Correlation Between Basal Metabolic Rate and Clinical Outcomes in Gastric Cancer Patients: A Retrospective Study.

OBJECTIVES: Hypermetabolism is associated with clinical prognosis of cancer patients. The aim of this study was to explore the association between basal metabolic rate (BMR) and postoperative clinical outcomes in gastric cancer patients. METHODS: We collected data of 958 gastric cancer patients admitted at our center from June 2014 to December 2018. The optimal cutoff value of BMR (BMR ≤1149 kcal/day) was obtained using the X-tile plot. Logistic and Cox regression analyses were then performed to evaluate the relevant influencing factors of clinical outcomes. Finally, R software was utilized to construct the nomogram. RESULTS: A total of 213 patients were defined as having a lower basal metabolic rate (LBMR). Univariate and multivariate analyses showed that gastric cancer patients with LBMR were more prone to postoperative complications and had poor long-term overall survival (OS). The established nomogram had good predictive power to assess the risk of OS in gastric cancer patients after radical gastrectomy (c-index was 0.764). CONCLUSIONS: Overall, LBMR on admission is associated with the occurrence of postoperative complications in gastric cancer patients, and this population has a poorer long-term survival. Therefore, there should be more focus on the perioperative management of patients with this risk factor before surgery.

Causal relationship between basal metabolic rate and kidney function: a bidirectional two-sample mendelian randomization study.

Background: The relationship between basal metabolic rate (BMR) and Chronic kidney disease (CKD) remains unclear and controversial. In this study, we investigated the causal role of BMR in renal injury, and inversely, whether altered renal function causes changes in BMR. Methods: In this two-sample mendelian randomization (MR) study, Genetic data were accessed from published genome-wide association studies (GWAS) for BMR ((n = 454,874) and indices of renal function, i.e. estimated glomerular filtration rate (eGFR) based on creatinine (n =1, 004, 040), CKD (n=480, 698), and blood urea nitrogen (BUN) (n =852, 678) in European. The inverse variance weighted (IVW) random-effects MR method serves as the main analysis, accompanied by several sensitivity MR analyses. We also performed a reverse MR to explore the causal effects of the above indices of renal function on the BMR. Results: We found that genetically predicted BMR was negatively related to eGFR, (ß= -0.032, P = 4.95*10-12). Similar results were obtained using the MR-Egger (ß= -0.040, P = 0.002), weighted median (ß= -0.04, P= 5.35×10-11) and weighted mode method (ß= -0.05, P=9.92×10-7). Higher BMR had a causal effect on an increased risk of CKD (OR =1.36, 95% CI = 1.11-1.66, P =0.003). In reverse MR, lower eGFR was related to higher BMR (ß= -0.64, P = 2.32×10-6, IVW analysis). Bidirectional MR supports no causal association was observed between BMR and BUN. Sensitivity analyses confirmed these findings, indicating the robustness of the results. Conclusion: Genetically predicted high BMR is associated with impaired kidney function. Conversely, genetically predicted decreased eGFR is associated with higher BMR.

Are resting metabolic rate and clinical symptoms affected by variation of serum thyroid stimulating hormone levels within the normal range in healthy and women with hypothyroidism? A case-control study.

BACKGROUND: It is unclear whether variation in thyroid stimulating hormone (TSH) levels within the reference range affect energy expenditure and clinical symptoms and even within the normal range of TSH levels, resting energy expenditure may alter. The aim of the present study was to determine whether treated hypothyroid subjects and healthy subjects with a low-normal TSH range (0.3-2.3 mIU/L) have better clinical outcomes and increased energy expenditure than those with a high-normal TSH range (2.3-4.3 mIU/L). METHODS: This was a case-control study of 160 overweight/obese women with TSH levels across the reference range of 0.3-4.3 mU/l. Subjects were paired in four groups: healthy subjects with low-normal target TSH (n = 40), healthy subjects with high-normal target TSH (n = 40), subjects with treated hypothyroidism with low-normal target TSH (n = 40), and subjects with treated hypothyroidism with high-normal target TSH (n = 40). Resting energy expenditure (RMR), dietary intake, body composition, physical activity, and biochemical markers were assessed. RESULTS: Subjects with low-normal (≤2.3 mU/L) and high-normal (>2.3 mU/L) TSH levels did not differ in terms of RMR, serum T3 levels, and clinical symptoms except fatigue (P = 0.013). However, serum fT4 levels were found to be significantly different between the study groups (P = 0.002). Serum fT4 concentration was the highest in subjects with treated hypothyroidism with low-normal target TSH. CONCLUSION: Variation in serum TSH levels within the reference range did not significantly affect REE and clinical symptoms except fatigue in healthy and women with hypothyroidism.

Multiple factor assessment for determining resting metabolic rate in young adults.

Existing formulas cannot fully explain the variation of resting metabolic rate (RMR). This study aims to examine potential influencing factors beyond anthropometric measurements and develop more accurate equations using accessible parameters. 324 healthy adults (230 females; 18-32 years old) participated in the study. Height, fat-free mass (FFM), fat mass (FM) and RMR were measured. Menstrual cycle, stress levels, living habits, and frequency of consuming caffeinated foods were collected. Measured RMR were compared with predictive values of the new equations and previous 11 equations. Mean RMR for men and women was 1825.2 ± 248.8 and 1345.1 ± 178.7 kcal/day, respectively. RMR adjusted for FFM0.66FM0.066 was positively correlated with BMI. The multiple regression model showed that RMR can be predicted in this population with model 1 (with FFM, FM, age, sex and daily sun exposure duration) or model 2 (with weight and height replacing FFM and FM). The accuracy was 75.31% in the population for predictive model 1 and 70.68% for predictive model 2. The new equations had overall improved performance when compared with existing equations. The predictive formula that consider daily sun exposure duration improve RMR prediction in young adults. Additional investigation is required among individuals in the middle-aged and elderly demographic.

Longitudinal changes in anthropometric, physiological, and physical qualities of international women's rugby league players.

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women's rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e.,[Formula: see text]) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ~2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their [Formula: see text] by 22% from testing point 1 (40 ml kg-1 min-1) to testing point 3 (49 ml kg-1 min-1) (p = ≤ 0.04). The [Formula: see text] of forwards increased by 10% from testing point 1 (41 ml kg-1 min-1) to testing point 3 (45 ml kg-1 min-1), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77-78 kg), fat mass percentage (backs: 25-26%; forwards: 30-31%), resting metabolic rate (backs: 7 MJ day-1; forwards: 7 MJ day-1), isometric mid-thigh pull (backs: 2106-2180 N; forwards: 2155-2241 N), isometric bench press (backs: 799-822 N; forwards: 999-1024 N), isometric prone row (backs: 625-628 N; forwards: 667-678 N) and bloods (backs: ferritin 21-29 ug/L, haemoglobin 137-140 g/L, iron 17-21 umol/L, transferrin 3 g/L, transferring saturation 23-28%; forwards: ferritin 31-33 ug/L, haemoglobin 141-145 g/L, iron 20-23 umol/L, transferrin 3 g/L, transferrin saturation 26-31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.

Among- and Within-Individual Variance in Metabolic Thermal Reaction Norms.

AbstractIn ectotherms, temperature has a strong effect on metabolic rate (MR), yet the extent to which the thermal sensitivity of MR varies among versus within individuals is largely unknown. This is of interest because significant among-individual variation is a prerequisite for the evolution of metabolic thermal sensitivity. Here, we estimated the repeatability (R) of the thermal sensitivity of MR in individual virgin, adult male Drosophila melanogaster (N=316) by taking repeated overnight measures of their MRs at two temperatures (~24°C and ~27°C). At the population level, thermal sensitivity decreased with locomotor activity, and older individuals showed a higher thermal sensitivity of MR than younger individuals. Taking these effects (and body mass) into account, we detected significant repeatability in both the centered intercept (Rint=0.52±0.04) and the slope (Rslp=0.21±0.07) of the metabolic thermal reaction norms, which respectively represent average MR and thermal sensitivity of MR. Furthermore, individuals with a higher overall MR also displayed greater increases in MR as temperature increased from ~24°C to ~27°C (rind=0.32±0.14). Average MR and thermal sensitivity of MR were also positively correlated within individuals (re=0.15±0.07). Our study represents a point of departure for future larger studies, in which more complex protocols (e.g., wider temperature range, breeding design) can be applied to quantify the causal components of variation in thermal sensitivity that are needed to make accurate predictions of adaptive responses to global warming.

No evidence for a signal in mammalian basal metabolic rate associated with a fossorial lifestyle.

A vast array of challenging environments are inhabited by mammals, such as living in confined spaces where oxygen levels are likely to be low. Species can exhibit adaptations in basal metabolic rate (BMR) to exploit such unique niches. In this study we use 801 species to determine the relationship between BMR and burrow use in mammals. We included pre-existing data for mammalian BMR and 16 life history traits. Overall, mammalian BMR is dictated primarily by environmental ambient temperature. There were no significant differences in BMR of terrestrial, semi-fossorial and fossorial mammals, suggesting that species occupying a subterranean niche do not exhibit baseline metabolic costs on account of their burrowing lifestyle. Fossorial mammals likely show instantaneous metabolic responses to low oxygen in tunnels, rather than exhibit adaptive long-term responses in their BMR.

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.

The thermoneutral zone in women takes an "arctic" shift compared to men.

Conventionally, women are perceived to feel colder than men, but controlled comparisons are sparse. We measured the response of healthy, lean, young women and men to a range of ambient temperatures typical of the daily environment (17 to 31 °C). The Scholander model of thermoregulation defines the lower critical temperature as threshold of the thermoneutral zone, below which additional heat production is required to defend core body temperature. This parameter can be used to characterize the thermoregulatory phenotypes of endotherms on a spectrum from "arctic" to "tropical." We found that women had a cooler lower critical temperature (mean ± SD: 21.9 ± 1.3 °C vs. 22.9 ± 1.2 °C, P = 0.047), resembling an "arctic" shift compared to men. The more arctic profile of women was predominantly driven by higher insulation associated with more body fat compared to men, countering the lower basal metabolic rate associated with their smaller body size, which typically favors a "tropical" shift. We did not detect sex-based differences in secondary measures of thermoregulation including brown adipose tissue glucose uptake, muscle electrical activity, skin temperatures, cold-induced thermogenesis, or self-reported thermal comfort. In conclusion, the principal contributors to individual differences in human thermoregulation are physical attributes, including body size and composition, which may be partly mediated by sex.

Characterizing Factors Associated with Excess Body Weight: A Descriptive Study Using Principal Component Analysis in a Population with Overweight and Obesity.

Obesity is a worldwide epidemic, making it crucial to understand how it can be effectively prevented/treated. Considering that obesity is a multifactorial condition, this article carried out a baseline cross-sectional study of the variables involved in the disorder. Eighty-four subjects with overweight/obesity were recruited. Dietary baseline information was obtained by analysing three 24 h recalls. Resting metabolic rate was measured using indirect calorimetry, physical activity was measured through accelerometry, cardiometabolic parameters were determined in blood samples and body composition via anthropometry and bioimpedance. A univariant and multivariate exploratory approach was carried out using principal component analysis (PCA). Large inter-individual variability was observed in dietetic, biochemical, and physical activity measurements (coefficient of variation ≥ 30%), but body composition was more uniform. Volunteers had an unbalanced diet and low levels of physical activity. PCA reduced the 26 analysed variables to 4 factors, accounting for 65.4% of the total data variance. The main factor was the "dietetic factor", responsible for 24.0% of the total variance and mainly related to energy intake, lipids, and saturated fatty acids. The second was the "cardiometabolic factor" (explaining 16.8% of the variability), the third was the "adiposity factor" (15.2%), and the last was the "serum cholesterol factor" (9.4%).

[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.

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.

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis.

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults.

BACKGROUND: Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. OBJECTIVES: To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. METHODS: TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. RESULTS: Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. CONCLUSIONS: These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Energy expenditure and dietary intake in professional female football players in the Dutch Women's League: Implications for nutritional counselling.

In contrast to male football players, research on the nutritional requirements of female football players is limited. This study aimed to assess total daily energy expenditure (TDEE) in professional female football players, along with body composition, physical activity and dietary intake. This observational study included 15 professional football players playing in the highest Dutch Women's League. TDEE was assessed by doubly labelled water over 14 days, along with resting metabolic rate (RMR; ventilated hood), fat-free mass (FFM; dual-energy x-ray absorptiometry), and dietary intake (24-h recalls). Physical activity energy expenditure (PAEE) was derived from subtracting RMR and estimated diet-induced thermogenesis (10%) from TDEE. TDEE was 2882 ± 278 kcal/day (58 ± 5 kcal/kg FFM) and significantly (p < 0.05) correlated with FFM (r = 0.62). PAEE was 1207 ± 213 kcal/d. Weighted energy intake was 2344 kcal [2023-2589]. Carbohydrate intakes were 3.2 ± 0.7, 4.4 ± 1.1 and 5.3 ± 1.9 g/kg body mass for rest, training and match days, respectively, while weighted mean protein intake was 1.9 ± 0.4 g/kg body mass. In conclusion, the energy requirements of professional female football players are moderate to high and can be explained by the substantial PAEE. To fuel these requirements, sports nutritionists should consider shifting the players' focus towards prioritizing adequate carbohydrate intakes, rather than emphasizing high protein consumption.

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.

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.

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.

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.

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.