Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners

This study aimed to investigate the resting metabolic rate (RMR) in cross-training practitioners (advanced and novice) using indirect calorimetry (IC) and compare it with predictive equations proposed in the scientific literature.

METHODS

A cross-sectional and comparative study analyzed 65 volunteers, both sexes, practicing cross-training (CT). Anthropometry and body composition were assessed, and RMR was measured by IC (FitMate PRO®), bioimpedance (BIA-InBody 570®), and six predictive equations. Data normality was tested by the Kolgomorov-Smirnov test and expressed as mean ± standard deviation with 95% confidence intervals (CI), chi-square test was performed to verify ergogenic resources, and a Bland-Altman plot (B&A) was made to quantify the agreement between two quantitative measurements. One-way ANOVA was applied to body composition parameters, two-way ANOVA with Bonferroni post hoc was used to compare the RMR between groups, and two-way ANCOVA was used to analyze the adjusted RMR for body and skeletal muscle mass. The effect size was determined using Cohen's d considering the values adjusted by ANCOVA. If a statistical difference was found, post hoc Bonferroni was applied. The significance level was p < 0.05 for all tests.

RESULTS

The main results indicated that men showed a higher RMR than women, and the most discrepant equations were Cunningham, Tinsley (b), and Johnstone compared to IC. Tinsley's (a) equation indicated greater precision in measuring the RMR in CM overestimated it by only 1.9%, and BIA and the Harris-Benedict in CW overestimated RMR by only 0.1% and 3.4%, respectively.

CONCLUSIONS

The BIA and Harris-Benedict equation could be used reliably to measure the RMR of females, while Tinsley (a) is the most reliable method to measure the RMR of males when measuring with IC is unavailable. By knowing which RMR equations are closest to the gold standard, these professionals can prescribe a more assertive diet, training, or ergogenic resources. An assertive prescription increases performance and can reduce possible deleterious effects, maximizing physical sports performance.

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.

Free-living competitive racewalkers and runners with energy availability estimates of <35 kcal·kg fat-free mass-1·day-1 exhibit peak serum progesterone concentrations indicative of ovulatory disturbances: a pilot study

Introduction

The release of luteinising hormone (LH) before ovulation is disrupted during a state of low energy availability (EA). However, it remains unknown whether a threshold EA exists in athletic populations to trigger ovulatory disturbances (anovulation and luteal phase deficiency) as indicated by peak/mid-luteal serum progesterone concentration (Pk-PRG) during the menstrual cycle.

Methods

We assessed EA and Pk-PRG in 15 menstrual cycles to investigate the relationship between EA and Pk-PRG in free-living, competitive (trained-elite) Guatemalan racewalkers (n = 8) and runners (n = 7) [aged: 20 (14-41) years; post-menarche: 5 (2-26) years; height: 1.53 ± 0.09 m; mass: 49 ± 6 kg (41 ± 5 kg fat-free mass "FFM")]. EA was estimated over 7 consecutive days within the follicular phase using food, training, and physical activity diaries. A fasted blood sample was collected during the Pk-PRG period, 6-8 days after the LH peak, but before the final 2 days of each cycle. Serum progesterone concentration was quantified using electrochemiluminescence immunoassay.

Results

Participants that reported an EA of <35 kcal·kg FFM-1·day-1 (n = 7) exhibited ovulatory disturbances (Pk-PRG ≤9.40 ng·mL-1). Athletes with EA ≥36 kcal·kg FFM-1·day-1 (n = 8) recorded "normal"/"potentially fertile" cycles (Pk-PRG >9.40 ng·mL-1), except for a single racewalker with the lowest reported protein intake (1.1 g·kg body mass-1·day-1). EA was positively associated with Pk-PRG [r(9) = 0.79, 95% confidence interval (CI): 0.37-0.94; p = 0.003; 1 - β = 0.99] after excluding participants (n = 4) that likely under-reported/reduced their dietary intake.

Conclusions

The result from the linear regression analysis suggests that an EA ≥ 36 kcal·kg FFM-1·day-1 is required to achieve "normal ovulation." The threshold EA associated with ovulatory disturbances in athletes and non-invasive means of monitoring the ovulatory status warrant further research.

Signs of low energy availability in elite male volleyball athletes but no association with risk of bone stress injury and patellar tendinopathy

Relative Energy Deficiency in Sport (RED-S) syndrome is associated with undesirable health and performance outcomes. The aetiology of RED-S syndrome is low energy availability (LEA). LEA has been reported in male athletes in various sports, but there is little information in team sports. Therefore, the aims of this study were to assess the point-prevalence of surrogate markers of LEA in elite male volleyball players and examine the association between low and normal total-testosterone (TES) on endocrine markers, resting metabolic rate, bone mineral density (BMD), and history of injury/illness. Using a cross-sectional design, 22 elite male volleyball players underwent anthropometric, dual-energy X-ray absorptiometry (DEXA or DXA) and resting metabolic rate testing, bloodwork, dietary analysis, the three-factor eating questionnaire-R18, injury/illness questionnaire and Victorian Institute of Sport Assessment - patellar tendon questionnaire. The primary finding of this investigation was that 36% of athletes had ≥2 surrogate markers of LEA. Although fasted insulin was lower and cortisol was higher in players with low-total TES, low BMD, low RMR and various other endocrine markers linked to LEA were not observed. More research is required to define surrogate markers of LEA in male athletes.HIGHLIGHTS Thirty-six percent of volleyball players had ≥2 surrogate markers of LEA.The Cunningham, 1991 predictive RMR equation and/or the cut-off point (<0.9) may be unsuitable for detecting energy conservation associated with LEA in large male athletes.There was no association between total-TES and risk of bone stress injury, illness and patellar tendinopathy.

High Energetic Demand of Elite Rowing - Implications for Training and Nutrition

Purpose: Elite rowers have large body dimensions, a high metabolic capacity, and they realize high training loads. These factors suggest a high total energy requirement (TER), due to high exercise energy expenditure (EEE) and additional energetic needs. We aimed to study EEE and intensity related substrate utilization (SU) of elite rowers during rowing (EEE_ROW) and other (EEE_NON-ROW) training.

Methods: We obtained indirect calorimetry data during incremental (N = 174) and ramp test (N = 42) ergometer rowing in 14 elite open-class male rowers (body mass 91.8 kg, 95% CI [87.7, 95.9]). Then we calculated EEE_ROW and SU within a three-intensity-zone model. To estimate EEE_NON-ROW, appropriate estimates of metabolic equivalents of task were applied. Based on these data, EEE, SU, and TER were approximated for prototypical high-volume, high-intensity, and tapering training weeks. Data are arithmetic mean and 95% confidence interval (95% CI).

Results: EEE_ROW for zone 1 to 3 ranged from 15.6 kcal·min⁻¹, 95% CI [14.8, 16.3] to 49.8 kcal·min⁻¹, 95% CI [48.1, 51.6], with carbohydrate utilization contributing from 46.4%, 95% CI [42.0, 50.8] to 100.0%, 95% CI [100.0, 100.0]. During a high-volume, a high-intensity, or a taper week, TER was estimated to 6,775 kcal·day⁻¹, 95% CI [6,651, 6,898], 5,772 kcal·day⁻¹, 95% CI [5,644, 5,900], or 4,626 kcal∙day⁻¹, 95% CI [4,481, 4,771], respectively.

Conclusion: EEE in elite open-class male rowers is remarkably high already during zone 1 training and carbohydrates are dominantly utilized, indicating relatively high metabolic stress even during low intensity rowing training. In high-volume training weeks, TER is presumably at the upper end of the sustainable total energy expenditure. Periodized nutrition seems warranted for rowers to avoid low energy availability, which might negatively impact performance, training, and health.

Current Predictive Resting Metabolic Rate Equations Are Not Sufficient to Determine Proper Resting Energy Expenditure in Olympic Young Adult National Team Athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Estimation of resting metabolic rate (RMR) is an important step for prescribing an individual’s energy intake. The purpose of this study was to evaluate the validity of portable indirect calorimeters and RMR prediction equations in muscular physique athletes. Twenty-seven males (n = 17; body mass index (BMI): 28.8 ± 2.0 kg/m2; body fat: 12.5% ± 2.7%) and females (n = 10; BMI: 22.8 ± 1.6 kg/m2; body fat: 19.2% ± 3.4%) were evaluated. The reference RMR value was obtained from the ParvoMedics TrueOne 2400 indirect calorimeter, and the Cosmed Fitmate and Breezing Metabolism Tracker provided additional RMR estimates. Existing RMR prediction equations based on body weight (BW) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Errors in RMR estimates were assessed using validity statistics, including t tests with Bonferroni correction, linear regression, and calculation of the standard error of the estimate, total error, and 95% limits of agreement. Additionally, new prediction equations based on BW (RMR (kcal/day) = 24.8 × BW (kg) + 10) and FFM (RMR (kcal/day) = 25.9 × FFM (kg) + 284) were developed using stepwise linear regression and evaluated using leave-one-out cross-validation. Nearly all existing BW- and FFM-based prediction equations, as well as the Breezing Tracker, did not exhibit acceptable validity and typically underestimated RMR. The ten Haaf and Weijs (PLoS ONE, 9: e1084602014 (2014)) and Cunningham (1980) (Am. J. Clin. Nutr. 33: 2372–2374 (1980)) FFM-based equations may produce acceptable RMR estimates, although the Cosmed Fitmate and newly developed BW- and FFM-based equations may be most suitable for RMR estimation in male and female physique athletes. Future research should provide additional external cross-validation of the newly developed equations to refine the ability to predict RMR in physique athletes.

Are increases in skeletal muscle mass accompanied by changes to resting metabolic rate in rugby athletes over a pre-season training period?

Optimising dietary energy intake is essential for effective sports nutrition practice in rugby athletes. Effective dietary energy prescription requires careful consideration of athletes' daily energy expenditure with the accurate prediction of resting metabolic rate (RMR) important due to its influence on total energy expenditure and in turn, energy balance. This study aimed to (a) measure rugby athletes RMR and (b) report the change in RMR in developing elite rugby players over a rugby preseason subsequent to changes in body composition and (c) explore the accurate prediction of RMR in rugby athletes. Eighteen developing elite rugby union athletes (age 20.2 ± 1.7 years, body mass 101.2 ± 14.5 kg, stature 184.0 ± 8.4 cm) had RMR (indirect calorimetry) and body composition (dual energy x-ray absorptiometry) measured at the start and end of a rugby preseason ∼14 weeks later. There was no statistically significant difference in RMR over the preseason period (baseline 2389 ± 263 kcal·day^-1 post 2373 ± 270 kcal·day^-1) despite a significant increase in lean mass of +2.0 ± 1.6 kg (P < 0.01) and non-significant loss of fat mass. The change in RMR was non-significant and non-meaningful; thus, this study contradicts the commonly held anecdotal perception that an increase in skeletal muscle mass will result in a significant increase in metabolic rate and daily energy needs. Conventional prediction equations generally under-estimated rugby athletes' measured RMR, and may be problematic for identifying low energy availability, and thus updated population-specific prediction equations may be warranted to inform practice.

Low RMRratio as a Surrogate Marker for Energy Deficiency, the Choice of Predictive Equation Vital for Correctly Identifying Male and Female Ballet Dancers at Risk

Ballet dancers are reported to have an increased risk for energy deficiency with or without disordered eating behavior. A low ratio between measured (m) and predicted (p) resting metabolic rate (RMRratio < 0.90) is a recognized surrogate marker for energy deficiency. We aimed to evaluate the prevalence of suppressed RMR using different methods to calculate pRMR and to explore associations with additional markers of energy deficiency. Female (n = 20) and male (n = 20) professional ballet dancers, 19–35 years of age, were enrolled. mRMR was assessed by respiratory calorimetry (ventilated open hood). pRMR was determined using the Cunningham and Harris–Benedict equations, and different tissue compartments derived from whole-body dual-energy X-ray absorptiometry assessment. The protocol further included assessment of body composition and bone mineral density, blood pressure, disordered eating (Eating Disorder Inventory-3), and for females, the Low Energy Availability in Females Questionnaire. The prevalence of suppressed RMR was generally high but also clearly dependent on the method used to calculate pRMR, ranging from 25% to 80% in males and 35% to 100% in females. Five percent had low bone mineral density, whereas 10% had disordered eating and 25% had hypotension. Forty percent of females had elevated Low Energy Availability in Females Questionnaire score and 50% were underweight. Suppressed RMR was associated with elevated Low Energy Availability in Females Questionnaire score in females and with higher training volume in males. In conclusion, professional ballet dancers are at risk for energy deficiency. The number of identified dancers at risk varies greatly depending on the method used to predict RMR when using RMRratio as a marker for energy deficiency.

Within-Day Energy Deficiency and Metabolic Perturbation in Male Endurance Athletes

Endurance athletes are at increased risk of relative energy deficiency associated with metabolic perturbation and impaired health. We aimed to estimate and compare within-day energy balance in male athletes with suppressed and normal resting metabolic rate (RMR) and explore whether within-day energy deficiency is associated with endocrine markers of energy deficiency. A total of 31 male cyclists, triathletes, and long-distance runners recruited from regional competitive sports clubs were included. The protocol comprised measurements of RMR by ventilated hood and energy intake and energy expenditure to predict RMR_ratio (measured RMR/predicted RMR), energy availability, 24-hr energy balance and within-day energy balance in 1-hr intervals, assessment of body composition by dual-energy X-ray absorptiometry, and blood plasma analysis. Subjects were categorized as having suppressed (RMR_ratio < 0.90, n = 20) or normal (RMR_ratio > 0.90, n = 11) RMR. Despite there being no observed differences in 24-hr energy balance or energy availability between the groups, subjects with suppressed RMR spent more time in an energy deficit exceeding 400 kcal (20.9 [18.8-21.8] hr vs. 10.8 [2.5-16.4], p = .023) and had larger single-hour energy deficits compared with subjects with normal RMR (3,265 ± 1,963 kcal vs. -1,340 ± 2,439, p = .023). Larger single-hour energy deficits were associated with higher cortisol levels (r = -.499, p = .004) and a lower testosterone:cortisol ratio (r = .431, p = .015), but no associations with triiodothyronine or fasting blood glucose were observed. In conclusion, within-day energy deficiency was associated with suppressed RMR and catabolic markers in male endurance athletes.

High resting metabolic rate among Amazonian forager-horticulturalists experiencing high pathogen burden

OBJECTIVES

Resting metabolic rate (RMR) reflects energetic costs of homeostasis and accounts for 60 to 75% of total energy expenditure (TEE). Lean mass and physical activity account for much RMR variability, but the impact of prolonged immune activation from infection on human RMR is unclear in naturalistic settings. We evaluate the effects of infection on mass-corrected RMR among Bolivian forager-horticulturalists, and assess whether RMR declines more slowly with age than in hygienic sedentary populations, as might be expected if older adults experience high pathogen burden.

MATERIALS AND METHODS

RMR was measured by indirect calorimetry (Fitmate MED, Cosmed) in 1,300 adults aged 20 to 90 and TEE was measured using doubly labeled water (n = 40). Immune biomarkers, clinical diagnoses, and anthropometrics were collected by the Tsimane Health and Life History Project.

RESULTS

Tsimane have higher RMR and TEE than people in sedentary industrialized populations. Tsimane RMR is 18 to 47% (women) and 22 to 40% (men) higher than expected using six standard prediction equations. Tsimane mass-corrected TEE is similarly elevated compared to Westerners. Elevated leukocytes and helminths are associated with excess RMR in multivariate regressions, and jointly result in a predicted excess RMR of 10 to 15%. After age 40, RMR declines by 69 kcal/decade (p < .0001). Controlling for lean mass and height accounts for 71% of age-related RMR decline, and adding indicators of infection minimally affects the age slope. The residual level of age-related decline from age 40 is 1.2% per decade.

CONCLUSION

High pathogen burden may lead to higher metabolic costs, which may be offset by smaller body mass or other energy-sparing mechanisms.

Basal Metabolic Rate of Adolescent Modern Pentathlon Athletes: Agreement between Indirect Calorimetry and Predictive Equations and the Correlation with Body Parameters

Purpose

The accurate estimative of energy needs is crucial for an optimal physical performance among athletes and the basal metabolic rate (BMR) equations often are not well adjusted for adolescent athletes requiring the use of specific methods, such as the golden standard indirect calorimetry (IC). Therefore, we had the aim to analyse the agreement between the BMR of adolescents pentathletes measured by IC and estimated by commonly used predictive equations.

Methods

Twenty-eight athletes (17 males and 11 females) were evaluated for BMR, using IC and the predictive equations Harris and Benedict (HB), Cunningham (CUN), Henry and Rees (HR) and FAO/WHO/UNU (FAO). Body composition was obtained using DXA and sexual maturity data were retrieved through validated questionnaires. The correlations among anthropometric variables an IC were analysed by T-student test and ICC, while the agreement between IC and the predictive equations was analysed according to Bland and Altman and by survival-agreement plotting.

Results

The whole sample average BMR measured by IC was significantly different from the estimated by FAO (p<0.05). Adjusting data by gender FAO and HR equations were statistically different from IC (p <0.05) among males, while female differed only for the HR equation (p <0.05).

Conclusion

The FAO equation underestimated athletes’ BMR when compared with IC (T Test). When compared to the golden standard IC, using Bland and Altman, ICC and Survival-Agreement, the equations underestimated the energy needs of adolescent pentathlon athletes up to 300kcal/day. Therefore, they should be used with caution when estimating individual energy requirements in such populations.

Irisin does not mediate resistance training-induced alterations in resting metabolic rate

PURPOSE

This study aimed to investigate the effects of a 6-month preventive resistance training program on resting metabolic rate (RMR) and its associations with fat-free mass (FFM) and the newly described myokine irisin as two potential mechanistic links between exercise training and RMR.

METHODS

In a randomized controlled trial, 74 sedentary healthy male and female participants either completed 6 months of high-repetition resistance training 3 d·wk in accordance with the American College of Sports Medicine recommendations (RT: n = 37; 47 ± 7 yr; body mass index, 25.0 ± 3.4 kg·m) or served as controls (CO: n = 37; 50 ± 7 yr; body mass index, 24.2 ± 3.2 kg·m). Strength (one-repetition maximum), RMR (indirect calorimetry), body fat (caliper method), and serum irisin concentration (enzyme-linked immunosorbent assay) were measured before and after 6 months of training.

RESULTS

Training led to an increase in strength (one-repetition maximum leg press, 16% ± 7%; P < 0.001). RMR increased in RT (1671 ± 356 vs 1843 ± 385 kcal·d, P < 0.001) but not in CO (1587 ± 285 vs 1602 ± 294 kcal·d, P = 0.97; group-time interaction, P < 0.01). Body weight (RT, -0.5 ± 2.4 kg; CO, 0.1 ± 2.3 kg), body fat percentage (RT, -1.1% ± 2.5%; CO, -0.7% ± 2.9%), and FFM (RT, 0.4 ± 2.1 kg; CO, 0.6 ± 1.9 kg) did not develop differently between groups (group-time interaction: P = 0.29, P = 0.54, and P = 0.59, respectively). Serum irisin concentration increased in CO (70.8 ± 83.4 ng·mL, P < 0.001) but not in RT (22.4 ± 92.6 ng·mL, P = 0.67; group-time interaction, P < 0.01). The change in RMR was not associated with the change in FFM (r = -0.11, P = 0.36) or irisin (r = -0.004, P = 0.97).

CONCLUSIONS

Preventive resistance training elicits an increase in RMR. However, in contrast to currently discussed hypotheses, this increase does not seem to be mediated by training-induced changes in FFM or circulating irisin concentration, which casts doubt in the meaning of irisin for human energy balance.

Accuracy of predicted resting metabolic rate and relationship between resting metabolic rate and cardiorespiratory fitness in obese men

PURPOSE

The purpose of this study is to examine that not only the relationship of the resting metabolic rate (RMR) and cardiorespiratory fitness(VO2peak), but also the comparison between measured and predicted results of RMR in obese men.

METHODS

60 obese men (body fat>32%) were recruited for this study. They did not participate in regular exercising programs at least 6 months. The RMR was measured with indirect calorimetry and predicted RMR using Herris-Benedicte equation. The cardiorespiratory fitness was determined by directly measuring the oxygen consumption (VO2peak) during the exercise on the treadmill.

RESULTS

The significance for the difference between the measured results and predicted result of RMR were tested by paired t-test. Correlation of measured date was obtained by Pearson correlation coefficient. The value of predicted RMR and measured RMR were significantly different in these obese subjects. (p < 0.001). The difference between RMR cardiorespiratory fitness and cardiorespiratory fitness showed significant correlation (r=0.67, p < 0.05).

CONCLUSION

The current formulas of predicted RMR have limited the evaluation of measured RMR for Korean obese men. Therefore, this study suggests that new formula should be designed for Korean in order to obtain more accurate results in obese.