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

doi:10.21203/rs.3.rs-4711548/v1

Download PDF

Research Article

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

https://doi.org/10.21203/rs.3.rs-4711548/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

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

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: (10WT) + (6.25HT) – (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 - (3.8AGE) + (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.

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 VO₂ consumption and calculates the RMR by estimating VCO₂ production from a fixed RQ of 0.85 based on the abbreviated Weir equation as it does not contain a VCO₂ 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 VO₂ 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), VO₂ (ml/min), Vp (l/min) and FeO₂ (%) 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

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/m²)	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
VO₂ (ml/min)	250.2±40.70	240.4±40.92
Vp (l/min)	32.5±5.37	33.0±6.50
FeO₂ (%)	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 R²	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/m²)	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 R²	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/m²)	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 R² for the model malign prostate cancer patient: 0.548
Adjusted R² 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).

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 R²:54,8% of the variance in REE among men with malign prostate group and accounting for R²:37,5% 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.

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.

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

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)

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.
Wiskin A, Davies J, Wootton S, Beattie R: Energy expenditure, nutrition and growth. Archives of disease in childhood 2011, 96(6):567-572.
Blundell JE, Caudwell P, Gibbons C, Hopkins M, Naslund E, King N, Finlayson G: Role of resting metabolic rate and energy expenditure in hunger and appetite control: a new formulation. Disease models & mechanisms 2012, 5(5):608-613.
Norgan NG: Energy Expenditure and Energy Balance. In: Encyclopedia of Food Sciences and Nutrition (Second Edition). edn. Edited by Caballero B. Oxford: Academic Press; 2003: 2103-2108.
Purcell S, Elliott S, Baracos V, Chu Q, Prado C: Key determinants of energy expenditure in cancer and implications for clinical practice. European Journal of Clinical Nutrition 2016, 70(11):1230-1238.
Einarsson S, Laurell G, Tiblom Ehrsson Y: An explorative study on energy balance in patients with head and neck cancer. Nutrition and cancer 2020, 72(7):1191-1199.
Prado CM, Gonzalez MC, Heymsfield SB: Body composition phenotypes and obesity paradox. Current Opinion in Clinical Nutrition & Metabolic Care 2015, 18(6):535-551.
Tek NA, Yurtdaş G, Cemali Ö, Bayazıt AD, Çelik ÖM, Uyar GÖ, Güneş BD, Özbaş B, Erten Y: A Comparison of the indirect calorimetry and Different energy equations for the Determination of resting energy expenditure of patients with renal transplantation. Journal of Renal Nutrition 2021, 31(3):296-305.
Tüccar TB, Tek NA: Determining the factors affecting energy metabolism and energy requirement in cancer patients. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences 2021, 26.
Delsoglio M, Achamrah N, Berger MM, Pichard C: Indirect calorimetry in clinical practice. Journal of clinical medicine 2019, 8(9):1387.
Purcell SA, Elliott SA, Baracos VE, Chu QS, Sawyer MB, Mourtzakis M, Easaw JC, Spratlin JL, Siervo M, Prado CM: Accuracy of resting energy expenditure predictive equations in patients with cancer. Nutrition in Clinical Practice 2019, 34(6):922-934.
Barcellos PS, Borges N, Torres DP: Resting energy expenditure in cancer patients: Agreement between predictive equations and indirect calorimetry. Clinical Nutrition ESPEN 2021, 42:286-291.
da Rocha EEM, Alves VGF, Silva MHN, Chiesa CA, da Fonseca RBV: Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice? Current Opinion in Clinical Nutrition & Metabolic Care 2005, 8(3):319-328.
Organization WH: Energy and protein requirements: report of a joint FAO/WHO/UNU expert consultation. In: Energy and protein requirements: report of a joint FAO/WHO/UNU expert consultation. edn.; 1985: 206-206.
Jouinot A, Vazeille C, Durand JP, Huillard O, Boudou-Rouquette P, Coriat R, Chapron J, Neveux N, De Bandt JP, Alexandre J: Resting energy expenditure in the risk assessment of anticancer treatments. Clinical Nutrition 2018, 37(2):558-565.
Löffler MC, Betz MJ, Blondin DP, Augustin R, Sharma AK, Tseng Y-H, Scheele C, Zimdahl H, Mark M, Hennige AM: Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application. Molecular metabolism 2021, 51:101237.
Battley EH: The advantages and disadvantages of direct and indirect calorimetry. Thermochimica acta 1995, 250(2):337-352.
de Oliveira FCE, Alves RDM, Zuconi CP, Ribeiro AQ, Bressan J: Agreement between different methods and predictive equations for resting energy expenditure in overweight and obese Brazilian men. Journal of the Academy of Nutrition and Dietetics 2012, 112(9):1415-1420.
Hipskind P, Glass C, Charlton D, Nowak D, Dasarathy S: Do handheld calorimeters have a role in assessment of nutrition needs in hospitalized patients? A systematic review of literature. Nutrition in Clinical Practice 2011, 26(4):426-433.
Cioffi I, Marra M, Pasanisi F, Scalfi L: Prediction of resting energy expenditure in healthy older adults: A systematic review. Clinical Nutrition 2021, 40(5):3094-3103.
Acar-Tek N, Ağagündüz D, Çelik B, Bozbulut R: Estimation of resting energy expenditure: validation of previous and new predictive equations in obese children and adolescents. Journal of the American College of Nutrition 2017, 36(6):470-480.
Harris JA, Benedict FG: A biometric study of human basal metabolism. Proceedings of the National Academy of Sciences 1918, 4(12):370-373.
Ireton-Jones CS, Turner Jr WW, Liepa GU, Baxter CR: Equations for the estimation of energy expenditures in patients with burns with special reference to ventilatory status. The Journal of burn care & rehabilitation 1992, 13(3):330-333.
Ireton‐Jones C, Jones JD: Improved equations for predicting energy expenditure in patients: the Ireton‐Jones equations. Nutrition in Clinical Practice 2002, 17(1):29-31.
Schofield WN: Predicting basal metabolic rate, new standards and review of previous work. Human nutrition Clinical nutrition 1985, 39:5-41.
Barcellos PS, Borges N, Torres DP: New equation to estimate resting energy expenditure in non-critically ill patients. Clinical nutrition ESPEN 2020, 37:240-246.
Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO: A new predictive equation for resting energy expenditure in healthy individuals. The American journal of clinical nutrition 1990, 51(2):241-247.
Souza MTP, Singer P, Ozorio GA, Rosa VM, Alves MMF, López RVM, Waitzberg DL: Resting energy expenditure and body composition in patients with head and neck cancer: An observational study leading to a new predictive equation. Nutrition 2018, 51:60-65.
Lin Y-C, Wang C-H, Ling HH, Pan Y-P, Chang P-H, Chou W-C, Chen F-P, Yeh K-Y: Inflammation Status and Body Composition Predict Two-Year Mortality of Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma under Provision of Recommended Energy Intake during Concurrent Chemoradiotherapy. Biomedicines 2022, 10(2):388.
Purcell SA, Elliott SA, Ryan AM, Sawyer MB, Prado CM: Accuracy of a portable indirect calorimeter for measuring resting energy expenditure in individuals with cancer. Journal of Parenteral and Enteral Nutrition 2019, 43(1):145-151.
Purcell SA, Johnson-Stoklossa C, Tibaes JRB, Frankish A, Elliott SA, Padwal R, Prado CM: Accuracy and reliability of a portable indirect calorimeter compared to whole-body indirect calorimetry for measuring resting energy expenditure. Clinical nutrition ESPEN 2020, 39:67-73.
Weir JdV: New methods for calculating metabolic rate with special reference to protein metabolism. The Journal of physiology 1949, 109(1-2):1.
Blond E, Maitrepierre C, Normand S, Sothier M, Roth H, Goudable J, Laville M: A new indirect calorimeter is accurate and reliable for measuring basal energy expenditure, thermic effect of food and substrate oxidation in obese and healthy subjects. e-SPEN, the European e-Journal of Clinical Nutrition and Metabolism 2011, 6(1):e7-e15.
Lupinsky L, Singer P, Theilla M, Grinev M, Hirsh R, Lev S, Kagan I, Attal-Singer J: Comparison between two metabolic monitors in the measurement of resting energy expenditure and oxygen consumption in diabetic and non-diabetic ambulatory and hospitalized patients. Nutrition 2015, 31(1):176-179.
Nieman DC, Austin MD, Benezra L, Pearce S, McInnis T, Unick J, Gross SJ: Validation of Cosmed’s FitMate™ in measuring oxygen consumption and estimating resting metabolic rate. Research in Sports Medicine 2006, 14(2):89-96.
Yeung SS, Trappenburg MC, Meskers CG, Maier AB, Reijnierse EM: Inadequate energy and protein intake in geriatric outpatients with mobility problems. Nutrition Research 2020, 84:33-41.
Kajale N, Khadilkar A, Oza C, Gondhalekar K, Khadilkar V: Resting metabolic rate and its association with body composition parameters in 9-to 18-year-old Indian children and adolescents. Nutrition 2022, 99:111652.
Compher C, Frankenfield D, Keim N, Roth-Yousey L, Group EAW: Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. Journal of the American Dietetic Association 2006, 106(6):881-903.
Maury-Sintjago E, Muñoz-Mendoza C, Rodríguez-Fernández A, Ruíz-De la Fuente M: Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women. Nutrients 2022, 14(15):3199.
Weststrate JA, Dekker J, Stoel M, Begheijn L, Deurenberg P, Hautvast JG: Resting energy expenditure in women: impact of obesity and body-fat distribution. Metabolism 1990, 39(1):11-17.
Lieffers JR, Mourtzakis M, Hall KD, McCargar LJ, Prado CM, Baracos VE: A viscerally driven cachexia syndrome in patients with advanced colorectal cancer: contributions of organ and tumor mass to whole-body energy demands. The American journal of clinical nutrition 2009, 89(4):1173-1179.
Prado CM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L, Baracos VE: Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. The lancet oncology 2008, 9(7):629-635.
Martin L, Birdsell L, MacDonald N, Reiman T, Clandinin MT, McCargar LJ, Murphy R, Ghosh S, Sawyer MB, Baracos VE: Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. Journal of clinical oncology 2013, 31(12):1539-1547.
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.
Reeves MM: Estimating patients' energy requirements: Cancer as a case study. Queensland University of Technology; 2004.
Ramirez-Zea M: Validation of three predictive equations for basal metabolic rate in adults. Public Health Nutrition 2005, 8(7a):1213-1228.
Xue J, Li S, Zhang Y, Hong P: Accuracy of predictive resting-metabolic-rate equations in chinese mainland adults. International Journal of Environmental Research and Public Health 2019, 16(15):2747.
Buch A, Diener J, Stern N, Rubin A, Kis O, Sofer Y, Yaron M, Greenman Y, Eldor R, Eilat-Adar S: Comparison of equations estimating resting metabolic rate in older adults with type 2 diabetes. Journal of Clinical Medicine 2021, 10(8):1644.
Schusdziarra V, Wolfschläger K, Hausmann M, Wagenpfeil S, Erdmann J: Accuracy of resting energy expenditure calculations in unselected overweight and obese patients. Annals of Nutrition and Metabolism 2014, 65(4):299-309.
Bosy-Westphal A, Müller MJ, Boschmann M, Klaus S, Kreymann G, Lührmann PM, Neuhäuser-Berthold M, Noack R, Pirke KM, Platte P: Grade of adiposity affects the impact of fat mass on resting energy expenditure in women. British journal of nutrition 2008, 101(4):474-477.
EFSA Panel on Dietetic Products N, Allergies: Scientific opinion on dietary reference values for energy. EFSA Journal 2013, 11(1):3005.

No competing interests reported.

Download PDF

Reviews received at journal
18 Aug, 2024
Reviews received at journal
14 Aug, 2024
Reviewers agreed at journal
11 Aug, 2024
Reviews received at journal
10 Aug, 2024
Reviewers agreed at journal
05 Aug, 2024
Reviewers agreed at journal
05 Aug, 2024
Reviews received at journal
28 Jul, 2024
Reviewers agreed at journal
23 Jul, 2024
Reviewers invited by journal
23 Jul, 2024
Editor invited by journal
11 Jul, 2024
Editor assigned by journal
11 Jul, 2024
Submission checks completed at journal
11 Jul, 2024
First submitted to journal
09 Jul, 2024

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Figures

Background

Methods

Study design and participants

Anthropometric Measurements and Body Composition Analysis

Resting Energy Expenditure (REE)

REE Prediction Equations

Statistical Analysis

Results

Insert Fig. 1 and Fig. 2 Here

Development and Validation of the new REE Predictive Equations

Discussion

Conclusion and future directions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1