25 211 61.9 14.3 23.8 -1.5 -58.8 17.3 210 Wang, 67 212 46.7 35.7 16.7 4.0 -49.0 18.8 220 IOM 39 211 59.5 23.8 16.7 2.3 -47.2 21.0 213 Henry 69 217 43.9 39.0 17.1 4.1 -39.8 24.9 226 21 kcal/kg/d -21 231 50.0 23.8 26.2 -1.2 -34.2 22.4 229 New Equation BPG -12 208 64.2 19.0 16.8 0.0 -49.0 19.9 202 *RMSE (kcal/d): Root mean squared error, REE: Resting Energy Expenditure and BIAS: mean difference between measured and predicted, BPG: Benign Prostate Group, MPG: Malign prostate cancer group, İOM: Institute of Medicine Insert Table 3 Here Most equations showed a drift, indicating a systematic error: the larger range of the measured values, the greater discrepancy between the highest and lowest values (Table 3 ). Differences, as well as lower and upper equalization limits, malign and benign prostate group values calculated with different equations and measures using the direct incalorimetric method, are shown in Bland-Altman plots (Fig. 1 and Fig. 2 , respectively). Only the Owen, equation of 21kcal/kg/day, and New Equation-MPG were found to have a normally distributed sample of malign prostate cancer subjects, and were therefore approved for use based on Bland-Altman plots. However, only the equation of 21kcal/kg/day and the New Equation-BPG showed a random distribution in prostate benign cancer subjects, so the other equations were deemed unsuitable for use in REE estimation. Insert Fig. 1 and Fig. 2 Here Development and Validation of the new REE Predictive Equations At first, we treated REE as the dependent variable and each of the other variables listed in Table 4 as the independent variables, running a univariate regression for each independent variable on its own. Univariate regression analysis revealed that all factors, besides groups, were statistically significant. Table 4 Result of malign and benign prostate group univariate regression analysis Variable malign prostate group Coefficients (95% CI) Adjusted R 2 p value Age (years) -4.650 (-17.860; 8.559) 0.000 0.481 Body Weight (kg) 15.906 (11.098; 20.714) 0.523 < 0.001 Height (cm) 17.248 (0.932; 33.564) 0.082 0.039 Waist circumference (cm) 16.811 (8.618; 25.003) 0.306 < 0.001 BMI (kg/m 2 ) 45.999 (29.701; 62.298) 0.411 < 0.001 FFM (kg) 29.335 (20.124; 38.546) 0.503 < 0.001 Fat Mass (kg) 21.894 (11.035; 32.752) 0.281 < 0.001 Body Fat Percentage (%) 16.283 (-0.681; 33.247) 0.065 0.059 PSA Total (ng/ml) -1.405 (-3.691; 0.881) 0.013 0.221 Variable benign prostate group Coefficients (95% CI) Adjusted R 2 p value Age (years) -6.270 (-20.463; 7.922) 0.000 0.377 Body Weight (kg) 13.094 (7.864; 18.324) 0.376 < 0.001 Height (cm) 9.725 (-6.227; 25.677) 0.012 0.225 Waist circumference (cm) 11.806 (4.644; 18.948) 0.218 0.002 BMI (kg/m 2 ) 33.961 (18.169; 49.761) 0.304 < 0.001 FFM (kg) 23.902 (13.964; 33.839) 0.356 < 0.001 Fat Mass (kg) 15.516 (5.870; 25.162) 0.189 0.002 Body Fat Percentage (%) 11.971 (-1.070; 25.012) 0.056 0.071 PSA Total (ng/ml) 9.112 (-8.284; 26.508) 0.003 0.296 Insert Table 4 Here Modeling and multiple regression analysis were performed on all relevant variables. The analysis variables were selected using the backwards method. In the end, the model included both body weight (WT), Height (HT), Fat Free Mass (FFM), Fat Mass (FM) and Prostate Specific Antigen Total (PSAT) as independent variables. Table 5 shows the outcomes of a multiple regression analysis. Table 5 Result of malign and benign prostate group canopy multiple regression analysis Variable malign prostate group Coefficients (95% CI) p value Constant 3192,258 (909,754; 5474,671) 0.007 WT 208,326 (103,911; 312,741) < 0.001 HT -20,285 (-35,329; 5,042) 0.011 FFM -187,549 (-295,044; 80,754) 0.001 FM -203,241 (-309,408; 97,074) < 0.001 PSAT 4,194 (1,654; 6,818) 0.003 Variable benign prostate group Coefficients (95% CI) p value Constant 615,922 (189,491; 1042,353) 0.006 WT 13,094 (7,864; 18,324) < 0.001 Adjusted R 2 for the model malign prostate cancer patient: 0.548 Adjusted R 2 for the model benign prostate cancer patient: 0.375 WT: Body Weight, FM: Fat Mass, HT: Height and PSAT: PSA Total Insert Table 5 Here The equation and results taken from Table V are given below. New prediction equation for malign prostate patient REE = 3192,258+(208,326* WT)-(20,285*HT)- (187,549* FFM)-(203,214*FM)+(4,194* PSAT) New prediction equation for benign prostate patient REE = 615,922+ (13,094* WT) Tables 3 show the results of an internal cross-validation test performed on this equation. The values of the newly developed (New Equation MPG) for men with prostate cancer are as follows: difference predicted-measured REE value: 0.6 kcal/d, SD of predictive REE: 151, the percentage of accurate prediction: 80.9, the percentage under-prediction: 7.0, the percentage over-prediction: 12.1, the percentage bias: 0.0, the percentage maximum negative error: -11.7, the percentage maximum negative error: 28.0, and RMSE: 149 kcal/d (Table 3 ). The values of the newly developed equation (New Equation BPG) for men with benign prostate group are as follows: difference predicted-measured REE value: -12 kcal/d, SD of predictive REE: 208, the percentage of accurate prediction: 64.2, the percentage under-prediction: 19.0, the percentage over-prediction: 16.8, the percentage bias: 0.0, the percentage maximum negative error: -49.0, the percentage maximum negative error: 19.9, and RMSE: 202 kcal/d (Table 3 ). Discussion An accurate calculation of energy expenditure (REE) is necessary for estimating energy needs in prostate cancer. The purpose of this research was to evaluate the accuracy of the established new equation for predicting REE in malign and benign prostate patients versus the accuracy of the previously used predictive equations based on REE measured by indirect calorimetry. In the present study findings demonstrated that the previous equations used to predict the REEs of adults with prostate cancer (over the age of 40) show a large disparity between the two, contain a large number of errors, and frequently result in over- or underestimating REE. The factors of fat-free mass (FFM), body size, age, gender, and body fat, among others, are the most important in determining resting metabolic rate [ 41 ]. The study emphasized the importance of total body fat mass, fat free mass, and abdominal adiposity in determining RMR [ 42 ]. Table 2 was present the subjects' primary characteristics, but no statistical significance was found. This situation was thought to be related to the homogeneous distribution of the groups. Many factors could contribute to errors in estimating REE in cancer patients. To begin, metabolic abnormalities that may occur in cancer patients (such as tumor energy demand, systemic inflammation, obesity, FFM, and fat mass (FM) metastases in advanced cancer stages can impact the performance of REE prediction equations [ 7 , 43 – 45 ]. When making nutritional recommendations for cancer patients, it is important to first determine whether or not their metabolism and energy expenditure have been altered [ 46 ]. With so many equations involved in estimating energy expenditure, it's easy to see how mistakes can add up over time, which can reduce the impact of the intervention. Consequently, the best equation for estimating REE has not yet been determined [ 47 , 48 ]. Evaluation of FitMate GS with canopy-hood REE with different predictive equations in malign and benign prostate group values was presented in Tables 3 . An accurate prediction was defined as one that fell within the range of 90% and 110% of the actual REE. Under prediction was defined as a prediction of less than 90% and over prediction as a prediction of more than 110%. It was found that the percentage of adult hospital patients whose REE were correctly predicted was low, ranging from 8–49% across all equations in a study [ 48 ]. In another study, the acurate of correctly predicted REE was found to vary between 9.6% and 62% in all equations [ 49 ]. In the present study, the percentage of accurate prediction REE obtained from REE predictive equations varied between 2.4% and 60.9% in previous equations. The percentage of accurate prediction REE obtained from the new equations was found to be 80.9% in the maling prostate cancer group. This rate has the highest accurate prediction rate with 64.2% in the benign prostate group for new equation. Bias was calculated as the percentage disparity between the predicted and measured REE. It was found in a study that the REE from FitMate GS exhibited a fanning effect, with smaller REE values exhibiting a narrower spread of biases, and a positive proportional bias being present. The FitMate GS showed little individual variation and high group precision because of its low bias [ 32 ]. As one study found in a sample of cancer patients, just over half had their REE predicted correctly using the Harris-Benedict, Owen et al., Mifflin et al., or 21 kcal/kg methods. Spread of bias was clearly visible for the group of cancer patients, with increasing REE values between measured REE and REE predicted by the equations of Cunningham et al. and Wang et al. The observed bias with REE predicted by the equation of Schofield showed a tendency toward underestimating measured REE with increasing REE values, but this trend was not statistically significant [ 46 ]. Using classical equations (Harris Benedict, Schofield, IretoneJones, and Mifflin-St.Jeor), researchers found that the mean of the measured REE was greater than the classical equations in a study of patients with digestive cancer [ 14 ]. Numerous studies have verified the Schofield Eq. (1985), making it one of the most widely used equations to date. When applied to critically ill patients and the majority of patients overall, it was deemed inadequate for estimating REEs [ 50 , 51 ]. Although not statistically significant, the bias observed with REE predicted from the equation of Schofield showed a tendency toward underestimation of measured REE with increasing REE values in a study on the determination of resting energy expenditure in patients receiving anticancer treatment [ 46 ]. Except for men over the age of 60, Henry and equations gave lower values than Schofield equations. Henry's equations were the most reliable in males [ 52 ]. One study highlighted the fact that the Schofield equation most suitable for adults [ 53 ]. The relevance of measuring REE by indirect calorimetry in order to better adequately provide nutritional support to cancer patients is supported by the Barcellos Equations, allowing for less predictive and more accurate mean REE estimation in cancer patients [ 14 ]. The results of this research showed that the Barcellos II equation is more accurate than the existing equations for estimating REEs. Patients in hospitals were less likely to develop malnutrition when REE was accurately estimated and used to guide nutritional intervention [ 28 ]. Cunningham proposed an alternative equation that takes into account the correlation between resting metabolic rate (RMR) and fat-free mass (FFM). In a study involving both normal-weight and obese male subjects, served as the basis in equation [ 54 ]. In the study, Cunningham equation gave the last one in the estimation of REE of male individuals [ 55 ]. Similarly, the IOM equation, used for predicting the REEs was found to have low accuracy and high bias and RMSE value [ 23 ]. The result, related to the all equation was presented in Tables 3 . This is widely assumed to have its roots in racial and genetic predispositions. Many studies, including those that provided funding for this one, have recommended that individual societies adopt the equations that have been tailored to their needs. Since none of the existing prediction equations were appropriate for a population of men with prostate cancer, in this study was developed a new prediction equation. Consistent with expectations, the new equation found that WT, height, FFM, FM and PSA Total were the strongest predictors of REE, accounting for R 2 :54,8% of the variance in REE among men with malign prostate group and accounting for R 2 :37,5% of the variance in REE among men with benign prostate group in Table 5 . The findings here corroborate those of other study. This study find was consistent with those of a previous one, which also founded that FM was a major factor in determining adults' REEs [ 56 ]. Genetics may also account for the variation in REE between populations, which is known to be linked to FM and WT. Since body composition equations are usually population-specific, they should be more appropriate for use as predictive equations for REE [ 57 ]. In our opinion, the information we gleaned from the outcomes of the malign and benign prostate group FitMate GS with canopy-hood multiple regression analysis will provide us more precision in predicting the REE particular to this group of PSAT value, which is incorporated into the malign prostate group equation. Conclusion and future directions In conclusion, this study demonstrated that there was substantial variation in REE estimation precision across the predictive equations. As a result, optimizing treatment and survival rates for people with prostate cancer necessitates estimating REEs. Our research was the first to synthesize a large number of REE predictive equations and use FitMate GS with canopy-hood to create a new prediction equation specifically for malign and benign prostate cancer patients. A new prediction equation was recommended for accurate prediction of the REEs of prostate cancer when indirect calorimetry was unavailable. They would be practical to determine the energy requirement for treating malign and benign prostate cancer by using the newly developed equations in the prediction of REE in the clinic. This study had some trengths and limitations. This is the first research to examine the REEs of patients with benign and malignant prostate cancer as determined by indirect calorimetry. The current study is the initial step in evaluating REE using new predictive equations in these groups. The findings are representative of this sample population, and it would be advantageous to apply the equations created for prostate cancer patients in studies with larger samples. Abbreviations BMR: Basal Metabolic Rate FFM: Fat Free Mass IOM: Institutes of Medicine REE: Resting Energy Expenditure RMSE: The root mean squared error SD: Strandard Deviation PSAT: Prostate Specific Antigen Total Declarations Ethics approval and consent to participate Ethical approval was obtained from the Gazi University Ethic Committie with decision number 4 on 7 April 2020. Clear explanations were provided for the parents with regard to the purpose of the study, after which written informed consent was obtained from all the parents in accordance with the Declaration of Helsinki (World Medical Association). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors declare that they have no competing interests. Authors’ contributions T.K.; investigation, conceptualization, data curation, formal analysis, methodology, writing - review & editing. N.A.T; investigation, conceptualization, methodology, supervision, project administration. S.Y. and T.S.S; supervision, review, and editing. All authors have read and agreed to the published version of the manuscript Acknowledgements All the men with malign and benign prostate cancer who agreed to take part in this study are deeply appreciated. Sincere gratitude is extended for their timely and enthusiastic assistance. None of the authors had a personal or financial conflict of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The Cosmed-FitMate GS Indirect Calorimetry with canopy-hood (Rome, Italy) system was made available to us by the Elsa Ortopedi firm, and we would also like to thank Mithat EMRİ and Erdem BİLİR for their essential contributions to the our study. Authors’ information 1 Gümüşhane University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Gümüşhane, Turkey (T.K.) 2 Gazi University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Ankara, Turkey(N.A.T.) 3 Gazi University, Faculty of Medicine, Department of Urology, Ankara, Turkey(S.Y. and T.S.S) References Platz EA: Energy imbalance and prostate cancer . The Journal of nutrition 2002, 132 (11):3471S-3481S. Valle-Mendiola A, Soto-Cruz I: Energy metabolism in cancer: the roles of STAT3 and STAT5 in the regulation of metabolism-related genes . Cancers 2020, 12 (1):124. Westerterp KR: Control of energy expenditure in humans . European journal of clinical nutrition 2017, 71 (3):340-344. 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Reeves MM, Battistutta D, Capra S, Bauer J, Davies PS: Resting energy expenditure in patients with solid tumors undergoing anticancer therapy . Nutrition 2006, 22 (6):609-615. Cordoza M, Chan L-N, Bridges E, Thompson H: Methods for Estimating Energy Expenditure in Critically Ill Adults . AACN advanced critical care 2020, 31 (3):254-264. Kruizenga HM, Hofsteenge GH, Weijs PJ: Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients . Nutrition & metabolism 2016, 13 (1):1-9. Almajwal AM, Abulmeaty M: New predictive equations for resting energy expenditure in normal to overweight and obese population . International Journal of Endocrinology 2019, 2019 . Kamimura MA, Avesani CM, Bazanelli AP, Baria F, Draibe SA, Cuppari L: Are prediction equations reliable for estimating resting energy expenditure in chronic kidney disease patients? Nephrology Dialysis Transplantation 2011, 26 (2):544-550. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4711548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334401124,"identity":"6ada2922-bfe6-4c50-9028-eb6535087ecb","order_by":0,"name":"Tevfik Koçak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACHhBxwAbMZoaKGeDXAdGShq4lgaCWwyRosec5fPDDjzPn7TZcO2P8uaDmjhwDe/M2CcYf93DbwtuWLNlz43byhts5ZtIzjj0zZuA5VibBkFCMWws/jxkDz4fbyQZALcw8bIcTGyRyzIBacLsMpIXxz4dzIC3Gn3n+Ha5vkH9DQAtvD9DwGwfsgFoMpHnbDicwSPAQ0HLmWLK0zJnkBMnbaWXSvH2HDdt40ootEtJwa2HvST748c0xO3u+28mbP/N8OyzPz354440PNri1wEBiA4zFBiIIawDGDxFqRsEoGAWjYKQCADWgUShfuWElAAAAAElFTkSuQmCC","orcid":"","institution":"Gümüşhane University","correspondingAuthor":true,"prefix":"","firstName":"Tevfik","middleName":"","lastName":"Koçak","suffix":""},{"id":334401125,"identity":"e7a11e12-54ed-4437-bfb4-5bff1259f956","order_by":1,"name":"Nilüfer Acar Tek","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Nilüfer","middleName":"Acar","lastName":"Tek","suffix":""},{"id":334401126,"identity":"a9de8bda-ea45-4394-a212-b43275e077d5","order_by":2,"name":"Süleyman YEŞİL","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Süleyman","middleName":"","lastName":"YEŞİL","suffix":""},{"id":334401127,"identity":"d789d980-1104-4851-a327-eef7f28e919b","order_by":3,"name":"Tevfik Sinan SÖZEN","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Tevfik","middleName":"Sinan","lastName":"SÖZEN","suffix":""}],"badges":[],"createdAt":"2024-07-09 11:06:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4711548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4711548/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12894-024-01648-9","type":"published","date":"2025-03-27T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62219053,"identity":"2cb7c221-4ba4-4f69-9cd3-5ad24ba3e9a7","added_by":"auto","created_at":"2024-08-11 12:10:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":784745,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman a scatter plot comparing the 11 different predictive equations for REE using data from a group of malign prostate cancer patients who were measured using an indirect calorimeter with a canopy.\u003c/p\u003e\n\u003cp\u003eTwo standard deviations (SDs) above and below the mean are shown by the dashed lines (limits of agreement)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4711548/v1/9d7f7bb1d5598553683b1b46.png"},{"id":62218401,"identity":"bccca763-fa37-41e2-b7e9-f0b14b0c1c27","added_by":"auto","created_at":"2024-08-11 12:02:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":798050,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman a scatter plot comparing the 11 different predictive equations for REE using data from a group of benign prostate cancer group who were measured using an indirect calorimeter with a canopy.\u003c/p\u003e\n\u003cp\u003eTwo standard deviations (SDs) above and below the mean are shown by the dashed lines (limits of agreement)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4711548/v1/9337316a9cbf22816ff7ff29.png"},{"id":79604901,"identity":"d3f75fb2-2b07-482b-8816-7776f2d90770","added_by":"auto","created_at":"2025-03-31 16:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5303057,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4711548/v1/37a59e9b-0a74-4dc4-941e-ddc38f37b288.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detection of resting energy expenditure in prostate cancer: Assessment of energy prediction equations","fulltext":[{"header":"Background","content":"\u003cp\u003eAn increased risk of prostate cancer is discussed in relation to an excessive energy intake in comparison to energy expenditure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Energy is essential for development, reproduction, structure maintenance, and environmental adaptation in all cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Total energy expenditure is the sum of all energy consumed by an organism over the course of a day [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The majority of energy needs are met by basal/resting metabolic rate (BMR/REE), which accounts for roughly 60\u0026ndash;70% of total energy expenditure [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Both the resting energy expenditure and the amount of physical activity contribute significantly to total energy expenditure, and both were modifiable in cancer patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prostate cancer patients may experience metabolic changes that alter their overall energy expenditure depending on the stage and type of the disease. The significance of resting energy expenditure in an individual's total energy expenditure is increased by the advanced age and declining physical activity of cancer patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Reduced physical performance and metabolic response, fatigue, incrased inflammatory, weight loss, malnutrition, and even obesity have all been linked to inaccurate estimates of total energy requirements [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the other hand, to prevent increases in fat free mass and aggravating comorbidities, energy recommendations for overweight or obese patients shouldn't be exaggerated [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To avoid undernutrition and obesity in patients, it is essential to accurately determine changes in energy expenditure and estimate each person's specific energy needs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For the patients' survival and mobilization, it was crucial to receive adequate and balanced nutrition tailored to their particular cancer. The accuracy of using predictive equations to estimate the energy requirements of patients with various cancer types may be low due to the fluctuating energy metabolism and metabolic variations of cancer patients. As a result the primary strategy should, whenever possible, be to evaluate each patient individually while measuring energy expenditure via indirect calorimetry. It will be more accurate to plan a unique nutrition therapy program by measuring REE with indirect calorimetry for the patient's treatment and essential comfort in order to prevent negative energy balance in those with prostate cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeasuring energy expenditure via pulmonary gas exchanges with indirect calorimetry (IC), is widely regarded as the gold standard. It's a non-invasive method that improves clinical outcomes by tailoring nutritional support prescriptions to patients' individual metabolic requirements. This method is accurate however necessity of equipment, expensive, time-consuming and requiring expert personal [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, equations have been developed to predict REE across all age groups since the 1980\u0026rsquo;s. A number of REE prediction equations have been developed healthy and clinical usage; these include the HarriseBenedict (1919), Schofield (1985), IretoneJones (2002), Mifflin-St.Jeor (1990), Barcellos Equation II (2020), Cunningham (1991), Owen (1986), Wang (2000), IOM (2004), Henry (2004), and 21 kcal/kg/d Silver (2013) models which are based on weight, age, BMI, height or gender and mid-upper arm circumference. Many studies have evaluate the accuracy of the resting energy expenditure prediction equations in diffent patients groups [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The use of previous predictive equations shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the malign and benign prostate group was discussed in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive Equations for REE in malign and benign prostate group used in the present study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor and Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredictive Equation for REE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarriseBenedict, 1919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 66.4730 + (13.7516WT) + (5.0033 HT) - (6.7550 AGE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg, HT:cm, AGE:years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchofield, 1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: (( 0.048 *WT\u0026thinsp;+\u0026thinsp;3.653) * 239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIretoneJones, 2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 1784 - (11 AGE ) + (5 WT) + (244 SEX) + (239T) + (804B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg AGE:years SEX: Male:0, Female:1 T: trauma (absence : 0 and presence : 1) B : burnt (absence : 0 and presence : 1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMifflin-St.Jeor, 1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: (10*WT) + (6.25*HT) \u0026ndash; (5*AGE)\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg, HT:cm, AGE:years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarcellos Equation II, 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 58(MUAC)\u0026thinsp;+\u0026thinsp;621.4( SEX ) \u0026minus;\u0026thinsp;425(O) \u0026minus;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMUAC: mid-upper arm circumference SEX: Male:0, Female:1 O: obesity (0: BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 and 1: BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m2).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCunningham1, 1991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 370\u0026thinsp;+\u0026thinsp;21.6*FM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFM:kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwen, 1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 879\u0026thinsp;+\u0026thinsp;10.2*WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang, 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 24.6*FFM\u0026thinsp;+\u0026thinsp;175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFFM:kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOM, 2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 293 - (3.8*AGE) + (456.4 *HT) + (10.12*WT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg, HT:cm, AGE:years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHenry, 2004\u003c/p\u003e \u003cp\u003eheight and weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026ndash;60\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: Men age 30\u0026ndash;60 years: 11.4 \u0026times; WT + 541 \u0026times; HT \u0026minus; 137\u003c/p\u003e \u003cp\u003ekcal/d: Men age\u0026thinsp;\u0026gt;\u0026thinsp;60 years: 11.4 \u0026times; WT + 541 \u0026times; HT \u0026minus; 256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHT:meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21 kcal/kg/d Silver, 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekcal/d: 21 \u0026times; WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWT:kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eWT: Weight, HT: Height, FM: Fat mass,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen directly measuring an individual's REEs is not possible, predictive equations may be used in practice instead. Thus, an accurate prediction of REE value is necessary for an accurate prediction of TEE [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].Since the invention of compact indirect calorimeters, routine REE testing has become feasible. Indirect calorimetry, which involves the measurement of oxygen consumption, allows for a quick and precise estimate of REE. It has recently been accepted as a valid surrogate for the gold standard of direct calorimetry [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. There were both advantages and disadvantages to using this method. Indirect calorimetric methods are more challenging to implement on a large scale due to the higher cost of measurement equipment and the need for trained personnel [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Other indirect measuring devices were challenging to use in cancer patients due to their mouthpiece and nasal designs. When it was come to comfort and ease of measurement, however, portable indirect measurement tools or those with a canopy-hood rather than a face mask are ideal [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Because of limitations imposed by clinical applications, scientists developed body composition parameters and anthropometric measurement systems predicated on REEs for adults and older patients. The use of predictive equations has become widely for field of REE predictions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Indirect calorimetric measurement values were the dependent variable, while demographics (age, gender, ethnicity), anthropometrics (weight, height), and body composition are the independent variables in these equations (adipose tissue, lean tissue). Similar to the regression analysis-based equations, these equations were developed using data collected from the population of healthy people at large to provide fast and easy solutions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A variety of clinically applicable predictive equations have been developed, and these were utilized to estimate REE [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. There were a number of equations developed in healthy cancer populations and that can be used to determine REE for people who have cancer. But there was still no agreement on what the best equation was for determining REE for different types of cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To date, some studies examining the accuracy of REE prediction equations have used in conclusion current equations were not providing valid and accurate predictions in cancer patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of the present study was to compare measured resting energy expenditure (REE) by an indirect calorimetry model (FitMate GS with canopy-hood) and established new equation to calculated by previous predictive equations in individuals with malign and benign prostate patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e The study was conducted between December 2020 and May 2021 and 40 individuals over the age of 40 who applied to the Urology Clinic of Gazi University Faculty of Medicine, with malign prostate cancer according to the pathology result and benign prostate prostate cancer in the pathology result. In order to measure REE in individuals with malign and benign prostate cancer by indirect calorimetry method, compare with REE values estimated by equations and develop the most appropriate equation for this group. Total 83 (41 malign prostate cancer and 42 benign prostate canceraged over 40 (65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30 years) volunteers were recruited in the study.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were included being over the age of 40, male, having pathology results indicating either prostate cancer (malign prostate tissue) or benign prostate tissue (without prostate cancer), and not having undergone surgical intervention. The exclusion criteria were; having endocrine and metabolic disorders, chronic kidney and liver disease, sleep disorders, psychiatric and cognitive disorder, heart failure, respiratory diseases such as asthma, influenza, colds, regular medication and have received chemotherapy and radiotherapy during the all measurements.. Ethics committee approval of the study was obtained from the Gazi University Faculty of Medicine Clinical Research Ethics Committee with decision number 4 on 7 April 2020. Signed informed consent was obtained from all patients, and this study was conducted by the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Measurements and Body Composition Analysis\u003c/h2\u003e \u003cp\u003eAnthropometric measurements and body composition analysis were performed by the researcher, who is a specialist dietitian, in the early morning hours after at least 8 hours of night fasting. Body weight measurement and body composition analysis (fat mass, percent of fat, fat free mass (FFM) were made by using the TANITA BC 601. The scale's specifications include a frequency measurement range of 50 kHz, a current measurement range of 100 \u0026micro;A, and a voltage measurement range of 150\u0026ndash;1200 Ω. Height was measured (cm) with feet close together and the head was at Frankfort plane with a 0.1 cm sensitive portable stadiometer. An individual's waist circumference (in centimeters) was recorded to the closest 0.1 centimeter by putting a nonelastic tape measure midway between the lowest rib border margin and the iliac crest at the end of a normal expiration without the tape compressing the skin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eResting Energy Expenditure (REE)\u003c/h2\u003e \u003cp\u003eREE measurements were made by using the Cosmed- FitMate GS Indirect Calorimetry with canopy-hood (Rome, Italy). FitMate is a reliable and valid system for measuring oxygen - FitMate GS Indirect Calorimetry with canopy-hood consumption and RMR in many studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Fitmate GS measures VO\u003csub\u003e2\u003c/sub\u003e consumption and calculates the RMR by estimating VCO\u003csub\u003e2\u003c/sub\u003e production from a fixed RQ of 0.85 based on the abbreviated Weir equation as it does not contain a VCO\u003csub\u003e2\u003c/sub\u003e sensor [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In healthy adults and outpatients, Fitmate GS with canopy-hood has been validated against the gold standard device DELTATRAC and Douglas bag. The Canopy-hood System is a convenient method of performing resting energy expenditure testing. It is ideal for subjects who may experience discomfort with mouthpieces or masks. It is an advanced metabolic measurement system option [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Routine procedures for measuring RMR were followed. Calibration was carried out before each measurement in accordance with the manufacturer's instructions. A canopy-hood was then placed over the patient\u0026rsquo;s head. When the VO\u003csub\u003e2\u003c/sub\u003e coefficient of variation was less than 10%, steady state was achieved. After the instrument stabilized for 15 minutes, RMR readings were taken. During the measurement, outpatients were lying in a supine position and instructed to limit movement, talking and to avoid sleeping [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Before the canopy-hood measurement, the malign and benign prostate group individuals were instructed to remain in the supine position for 15 minutes. Then, Fitmate GS RMR measurement was made with the canopy-hood [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].The measurements were made during the early morning period between 8.00am and 10.00pm in the morning after at least 8 hours of night hunger. The participant did not engage in heavy exercise and consumed their usual diet in the day leading up to the REE measurement. Specifically, the method developed by Compher et al. (2006) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result; For FitMate GS with canopy-hood REE (kcal/day), VO\u003csub\u003e2\u003c/sub\u003e (ml/min), Vp (l/min) and FeO\u003csub\u003e2\u003c/sub\u003e (%) measurements were evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eREE Prediction Equations\u003c/h2\u003e \u003cp\u003eEleven different REE calculation equations were used to evaluate the accuracy of the measured and calculated REE values. The study included these equations; HarriseBenedict-1919, Schofield-1985, IretoneJones-2002, Mifflin-St.Jeor-1990, Barcellos Equation II-2020, Cunningham-1991, Owen-1986, Wang-2000, Institutes of Medicine (IOM)-2004, Henry-2004 and 21 kcal/kg/day (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS (The Statistical Package for Social Sciences) Version 22.0 (SPSS Inc., Chicago, IL, USA). Data were presented mean and standard deviation (SD). The normality of data distribution was evaluated by using Shapiro-Wilk or Kolmogorov-Smirnov tests. The two-tailed Student\u0026rsquo;s t-test was used to compare differences in the mean values of normally distributed variables between malign and benign prostate group. Mann-Whitney U test was used to compare malign and benign prostate group for not normally distributed.\u003c/p\u003e \u003cp\u003eThe aim of this research was to establish and validate new equations in men diagnosed with malign and benign prostate cancer.\u003c/p\u003e \u003cp\u003eMeasured REE-related variables were analyzed statistically with simple linear regression. Measured REE was used in a stepwise multiple regression analysis (backward selection technique), which included integrating all factors with a p value in the simple linear regression analysis of less than 0.20.\u003c/p\u003e \u003cp\u003eThe predictive equations' accuracy was determined both for individuals and for the entire population. Accuracy at the group level was determined by calculating the average percentage difference between the predicted and measured REE. Individual accuracy was evaluated by the proportion of patients who\u0026rsquo;s predicted REE was within \u0026plusmn;10% of their measured REE. Accurate predictions were determined to be between 90% and 110% of the measured REE, with values below 90% being labeled as an under-prediction and values above 110% as an over-prediction. A more accurate representation of the prediction made by this model in our data set was found by calculating its root mean squared error (RMSE). Furthermore, the bias (mean difference and standard deviation of the differences) and the 95% confidence intervals for the bias were calculated to evaluate the degree of agreement between indirect calorimetry and the 11 equations of interest. In this study, we compared malign and benign prostate cancer patients whose REE was measured with an indirect calorimeter to those whose REE was calculated using different predictive equations, and we did so use a Bland-Altman plot and analysis. Both the mean difference and the limits of agreement, which were calculated as the mean difference plus or minus 2 times the standard deviation of the differences, were plotted as horizontal lines in Bland-Altman plots of individuals with malign and benign prostate group. In all cases, significance was determined using a two-tailed p value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eHere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe primary characteristics of the prostate subjects are shown in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. The mean age body weight, BMI, fat percentage, body fat mass, FFM were not significant different between two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Malign prostate patient group PSA Total (PSAT) and measured REE values (4.93\u0026plusmn;5.44 ng/ml, 1722.9\u0026plusmn;272.69kcal/d respectively) were statisticaly significant higher than benign group (1.76\u0026plusmn;0.73ng/ml, 1670.5\u0026plusmn;266.76 kcal/d respectively) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the malign prostate patients, the highest density was found in the tumor grade group as group four 13(31.7%), and D\u0026apos;Amico risk classification as (high risk) 17(41.5%) in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe Main Characteristics of Subjects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMalign prostate patient group (n:41)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBenign prostate patient group (n:42)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0\u0026plusmn;6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.5\u0026plusmn;5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.0\u0026plusmn;12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.5\u0026plusmn;12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.0\u0026plusmn;5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.2\u0026plusmn;5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u0026plusmn;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u0026plusmn;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent of body fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u0026plusmn;4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u0026plusmn;6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u0026plusmn;6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u0026plusmn;7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.8\u0026plusmn;6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.6\u0026plusmn;6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA Total (ng/ml)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93\u0026plusmn;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasured REE (kcal/day) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1722.9\u0026plusmn;272.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1670.7\u0026plusmn;266.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e (ml/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.2\u0026plusmn;40.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.4\u0026plusmn;40.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVp (l/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.5\u0026plusmn;5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.0\u0026plusmn;6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.9\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTumor Grade (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (22,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (14,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (22,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (31,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (9,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eD\u0026rsquo;Amico risk classification (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (24,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (34,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (41,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e** The difference between male and female groups were found statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BMI: Body Mass Index, FM: Fat Mass, FFM: Fat Free Mass, PSA: Prostate Specific Antigen, REE: Resting Energy Expenditure\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e \u003cstrong\u003eHere\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e shows Measured and calculated REE values of malign and benign prostate groups are compared in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. The malign prostate cancer group predicted-measured (kcal/d) differences ranged from \u0026minus;\u0026thinsp;630 (Barcellos II equation) to 330 (Mifflin-St.Jeor equation) after the comparison. This measured for benign prostate group ranged from \u0026minus;\u0026thinsp;687 (Barcellos II equation) to 297 (Mifflin-St.Jeor equation) after the comparison. The bias of the equations varied from \u0026minus;\u0026thinsp;36.5% (Barcellos II Equation) to 0.0% (New Equation-MPG) in maling group. This measured for benign group ranged from \u0026minus;\u0026thinsp;41.1% (Barcellos II Equation) to 0.0% (New Equation BPG). The highest RMSE value was Barcellos II Eq.\u0026nbsp;(691 kcal/d) and also smallest RMSE value was the New Equation MP (149 kcal/d) in malign prostate patients. Similarly in benign group New Equation-BPG was the smallest RMSE (202 kcal/d) and the Barcellos II equation was the highest RMSE (734 kcal/d) in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. In malign prostate patients, the percentage of accurate-prediction value of equations varied from 7.3 (Barcellos II) to 80.9 (New Equation MPG). This measured for benign prostate patients accurate-prediction value of equations varied from 2.4 (Barcellos II) to 64.2 (New Equation BPG). The under-prediction values varied from 0% (Barcellos II and Mifflin-St.Jeor equation) to 39.0% (Cunningham and Henry equation) in addition the highest over-prediction value was found in the Barcellos II Eq.\u0026nbsp;(92.7%). The benign prostate group under-prediction value varies from 0% (Barcellos II equation) to 40.5% (Cunningham equation). The highest over-prediction value was found in the Barcellos II Eq.\u0026nbsp;(97.6%). In addition, the maximum negative error values in malign group varied from-0.3% (Mifflin-St.Jeor equation) to -103.2% (Barcellos II equation). The maximum positive error value was the highest for the Mifflin-St.Jeor equation at 33.0% and lowest for the Barcellos II equation at 4.4%. The benign prostate group maximum negative error varied from-18.2% (Mifflin-St.Jeor equation) to -131.7% (Barcellos II equation). In benign prostate cancer group the maximum positive error value was the highest for the Mifflin-St.Jeor equation at 35.4% and lowest for the Barcellos II equation at 3.1%. (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEvaluation of measured REE with different predictive equations malign and benign prostate group based on differences predicted-measured, SD of predictive REE, percentage of accuracy, under-prediction, over-prediction, bias, maximum negative error, maximum positive error, and RMSE\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eREE predictive equations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003cp\u003epredicted-measured REE kcal/d\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccurate-prediction %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnder-prediction %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOver-prediction %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIAS %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum negative\u003c/p\u003e\n \u003cp\u003eError %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum positive\u003c/p\u003e\n \u003cp\u003eError %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003eMalign prostate group (MPG)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarris Benedict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchofield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIretoneJones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMifflin-St.Jeor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarcellos Equation II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-103.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCunningham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwen,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHenry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 kcal/kg/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Equation MPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign prostate group (BPG)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarris Benedict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchofield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIretoneJones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMifflin-St.Jeor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarcellos Equation II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-131.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCunningham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwen,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHenry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 kcal/kg/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Equation BPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*RMSE (kcal/d): Root mean squared error, REE: Resting Energy Expenditure and BIAS: mean difference between measured and predicted, BPG: Benign Prostate Group, MPG: Malign prostate cancer group, İOM: Institute of Medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e \u003cstrong\u003eHere\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost equations showed a drift, indicating a systematic error: the larger range of the measured values, the greater discrepancy between the highest and lowest values (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). Differences, as well as lower and upper equalization limits, malign and benign prostate group values calculated with different equations and measures using the direct incalorimetric method, are shown in Bland-Altman plots (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, respectively). Only the Owen, equation of 21kcal/kg/day, and New Equation-MPG were found to have a normally distributed sample of malign prostate cancer subjects, and were therefore approved for use based on Bland-Altman plots. However, only the equation of 21kcal/kg/day and the New Equation-BPG showed a random distribution in prostate benign cancer subjects, so the other equations were deemed unsuitable for use in REE estimation.\u003c/p\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eInsert Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e Here\u003c/h2\u003e\n \u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eDevelopment and Validation of the new REE Predictive Equations\u003c/h2\u003e\n \u003cp\u003eAt first, we treated REE as the dependent variable and each of the other variables listed in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e as the independent variables, running a univariate regression for each independent variable on its own. Univariate regression analysis revealed that all factors, besides groups, were statistically significant.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResult of malign and benign prostate group univariate regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable malign prostate group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-17.860; 8.559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.098; 20.714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.932; 33.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.618; 25.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(29.701; 62.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(20.124; 38.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat Mass (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.035; 32.752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Fat Percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.681; 33.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA Total (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.691; 0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable benign prostate group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-20.463; 7.922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.864; 18.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-6.227; 25.677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.644; 18.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(18.169; 49.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.964; 33.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat Mass (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.870; 25.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Fat Percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.070; 25.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA Total (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-8.284; 26.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e \u003cstrong\u003eHere\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eModeling and multiple regression analysis were performed on all relevant variables. The analysis variables were selected using the backwards method. In the end, the model included both body weight (WT), Height (HT), Fat Free Mass (FFM), Fat Mass (FM) and Prostate Specific Antigen Total (PSAT) as independent variables. Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e shows the outcomes of a multiple regression analysis.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResult of malign and benign prostate group canopy multiple regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003cp\u003emalign prostate group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoefficients (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3192,258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(909,754; 5474,671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208,326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(103,911; 312,741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20,285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-35,329; 5,042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-187,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-295,044; 80,754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-203,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-309,408; 97,074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1,654; 6,818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable benign prostate group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e615,922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(189,491; 1042,353)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7,864; 18,324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e for the model malign prostate cancer patient: 0.548\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e for the model benign prostate cancer patient: 0.375\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eWT: Body Weight, FM: Fat Mass, HT: Height and PSAT: PSA Total\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e \u003cstrong\u003eHere\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe equation and results taken from Table V are given below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNew prediction equation for malign prostate patient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eREE\u0026thinsp;=\u0026thinsp;3192,258+(208,326* WT)-(20,285*HT)- (187,549* FFM)-(203,214*FM)+(4,194* PSAT)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNew prediction equation for benign prostate patient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eREE\u0026thinsp;=\u0026thinsp;615,922+ (13,094* WT)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTables\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e show the results of an internal cross-validation test performed on this equation. The values of the newly developed (New Equation MPG) for men with prostate cancer are as follows: difference predicted-measured REE value: 0.6 kcal/d, SD of predictive REE: 151, the percentage of accurate prediction: 80.9, the percentage under-prediction: 7.0, the percentage over-prediction: 12.1, the percentage bias: 0.0, the percentage maximum negative error: -11.7, the percentage maximum negative error: 28.0, and RMSE: 149 kcal/d (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). The values of the newly developed equation (New Equation BPG) for men with benign prostate group are as follows: difference predicted-measured REE value: -12 kcal/d, SD of predictive REE: 208, the percentage of accurate prediction: 64.2, the percentage under-prediction: 19.0, the percentage over-prediction: 16.8, the percentage bias: 0.0, the percentage maximum negative error: -49.0, the percentage maximum negative error: 19.9, and RMSE: 202 kcal/d (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAn accurate calculation of energy expenditure (REE) is necessary for estimating energy needs in prostate cancer. The purpose of this research was to evaluate the accuracy of the established new equation for predicting REE in malign and benign prostate patients versus the accuracy of the previously used predictive equations based on REE measured by indirect calorimetry. In the present study findings demonstrated that the previous equations used to predict the REEs of adults with prostate cancer (over the age of 40) show a large disparity between the two, contain a large number of errors, and frequently result in over- or underestimating REE.\u003c/p\u003e \u003cp\u003eThe factors of fat-free mass (FFM), body size, age, gender, and body fat, among others, are the most important in determining resting metabolic rate [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The study emphasized the importance of total body fat mass, fat free mass, and abdominal adiposity in determining RMR [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was present the subjects' primary characteristics, but no statistical significance was found. This situation was thought to be related to the homogeneous distribution of the groups.\u003c/p\u003e \u003cp\u003eMany factors could contribute to errors in estimating REE in cancer patients. To begin, metabolic abnormalities that may occur in cancer patients (such as tumor energy demand, systemic inflammation, obesity, FFM, and fat mass (FM) metastases in advanced cancer stages can impact the performance of REE prediction equations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. When making nutritional recommendations for cancer patients, it is important to first determine whether or not their metabolism and energy expenditure have been altered [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. With so many equations involved in estimating energy expenditure, it's easy to see how mistakes can add up over time, which can reduce the impact of the intervention. Consequently, the best equation for estimating REE has not yet been determined [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Evaluation of FitMate GS with canopy-hood REE with different predictive equations in malign and benign prostate group values was presented in Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. An accurate prediction was defined as one that fell within the range of 90% and 110% of the actual REE. Under prediction was defined as a prediction of less than 90% and over prediction as a prediction of more than 110%. It was found that the percentage of adult hospital patients whose REE were correctly predicted was low, ranging from 8–49% across all equations in a study [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In another study, the acurate of correctly predicted REE was found to vary between 9.6% and 62% in all equations [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the present study, the percentage of accurate prediction REE obtained from REE predictive equations varied between 2.4% and 60.9% in previous equations. The percentage of accurate prediction REE obtained from the new equations was found to be 80.9% in the maling prostate cancer group. This rate has the highest accurate prediction rate with 64.2% in the benign prostate group for new equation.\u003c/p\u003e \u003cp\u003eBias was calculated as the percentage disparity between the predicted and measured REE. It was found in a study that the REE from FitMate GS exhibited a fanning effect, with smaller REE values exhibiting a narrower spread of biases, and a positive proportional bias being present. The FitMate GS showed little individual variation and high group precision because of its low bias [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As one study found in a sample of cancer patients, just over half had their REE predicted correctly using the Harris-Benedict, Owen et al., Mifflin et al., or 21 kcal/kg methods. Spread of bias was clearly visible for the group of cancer patients, with increasing REE values between measured REE and REE predicted by the equations of Cunningham et al. and Wang et al. The observed bias with REE predicted by the equation of Schofield showed a tendency toward underestimating measured REE with increasing REE values, but this trend was not statistically significant [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Using classical equations (Harris Benedict, Schofield, IretoneJones, and Mifflin-St.Jeor), researchers found that the mean of the measured REE was greater than the classical equations in a study of patients with digestive cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies have verified the Schofield Eq.\u0026nbsp;(1985), making it one of the most widely used equations to date. When applied to critically ill patients and the majority of patients overall, it was deemed inadequate for estimating REEs [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although not statistically significant, the bias observed with REE predicted from the equation of Schofield showed a tendency toward underestimation of measured REE with increasing REE values in a study on the determination of resting energy expenditure in patients receiving anticancer treatment [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExcept for men over the age of 60, Henry and equations gave lower values than Schofield equations. Henry's equations were the most reliable in males [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. One study highlighted the fact that the Schofield equation most suitable for adults [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The relevance of measuring REE by indirect calorimetry in order to better adequately provide nutritional support to cancer patients is supported by the Barcellos Equations, allowing for less predictive and more accurate mean REE estimation in cancer patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The results of this research showed that the Barcellos II equation is more accurate than the existing equations for estimating REEs. Patients in hospitals were less likely to develop malnutrition when REE was accurately estimated and used to guide nutritional intervention [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cunningham proposed an alternative equation that takes into account the correlation between resting metabolic rate (RMR) and fat-free mass (FFM). In a study involving both normal-weight and obese male subjects, served as the basis in equation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In the study, Cunningham equation gave the last one in the estimation of REE of male individuals [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Similarly, the IOM equation, used for predicting the REEs was found to have low accuracy and high bias and RMSE value [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The result, related to the all equation was presented in Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This is widely assumed to have its roots in racial and genetic predispositions. Many studies, including those that provided funding for this one, have recommended that individual societies adopt the equations that have been tailored to their needs.\u003c/p\u003e \u003cp\u003eSince none of the existing prediction equations were appropriate for a population of men with prostate cancer, in this study was developed a new prediction equation. Consistent with expectations, the new equation found that WT, height, FFM, FM and PSA Total were the strongest predictors of REE, accounting for R\u003csup\u003e2\u003c/sup\u003e:54,8% of the variance in REE among men with malign prostate group and accounting for R\u003csup\u003e2\u003c/sup\u003e:37,5% of the variance in REE among men with benign prostate group in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The findings here corroborate those of other study. This study find was consistent with those of a previous one, which also founded that FM was a major factor in determining adults' REEs [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Genetics may also account for the variation in REE between populations, which is known to be linked to FM and WT. Since body composition equations are usually population-specific, they should be more appropriate for use as predictive equations for REE [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In our opinion, the information we gleaned from the outcomes of the malign and benign prostate group FitMate GS with canopy-hood multiple regression analysis will provide us more precision in predicting the REE particular to this group of PSAT value, which is incorporated into the malign prostate group equation.\u003c/p\u003e "},{"header":"Conclusion and future directions","content":"\u003cp\u003eIn conclusion, this study demonstrated that there was substantial variation in REE estimation precision across the predictive equations. As a result, optimizing treatment and survival rates for people with prostate cancer necessitates estimating REEs. Our research was the first to synthesize a large number of REE predictive equations and use FitMate GS with canopy-hood to create a new prediction equation specifically for malign and benign prostate cancer patients. A new prediction equation was recommended for accurate prediction of the REEs of prostate cancer when indirect calorimetry was unavailable. They would be practical to determine the energy requirement for treating malign and benign prostate cancer by using the newly developed equations in the prediction of REE in the clinic.\u003c/p\u003e\u003cp\u003eThis study had some trengths and limitations. This is the first research to examine the REEs of patients with benign and malignant prostate cancer as determined by indirect calorimetry. The current study is the initial step in evaluating REE using new predictive equations in these groups. The findings are representative of this sample population, and it would be advantageous to apply the equations created for prostate cancer patients in studies with larger samples.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMR: Basal Metabolic Rate\u003c/p\u003e\n\u003cp\u003eFFM: Fat Free Mass\u003c/p\u003e\n\u003cp\u003eIOM: Institutes of Medicine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eREE: \u0026nbsp;Resting Energy Expenditure\u003c/p\u003e\n\u003cp\u003eRMSE: The root mean squared error\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD: Strandard Deviation\u003c/p\u003e\n\u003cp\u003ePSAT:\u0026nbsp;Prostate Specific Antigen\u0026nbsp;Total\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Gazi University Ethic Committie\u0026nbsp;with decision number 4 on 7 April 2020. Clear explanations were provided for the parents with regard to the purpose of the study, after which written informed consent was obtained from all the parents in accordance with the Declaration of Helsinki (World Medical Association).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.K.; investigation, conceptualization, data curation, formal analysis, methodology, writing - review \u0026amp; editing. N.A.T; investigation, conceptualization, methodology, supervision, project administration. S.Y. and T.S.S; supervision, review, and editing. All authors have read and agreed to the published version of the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the men with\u0026nbsp;malign and benign prostate\u0026nbsp;cancer who agreed to take part in this study are deeply appreciated. Sincere gratitude is extended for their timely and enthusiastic assistance.\u0026nbsp;None of the authors had a personal or financial conflict of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;The Cosmed-FitMate GS Indirect Calorimetry with\u0026nbsp;canopy-hood\u0026nbsp;(Rome, Italy) system was made available to us by the Elsa Ortopedi firm, and we would also like to thank Mithat EMRİ and Erdem BİLİR for their essential contributions to the our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e G\u0026uuml;m\u0026uuml;şhane University, Faculty of Health Sciences, Department of Nutrition and Dietetics, G\u0026uuml;m\u0026uuml;şhane, Turkey (T.K.) \u003csup\u003e2\u003c/sup\u003e Gazi University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Ankara, Turkey(N.A.T.)\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e Gazi University, Faculty of Medicine, Department of Urology, Ankara, Turkey(S.Y. and T.S.S)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePlatz EA: \u003cstrong\u003eEnergy imbalance and prostate cancer\u003c/strong\u003e. \u003cem\u003eThe Journal of nutrition \u003c/em\u003e2002, \u003cstrong\u003e132\u003c/strong\u003e(11):3471S-3481S.\u003c/li\u003e\n\u003cli\u003eValle-Mendiola A, Soto-Cruz I: \u003cstrong\u003eEnergy metabolism in cancer: the roles of STAT3 and STAT5 in the regulation of metabolism-related genes\u003c/strong\u003e. \u003cem\u003eCancers \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(1):124.\u003c/li\u003e\n\u003cli\u003eWesterterp KR: \u003cstrong\u003eControl of energy expenditure in humans\u003c/strong\u003e. \u003cem\u003eEuropean journal of clinical nutrition \u003c/em\u003e2017, \u003cstrong\u003e71\u003c/strong\u003e(3):340-344.\u003c/li\u003e\n\u003cli\u003eWiskin A, Davies J, Wootton S, Beattie R: \u003cstrong\u003eEnergy expenditure, nutrition and growth\u003c/strong\u003e. \u003cem\u003eArchives of disease in childhood \u003c/em\u003e2011, \u003cstrong\u003e96\u003c/strong\u003e(6):567-572.\u003c/li\u003e\n\u003cli\u003eBlundell JE, Caudwell P, Gibbons C, Hopkins M, Naslund E, King N, Finlayson G: \u003cstrong\u003eRole of resting metabolic rate and energy expenditure in hunger and appetite control: a new formulation\u003c/strong\u003e. \u003cem\u003eDisease models \u0026amp; mechanisms \u003c/em\u003e2012, \u003cstrong\u003e5\u003c/strong\u003e(5):608-613.\u003c/li\u003e\n\u003cli\u003eNorgan NG:\u003cstrong\u003e Energy Expenditure and Energy Balance\u003c/strong\u003e. 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Queensland University of Technology; 2004.\u003c/li\u003e\n\u003cli\u003eRamirez-Zea M: \u003cstrong\u003eValidation of three predictive equations for basal metabolic rate in adults\u003c/strong\u003e. \u003cem\u003ePublic Health Nutrition \u003c/em\u003e2005, \u003cstrong\u003e8\u003c/strong\u003e(7a):1213-1228.\u003c/li\u003e\n\u003cli\u003eXue J, Li S, Zhang Y, Hong P: \u003cstrong\u003eAccuracy of predictive resting-metabolic-rate equations in chinese mainland adults\u003c/strong\u003e. \u003cem\u003eInternational Journal of Environmental Research and Public Health \u003c/em\u003e2019, \u003cstrong\u003e16\u003c/strong\u003e(15):2747.\u003c/li\u003e\n\u003cli\u003eBuch A, Diener J, Stern N, Rubin A, Kis O, Sofer Y, Yaron M, Greenman Y, Eldor R, Eilat-Adar S: \u003cstrong\u003eComparison of equations estimating resting metabolic rate in older adults with type 2 diabetes\u003c/strong\u003e. \u003cem\u003eJournal of Clinical Medicine \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e(8):1644.\u003c/li\u003e\n\u003cli\u003eSchusdziarra V, Wolfschl\u0026auml;ger K, Hausmann M, Wagenpfeil S, Erdmann J: \u003cstrong\u003eAccuracy of resting energy expenditure calculations in unselected overweight and obese patients\u003c/strong\u003e. \u003cem\u003eAnnals of Nutrition and Metabolism \u003c/em\u003e2014, \u003cstrong\u003e65\u003c/strong\u003e(4):299-309.\u003c/li\u003e\n\u003cli\u003eBosy-Westphal A, M\u0026uuml;ller MJ, Boschmann M, Klaus S, Kreymann G, L\u0026uuml;hrmann PM, Neuh\u0026auml;user-Berthold M, Noack R, Pirke KM, Platte P: \u003cstrong\u003eGrade of adiposity affects the impact of fat mass on resting energy expenditure in women\u003c/strong\u003e. \u003cem\u003eBritish journal of nutrition \u003c/em\u003e2008, \u003cstrong\u003e101\u003c/strong\u003e(4):474-477.\u003c/li\u003e\n\u003cli\u003eEFSA Panel on Dietetic Products N, Allergies: \u003cstrong\u003eScientific opinion on dietary reference values for energy\u003c/strong\u003e. \u003cem\u003eEFSA Journal \u003c/em\u003e2013, \u003cstrong\u003e11\u003c/strong\u003e(1):3005.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, resting energy expenditure, indirect calorimetry, predictive equations","lastPublishedDoi":"10.21203/rs.3.rs-4711548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4711548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn accurate calculation of energy expenditure (REE) is necessary for estimating energy needs in prostate cancer. The purpose of this research was to evaluate the accuracy of the established new equation for predicting REE in malign and benign prostate patients versus the accuracy of the previously used predictive equations based on REE measured by indirect calorimetry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubjects with 41 malign prostate and 42 benign prostate subtects were both over the age of 40 (65.3 ± 6.30 years) and recruited for the study. Cosmed-FitMate GS Indirect Calorimetry with Canopy-hood (Rome, Italy) was used to measure REE. A full body composition analysis and anthropometric measurements were taken.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalign prostate group PSA Total and measured REE values (4.93±5.44 ng/ml, 1722.9±272.69kcal/d respectively) were statisticaly significantly higher than benign group (1.76±0.73ng/ml, 1670.5±266.76 kcal/d respectively) (p \u0026lt; 0.05). Malign (MPG) and benign prostate groups (BPG) have the highest percentage of the accurate-prediction value of equations 80.9% (New EquationMPG) and 64.2% (New EquationBPG). The bias of the equations varied from-36.5% (Barcellos II Equation) to 19.2% (Mifflin-St. Jeor equation) for malign prostate group and varied from − 41.1% (Barcellos II Equation) to 17.7% (Mifflin-St.Jeor equation) in benign prostate group. The smallest RMSE values in the malign and benign prostate group were New EquationMPG (149 kcal/d) and New EquationBPG (202 kcal/d). The new specific equation for malign prostate cancer: REE = 3192,258+(208,326* body weight(WT)) - (20,285* height(HT)) - (187,549* Fat Free Mass(FFM)) - (203,214* Fat Mass(FM)) + (4,194* Prostate Specific Antigen Total(PSAT)). The new specific equation for benign prostate group: REE = 615,922+ (13,094* WT). Bland-Altman plots reveal an equally random distribution of new equations in malign and benign prostate group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe majority of the previously developed predictive equations for REE were inaccurate and biased. The new specific equation for malign prostate cancer that we created enabled us to develop prostate cancer-specific energy prediction equations with the PSAT parameter. In any case, the new predictive equations enable clinicians to estimate REE in people with malign and benign prostate groups with sufficient and most acceptable accuracy.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Detection of resting energy expenditure in prostate cancer: Assessment of energy prediction equations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:02:24","doi":"10.21203/rs.3.rs-4711548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-19T09:49:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-18T20:31:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T18:12:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294207990551554976660630602693556632549","date":"2024-08-11T14:23:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-10T13:31:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309199437737232179873038554246476867452","date":"2024-08-05T21:14:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192510217257043023174736480503042098082","date":"2024-08-05T15:51:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-28T11:59:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179185729350418560522711671572459849479","date":"2024-07-23T19:27:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T17:43:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-11T14:29:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-11T14:28:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T14:27:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2024-07-09T11:03:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69cb5715-934b-4735-8240-36ba1f1bd842","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:02:03+00:00","versionOfRecord":{"articleIdentity":"rs-4711548","link":"https://doi.org/10.1186/s12894-024-01648-9","journal":{"identity":"bmc-urology","isVorOnly":false,"title":"BMC Urology"},"publishedOn":"2025-03-27 15:57:38","publishedOnDateReadable":"March 27th, 2025"},"versionCreatedAt":"2024-08-11 12:02:24","video":"","vorDoi":"10.1186/s12894-024-01648-9","vorDoiUrl":"https://doi.org/10.1186/s12894-024-01648-9","workflowStages":[]},"version":"v1","identity":"rs-4711548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4711548","identity":"rs-4711548","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}