Studying factors that contribute to our understanding of maintaining normal energy balance are of paramount significance following spinal cord injury (SCI). Accurate determination of energy needs is crucial for providing nutritional guidance and managing the increasing prevalence of malnutrition or obesity after SCI. BMR represents 75–80 % of the total energy expenditure in persons with SCI. Accurately measuring BMR is an important component for calculating total energetic needs in this population. Indirect calorimetry is considered the gold-standard technique for measuring BMR. However, technical challenges may limit its applications in large cohort studies and alternatively rely on prediction equations. Previous work has shown that BMR changes in response to disuse and exercise in the range of 15–120 %. Factors including sex, level of injury and type of assistive devices may influence BMR after SCI. RMR is erroneously used interchangeably for BMR, which may result in overestimation of energetic intake when developing nutritional plans. To address this concern, we comprehensively reviewed studies that conducted BMR (n=15) and RMR (n=22) in persons with SCI. The results indicated that RMR is 9 % greater than BMR in persons with SCI. Furthermore, the SCI-specific prediction equations that incorporated measures of fat-free mass appeared to accurately predict BMR. Overall, the current findings highlighted the significance of measuring BMR as well as encouraging the research and clinical community to effectively establish countermeasures to combat obesity after SCI.

This systematic review and meta-analysis compared the effects of different rates of weight loss (WL), but equivalent total WL, on body composition and RMR. Studies examining gradual v. rapid WL on body composition and RMR in participants with overweight/obesity published up to October 2019 were identified through PubMed, the Cochrane Library, Web of Science, Embase, Scopus and Ovid databases. Meta-analysis was carried out using a fixed or random effects model as appropriate. Although the magnitude of WL was similar (mean difference 0·03 kg, 95 % CI –0·65, 0·71), gradual WL promoted greater reductions in fat mass (FM) (–1 kg, 95 % CI –1·70, –0·29) and body fat percentage (BFP) (–0·83 %, 95 % CI –1·49, –0·17). Gradual WL significantly preserved RMR compared with rapid WL (407·48 kJ, 95 % CI 76·76, 118·01). However, there was no significant difference in waist and hip circumferences, waist:hip ratio and fat-free mass (FFM) between gradual and rapid WL. The present systematic review and meta-analysis indicates beneficial effects of gradual WL, as compared with rapid WL, on FM, BFP and RMR in individuals with overweight/obesity. However, FFM changes and anthropometric indices did not significantly differ following different rates of WL.

Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m2). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.

Objective measurement of RMR may be important for optimal nutritional care but is hindered by the price and practicality of the metabolic monitoring device. This study compared two metabolic monitoring devices for measuring RMR and VO2 and compared the measured RMR with the predicted RMR calculated from equations. RMR was measured using QUARK RMR (reference device) and Fitmate GS (COSMED) in a random order for 30 min, each on fasted participants. In total, sixty-eight adults participated (median age 22 years, interquartile range 21–32). Pearson correlation showed that RMR (r 0·86) and VO2 (r 0·86) were highly correlated between the two devices (P < 0·05). Intraclass correlation coefficients (ICC) showed good relative agreements regarding RMR (ICC = 0·84) and VO2 (ICC = 0·84) (P < 0·05). RMR measured by QUARK RMR was significantly higher (649 (sd 753) kJ/d) than Fitmate GS. Equations significantly overpredicted RMR. Accurate RMR (i.e. within ±10 % of the RMR measured by QUARK RMR) was found among 38 % of the participants for Fitmate GS and among 46–68 % depending on the equations. Bland–Altman analysis showed a low absolute agreement with QUARK RMR at an individual level for both Fitmate GS (limits of agreement (LOA): −828 to +2125 kJ/d) and equations (LOA ranged from −1979 to +1879 kJ/d). In conclusion, both Fitmate GS and predictive equations had low absolute agreements with QUARK RMR at an individual level. Therefore, these limitations should be considered when determining RMR using Fitmate GS or equations.

The micro-organisms which inhabit the human gut (i.e. the intestinal microbiota) influence numerous human biochemical pathways and physiological functions. The present review focuses on two questions, ‘Are intestinal microbiota effects measurable and meaningful?’ and ‘What research methods and variables are influenced by intestinal microbiota effects?’. These questions are considered with respect to doubly labelled water measurements of energy expenditure, heat balance calculations and models, measurements of RMR via indirect calorimetry, and diet-induced energy expenditure. Several lines of evidence suggest that the intestinal microbiota introduces measurement variability and measurement errors which have been overlooked in research studies involving nutrition, bioenergetics, physiology and temperature regulation. Therefore, we recommend that present conceptual models and research techniques be updated via future experiments, to account for the metabolic processes and regulatory influences of the intestinal microbiota.

Nighttime eating is often associated with a negative impact on weight management and cardiometabolic health. However, data from recent acute metabolic studies have implicated a benefit of ingesting a bedtime snack for weight management. The present study compared the impact of ingesting a milk snack containing either 10 (BS10) or 30 g (BS30) protein with a non-energetic placebo (BS0) 30 min before bedtime on next morning metabolism, appetite and energy intake in mildly overweight males (age: 24·3 (sem 0·8) years; BMI: 27·4 (sem 1·1) kg/m2). Next morning measurements of RMR, appetite and energy intake were measured using indirect calorimetry, visual analogue scales and an ad libitum breakfast, respectively. Bedtime milk ingestion did not alter next morning RMR (BS0: 7822 (sem 276) kJ/d, BS10: 7482 (sem 262) kJ/d, BS30: 7851 (sem 261) kJ/d, P=0·19) or substrate utilisation as measured by RER (P=0·64). Bedtime milk ingestion reduced hunger (P=0·01) and increased fullness (P=0·04) during the evening immediately after snack ingestion, but elicited no effect the next morning. Next morning breakfast (BS0: 2187 (sem 365) kJ, BS10: 2070 (sem 336) kJ, BS30: 2582 (sem 384) kJ, P=0·21) and 24 h post-trial (P=0·95) energy intake was similar between conditions. To conclude, in mildly overweight adults, compared with a non-energetic placebo, a bedtime milk snack containing 10 or 30 g of protein does not confer changes in next morning whole-body metabolism and appetite that may favour weight management.

Diet-induced weight loss (WL) is associated with reduced resting and non-resting energy expenditure (EE), driven not only by changes in body composition but also potentially by adaptive thermogenesis (AT). When exactly this happens, during progressive WL, remains unknown. The aim of this study was to determine the timeline of changes in RMR and exercise-induced EE (EIEE), stemming from changes in body composition v. the presence of AT, during WL with a very-low-energy diet (VLED). In all, thirty-one adults (eighteen men) with obesity (BMI: 37 (sem 4·5) kg/m2; age: 43 (sem 10) years) underwent 8 weeks of a VLED, followed by 4 weeks of weight maintenance. Body weight and composition, RMR, net EIEE (10, 25 and 50 W) and AT (for RMR (ATRMR) and EIEE (ATEIEE)) were measured at baseline, day 3 (2 (sem 1) % WL), after 5 and 10 % WL and at weeks 9 (16 (sem 2) %) and 13 (16 (sem 1) %). RMR and fat mass were significantly reduced for the first time at 5 % WL (12 (sem 8) d) (P<0·01 and P<0·001, respectively) and EIEE at 10 % WL (32 (sem 8) d), for all levels of power (P<0·05), and sustained up to week 13. ATRMR was transiently present at 10 % WL (−460 (sem 690) kJ/d, P<0·01). A fall in RMR should be anticipated at ≥5 % WL and a reduction in EIEE at ≥10 % WL. Transient ATRMR can be expected at 10 % WL. These physiological adaptations may make progressive WL difficult and will probably contribute to relapse.

This study aimed to characterise lean and obese phenotypes according to diet and body composition, and to compare fasting and postprandial appetite and metabolic profiles following a high-fat test meal. A total of ten lean (BMI<25 kg/m2) high-fat (LHF), ten lean low-fat (LLF; >40 and <30 % energy from fat) and ten obese (BMI>30 kg/m2) high-fat consumers (OHF; >40 % energy from fat) were recruited. Before and following the test meal (4727 kJ (1130 kcal), 77 % fat, 20 % carbohydrate (CHO) and 3 % protein), fasting plasma glucose, insulin, leptin, ghrelin, peptide YY (PYY), RER, RMR and subjective appetite ratings (AR) were measured for 6 h. Thereafter, subjects consumed a self-selected portion of a standardised post-test meal (40 % fat, 45 % CHO and 15 % protein) and reported AR. Fasting (P=0·01) and postprandial (P<0·001) fat oxidation was significantly higher in LHF than in LLF but was not different between LHF and OHF. Although similar between the lean groups, fasting and postprandial energy expenditures were significantly higher in OHF compared with LHF (P<0·01). Despite similar AR across groups, LLF consumed a relatively greater quantity of the post-test meal than did LHF (7·87 (sd 2·96) v. 7·23 (sd 2·67) g/kg, P=0·013). The lean groups showed appropriate changes in plasma ghrelin and PYY following the test meal, whereas the OHF group showed a blunted response. In conclusion, the LHF phenotype had a greater capacity for fat oxidation, which may be protective against weight gain. OHF individuals had a blunted appetite hormone response to the high-fat test meal, which may subsequently increase energy intake, driving further weight gain.

The Deltatrac™ II Metabolic Monitor (Datex-Ohmeda Inc.) is considered the standard reference machine in indirect calorimetry; however, it is no longer commercially available thus there is a need for new machines. The gas exchange measurement (GEM; GEM Nutrition Ltd) and the ECAL (Health Professional Solutions) are alternative measuring systems. The aim of this study was to compare the ECAL and GEM with Deltatrac for measures of RMR and the GEM to the Deltatrac for measures of diet-induced thermogenesis (DIT). Twenty healthy participants were tested on test day 1 (T1) and test day 2 (T2). RMR was measured in a randomised order for 30 min on the Deltatrac, the GEM and the ECAL. Following this, a 1553 kJ meal was consumed and DIT was measured on the Deltatrac and the GEM in alternating 15 min intervals for 4 h. The GEM reported consistently higher values than the Deltatrac for VO2, VCO2, RMR and fat oxidation (P < 0·005). The ECAL was significantly higher than the Deltatrac for measures of VO2, RMR, carbohydrate oxidation (T2) and respiratory quotient and fat oxidation (T1, T2) (P < 0·05). There were no significant differences within repeated RMR measures on the ECAL, the GEM or the Deltatrac. DIT measures were consistently higher on the GEM (T1) (P < 0·005); however, there were no significant differences between repeated measures. The findings suggest that while the GEM and the ECAL were not accurate alternatives to the Deltatrac, they may be reliable for repeated measures.

The purpose of the present study was to investigate whether whey protein (WP), casein protein (CP), carbohydrate (CHO) or a non-energy-containing placebo (PLA) consumed before sleep alters morning appetite and resting energy expenditure (REE) in active men. A total of eleven men (age: 23·6 (sem 1·0) years; body fat: 16·3 (sem 2·5) %) participated in this randomised, double-blind, cross-over study. A single dose of WP (30 g), CP (30 g), CHO (33 g) or PLA was consumed 30 min before sleep, and each trial was separated by 48–72 h. The next morning (05.00–08.00 hours), measurements of satiety, hunger and desire to eat and REE were taken. After a 30 min equilibration period, REE in the supine position was measured for 60 min. An analysis of 10 min mean intervals over the final 50 min of the measurement period was conducted. Statistical analyses were conducted using repeated-measures ANOVA for metabolic variables, and a one-way ANOVA was used for measuring changes in appetite markers. Group differences were examined by Tukey's post hoc analysis. There were no significant differences in appetite measures among the groups. There was a main group effect for REE. The predicted REE was significantly greater after consumption of the WP (8151 (sem 67) kJ/d), CP (8126 (sem 67) kJ/d) and CHO (7988 (sem 67) kJ/d) than after that of the PLA (7716 (sem 67) kJ/d, P <0·0001). There were no significant differences between the WP and CP groups in any metabolic measurements. Night-time consumption of WP, CP or CHO, in the hours close to sleep, elicits favourable effects on the next-morning metabolism when compared with that of a PLA in active young men.

The present study investigated the effect of 2 weeks of energy restriction on whole body, liver and skeletal muscle energy handling. We measured whole-body oxygen consumption, as well as mitochondrial protein mass, respiratory capacity and energetic coupling in liver and skeletal muscle from food-restricted (FR) rats, age- and weight-matched controls. We also assessed markers of oxidative damage and antioxidant defences. The present results show that, in response to energy restriction, an adaptive decrease in whole-body energy expenditure is coupled with structural and functional changes in mitochondrial compartment, both in liver and skeletal muscle. In fact, liver mitochondrial mass per g of liver significantly increased, whereas total hepatic mitochondrial oxidative capacity was lower in FR than in control rats, because of a significant decrease in liver contribution to total body weight. In skeletal muscle, sub-sarcolemmal (SS) mitochondrial respiratory capacity, as well as SS and inter-myofibrillar (IMF) mitochondrial protein mass per g of tissue, was significantly lower in FR rats, compared to controls. Finally, a decrease in oxidative damage was found in liver but not in skeletal muscle mitochondria from FR rats, whereas an increase in antioxidant defence was found in both tissues. From the present results, it appears that skeletal muscle is involved in the decrease in energy expenditure induced by energy restriction. Energy sparing is achieved through changes in the activity (SS), mass (SS and IMF) and efficiency (IMF) of mitochondrial compartment.