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

BACKGROUND: Health-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. METHODS: One 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. RESULTS: Median 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. CONCLUSION: In 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.

Basal metabolic rate and the risk of urolithiasis: a two-sample Mendelian randomization study.

OBJECTIVE: Few studies have investigated the impact of basal metabolic rate (BMR) on the development of urolithiasis, and the causal relationship is yet to be established. In this study, a two-sample Mendelian randomization (MR) analysis was utilized to identify the causal relationship between BMR and risk of urolithiasis. METHOD: Genetic instruments for BMR were drawn from a public genome-wide association study (GWAS). Summary dates on BMR and urolithiasis were obtained from a GWAS meta-analysis with sample sizes of 454,874 and 212,453, respectively. The inverse-variance weighted (IVW) method was provided as the main approach to estimate the causal relationship. The weighted-median method and the MR-Egger method were used as supplements to the IVW method. In addition, we conducted sensitivity analyses, including heterogeneity tests, pleiotropy tests and leave-one-out analysis, to assess the robustness of the outcomes. Furthermore, the funnel plot asymmetry was visually inspected to evaluate possible bias. RESULTS: The inverse-variance weighted data revealed that genetically predicted BMR significantly decreased the risk of urolithiasis [beta coefficient (beta): - 0.2366, odds ratio (OR): 0.7893, 95% confidence interval (CI) 0.6504-0.9579, p = 0.0166]. CONCLUSIONS: BMR has causal effects on urolithiasis in an MR study, and the risk of urolithiasis in patients with lower levels of BMR is higher.

[Characteristics of resting energy expenditure and evaluation of prediction formulas in young men with different body mass indexes].

OBJECTIVE: To compare the resting energy expenditure (REE) characteristics among young men with different body mass indexes (BMI). METHODS: Thirty young men [average age was (26.93±4.16) years] were enrolled in this study. They underwent resting metabolism tests in the Department of Sports Medicine of Peking University Third Hospital from December 2017 to June 2021. The resting metabolic rate (RMR) was measured by indirect calorimetry, the body composition was measured by bioresistance antibody component analyzer. The REE characteristics were analyzed, and 11 predictive equations were used to estimate RMR and compared with the measured value. The differences were analyzed by paired t-test and intra-class correlation coefficient (ICC). RESULTS: The RMR of the overall 30 young men was (1 960.17±463.11) kcal/d (1 kcal=4.186 8 kJ). Including (1 744.33±249.62) kcal/d in those with normal BMI, which was significantly lower than that in those who were overweight or obese [(2 104.06± 520.32) kcal/d, P < 0.01], but the weight-corrected RMR in those with normal BMI was significantly higher than that in those who were overweight or obese [(24.02±2.61) kcal/(kg·d) vs. (19.98±4.38) kcal/(kg·d), P < 0.01]. The RMR was significantly and positively correlated with body weight, adiposity, lean body mass, body surface area, and extracellular fluid in the subjects with diffe-rent BMI (all P < 0.05). The predicted values of the 11 prediction equations were not in good agreement with the measured values (all ICC < 0.75), with relatively high agreement between the predicted and measured values of the World Health Organization (WHO) equation in overweight obese young men (ICC=0.547, P < 0.01). CONCLUSION: There were significant differences in RMR among young men with different BMI, and the RMR after weight correction should be considered for those who were overweight or obese. The consistency between the predicted values of different prediction equations and the actual measured values of RMR was relatively poor, and it is recommended to accurately measure RMR by indirect calorimetry. For overweight or obese young men, the WHO prediction equation can be considered to calculate RMR, but it is necessary to establish an RMR prediction equation applicable to different BMI populations.

Low resting metabolic rate and increased hunger due to ß-MSH and ß-endorphin deletion in a canine model.

Mutations that perturb leptin-melanocortin signaling are known to cause hyperphagia and obesity, but energy expenditure has not been well studied outside rodents. We report on a common canine mutation in pro-opiomelanocortin (POMC), which prevents production of ß-melanocyte-stimulating hormone (ß-MSH) and ß-endorphin but not α-MSH; humans, similar to dogs, produce α-MSH and ß-MSH from the POMC propeptide, but rodents produce only α-MSH. We show that energy expenditure is markedly lower in affected dogs, which also have increased motivational salience in response to a food cue, indicating increased wanting or hunger. There was no difference in satiety at a modified ad libitum meal or in their hedonic response to food, nor disruption of adrenocorticotropic hormone (ACTH) or thyroid axes. In vitro, we show that ß-MSH signals comparably to α-MSH at melanocortin receptors. These data implicate ß-MSH and ß-endorphin as important in determining hunger and moderating energy expenditure and suggest that this role is independent of the presence of α-MSH.

Variation in Resting Metabolic Rate Affects Identification of Metabolic Change in Geographically Distinct Cohorts of Patients With ALS.

BACKGROUND AND OBJECTIVES: Altered metabolism is observed in amyotrophic lateral sclerosis (ALS). However, without a standardized methodology to define metabolic changes, our understanding of factors contributing to and the clinical significance of altered metabolism in ALS is limited. METHODS: We aimed to determine how geographic variation in metabolic rates influences estimates and accuracy of predicted resting energy expenditure (REE) in patients with ALS and controls, while validating the effectiveness of cohort-specific approaches in predicting altered metabolic rate in ALS. Participants from 3 geographically distinct sites across Australia, China, and the Netherlands underwent REE assessments, and we considered 22 unique equations for estimating REE. Analyses evaluated equation performance and the influence of demographics on metabolic status. Comparisons were made using standardized and local reference values to identify metabolic alterations. RESULTS: 606 participants were included from Australia (patients with ALS: 140, controls: 154), the Netherlands (patients with ALS: 79, controls: 37) and China (patients with ALS: 67, controls: 129). Measured REE was variable across geographic cohorts, with fat-free mass contributing to this variation across all patients (p = 0.002 to p < 0.001). Of the 22 predication equations assessed, the Sabounchi Structure 4 (S4) equation performed relatively well across all control cohorts. Use of prediction thresholds generated using data from Australian controls generally increased the prevalence of hypermetabolism in Chinese (55%, [43%-67%]) and Dutch (44%, [33%-55%]) cases when compared with Australian cases (30%, [22%-38%]). Adjustment of prediction thresholds to consider geographically distinct characteristics from matched control cohorts resulted in a decrease in the proportion of hypermetabolic cases in Chinese and Dutch cohorts (25%-31% vs 55% and 20%-34% vs 43%-44%, respectively), and increased prevalence of hypometabolism in Dutch cases with ALS (1% to 8%-10%). DISCUSSION: The identification of hypermetabolism in ALS is influenced by the formulae and demographic-specific prediction thresholds used for defining alterations in metabolic rate. A consensus approach is needed for identification of metabolic changes in ALS and will facilitate improved understanding of the cause and clinical significance of this in ALS.

The relationship between mitochondrial respiration, resting metabolic rate and blood cell count in great tits.

Although mitochondrial respiration is believed to explain a substantial part of the variation in resting metabolic rate (RMR), few studies have empirically studied the relationship between organismal and cellular metabolism. We therefore investigated the relationship between RMR and mitochondrial respiration of permeabilized blood cells in wild great tits (Parus major L.). We also studied the correlation between mitochondrial respiration traits and blood cell count, as normalizing mitochondrial respiration by the cell count is a method commonly used to study blood metabolism. In contrast to previous studies, our results show that there was no relationship between RMR and mitochondrial respiration in intact blood cells (i.e. with the ROUTINE respiration). However, when cells were permeabilized and interrelation re-assessed under saturating substrate availability, we found that RMR was positively related to phosphorylating respiration rates through complexes I and II (i.e. OXPHOS respiration) and to the mitochondrial efficiency to produce energy (i.e. net phosphorylation efficiency), though variation explained by the models was low (i.e. linear model: R2=0.14 to 0.21). However, unlike studies in mammals, LEAK respiration without [i.e. L(n)] and with [i.e. L(Omy)] adenylates was not significantly related to RMR. These results suggest that phosphorylating respiration in blood cells can potentially be used to predict RMR in wild birds, but that this relationship may have to be addressed in standardized conditions (permeabilized cells) and that the prediction risks being imprecise. We also showed that, in our conditions, there was no relationship between any mitochondrial respiration trait and blood cell count. Hence, we caution against normalising respiration rates using this parameter as is sometimes done. Future work should address the functional explanations for the observed relationships, and determine why these appear labile across space, time, taxon, and physiological state.

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis.

BACKGROUND: Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns. METHODS: A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively. RESULTS: Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%). CONCLUSION: For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Trajectories of resting energy expenditure and performance of predictive equations in children hospitalized with an acute illness and malnutrition: a longitudinal study.

There is scarce data on energy expenditure in ill children with different degrees of malnutrition. This study aimed to determine resting energy expenditure (REE) trajectories in hospitalized malnourished children during and after hospitalization. We followed a cohort of children in Bangladesh and Malawi (2-23 months) with: no wasting (NW); moderate wasting (MW), severe wasting (SW), or edematous malnutrition (EM). REE was measured by indirect calorimetry at admission, discharge, 14-and-45-days post-discharge. 125 children (NW, n = 23; MW, n = 29; SW, n = 51; EM, n = 22), median age 9 (IQR 6, 14) months, provided 401 REE measurements. At admission, the REE of children with NW and MW was 67 (95% CI [58, 75]) and 70 (95% CI [63, 76]) kcal/kg/day, respectively, while REE in children with SW was higher, 79 kcal/kg/day (95% CI [74, 84], p = 0.018), than NW. REE in these groups was stable over time. In children with EM, REE increased from admission to discharge (65 kcal/kg/day, 95% CI [56, 73]) to 79 (95% CI [72, 86], p = 0.0014) and was stable hereafter. Predictive equations underestimated REE in 92% of participants at all time points. Recommended feeding targets during the acute phase of illness in severely malnourished children exceeded REE. Acutely ill malnourished children are at risk of being overfed when implementing current international guidelines.

Metabolic adjustments to winter severity in two geographically separated great tit (Parus major) populations.

Understanding the potential limits placed on organisms by their ecophysiology is crucial for predicting their responses to varying environmental conditions. A main hypothesis for explaining avian thermoregulatory mechanisms is the aerobic capacity model, which posits a positive correlation between basal (basal metabolic rate [BMR]) and summit (Msum) metabolism. Most evidence for this hypothesis, however, comes from interspecific comparisons, and the ecophysiological underpinnings of avian thermoregulatory capacities hence remain controversial. Indeed, studies have traditionally relied on between-species comparisons, although, recently, there has been a growing recognition of the importance of intraspecific variation in ecophysiological responses. Therefore, here, we focused on great tits (Parus major), measuring BMR and Msum during winter in two populations from two different climates: maritime-temperate (Gontrode, Belgium) and continental (Zvenigorod, Russia). We tested for the presence of intraspecific geographical variation in metabolic rates and assessed the predictions following the aerobic capacity model. We found that birds from the maritime-temperate climate (Gontrode) showed higher BMR, whereas conversely, great tits from Zvenigorod showed higher levels of Msum. Within each population, our data did not fully support the aerobic capacity model's predictions. We argued that the decoupling of BMR and Msum observed may be caused by different selective forces acting on these metabolic rates, with birds from the continental-climate Zvenigorod population facing the need to conserve energy for surviving long winter nights (by keeping their BMR at low levels) while simultaneously being able to generate more heat (i.e., a high Msum) to withstand cold spells.

Sex-specific effects of immune challenges on green anole lizard metabolism.

Immune responses can increase survival, but they can also incur a variety of costs that may lead to phenotypic trade-offs. The nature of trade-offs between immune activity and other components of the phenotype can vary and depend on the type and magnitude of immune challenge, as well as the energetic costs of simultaneously expressing other traits. There may also be sex-specific differences in both immune activity and trade-offs, particularly with regard to energy expenditure that might differ between males and females during the breeding season. Females are generally expected to invest less in nonspecific immune responses compared to males due to differences in the allocation of resources to reproduction, which may lead to sex differences in the metabolic costs of immunity. We tested for sex-specific differences in metabolic costs of different types of immune challenges in Anolis carolinensis lizards, including lipopolysaccharide (LPS) injection and wounding. We also tested for differences in immune prioritization by measuring bacterial killing ability (BKA). We predicted males would show a greater increase in metabolism after immune challenges, with combined immune challenges eliciting the greatest response. Furthermore, we predicted that metabolic costs would result in decreased BKA. LPS injection increased the resting metabolic rate (RMR) of males but not females. Wounding did not affect RMR of either sex. However, there was an inverse relationship between BKA and wound healing in LPS-injected lizards, suggesting dynamic tradeoffs among metabolism and components of the immune system.

The association between the basal metabolic rate and cardiovascular disease: A two-sample Mendelian randomization study.

BACKGROUND: Mendelian randomization analysis was applied to elucidate the causal relationship between the basal metabolic rate (BMR) and common cardiovascular diseases. METHOD: We choose BMR as exposure. BMR is the metabolic rate of the body when the basic physiological activities (blood circulation, breathing and constant body temperature) are maintained. The normal BMR is 1507 kcal/day for men and 1276 kcal/day for women. The dataset was drawn from the public GWAS dataset (GWAS ID: ukb-a-268), collected and analysed by UK biobank, containing 331,307 European males and females. SNPs independently and strongly associated with BMR were used as instrumental variables in the inverse variance weighted analysis. MR-Egger, weighted median, MR pleiotropy residual sum, and outlier methods were also performed, and the sensitivity was evaluated using horizontal pleiotropy and heterogeneity analyses to ensure the stability of the results. RESULTS: An increased BMR is associated with a higher risk of cardiomyopathy (odds ratio [OR] = 2.00, 95% confidence interval [CI], 1.57-2.54, p = 1.87 × 10-8), heart failure (OR = 1.39, 95% CI, 1.27-2.51, p = 8.1 × 10-13), and valvular heart disease (OR = 1.18, 95% CI, 1.10-1.27, p = .00001). However, there was no clear association between BMR and the subtypes of other cardiovascular diseases, such as coronary disease (OR = .96, 95% CI, .85-1.08, p = .48651) and atrial fibrillation (AF) (OR = 1.85, 95% CI, 1.70-2.02, p = 6.28 × 10-44). CONCLUSION: Our study reveals a possible causal effect of BMR on the risk of cardiomyopathy, heart failure and valvular disease, but not for coronary disease and AF.

Measured versus estimated energy requirement in hospitalized patients.

BACKGROUND & AIM: Failure to identify a patient's energy requirement has a variety of consequences both physiological and economical. Previous studies have shown that predictive formulas, including the Harris Benedict equation (HB), both over- and underestimates energy requirement in severely ill patients and healthy younger adults, compared to the golden standard, indirect calorimetry (IC). The comparison between measured and estimated energy requirements in hospitalized patients in regular wards is underreported. The aim of this study was to assess the agreement between measured energy requirements and requirements estimated by HB in the individual hospitalized patients, and to investigate whether those findings were associated with other specific patient characteristics. METHODS: IC (n = 86) was used to measure resting energy expenditure (REE) and bioimpedance analysis (BIA) (n = 67) was used for body composition in patients admitted to Aalborg University Hospital. Furthermore, height, weight, body mass index, calf circumference, while information regarding hospital ward, vital values, dieticians estimated energy requirements and blood samples were collected in the patients' electronic medical records. Bland-Altman plots, multiple linear regression analysis, and Chi2 tests were performed. RESULTS: On average a difference between IC compared with the HB (6.2%), dietitians' estimation (7.8%) and BIA (4.50%) was observed (p < 0.05). Association between REE and skeletal muscle mass (SMM) (R2 = 0.58, ß = 149.0 kJ), body fat mass (BFM) (R2 = 0.51, ß = 59.1 kJ), and weight (R2 = 0.62, ß = 45.6 kJ) were found (p < 0.05). A positive association between measured REE and HB were found in the following variables (p < 0.05): CRP, age, surgical patients, and respiratory rate. CONCLUSION: This study found a general underestimation of estimated energy expenditure compared to measured REE. A positive correlation between measured REE and SMM, BRM and weight was found. Lastly, the study found a greater association between CRP, age, surgical patients, and respiratory rate and a general greater than ±10% difference between measured and estimation of energy requirements.

Basal metabolic rate using indirect calorimetry among individuals living with overweight or obesity: The accuracy of predictive equations for basal metabolic rate.

BACKGROUND & AIMS: Weight reduction programs in people with overweight or obesity can be informed by indirect calorimetry (IC) which is the gold standard to measure basal metabolic rate (BMR). Since IC is labor intensive and expensive, predictive equations are often used as an alternative. In this study the accuracy rate was assessed and bias statistics of predictive equations were compared to IC among subjects with overweight or obesity. Secondly, differences in clinical features between individuals with over-, accurate or underestimation of their BMR were evaluated. METHODS: This cross sectional study included 731 subjects from the outpatient obesity clinic of the Antwerp University Hospital, Belgium. Fourteen equations were evaluated. Overestimation and underestimation was defined as >10 % and <10 % of measured BMR. RESULTS: In the total population, mean age was 43 ± 13 years, mean BMI 35.6 ± 5.8 kg/m2 and 79.5 % were female. The highest accuracy rates were reached by the Henry (73 %), Ravussin (73 %) and Mifflin St. Jeor (73 %) equations. In the total population, the Mifflin St. Jeor and Henry equation were unbiased. The Akern, Livingston and Ravussin equations were biased to underestimation. All other equations were biased to overestimation. Subjects with an underestimation of BMR had significantly higher waist-hip ratio (1.02 ± 0.13 vs 0.91 ± 0.11; P < 0.001), higher visceral adipose tissue (239 ± 96 vs 162 ± 93; P < 0.001), lower fat free mass (kg) (67.6 (45.4-95.9) vs 54.0 (39.6-95.5); P < 0.001) and a higher prevalence of the Metabolic Syndrome (24 (77.4) vs 112 (37.5); P < 0.001). Individuals with an overestimation of BMR had significantly higher subcutaneous adipose tissue (545 ± 149 vs 612 ± 149; P < 0.05), lower fasting plasma insulin (81 (10-2019) vs 67 (27-253); P < 0.001) and lower 2-h plasma glucose (132 (30-430) vs 116 (43-193); P < 0.001) during OGTT. CONCLUSIONS: In this study, the Henry and Mifflin St. Jeor equations have the highest accuracy and lowest bias to estimate the basal metabolic rate in a Caucasian, predominantly female, population living with overweight or obesity. Visceral and subcutaneous adipose tissue and presence of metabolic syndrome were significantly different in individuals with over- or underestimation of BMR.

Metabolic scaling as an emergent outcome of variation in metabolic rate.

The allometric scaling of metabolic rate and what drives it are major questions in biology with a long history. Since the metabolic rate at any level of biological organization is an emergent property of its lower-level constituents, it is an outcome of the intrinsic heterogeneity among these units and the interactions among them. However, the influence of lower-level heterogeneity on system-level metabolic rate is difficult to investigate, given the tightly integrated body plan of unitary organisms. In this context, social insects such as honeybees can serve as important model systems because unlike unitary organisms, these superorganisms can be taken apart and reassembled in different configurations to study metabolic rate and its various drivers at different levels of organization. This commentary discusses the background of such an approach and how combining it with artificial selection to generate heterogeneity in metabolic rate with an analytical framework to parse out the different mechanisms that contribute to the effects of heterogeneity can contribute to the various models of metabolic scaling. Finally, the absence of the typical allometric scaling relationship among different species of honeybees is discussed as an important prospect for deciphering the role of top-down ecological factors on metabolic scaling. This article is part of the theme issue 'The evolutionary significance of variation in metabolic rates'.

The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players.

Determining resting metabolic rate (RMR) is an important aspect when calculating energy requirements for professional rugby union players. Prediction equations are often used for convenience to estimate RMR. However, the accuracy of current prediction equations for professional rugby union players remains unclear. The aims of this study were to examine the RMR of professional male rugby union players compared to nine commonly used prediction equations and develop and validate RMR prediction equations specific to professional male rugby union players. One hundred and eight players (body mass (BM) = 102.9 ± 13.3 kg; fat-free mass (FFM) = 84.8 ± 10.2 kg) undertook Dual-energy X-ray Absorptiometry scans to assess body composition and indirect calorimetry to determine RMR. Mean RMR values of 2585 ± 176 kcal∙day-1 were observed among the group with forwards (2706 ± 94 kcal·day-1), demonstrating significantly (p < 0.01; d = 1.93) higher RMR compared to backs (2465 ± 156 kcal·day-1), which appeared to be due to their higher BM and FFM measures. Compared to the measured RMR for the group, seven of the nine commonly used prediction equations significantly (p < 0.05) under-estimated RMR (-104-346 kcal·day-1), and one equation significantly (p < 0.01) over-estimated RMR (192 kcal·day-1). This led to the development of a new prediction equation using stepwise linear regression, which determined that the strongest predictor of RMR for this group was FFM alone (R2 = 0.70; SEE = 96.65), followed by BM alone (R2 = 0.65; SEE = 104.97). Measuring RMR within a group of professional male rugby union players is important, as current prediction equations may under- or over-estimate RMR. If direct measures of RMR cannot be obtained, we propose the newly developed prediction equations be used to estimate RMR within professional male rugby union players. Otherwise, developing team- and/or group-specific prediction equations is encouraged.

Elevational variation in metabolic rate, feeding capacity and their associations in the Asiatic toad Bufo gargarizans.

Foraging behavior is known to place demands on the metabolic characteristics of anurans. Active foragers feeding on sedentary prey typically have high aerobic capacity and low anaerobic capacity, whereas sit-and-wait foragers feeding on active and mobile prey have the opposite pattern. Thus, the energetic demands of foraging may influence their metabolic adaptations to harsh environments, such as high elevations. Anurans that engage in active foraging have been found to increase maximum metabolic rate (MMR) and aerobic scope (AS, the difference between MMR and resting metabolic rate, RMR) at high elevations. However, data are lacking in amphibian ambush foragers. In this study, we examined the RMR, MMR, AS, and feeding capacity of a sit-and-wait forager ─the Asiatic toad (Bufo gargarizans), from two populations that are in close geographic proximity but differ by 1350 m in elevation. Our results show that there is no elevational variation in RMR and feeding capacity in either males or females. However, there are sex-specific variations in MMR and AS along an elevational gradient; females from high elevations have lower MMR and smaller net AS than their counterparts from low elevations while males maintain similar MMR and net AS across elevations. Furthermore, aerobic performances do not appear to be associated with feeding capacity at either the individual or population level. Our results support the hypothesis that sit-and-wait foragers may not increase their aerobic capacity as a strategy in hypoxic and low food availability environments and the role of sex in these adaptive adjustments should not be overlooked.

Energy Expenditure of Elite Male and Female Professional Tennis Players During Habitual Training.

Understanding the daily energy expenditure of athletes during training is important to support recovery, adaptation, and the maintenance of performance. The aim of the current research was to assess the total daily energy expenditure (TDEE) and the acute energy expenditure (EE) of tennis training sessions during habitual training of elite tennis players. Using a cohort study design, 27 (n = 10, male; age; 22.3 ± 3.2 years and n = 17, female; age: 23.8 ± 3.5 years) elite singles tennis players were assessed for TDEE and tennis training EE. Using Actiheart activity monitors during a 2- to 5-day training period, male players were analyzed for 26 days and 33 (1.3 ± 0.5 sessions/day) tennis training sessions, and female players for 43 days and 58 (1.2 ± 0.4 sessions/day) tennis training sessions. Male TDEE (4,708 ± 583 kcal/day) was significantly higher than female (3,639 ± 305 kcal/day). Male absolute and relative tennis training EEs (10.2 ± 2.3 kcal/min and 7.9 ± 1.4 kcal·hr-1·kg-1) were significantly higher than those of females (7.6 ± 1.0 kcal/min and 6.8 ± 0.9 kcal·hr-1·kg-1). The resting metabolic rate was assessed via indirect calorimetry. The physical activity level for both groups was 2.3 AU. The TDEE of male and female players during habitual training now highlights the continual cycle of high energy demands experienced by the elite tennis player. The broad ranges of TDEE and EE reported here suggest individual assessment and nutritional planning be prioritized, with a particular focus on carbohydrate requirements.

Maximal sustainable energy intake during transatlantic ocean rowing is insufficient for total energy expenditure and skeletal muscle mass maintenance.

Studies of extreme endurance have suggested that there is an alimentary limit to energy intake (EI) of ∼2.5 × resting metabolic rate (RMR). To gain further insight, this study aimed to simultaneously measure EI, total energy expenditure (TEE) body mass and muscle mass in a large cohort of males and females of varying ages during a transatlantic rowing race. Forty-nine competitors (m = 32, f = 17; age 24-67 years; time at sea 46 ± 7 days) in the 2020 and 2021 Talisker Whisky Atlantic Challenge rowed 12-18 hday-1 for ∼3000 miles. TEE was assessed in the final week of the row using 2 H2 18 O doubly labelled water, and EI was analysed from daily ration packs over this period. Thickness of relatively active (vastus lateralis, intermedius, biceps brachaii and rectus abdominus) and inactive (gastrocnemius, soleus and triceps) muscles was measured pre (<7 days) and post (<24 h) row using ultrasound. Body mass was measured and used to calculate RMR from standard equations. There were no sex differences in males and females in EI (2.5 ± 0.5 and 2.3 ± 0.4 × RMR, respectively, P = 0.3050), TEE (2.5 ± 1.0 and 2.3 ± 0.4 × RMR, respectively, P = 0.5170), or body mass loss (10.2 ± 3.1% and 10.0 ± 3.0%, respectively, P = 0.8520), and no effect of age on EI (P = 0.5450) or TEE (P = 0.9344). Muscle loss occurred exclusively in the calf (15.7% ± 11.4% P < 0.0001), whilst other muscles remained unchanged. After 46 days of prolonged ultra-endurance ocean rowing incurring 10% body mass loss, maximal sustainable EI of ∼2.5 × RMR was unable to meet total TEE suggesting that there is indeed a physiological capacity to EI.

Effect of basal metabolic rate on rheumatoid arthritis: a Mendelian randomization study.

PURPOSE: Basal metabolic rate (BMR) as one of the most basic and significant indicators of metabolism has been associated with human health. Previous studies showed that the development of rheumatoid arthritis (RA) is linked to BMR; however, the causal relationship between BMR and RA is unknown. Thus, we aimed to explore the causal relationship between BMR and RA as well as RA-related factors. METHODS: Mendelian randomization (MR) analysis was performed on collected genome-wide association studies information. The effect of horizontal pleiotropy was detected by MR-PRESSO and MR-Radial. Five MR analysis methods were applied, including inverse variance weighted, MR-Egger, weighted median, weighted mode, and simple mode. Four sensitivity analysis methods were used for the validation of the significant MR analysis results. A two-component mixture of regressions method was additionally used to validate single nucleotide polymorphisms and to verify results. RESULTS: Genetically, there is a causal effect of BMR on overall RA (odds ratio = 1.25, 95% confidence interval: 1.07-1.47, PIVW = .006), seropositive RA (odds ratio = 1.20, 95% confidence interval: 1.01-1.44, PIVW = .035), and seronegative RA (odds ratio = 1.36, 95% confidence interval: 1.04-1.78, PIVW = .023). Sensitivity analyses validated the robustness of the above associations. No evidence supported the effect of RA on BMR. Moreover, BMR showed no causal relationship with rheumatoid factor, C-reactive protein, erythrocyte sedimentation rate, interleukin-1ß, tumor necrosis factor-α, and matrix metallopeptidase 3. CONCLUSION: MR results implied the causal effect of BMR on RA and raised our attention to the importance of BMR in RA's pathology.