Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle

Measurement of resting energy expenditure and its accuracy in women with breast cancer

BACKGROUND & AIMS

Breast cancer (BC) is frequently linked with obesity, metabolic syndrome, and sarcopenia. Therefore, measuring or accurately estimating resting energy expenditure (REE) is crucial for tailoring nutritional needs, managing weight and prevent under- or over-nutrition. We aimed to measure and compare REE between women with BC and a matched control group. Moreover, the prediction accuracy of selected formulas was evaluated.

METHODS

Women aged ≥18 years with newly diagnosis of BC (stage 0-III) and body mass index (BMI) ≤ 30 kg/m2 were included in this cross-sectional analysis. Anthropometry, indirect calorimetry, and bioelectrical impedance analysis (BIA) were performed. Patients with BC data were compared to healthy women with similar age and BMI range. Measured REE (mREE) was compared against 15 predictive equations. Agreement between methods was evaluated using Bland-Altman analysis.

RESULTS

We included 106 women with BC (age 49.9 ± 11.1 years and BMI 24.5 ± 2.8 kg/m2) and 75 women as control group. There were no differences in age, anthropometry, and BIA variables between groups, except for percentage fat mass. Measured REE values, alone and adjusted for fat-free mass (FFM) and age, were higher in patients with BC compared to controls (+4.3 % and +6.1 %, respectively). Regarding REE prediction, most of the selected equations underestimated mREE. Precision varied widely, with the two Marra equations showing the highest agreement (73 % and 74.5 %) along with the Müller equation (74 %), however, the wide limit of agreement range indicates substantial variability.

CONCLUSIONS

Women with early-stage BC exhibited higher mREE compared to controls, albeit its clinical significance is unknown. None of the selected predictive equations provided accurate and precise REE estimates in this group. Although the Marra equation displayed the highest agreement, further studies are needed to evaluate REE variability and its prediction in women with BC.

Sex Differences in the Energy System Contribution during Sprint Exercise in Speed-Power and Endurance Athletes

Background/Objectives: A high level of specific metabolic capacity is essential for maximal sprinting in both male and female athletes. Various factors dictate sex differences in maximal power production and energy utilization. This study aims to compare the contribution of energy systems between male and female athletes with similar sport-specific physiological adaptations during a 15-s sprint exercise. Methods: The endurance group consisted of 17 males (23 ± 7 y) and 17 females (20 ± 2 y). The speed-power group included 14 males (21.1 ± 2.6 y) and 14 females (20 ± 3 y). The contribution of phosphagen, glycolytic, and aerobic systems was determined using the three-component PCr-LA-O2 method. Results: Significant differences were observed in the energy expenditure for all systems and total energy expenditure between males and females in both groups (p = 0.001-0.013). The energy expenditure in kJ for individual systems (phosphagen-glycolytic-aerobic) was 35:25:7 vs. 20:16:5 in endurance males vs. female athletes, respectively. In the speed-power group, male athletes expended 33:37:6 kJ and female athletes expended 21:25:4 kJ, respectively. The percentage proportions did not differ between males and females in any system. The contribution of the phosphagen-glycolytic-aerobic systems was 52:37:11 vs. 48:39:13 in endurance male and female athletes, respectively. For speed-power males vs. female athletes, the proportions were 42:50:8 vs. 41:50:9, respectively. Conclusions: Despite the differences in body composition, mechanical output, and absolute energy expenditure, the energy system contribution appears to have a similar metabolic effect between male and female athletes engaged in sprint exercises with similar sport-related adaptations. The magnitude and profile of sex differences are related to sports discipline.

Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis

BACKGROUND/OBJECTIVES

Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults.

SUBJECTS/METHODS

This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values.

RESULTS

In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized β [95% CI]; 28.072 [2.188-53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856-925] vs. 1077 [1033-1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index.

CONCLUSIONS

PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

Development and validation of novel equation for prediction of resting energy expenditure in active Saudi athletes

Being the most stable component of energy expenditure, resting metabolic rate (RMR) is usually used in the calculation of energy requirements for athletes. An adequate energy prescription is essential in supporting athlete development. This work aims to develop and validate an equation for calculating energy requirements for Arabic Saudi athletes. This cross-sectional study included 171 active athletes aged 18 to 45 years. The sample was divided into a development group (n = 127) and a validation group (n = 44). Anthropometry, indirect calorimetry, and body composition analysis via bioelectric impedance analysis were performed on all participants. The novel predictive equations were created by using stepwise linear regression analyses. The accuracy of the novel equations was compared with 10 equations, and Bland and Altman plots were used to estimate the limits of agreement between measured RMR and novel equations. The first novel equation used a set of basic measures, including weight, gender, and age, was [RMR = 1137.094 + (Wt × 14.560)-(Age × 18.162) + (G × 174.917)] (R = 0.753, and R2 = 0.567, wt = weight, G = gender; for male use 1 and female 0). The second equation used fat-free mass, age, and weight [RMR = 952.828 + (fat-free mass × 10.970)-(Age × 18.648) + (Wt × 10.297)] (R = 0.760 and R2 = 0.577). Validation of the second novel equation increased the prediction of measured RMR to 72.7% and reduced the amount of bias to 138.82 ± 133.18 Kcal. Finally, the new set of equations was designed to fit available resources in clubs and showed up to 72.73% accurate prediction and good agreement with measured RMR by Bland and Altman plots.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

Predicting resting energy expenditure among athletes: a systematic review

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population - participants involved in organized sport; intervention - resting energy expenditure was obtained by calorimetry; comparator - equations to estimate REE; outcomes - comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athletic samples were obtained but, few studies tested their validity across independent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants.

Dietary Habits Are Related to Phase Angle in Male Patients with Non-Small-Cell Lung Cancer

Introduction: Lung cancer constitutes the most common cause of cancer death. Phase angle (PhA) has been related to lung cancer prognosis, which implies that the identification of dietary or other factors that could predict or modify PhA may have beneficial effects. Νutritional interventions have been linked with positive changes in PhA in certain types of cancer. Aim: The present study aimed to investigate the relationships between dietary habits/nutrition and PhA in NSCLC patients. Methods: The sample consisted of 82 male patients with non-small-cell lung cancer (NSCLC; stage IV) from the 'Theageneio' Cancer Hospital (Thessaloniki, Greece). Several parameters were assessed, such as body mass index (BMI), lean mass, PhA, Mediterranean diet score (MedDietScore), dietary patterns, smoking, resting metabolic rate, resting oxygen consumption (VO2), ventilation rate, and physical activity. Results: According to our results, a dietary pattern rich in potatoes and animal proteins (meat and poultry) was a significant determinant of PhA (B ± SE, p: 0.165 ± 0.08, p = 0.05) in multiple linear regression models after adjusting for age, smoking, lean tissue, and MedDietScore. Conclusion: In conclusion, dietary patterns may affect PhA, suggesting the crucial role of protein in cancer management and the prevention of sarcopenia.