Resting Metabolic Rate Measurements Research Articles

Impact of Acute Dietary and Exercise Manipulation on Next Day RMR Measurements and DXA Body Composition Estimates.

To investigate the effects of acute diet and exercise manipulation on resting metabolic rate (RMR) measurement variability and dual energy x-ray absorptiometry (DXA) body composition estimates. 10 male and 10 female endurance athletes (12 cyclists, 5 triathletes, 4 runners) of Tier 2 (n = 18) to Tier 3 (n = 2) calibre underwent five conditions using a Latin square counterbalance design. For 24 hours, athletes consumed a diet providing excessive energy availability (75 kcal‧kg fat free mass (FFM)-1) without exercise (GEArest), high energy availability (45 kcal‧kg FFM-1) without (HEArest) or with exercise (HEAex), or low-energy availability (15 kcal‧kg FFM-1) without (LEArest) or with exercise (LEAex). Exercise involved two bouts of cycling (morning bout: 149 ± 34 min at 55% of maximal aerobic capacity (VO2 max); afternoon bout: 60 min at 65% of VO2 max) that resulted in a cumulative exercise energy expenditure of 30 kcal‧kg FFM-1. The following day, RMR and DXA measurements occurred after a 10-hour fast and 12-hours post-exercise. There were no sex differences in relative RMR (p = 0.132) nor effects of any of the five conditions on RMR (p = 0.353). For both male and female athletes, FFM estimates were decreased following the LEArest (-0.84 ± 0.66 kg; p = 0.001) and LEAex (-0.65 ± 0.86 kg; p = 0.016) conditions compared to the GEArest condition and following the LEArest (-0.73 ± 0.51 kg; p = 0.001) and LEAex (-0.54 ± 0.79 kg; p = 0.024) conditions compared to the HEAex condition. There was no effect of condition on fat mass estimates (p = 0.819). Acute periods of diet and exercise manipulation did not create artifacts in next-day RMR measurements. However, as changes in estimates of FFM were seen, diet and exercise should be controlled in the 24-hours prior to DXA scans.

The Temporal Effects of Altitude and Low Energy Availability Manipulation on Resting Metabolic Rate in Female Race Walkers.

To investigate the temporal effects of ~1,800 m altitude exposure and energy availability (EA) manipulation on resting metabolic rate (RMR). Twenty elite female race walkers underwent a 3-week training camp at an altitude of ~1,800 m. During the first two weeks, athletes consumed a high EA (HEA) diet of 45 kcal·kg fat free mass (FFM) -1 ·day -1 . During the final week, half the athletes consumed a low EA (LEA) diet of 15 kcal·kg FFM -1 ·day -1 while the others continued on a HEA diet. Athletes followed individualized training plans throughout the study. To assess the effect of altitude on RMR, athletes in the HEA group had RMR measured at baseline (~580 m) prior to altitude exposure (Pre-alt), at 36-hours (36 h-alt), 2 weeks (Wk2-alt) and 3 weeks into altitude exposure (Wk3-alt), and at 36 hours post-altitude exposure at ~580 m (36 h-post). To assess the effect of LEA exposure on RMR while at altitude, athletes in the LEA group underwent RMR measurements at Pre-alt and before (Wk2-alt) and after the 7-days of LEA (Wk3-alt). Compared to Pre-alt, the RMR of HEA athletes was increased at 36 h-alt (+5.3 ± 3.1%; p = 0.026) and Wk2-alt (+4.9 ± 4.9%; p = 0.049), but was no longer elevated at Wk3-alt (+1.7 ± 4.2%; p = 0.850). The RMR of HEA athletes at 36 h-post was lower than all timepoints at altitude (p < 0.05) but was not different from Pre-alt (-3.9 ± 7.2%; p = 0.124). The 7-day period of LEA exposure at altitude did not affect RMR (p = 0.347). RMR was transiently increased with ~1,800 m altitude exposure in female athletes and was unaffected by short-term LEA. However, the altitude-induced increase was small (~25-75 kcal/day) and was unlikely to have clinically significant implications for daily energy requirements.

Resting Metabolic Rate Does Not Predict Peak Metabolic Rate in the Glanville Fritillary Butterfly.

Peak metabolic rate reflects maximal performance and may have direct fitness consequences, whereas resting metabolic rate (RMR) represents the maintenance cost of the whole animal. These traits may be linked, which has significant implications for the evolution of both traits. In vertebrates, a positive correlation between RMR and aerobic capacity has been proposed to explain the origin of endothermy. However, as studies on the relationship between RMR and aerobic capacity have focused on vertebrates, we know much less about these traits in ectothermic insects. I measured RMR in the Glanville fritillary butterfly (Melitaea cinxia) using two configurations: one optimized for measuring flight metabolic rate and the other optimized for RMR. The relationship between RMR and body mass was similar for the two configurations. Body mass explained 82% of the variation in RMR when it was measured using the "flight" configuration at 32°C, and 91% when using the "rest" configuration at 23°C. The Q10 coefficient calculated based on the two RMR measurements was 2.8. Mass-independent RMR was positively correlated between measurements obtained using the two instrument configurations. However, neither measure of RMR was correlated with peak metabolic rate, which indicates that RMR cannot be used as a surrogate measure for aerobic capacity in the Glanville fritillary. Ectothermic insects may be able to combine high metabolic capacity with no apparent increase in maintenance cost. Even though RMR is among the most frequently measured physiological variables, it may have limited predictive power when it comes to questions related to activity or aerobic capacity, or in the case of butterflies, flight performance.

The relationship between resting metabolic rate and quality of life is moderated by age and body composition in women: a cross-sectional study

BackgroundHealth-related quality of life (HRQOL) is related to body composition, which is also related to resting metabolic rate (RMR). RMR can be increased by exercise and diet interventions that are not dependent on changes in body composition, so a link between RMR and HRQOL may provide interventions that directly improve HRQOL in women.MethodsOne hundred twenty women (median age 63.5 [IQR: 53.0–71.0] years) completed one-time measurement of body composition (multi-frequency bioelectrical impedance), RMR (handheld calorimetry), and HRQOL (RAND-36). Physical (PCS) and mental (MCS) composite scores were calculated for the RAND-36. Pearson correlations were used to identify relationships between RMR, body composition, and HRQOL. Variables at the p < .01 level were entered into multiple regression models.ResultsMedian body mass index was 26.1 [IQR: 23.2–30.9] kg/m2 and median lean mass index was 16.1 [IQR: 14.6–17.3] kg/m2. Body composition consisted of fat mass (median 27.2 [IQR: 20.3–34.7] kg) and lean mass (median 42.7 [IQR: 38.2–46.9] kg). Median RMR was 1165.0 [IQR: 1022.5–1380.0] kcal/day. Median HRQOL scores were PCS (84.0 [IQR: 74.0–93.0]) and MCS (85.0 [IQR: 74.3–90.0]). RMR was not directly related to PCS, but was directly and negatively related to MCS (p = .002). RMR was significantly and positively related to body composition (lean mass: p < .001; fat mass: p < .001), body mass index (p = .005), and lean mass index (p < .001); but only fat mass (PCS: p < .001; MCS: p < .001) and body mass index (PCS: p < .001; MCS: p < .001) were related to HRQOL, although the relationship was negative. In addition, age was found to be significantly negatively related to RMR (p < .001) and PCS (p = .003). Regression models confirmed the moderating influence of age and body composition on the relationship between RMR and HRQOL. RMR, age, fat mass, and body mass index explained 24% (p < .001) of variance in PCS; and RMR, fat mass, and body mass index explained 15% (p < .001) of variance in MCS.ConclusionIn women, the relationship between RMR and HRQOL is moderated by age and body composition. Understanding these pathways will allow clinicians and researchers to direct interventions more effectively.

The Influence of Energy Balance and Availability on Resting Metabolic Rate: Implications for Assessment and Future Research Directions.

Resting metabolic rate (RMR) is a significant contributor to an individual's total energy expenditure. As such, RMR plays an important role in body weight regulation across populations ranging from inactive individuals to athletes. In addition, RMR may also be used to screen for low energy availability and energy deficiency in athletes, and thus may be useful in identifying individuals at risk for the deleterious consequences of chronic energy deficiency. Given its importance in both clinical and research settings within the fields of exercise physiology, dietetics, and sports medicine, the valid assessment of RMR is critical. However, factors including varying states of energy balance (both short- and long-term energy deficit or surplus), energy availability, and prior food intake or exercise may influence resulting RMR measures, potentially introducing error into observed values. The purpose of this review is to summarize the relationships between short- and long-term changes in energetic status and resulting RMR measures, consider these findings in the context of relevant recommendations for RMR assessment, and provide suggestions for future research.

Two novel models evaluating the determinants of resting metabolic rate in Indian children

Background: Resting metabolic rate (RMR) quantifies the minimal energy required to sustain vital body functions and is a crucial component of childhood development. Mean RMR per unit body mass (RMR/BM) has very accurately been modelled in references for Caucasian adolescents.Objectives: Here we address the extent to which such a model can be adapted to explain RMR/BM in Indian children.Subjects and Methods: The multicenter study (MCS) is a cross-sectional dataset on 495 children (235 girls and 260 boys) aged 9 to 19 years with anthropometric, body composition, and RMR measurements. The RMR-ultrasonography study (RMR-USG) consists of anthropometric data, RMR, and liver and kidney volume measured through ultrasonography in nine girls and nine boys aged 6 to 8 years.Results: The mean RMR/BM in Indian children is significantly lower compared to their Caucasian counterparts, except in boys in the age group 9–13 years. We present two novel phenomenological models that describe the mean RMR/BM stratified by age in Indian children and adolescents. The first is a modified Wang model in which the relative masses of four major organs are assumed to be uniformly lowered for Indian children. Theoretical predictions of liver size are not uniformly borne out in a pilot validation study; however, the relative mass of the kidney is found to be significantly lower. The second model demonstrates that changes in body composition alone can also explain the Indian data.Conclusion: A modified Wang model in which the relative masses of four major organs are assumed to be uniformly lower in Indian children and differences in body composition can be used to estimate mean RMR/BM by age in Indian children; however, understanding the mechanistic basis of variation in RMR/BM remains an open problem.

Revised Harris-Benedict Equation: New Human Resting Metabolic Rate Equation.

This paper contains a revision of the Harris-Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris-Benedict, Mifflin-St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) - (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) - (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) - (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) - (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease).

Comparison of Actual and Predicted Resting Metabolic Rate in Women with Lipedema.

Background: An adequate dietary energy supply is particularly important in patients with lipedema as it promotes weight and fat loss. Accurate estimation of resting metabolic rate (RMR) allows implementing a proper calorie restriction diet in patients with lipedema. Our study aimed to compare actual resting metabolic rate (aRMR) with predicted resting metabolic rate (pRMR) in women with lipedema and to determine the association between individual body composition parameters, body mass index, and aRMR. Methods and Results: A total of 108 women diagnosed with lipedema were enrolled in the study. aRMR was obtained by indirect calorimetry (IC) using FitMate WM metabolic system (Cosmed, Rome, Italy). pRMR was estimated with predictive equations and bioelectric impedance analysis (BIA). All body composition parameters were based on BIA. The mean aRMR in the study group was 1705.2 ± 320.7 kcal/day. This study found the agreement of predictive equations compared to IC is low (<60%). Most methods of predicted RMR measurement used in our study significantly underpredicted aRMR in patients with lipedema. Therefore, the most applied equations remain useless in clinical practice in this specific population due to large individual differences among the studied women. Conclusions: IC is the best tool to evaluate RMR in evaluated patients with lipedema. It is necessary to propose a new equation to RMR determination in clinical practice.

Accuracy Of Bioimpendance Analysis For Measuring Body Compositionand Resting Metabolism In Youth.

PURPOSE: This study assessed the accuracy of bioimpedance analysis (BIA) for measuring body composition and resting metabolic rate (RMR) against criterion measures in youth. METHODS: Youth of varying sex, age (child, 7-12 y; teen, 13-17 y), weight status (healthy weight, BMI < 85th %ile; overweight, BMI ≥ 85th %ile; obese, BMI ≥ 95th %ile), and pubertal status (years from peak height velocity) were recruited from Athens and surrounding counties of Southeast Ohio. Using paired t-tests, fat mass (FM; kg), fat free mass (FFM; kg), visceral adipose tissue (VAT; cm2) and RMR (kcal/day) measures from a multi-frequency, tetrapolar, eight-point BIA device were compared to dual-energy X-ray absorptiometry (DXA) and a portable metabolic analyzer, respectively. Relationships among all measures were assessed using correlation analyses. Accuracy was calculated as the root mean squared error (RMSE) and bias between methods. RESULTS: In total, 130 youth (11.9 ± 2.9 y; 68 female, 85 healthy weight, 77 children, 61 pre-pubertal) participated in the study. BIA measures of FM was greater in girls (16.0 ± 1.2 kg), and obese (27.6 ± 2.5 kg) compared to boys (13.2 ± 0.9 kg) and healthy weight youth (10.9 ± 0.4 kg). FFM was higher in boys (41.8 ± 1.8 kg), teens (49.2 ± 1.4 kg), and post-pubertal youth (52.0 ± 9.6 kg) compared to their counterparts (35.3 ± 1.2, 30.9 ± 0.9, and 28.5 ± 5.4 kg, respectively). BIA-estimated RMR was higher in boys (1276 ± 40 kcal/day), teens (1434 ± 29 kcal/day), and post-pubertal youth (1493 ± 207 kcal/day) compared to their counterparts (1133 ± 25, 1038 ± 19, 985 ± 116 kcal/day, respectively). Assessing the entire sample, BIA reported lower FFM (38.4 ± 1.1 kg) and higher FM (11.8 ± 0.8 kg) and VAT (50.4 ± 4.1 cm2) compared to DXA (32.6 ± 1.0 kg, 14.7 ± 0.8 kg, 45.8 ± 2.7 cm2), whereas estimated and measured RMR values were similar (1199 ± 24 vs. 1145 ± 254 kcal/day; p > 0.05). Correlations between methods ranged from r = 0.71 to 1.00 (p < 0.05). BIA overestimated FM by 2.8 kg and underestimated FFM by 5.7 kg. CONCLUSIONS: While measures between methods were statistically different, the magnitude of these differences were physiologically insignificant and strong relationships between BIA and criterion measures were observed. Future research should assess the sensitivity of BIA to body composition and RMR changes across growth trajectories.

Acute Effects of Whole-Body Vibration on Resting Metabolic Rate and Substrate Utilisation in Healthy Women.

Simple SummaryWhole-body vibration induces a number of physiological responses and is used both in physical training, and in the treatment of patients with various diseases. Whole-body vibration increases blood flow, skin temperature, bone density, muscle strength and affects the functions of the nervous system. The effect of whole-body vibration on metabolism is interesting. It has been suggested that whole-body vibration shifts energy metabolism towards carbohydrate utilisation and that this may increase post-exercise oxygen uptake. Increased resting metabolism (increased energy expenditure) during whole-body vibration may indicate that whole-body vibration can be used as an adjunct to treatment programs for overweight or metabolic disorders.The aim of the study was to determine the acute effects of single-whole-body vibration (WBV) on resting metabolic rate (RMR) and carbohydrate–lipid profile of blood in young, healthy women. The participants, in a randomised controlled crossover study, participated in two trials: WBV and a vibration simulation (placebo). The WBV was performed in the prone position and cycloidal-oscillatory vibration was used. The RMR measurement (calorimetry) was performed: before the WBV, during WBV, immediately after the completion of WBV, and 1 h after the completion of WBV. For biochemical analyses, venous blood was collected. During WBV, there was a significant increase in RMR compared to baseline. Immediately after and 1 h following the end of the WBV, RMR was close to baseline levels (p > 0.05). The increased energy expenditure resulted from the increased utilisation of carbohydrates and proteins during the vibration. In the placebo condition, there were no significant changes over time in the level of the studied indices during calorimetry. The WBV had no significant effects on the level of glucose in the blood. The applied vibration did not significantly affect the concentration of the analysed lipid indices, which were within the physiological norms for all measurements. Results indicate the need for further research to establish the physiological mechanisms underlying the observed effects of WBV on resting metabolic rate.

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke

ObjectiveTo measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke. DesignCross-sectional study. SettingHospital. ParticipantsSurvivors of stroke (N=71). InterventionNot applicable. Main Outcome MeasuresRMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorptiometry scan and treadmill test for peak oxygen consumption (V̇o2peak). RMR was calculated using 9 established equations. ResultsRMR measured (1552±319 kcal/d) was significantly lower than 9 predicted RMR values (all P<.001), with the best being McArdle-Katch (1652±233 kcal/d), Livingston (1677±230 kcal/d), and Mifflin (1707±242 kcal/d). The Institute of Medicine of the National Academies (2437±386 kcal/d) had the largest discrepancy with measured RMR. Predicted RMR determined with 8 of 9 equations was between 9% and 18% greater than measured RMR. Appendicular lean mass (r=0.64, P<.001), total lean mass (r=0.64, P<.001), and V̇o2peak (r=0.41, P<.001) were associated with measured RMR. ConclusionsRMR predictive equations established in adults without stroke are not appropriate for the population with stroke population, indicating the need to measure RMR until a more accurate predictive equation is developed. This could support modifications to nutritional intake guidelines in patients with conditions of muscle atrophy. If measurement of RMR is not feasible, the Katch-McArdle equation should be used to estimate RMR in a patient with stroke because on average it provides the lowest percentage overestimate compared with other equations.

Reliability of resting metabolic rate between and within day measurements using the Vyntus CPX system and comparison against predictive formulas.

Background: To detect longitudinal changes of resting metabolic rate (RMR) resulting from the effects of energetic stress, reliable RMR measurements are crucial. The Vyntus CPX is a new automated indirect calorimetry system for which RMR reliability has not been determined. Additionally, its agreement with common predictive RMR formulas is unknown. Aim: To determine the within and between-day reliability of RMR measurements using the Vyntus CPX system and its agreement with predictive RMR formulas. Methods: Young (31 ± 7 years) healthy participants (n = 26, 12 females, 14 males) completed three measurements of RMR, two consecutive measures on the same day, one the day before/after, all under standardised conditions. Reliability was assessed with pairwise comparisons of between-day at the same time (BDST), within day consecutive measurements (WDCM) and between-day different time (BDDT), for parameters of reliability (mean change (MC), intraclass correlation (ICC) and typical error of measurement (TEM)). Measured RMR values (kcal/day) were compared against predictive values of 4 common formulas. Results: Parameters of reliability (mean, (95% confidence interval)) were: -BDST: MC, 0.2(-2.3-2.7)% (p = 0.67); ICC, 0.92(0.84-0.97); TEM, 4.5(3.5-6.2)%. -WDCM: MC, -2.5(-6.2-1.3)% (p = 0.21); ICC, 0.88(0.74-0.88); TEM, 7.0(5.4-9.8)%. -BDDT: MC, -1.5(-4.8-1.9)% (p = 0.57); ICC, 0.90(0.76-0.95); TEM, 6.1(4.8-8.5)%. RMRratios (measured/predicted) were: 1.04 ± 0.14 (Nelson, p = 0.13), 1.03 ± 0.10 (Mifflin, p = 0.21), 0.98 ± 0.09 (Harris-benedict, p = 0.30), 0.95 ± 0.11 (Cunningham1980, p = 0.01), 1.00 ± 0.12 (Cunningham1991, p = 0.90) and 0.96 ± 0.13 (DXA, p = 0.03). Conclusions: The Vyntus CPX is reliable and measured RMR values agreed with four predictive formulas but are lower than Cunningham1980 and DXA RMR estimates for this population.

Feasibility of Using Bioelectrical Impedance Analysis for Assessing Youth Weight and Health Status: Preliminary Findings.

Background. This study assessed the accuracy of bioimpedance analysis (BIA) for measuring body composition and resting metabolic rate (RMR) in fasted and non-fasted state and the prospect of using phase angle (PA) to indicate cellular health in youth. Methods. BIA body composition, RMR, and hydration measures were compared to dual-energy x-ray absorptiometry (DXA), MedGem metabolic analyzer, and urine specific gravity, respectively, at baseline in a fasted state using one-way ANOVAs. Repeated BIAs at 0, 30, 60, 90, and 120 min post-prandial were compared to baseline using repeated-measures ANOVA. Correlations were used to assess the relationship among PA and health (blood lipids and glucose, resting BP) and fitness (grip strength and a 3 min step test) measures. Results. BIA scans (N = 58; 11.4 ± 2.9 y) measured lower body fat % (BF%) in healthy weight youth (BMI < 85th percentile; 16.4 ± 1.1 vs. 25.1 ± 1.0%) and lower visceral adipose tissue (VAT) in males (44.5 ± 2.9 vs. 34.1 ± 6.0 cm2) than DXA and higher RMR in all youth (1244 ± 41 vs. 1104 ± 39 kcals/day), healthy weight (1231 ± 48 vs. 1049 ± 44 kcals/day), and teens (1541 ± 62 vs. 1234 ± 72 kcals/day) than MedGem. Compared to baseline, immediate post-prandial values were significantly higher for BF% (21.4 ± 1.4 vs. 22.0 ± 1.4%) and VAT (45.4 ± 6.1 vs. 46.2 ± 6.2 cm2). PA was significantly correlated with BF% (r = −0.33; p = 0.01), fat-free mass (r = 0.59; p < 0.001), grip strength (r = 0.56; p < 0.001). Conclusions. While more data are needed to confirm these preliminary findings, the results suggest caution is necessary in using BIA to assess aspects of youth health and weight status, especially in males, healthy weight, and teens. However, these preliminary findings do indicate that phase angle maybe be a valuable, non-invasive tool for identifying youth who are heading towards obesity and/or obesity-related health consequences.

Long-term Resting Metabolic Rate Analysis in Pregnancy and Weight Loss Interventions

In this paper, we first studied the change in resting metabolic rate (RMR) of 4 women during their pregnancy period. We retrospectively analyzed published data, which lacked rigorous statistical analysis. We introduced new data that helps to define RMR baseline variabilities and further compare the RMR fluctuations in steady physiological conditions (no pregnancy, no weight/diet/exercise regime change) to assess “true” RMR changes that can guide healthy weight management in pregnancy and other conditions. For each subject, the change in the RMR values were computed as the difference between the values during the metabolic rate inspection period and the baseline values. This difference was compared against the difference values of a reference subject, using a two-sided paired t-test at the significance level of 5%. Our results indicated that some subjects exhibit a statistically significant increase, some exhibit a decrease while others show no significant statistical variation in RMR values during pregnancy. These are important findings that demystify the old idea that the RMR of a pregnant woman “always” increases since she is generating a new life; rather, individualized physiological processes can produce metabolic changes that cannot be generalized and need individual RMR measurements throughout pregnancy. The insights gained from this study were then applied to retrospectively analyze the RMR of 20 subjects during a 6-month pilot weight loss intervention with 89% efficiency in weight loss. Our analysis revealed that there was no significant decrease in metabolic activities at the end of the program. Although this contradicts the belief that weight loss is associated with a decrease in metabolic activities, our results can be explained by the fact that subjects adhered to a healthy nutritional diet and regular exercise during the pro- gram; thus, the effect of weight loss on decreasing the RMR was counter-balanced by the effect of healthier diet and exercise on increasing the RMR, which helped in maintaining a steady and healthy metabolic rate. Both studies, pregnancy and weight loss interventions indicated that changes in the metabolic rate of pregnant women and individuals undergoing weight loss interventions are unpredictable, therefore there is an urgent need to implement personalized practices of weight management by periodically measuring RMR and adjusting food caloric intakes based on the individual’s metabolic rate.

Measured Versus Estimated Resting Metabolic Rate in Heart Failure With Preserved Ejection Fraction.

Obesity is common in heart failure with preserved ejection fraction (HFpEF), and a hypocaloric diet can improve functional capacity. Malnutrition, sarcopenia, and frailty are also frequently present, and calorie restriction could harm some patients. Resting metabolic rate (RMR) is an essential determinant of caloric needs; however, it is rarely measured in clinical practice. The accuracy of commonly used predictive equations in HFpEF is unknown. RMR was measured with indirect calorimetry in 43 patients with HFpEF undergoing right heart catheterization at the University of Michigan, and among 49 participants in the SECRET trial (Study of the Effects of Caloric Restriction and Exercise Training in Patients With Heart Failure and a Normal Ejection Fraction); SECRET patients also had dual-energy X-ray absorptiometry body composition measures. Measured RMR was compared with RMR estimated using the Harris Benedict, Mifflin-St Jeor, World Health Organization, and Academy for Nutrition and Dietetics equations. All predictive equations overestimated RMR (by >10%, P<0.001 for all), with mean (95% CI) differences Harris Benedict equation +250 (186-313), Mifflin-St. Jeor equation +169 (110-229), World Health Organization equation +300 (239-361), and Academy for Nutrition and Dietetics equation +794 (890-697) kcal/day. Results were similar across both patient groups, and the discrepancy between measured and estimated RMR tended to increase with body mass index. In SECRET, measured RMR was closely associated with lean body mass (ρ=0.74; by linear regression adjusted for age and sex: β=27 [95% CI, 18-36] kcal/day per kg, P<0.001; r2=0.56). Commonly used predictive equations systematically overestimate measured RMR in patients with HFpEF. Direct measurement of RMR may be needed to effectively tailor dietary guidance in this population. Registration: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT00959660.

Evidence of Torpor in a Tropical Passerine, the Scarlet-Backed Flowerpecker Dicaeum cruentatum

The Scarlet-backed Flowerpecker Dicaeum cruentatum (Dicaeidae, Passeriformes), a sedentary species of Southeast Asia, is among the smallest passerine birds (5–6 g). Despite its very small size, it feeds mainly on plant foods, such as berries, nectar, and green seeds. We found that in conditions of likely food shortage Scarlet-backed Flowerpeckers were able to greatly reduce their metabolic rate at a relatively high ambient temperature (26–28°C). This is within the thermoneutral zone of most tropical birds, although slightly cooler than the normal lower critical temperature of flowerpeckers. In this state, the resting metabolic rate (RMR) of three individual, free-living Scarlet-backed Flowerpeckers from Vietnam averaged 1.57 mL O2/g*h, which was 3.4 times lower than their non-torpid RMR, measured at the same ambient temperature (Ta=∼27°C) and 2.5 time lower than their basal metabolic rate (BMR) measured at Ta=∼31°C. We did not measure the body temperature (Tb) of these individuals, but the dramatically low oxygen consumption provides evidence of torpor, an energy-saving physiological state, which is very rare among passerine birds. The skin Tb of active flowerpeckers, just before nocturnal RMR measurements, averaged 41.1°C (their cloacal Tb was 41.2°C), while in resting non-torpid birds at night the skin Tb averaged 36.3°C. Our report is the first quantitative evidence of torpor in the family Dicaeidae.

Fluctuating asymmetry, a marker of poor growth quality, is associated with adult male metabolic rate

Life history theory, a branch of evolutionary theory, predicts the existence of trade-offs in energetic allocation between competing physiological functions. The core metabolic cost of self-maintenance, measured by resting metabolic rate (RMR), represents a large component of human daily energy expenditure. Despite strong selective pressures for energetic frugality and high observed interindividual variation in RMR, the link between RMR and energetic allocation to life-history traits remains understudied in humans. In a sample of 105 (m = 57, f = 48), we investigated the relationship between adult RMR and investment in growth quality, as measured by fluctuating asymmetry (FA). Measurement of RMR and FA in university rowers revealed a significant positive correlation amongst males (n = 57, r = 0.344, p = 0.005, 1-tailed; standardized 95% CI, 0.090 to 0.598). Convincing evidence for a correlation among females was not found (n = 48, r = 0.142, p = 0.169, 1-tailed, standardized 95% CI, -0.152 to 0.435). The data suggest that low-quality asymmetrical growth is associated with later-life metabolic inefficiencies in males. Energetic investment in processes (likely concerning the stress-response) unrelated to growth during childhood may thereby trade-off against adult metabolic efficiency. We suggest that the presence of a relationship between RMR and FA in males but not females may be explained by the additional metabolic strain associated with larger body size and increased male muscularity, which may amplify the inefficiencies arising from low-quality growth.

Underestimation of resting metabolic rate using equations compared to indirect calorimetry in normal-weight subjects: Consideration of resting metabolic rate as a function of body composition

<h2>Summary</h2><h3>Background & aims</h3> Resting metabolic rate (RMR) can either be estimated using specific equations or measured with indirect calorimetry (iCal). The calculation formulas usually exclude body composition. In addition to age, height, weight, sex and disease, fat-free mass (FFM) and fat mass (FM), in particular, influence energy expenditure. The aim of the present study was to compare the difference between calculated RMR using various predictive formulas reported in the literature and the values from measured iCal. Additionally, differing muscle mass and FM were considered in the calculations. Our hypothesis was that the accuracy of the calculation formulas depends on body constitution. <h3>Methods</h3> This monocentric exploratory pilot study with an observational cross-sectional design included 69 healthy normal-weight adults. Participants were assigned to groups depending on constitution (slender, athletic, full slender). RMR was estimated with a set of 12 clinically relevant predictive equations. <h3>Results</h3> The comparison of the calculated and measured values showed that the prevalent equations under-rather than overestimated the RMR. The equation from the World Health Organization (WHO) had the most accurate prediction range, from 90% to 110% of the RMR measured by iCal, followed by the Harris–Benedict formula and its modifications according to Roza–Shizgal and Mifflin. The agreement between predicting formulas and measured RMR improved in the group with higher fat mass. <h3>Conclusions</h3> Body composition has a significant influence on RMR, mainly through muscle mass. The equations used correlated well with iCal in only 40%–60% of the studied population. Measurements and calculations varied considerably for subjects with greater muscle mass, whereas they were more consistent for subjects with greater FM. Increased emphasis should be placed on the measurement of RMR using iCal.

Development of a New Equation for the Prediction of Resting Metabolic Rate in Sri Lankan Adults.

Resting metabolic rate (RMR) is the key determinant of the energy requirement of an individual. Measurement of RMR by indirect calorimetry is not feasible in field settings and therefore equation-based calculations are used. Since a valid equation is not available for Sri Lankans, it is important to develop a new population-specific equation for field use. The study objective was to develop a new equation for the prediction of RMR in healthy Sri Lankans using a reference method, indirect calorimetry. RMR data were collected from fifty-seven (male 27) adults aged 19 to 60 years. They were randomly assigned to validation (n = 28) and cross-validation (n = 19) groups using the statistical package R (version 3.6.3). Height, weight, and RMR were measured. Multivariable fractional polynomials (MFP) were used to determine explanatory variables and their functional forms for the model. A variable shrinkage method was used to find the best fit predictor coefficients of the equation. The developed equation was cross-validated on an independent group. Weight and sex code (male = 1; female = 0) were identified as reliable independent variables. The new equation developed was RMR (kcal/day) = 284.5 + (13.2 x weight) + (133.0 x sex code). Independent variables of the prediction equation were able to predict 88.5% of the variance. Root mean square error (RMSE) of the prediction equation in validation and cross-validation was 88.11 kcal/day and 79.03 kcal/day, respectively. The equation developed in this study is suitable for predicting RMR in Sri Lankan adults.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago.

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity. We evaluated the accuracies of established and locally developed RMR prediction equations (RMRP) among adults. Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMRM) with a MedGem® indirect calorimeter according to recommended procedures. RMRP were compared to RMRM with values ± 10% of RMRM deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMRP equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study. Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMRP among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMRP. Locally developed RMRP had accuracies comparable to their top-ranked established RMRP counterparts. Accuracies of established RMRP depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMRP provide useful alternatives to established RMRP.