Resting Energy Expenditure of Master Athletes: Accuracy of Predictive Equations and Primary Determinants

Effect of Intake of Bifidobacteria and Dietary Fiber on Resting Energy Expenditure: A Randomized, Placebo-Controlled, Double-Blind, Parallel-Group Comparison Study

Bifidobacterium animalis subsp. lactis GCL2505 in combination with inulin has been shown to have several health benefits, including an improvement in the intestinal microbiota and a reduction in human visceral fat. Previous studies have suggested that the visceral fat reduction of GCL2505 and inulin may be achieved by improving daily energy expenditure. This parallel, placebo-controlled, randomized, double-blind study was conducted to evaluate the effects of GCL2505 and inulin on resting energy expenditure (REE) in overweight or mildly obese Japanese adults (n = 44). Participants ingested 1 × 1010 colony forming units of GCL2505 and 5.0 g of inulin daily for 4 weeks. REE score at week 4 was set as the primary endpoint. At week 4, the REE score of the GCL2505 and inulin group was significantly higher than that of the placebo group, with a difference of 84.4 kcal/day. In addition, fecal bifidobacteria counts were significantly increased in the GCL2505 and inulin group. Our results indicated that the intake of GCL2505 and inulin improves energy balance, which is known to be a major factor of obesity, by modulating the microbiota in the gut. This is the first report to demonstrate the effects of probiotics and dietary fiber on REE in humans.

Bone health and the masters runner

Masters runners are often defined as those ages 35 years and older who train and compete in running events. These runners represent a growing population of the overall running community and experience running-related injuries including bone stress injuries (BSIs). Similar to younger runners, health considerations in masters runners include the goal to optimize bone health with focus on mitigating age-associated loss of bone strength and preventing BSIs through a combination of ensuring appropriate physical activity, optimizing nutrition, and correcting faulty biomechanics. Importantly, BSIs in masters runners may include characteristics of both overuse injury from insufficient recovery and failure of bone weakened by age-related loss of bone (insufficiency fractures). This narrative review covers the limited available research on strategies to optimize bone health in masters runners. Applying knowledge on masters athletes and extrapolating from other populations, we propose strategies on treatment and prevention of BSIs. Finally, the review highlights gaps in knowledge that require further age-specific discoveries to advance treatment and prevention.

A natural sustained-intestinal release formulation of red chili pepper extracted capsaicinoids (Capsifen®) safely modulates energy balance and endurance performance: a randomized, double-blind, placebo-controlled study

Introduction

Overweight and obesity are major public health concerns, with a sharp increase in prevalence over the last few decades. The primary cause is an imbalance between calorie intake and expenditure due to a rise in calorie-rich processed food and reduced physical activity. Energy balance in humans involves complex processes including thermogenesis, a crucial factor in regulating energy expenditure.

Methods

In this randomized, double-blinded, placebo-controlled three-arm three-sequence study, we investigated the efficacy of Capsifen® (CapF), a pungency-masked sustained-intestinal release formulation of red chili extract, on energy expenditure, fat oxidation, and endurance using the Quark C-PET system in healthy overweight participants, with and without exercise. In the study, 105 healthy participants were randomized to receive either placebo, CapF 100 mg/day, or CapF 200 mg/day for 28 days.

Results

CapF demonstrated a dose-dependent response to increased energy expenditure and fatty acid oxidation with a concomitant reduction in body weight. Both CapF 100 and CapF 200 also increased the time to exhaustion.

Discussion

These results demonstrate the plausible efficacy of CapF in energy expenditure and physical performance in otherwise healthy adults who have a high body mass index.

Clinical trial registration

https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MjQzNTg=&Enc=&userName=CTRI/2018/04/013157 dated 04 October 2018.

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.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Predicting resting energy expenditure: a critical appraisal

BACKGROUND

The most commonly used prediction models for resting energy expenditure (REE) are Harris-Benedict (1919), Schofield (1985), Owen (1986), and Mifflin-St Jeor (1990), based on height, weight, age and gender, and Cunningham (1991), based on body composition.

METHODS

Here, the five models are compared with reference data, consisting of individual REE measurements (n = 353) from 14 studies, covering a large range of participant characteristics.

RESULTS

For white adults, prediction of REE with the Harris-Benedict model approached measured REE most closely, with estimates within 10% for more than 70% of the reference population.

DISCUSSION

Sources of differences between measured and predicted REE include measurement validity and measurement conditions. Importantly, a 12- to 14-h overnight fast may not be sufficient to reach post-absorptive conditions and may explain differences between predicted REE and measured REE. In both cases complete fasting REE may not have been achieved, especially in participants with high energy intake.

CONCLUSION

In white adults, measured resting energy expenditure was closest to predicted values with the classic Harris-Benedict model. Suggestions for improving resting energy expenditure measurements, as well as prediction models, include the definition of post-absorptive conditions, representing complete fasting conditions with respiratory exchange ratio as indicator.

Accuracy of equations for estimating resting energy expenditure in children and adolescents living with phenylketonuria

BACKGROUND

Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population.

METHODS

An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations.

RESULTS

Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2  = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942.

CONCLUSION

Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

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.

Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers

Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR estimated by 14 predictive equations on the basis of using body weight/fat-free mass. All equations underestimated RMR in male and female climbers, except for De Lorenzo's equation in the group of women. The De Lorenzo equation demonstrated the highest correlation with RMR in both groups. The results of the Bland-Altman tests revealed an increasing measurement error with increasing metabolism for most of the predictive equations in male and female climbers. All equations had low measurement reliability according to the intraclass correlation coefficient. Compared with the indirect calorimetry measurement results, none of the studied predictive equations demonstrated high reliability. There is a need to develop a highly reliable predictive equation to estimate RMR in sport climbers.

A New Approach to Improve the Validity of Doubly Labeled Water to Assess CO2 Production during High-Energy Turnover

PURPOSE

Accurate measurement of energy expenditure (EE) using doubly labeled water depends on the estimate of total body water (TBW). The aims of this study were to 1) assess the accuracy of a new approach for estimating TBW and EE during high-energy turnover and 2) assess the accuracy of day-to-day assessment of EE with this new approach.

METHODS

EE was measured in six healthy subjects (three male) for 5 consecutive days using three doubly labeled water methods: 1) the plateau, 2) slope-intercept, and 3) overnight-slope method, with whole-room indirect calorimetry as reference method. Urine samples were collected every evening and morning. High EE (physical activity level of >2.5) was achieved by cycling 4 h·d-1.

RESULTS

Physical activity level was 2.8 ± 0.1. TBW values were 41.9 ± 6.1, 38.4 ± 5.7, and 40.4 ± 5.8 L for the plateau, slope-intercept, and overnight-slope methods, respectively. The overnight-slope method showed the highest accuracy in estimated CO2 production, when compared with indirect calorimetry over the complete 5-d period (mean ± SD difference, 0.9% ± 1.6%). The plateau method significantly overestimated CO2 production by 4.7% ± 2.6%, whereas the slope-intercept method underestimated CO2 production (-3.4% ± 2.3%). When CO2 production was assessed per day, the overnight-slope method showed an average difference of 9.4% ± 4.5% to indirect calorimetry.

CONCLUSIONS

The overnight-slope method resulted in a more accurate estimation of CO2 production and EE compared with the plateau or slope-intercept method over a 5-d period in high physical activity conditions. Day-to-day determination of EE using the overnight-slope method was more accurate than diet recall and several standard prediction equations in athletes.

The Effects of Lifestyle and Diet on Gut Microbiota Composition, Inflammation and Muscle Performance in Our Aging Society

Living longer is associated with an increased risk of chronic diseases, including impairments of the musculoskeletal and immune system as well as metabolic disorders and certain cancers, each of which can negatively affect the relationship between host and microbiota up to the occurrence of dysbiosis. On the other hand, lifestyle factors, including regular physical exercise and a healthy diet, can affect skeletal muscle and immune aging positively at all ages. Accordingly, health benefits could partly depend on the effect of such interventions that influence the biodiversity and functionality of intestinal microbiota. In the present review, we first discuss the physiological effects of aging on the gut microbiota, immune system, and skeletal muscle. Secondly, we describe human epidemiological evidence about the associations between physical activity and fitness and the gut microbiota composition in older adults. The third part highlights the relevance and restorative mechanisms of immune protection through physical activity and specific exercise interventions during aging. Fourth, we present important research findings on the effects of exercise and protein as well as other nutrients on skeletal muscle performance in older adults. Finally, we provide nutritional recommendations to prevent malnutrition and support healthy active aging with a focus on gut microbiota. Key nutrition-related concerns include the need for adequate energy and protein intake for preventing low muscle mass and a higher demand for specific nutrients (e.g., dietary fiber, polyphenols and polyunsaturated fatty acids) that can modify the composition, diversity, and metabolic capacity of the gut microbiota, and may thus provide a practical means of enhancing gut and systemic immune function.

Nutrition for Older Athletes: Focus on Sex-Differences

Regular physical exercise and a healthy diet are major determinants of a healthy lifespan. Although aging is associated with declining endurance performance and muscle function, these components can favorably be modified by regular physical activity and especially by exercise training at all ages in both sexes. In addition, age-related changes in body composition and metabolism, which affect even highly trained masters athletes, can in part be compensated for by higher exercise metabolic efficiency in active individuals. Accordingly, masters athletes are often considered as a role model for healthy aging and their physical capacities are an impressive example of what is possible in aging individuals. In the present review, we first discuss physiological changes, performance and trainability of older athletes with a focus on sex differences. Second, we describe the most important hormonal alterations occurring during aging pertaining regulation of appetite, glucose homeostasis and energy expenditure and the modulatory role of exercise training. The third part highlights nutritional aspects that may support health and physical performance for older athletes. Key nutrition-related concerns include the need for adequate energy and protein intake for preventing low bone and muscle mass and a higher demand for specific nutrients (e.g., vitamin D and probiotics) that may reduce the infection burden in masters athletes. Fourth, we present important research findings on the association between exercise, nutrition and the microbiota, which represents a rapidly developing field in sports nutrition.