People with severe mental illness (SMI), including schizophrenia and related psychoses and bipolar disorder, are at greater risk for obesity compared with people without mental illness. An altered resting metabolic rate (RMR) may be a key driving factor; however, published studies have not been systematically reviewed. This systematic review and meta-analysis aimed to determine whether the RMR of people with SMI assessed by indirect calorimetry differs from (i) controls, (ii) predictive equations and (iii) after administration of antipsychotic medications. Five databases were searched from database inception to March 2022. Thirteen studies providing nineteen relevant datasets were included. Study quality was mixed (62 % considered low quality). In the primary analysis, RMR in people with SMI did not differ from matched controls (n 2, standardised mean difference (SMD) = 0·58, 95 % CI −1·01, 2·16, P = 0·48, I2 = 92 %). Most predictive equations overestimated RMR. The Mifflin–St. Jeor equation appeared to be most accurate (n 5, SMD = −0·29, 95 % CI −0·73, 0·14, P = 0·19, I2 = 85 %). There were no significant changes in RMR after antipsychotic administration (n 4, SMD = 0·17, 95 % CI −0·21, 0·55, P = 0·38, I2 = 0 %). There is little evidence to suggest there is a difference in RMR between people with SMI and people without when matched for age, sex, BMI and body mass, or that commencement of antipsychotic medication alters RMR.

Studies exploring the accuracy of equations calculating resting energy expenditure (REE) in patients with Crohn’s disease (CD) are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in CD patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. One hundred and eighty-six CD outpatients (102 males) with mean age 41·3 (sd 14·1) years and 37·6 % with active disease were evaluated. Mean mREE in the total sample was 7255 (sd 1854) kJ/day. All equations underpredicted REE and showed moderate correlations with mREE (Pearson’s r or Spearman’s rho 0·600–0·680 for current weight, all P-values < 0·001). Accuracy was low for all equations at the individual level (28–42 and 25–40 % for current and adjusted body weight, respectively, 19–33 % for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, BMI and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with CD.

Dietary interventions to delay carbohydrate digestion or absorption can effectively prevent hyperglycaemia in the early postprandial phase. L-arabinose can specifically inhibit sucrase. It remains to be assessed whether co-ingestion of L-arabinose with sucrose delays sucrose digestion, attenuates subsequent glucose absorption and impacts hepatic glucose output. In this double-blind, randomised crossover study, we assessed blood glucose kinetics following ingestion of a 200-ml drink containing 50 g of sucrose with 7·5 g of L-arabinose (L-ARA) or without L-arabinose (CONT) in twelve young, healthy participants (24 ± 1 years; BMI: 22·2 ± 0·5 kg/m2). Plasma glucose kinetics were determined by a dual stable isotope methodology involving ingestion of (U-13C6)-glucose-enriched sucrose, and continuous intravenous infusion of (6,6–2H2)-glucose. Peak glucose concentrations reached 8·18 ± 0·29 mmol/l for CONT 30 min after ingestion. In contrast, the postprandial rise in plasma glucose was attenuated for L-ARA, because peak glucose concentrations reached 6·62 ± 0·18 mmol/l only 60 min after ingestion. The rate of exogenous glucose appearance for L-ARA was 67 and 57 % lower compared with CONT at t = 15 min and 30 min, respectively, whereas it was 214 % higher at t = 150 min, indicating a more stable absorption of exogenous glucose for L-ARA compared with CONT. Total glucose disappearance during the first hour was lower for L-ARA compared with CONT (11 ± 1 v. 17 ± 1 g, P < 0·0001). Endogenous glucose production was not differentially affected at any time point (P = 0·27). Co-ingestion of L-arabinose with sucrose delays sucrose digestion, resulting in a slower absorption of sucrose-derived glucose without causing adverse effects in young, healthy adults.

Reducing sedentary behaviour (SB) and increasing physical activity (PA) by sitting less and standing/walking more is advised to prevent chronic diseases. However, the mechanisms underlying this recommendation are not well established, especially in individuals with obesity living in low-income regions. The present study evaluated whether there are associations between PA indicators (PAI – standing time, walking time and the number of steps/d) and SB indicators (SBI – sitting/lying down time) with the hormonal profile and resting energy expenditure (REE) of adult women living in a low-income region. This is a cross-sectional study. We collected data on hormones (insulin resistance, leptin and thyroid axis), body composition (tetrapolar bioimpedance), REE (indirect calorimetry), and PAI and SBI (triaxial accelerometers, ActivPAL). Multivariable linear models adjusting for age and fat-free mass were performed. Fifty-eight women (mean age of 31 years and BMI of 33 kg/m2) were included. The mean sitting/lying down time and standing time were 16·08 and 5·52 h/d, respectively. Sitting/lying down time showed a direct association with free thyroxine (FT4) (β = 0·56 ng/dl; 95 % CI = −1·10, −0·02). Standing time showed a direct association with FT4 (β = 0·75 ng/dl; 95 % CI = 0·01; 1·48) and inverse association with free triiodothyronine (β = −2·83 pg/ml 95 % CI = −5·56, −0·10). There were no associations between PAI and SBI with the REE, insulin resistance, leptin and thyroid-stimulating hormone. Thus, decreased SB is associated with thyroid hormones levels but not with REE, insulin resistance or leptin in women with obesity living in low-income regions.

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 study aimed to examine the effects of diurnal Ramadan fasting (RF) on substrate oxidation, energy production, blood lipids and glucose as well as body composition. Nine healthy Muslim men (fasting (FAST) group) and eight healthy non-practicing men (control (CNT) group) were assessed pre- and post-RF. FAST were additionally assessed at days 10, 20 and 30 of RF in the morning and evening. Body composition was determined by hydrodensitometry, substrate oxidation and energy production by indirect calorimetry, blood metabolic profile by biochemical analyses and energy balance by activity tracker recordings and food log analyses. A significant group×time interaction revealed that chronic RF reduced body mass and adiposity in FAST, without changing lean mass, whereas CNT subjects remained unchanged. In parallel to these findings, a significant main diurnal effect (morning v. evening) of RF on substrate oxidation (a shift towards lipid oxidation) and blood metabolic profile (a decrease in glucose and an increase in total cholesterol and TAG levels, respectively) was observed, which did not vary over the course of the Ramadan. In conclusion, although RF induces diurnal metabolic adjustments (morning v. evening), no carryover effect was observed throughout RF despite the extended daily fasting period (18·0 (sd 0·3) h) and changes in body composition.

Assessing energy requirements is a fundamental activity in clinical dietetic practice. The aim of this study was to investigate which resting energy expenditure (REE) predictive equations are the best alternatives to indirect calorimetry before and after an interdisciplinary therapy in Brazilian obese women. In all, twelve equations based on weight, height, sex, age, fat-free mass and fat mass were tested. REE was measured by indirect calorimetry. The interdisciplinary therapy consisted of nutritional, physical exercise, psychological and physiotherapy support during the course of 1 year. The average differences between measured and predicted REE, as well as the accuracy at the ±10 % level, were evaluated. Statistical analysis included paired t tests, intraclass correlation coefficients and Bland–Altman plots. Validation was based on forty obese women (BMI 30–39·9 kg/m2). Our major findings demonstrated a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid, obese women. The equations reported by Harris–Benedict and FAO/WHO/United Nations University (UNU) were the only ones that did not show significant differences compared with indirect calorimetry and presented a bias <5 %. The Harris–Benedict equation provided 40 and 47·5 % accurate predictions before and after therapy, respectively. The FAO equation provided 35 and 47·5 % accurate predictions. However, the Bland–Altman analysis did not show good agreement between these equations and indirect calorimetry. Therefore, the Harris–Benedict and FAO/WHO/UNU equations should be used with caution for obese women. The need to critically re-assess REE data and generate regional and more homogeneous REE databases for the target population is reinforced.

Energy expenditure (EE) can be estimated based on respiratory gas exchange measurements, traditionally done in respiration chambers by indirect calorimetry (IC). However, the 13C-bicarbonate technique (13C-BT) might be an alternative minimal invasive method for estimation of CO2 production and EE in the field. In this study, four Shetland ponies were used to explore the effect of feeding on CO2 production and EE measured simultaneously by IC and 13C-BT. The ponies were individually housed in respiration chambers and received either a single oral or intravenous (IV) bolus dose of 13C-labelled sodium bicarbonate (NaH13CO3). The ponies were fed haylage 3 h before (T−3), simultaneously with (T0) or 3 h after (T+3) administration of 13C-bicarbonate. The CO2 produced and O2 consumed by the ponies were measured for 6 h with both administration routes of 13C-bicarbonate at the three different feeding times. Feeding time affected the CO2 production (P<0.001) and O2 consumption (P<0.001), but not the respiratory quotient (RQ) measured by IC. The recovery factor (RF) of 13C in breath CO2 was affected by feeding time (P<0.01) and three different RF were used in the calculation of CO2 production measured by 13C-BT. An average RQ was used for the calculations of EE. There was no difference between IC and 13C-BT for estimation of CO2 production. An effect of feeding time (P<0.001) on the estimated EE was found, with higher EE when feed was offered (T0 and T+3) compared with when no feed was available (T−3) during measurements. In conclusion, this study showed that feeding time affects the RF and measurements of CO2 production and EE. This should be considered when the 13C-BT is used in the field. IV administration of 13C-bicarbonate is recommended in future studies with horses to avoid complex 13C enrichment-time curves with maxima and shoulders as observed in several experiments with oral administration of 13C-bicarbonate.

The effects of dietary carbohydrate and fat on feline health are not well understood. The effects of feeding diets moderately high in fat (HF; n 10; 30 % fat, 26 % carbohydrate as fed) or carbohydrate (HC; n 10; 11 % fat, 47 % carbohydrate), for 84 d, were investigated in healthy, adult cats (3·5 (sd 0·5) years). Data on indirect calorimetry, blood biomarkers, activity, play and cognition were collected at baseline, and at intervals throughout the study. Body composition was measured by dual-energy X-ray absorptiometry at baseline and on day 85. There were no significant main effects of diet on body weight and composition. When data were analysed over study day within diet, cats fed HF diets experienced a significant increase in body fat (P = 0·001) and body weight (P = 0·043) in contrast to cats consuming the HC diet that experienced no change in body fat or body weight (P = 0·762) throughout the study. Overall, energy expenditure was similar between diets (P = 0·356 (fasted), P = 0·086 (postprandial)) and respiratory quotient declined with exposure to the HF diet and increased with exposure to the HC diet (P < 0·001; fasted and postprandial). There was no difference in insulin sensitivity as an overall effect of diet (P = 0·266). Activity declined from baseline with exposure to both diets (HC: P = 0·002; HF: P = 0·01) but was not different between diets (P = 0·247). There was no effect of diet on play (P = 0·387) and cats consuming either the HF or HC diet did not successfully learn the cognitive test. Overall, cats adapt to dietary macronutrient content, and the implications of feeding HC and HF diets on risk for adiposity as driven by metabolic and behavioural mechanisms are discussed.

Lactose malabsorption is associated with rapid production of high levels of osmotic compounds, such as organic acids and SCFA in the colon, suspected to contribute to the onset of lactose intolerance. Adult rats are lactase deficient and the present study was conducted to evaluate in vivo the metabolic consequences of acute lactose ingestion, including host–microbiota interactions. Rats received diets of 25 % sucrose (S25 control group) or 25 % lactose (L25 experimental group). SCFA and lactic acid were quantified in intestinal contents and portal blood. Expression of SCFA transporter genes was quantified in the colonic mucosa. Carbohydrate oxidation (Cox) and lipid oxidation (Lox) were computed by indirect calorimetry. Measurements were performed over a maximum of 13 h. Time, diet and time × diet variables had significant effects on SCFA concentration in the caecum (P< 0·001, P= 0·004 and P= 0·007, respectively) and the portal blood (P< 0·001, P= 0·04 and P< 0·001, respectively). Concomitantly, expression of sodium monocarboxylate significantly increased in the colonic mucosa of the L25 group (P= 0·003 at t= 6 h and P< 0·05 at t= 8 h). During 5 h after the meal, the L25 group's changes in metabolic parameters (Cox, Lox) were significantly lower than those of the S25 group (P= 0·02). However, after 5 h, L25 Cox became greater than S25 (P= 0·004). Thus, enhanced production and absorption of SCFA support the metabolic changes observed in calorimetry. These results underline the consequences of acute lactose malabsorption and measured compensations occurring in the host's metabolism, presumably through the microbiota fermentations and microbiota–host interactions.

The Compendium of Physical Activities (CPA) provides the energy expenditure (EE) for hundreds of daily activities reported in metabolic equivalents (MET). It remains to be determined if the metabolic changes of pregnancy alter the use of the CPA MET (METCPA) in this population. The energy cost of rest, activities of daily living (ADL; typing, folding laundry and sweeping) and treadmill walking [2.0, 2.5, 3.0 mph (0% incline), 3.0 mph (3% incline)] were compared with the METCPA from the 2000 and 2011 CPA in 30 pregnant women (10–14 weeks gestation) using indirect calorimetry (IC). The METCPA for each activity was compared against two measured IC values: METabsolute (3.5 ml O2/kg/min) and METratio (EEactivity/EErest). Means for both comparisons were tested by one-sample t-test. Measured MET correlated with the 2011 METCPA: METabsolutev. METCPAR 2 = 0.906, P < 0.0001; METratiov. METCPAR 2 = 0.861, P < 0.0001. Differences between measured MET values and the 2011 METCPA ranged from 16% underestimation to 48% overestimation. Using the absolute definition, the METCPA significantly overestimated the ADL (P < 0.0005); yet, no significant differences were found between walking at 0% grade and METCPA. Conversely, only folding laundry was significantly different with the ratio definition, whereas walking at a level grade was significantly underestimated (P < 0.0001). Similar observations were found using the 2000 CPA. The use of the METCPA to estimate EE in pregnant women can result in significant over- or underestimation, depending on the activity and the definition of the MET that is used.

The objective of the present study was to investigate the relationship between energy expenditure (EE), biochemical and anthropometric nutritional status and severity scales in critically ill children. We performed a prospective observational study in forty-six critically ill children. The following variables were recorded before starting nutrition: age, sex, diagnosis, weight, height, risk of mortality according to the Paediatric Risk Score of Mortality (PRISM), the Revised Paediatric Index of Mortality (PIM2) and the Paediatric Logistic Organ Dysfunction (PELOD) scales, laboratory parameters (albumin, total proteins, prealbumin, transferrin, retinol-binding protein, cholesterol and TAG, and nitrogen balance) and EE measured by indirect calorimetry. The results showed that there was no relationship between EE and clinical severity evaluated using the PRISM, PIM2 and PELOD scales or with the anthropometric nutritional status or biochemical alterations. Finally, it was concluded that neither nutritional status nor clinical severity is related to EE. Therefore, EE must be measured individually in each critically ill child using indirect calorimetry.

The objective of the present study is to measure basal energy expenditure (BEE) using the Cosmed K4b2 portable metabolic system (Rome, Italy) and to develop a new predictive equation for BEE in southern Chinese adults. A total of 165 healthy Chinese adults aged 18–45 years with normal body weight were involved in the present study. BEE was measured by Cosmed K4b2. Body composition was determined by body composition analysers (ImpediMed DF50, QLD, Australia). Multiple linear regression analysis and correlation analysis were applied to develop a new optimal equation for predicting BEE of southern healthy Chinese adults. Measured BEE (mBEE) of southern Chinese healthy adults was 5513 (sem 96) kJ/d, which was similar to the results predicted by the equation developed by of Liu 5579 (sem 57) kJ/d (P = 0·37) and significantly lower than those from equations developed by Henry (5763 (sem 54) kJ/d), Schofield (5898 (sem 58) kJ/d) and Harris–Benedict (HB; 5863 (sem 51) kJ/d) (all P = 0·001). The optimal equation developed by our data was BEE (kJ/d) = 277+89 weight (kg)+600 sex (male = 1 and female = 0) (r2 = 0·48, n 165). For males, BEE (kJ/d) = 105 weight (kg) − 58 (r2 = 0·27, n 79); for females, BEE (kJ/d) = 69 weight (kg)+1335 (r2 = 0·24, n 86). In conclusion, the mBEE of southern Chinese healthy adults was 5513 (sem 96) kJ/d. The BMR of Chinese adults of normal weight is overestimated by widely used prediction equations developed by Henry, Schofield and HB. The equation developed in the present study (equation 7) can be used in predicting BEE for Chinese adults aged 18–45 years with normal body weight.

The use of activity monitors (triaxial accelerometers) to estimate total energy expenditure in kilocalories is dependent on the estimation of resting energy expenditure (REE). However, the REE estimated by activity monitors has not been validated against more precise techniques, such as indirect calorimetry (IC). Therefore, the objective of the present study was to compare REE estimated by the Actical activity monitor (ActMon) to that measured by IC and standard prediction equations of REE. Fifty healthy adults between 18 and 43 years of age were measured for weight and percentage of body fat using a digital scale and bioelectrical impedance. The REE estimated by the ActMon was only 129 kJ/d higher, but not statistically different (P>0·05), than the REE measured with IC. Using multiple linear regression, there was a positive relationship for men, but not for women, between fat mass (kg) and percentage of body fat and the difference in REE estimated by the ActMon compared to IC (P < 0·001). Therefore, in the cohort studied, the use of an activity monitor to estimate REE is valid when compared to IC, but not to a standard prediction equation of REE.

To avoid any negative outcomes associated with under- or overfeeding it is essential to estimate nutrient requirements before commencing nutrition support. The energy requirements of an individual vary with current and past nutritional status, clinical condition, physical activity and the goals and likely duration of treatment. The evidence-base for prediction methods in current use, however, is poor and the equations are thus open to misinterpretation. In addition, most methods require an accurate measurement of current weight, which is problematic in some clinical situations. The estimation of energy requirements is so challenging in some conditions, e.g. critical illness, obesity and liver disease, that it is recommended that expenditure be measured on an individual basis by indirect calorimetry. Not only is this technique relatively expensive, but in the clinical setting there are several obstacles that may complicate, and thus affect the accuracy of, any such measurements. A review of relevant disease-specific literature may assist in the determination of energy requirements for some patient groups, but the energy requirements for a number of clinical conditions have yet to be established. Regardless of the method used, estimated energy requirements should be interpreted with care and only used as a starting point. Practitioners should regularly review the patient and reassess requirements to take account of any major changes in clinical condition, nutritional status, activity level and goals of treatment. There is a need for large randomised controlled trials that compare the effects of different levels of feeding on clinical outcomes in different disease states and care settings.

Total energy expenditure (EE) can be assessed in children by the heart-rate (HR) monitoring technique calibrated against open-circuit indirect calorimetry (IC). In this technique, sleeping EE is usually estimated as the lowest value of a 30 min resting EE measurement×0·90 to give an average for the total sleeping period. However, sleeping is a dynamic process in which sleeping EE is modulated by the effect of factors such as body movement and different sleep stages. The aim of the present study was to determine a new method to improve the sleeping EE measurement by taking into account body movements during sleep. Twenty-four non-obese children participated in the present study. All subjects passed through a calibration period. HR and EE measured by IC were simultaneously collected during resting, the postprandial period, and during different levels of activity. Different methods for computing sleeping EE (resting EE×0·90 with different breakpoints (‘flex point’ HR with linear regression or ‘inflection point’ (IP) HR with the third order polynomial regression equation (P3)) were compared with EE measured for least 2·0 h in eight sleeping children. The best method of calculation was then tested in sixteen children undergoing HR monitoring and with a body movement detector. In a subset of eight children undergoing simultaneous sleeping EE measurement by IC and HR, the use of the equation resting EE×0·8 when HR<IP and P3 when HR>IP during the sleeping period gave the lowest difference (1 (SD 5·4) %) compared with other methods (linear or polynomial regressions). The new formula was tested in an independent subset of sixteen other children. The difference between sleeping ee computed with the formula resting EE×0·90 and sleeping EE computed with resting EE×0·8 when HR<IP and the P3 equation when HR>IP during sleeping periods was significant (13 (sd 5·9)%) only for active sleeping subjects (n 6 of 16 subjects). The correlation between the difference in the results from the two methods of calculation and body movements was close (r 0·63, P<0·005, Spearman test) as well as computed sleeping EE (Spearman test, r 0·679, P<0·001), indicating that this new method is reliable for computing sleeping EE with HR monitoring if children are moving during sleep and improves the total EE assessment.

The objectives of the present study were to determine whether differences in usual physical activity affect BMR, sleeping energy expenditure (EE), and EE during seated activities between athletic and non-athletic adolescents, and to establish individual relationships between heart rate and EE. Adolescents (n 49, four groups of eleven to fifteen boys or girls aged 16–19 years) participated in the study. Body composition was measured by the skinfold-thickness method and maximum O2 consumption (VO2max) by a direct method (respiratory gas exchange) on a cycloergometer. The subjects each spent 36 h in one of two large whole-body calorimeters. They followed a standardized activity programme including two periods of exercise simulating their mean weekly physical activities. Fat-free mass (FFM), VO2max, daily EE and EE during sleep and seated activities were significantly higher in athletic than in non-athletic subjects of both sexes. VO2max, daily EE and EE during exercise adjusted for FFM were higher in athletic than in non-athletic adolescents (P < 0·001), whereas sleeping EE, BMR and EE during seated activities and adjusted for FFM were not significantly different between athletic and non-athletic adolescents. However, sex differences in EE remained significant. Thus, differences in EE between athletic and non-athletic adolescents resulted mainly from differences in FFM and physical exercise. Usual activity did not significantly affect energy utilization of substrates. Finally, individual relationships were computed between heart rate and EE with activity programmes simulating the usual activities of athletic and non-athletic adolescents with the goal of predicting EE of the same subjects in free-living conditions.