Role of resting metabolic rate and energy expenditure in hunger and appetite control : a new formulation

A long-running issue in appetite research concerns the influence of energy expenditure on energy intake. More than 50 years ago, Otto G. Edholm proposed that "the differences between the intakes of food [of individuals] must originate in differences in the expenditure of energy". However, a relationship between energy expenditure and energy intake within any one day could not be found, although there was a correlation over 2 weeks. This issue was never resolved before interest in integrative biology was replaced by molecular biochemistry. Using a psychobiological approach, we have studied appetite control in an energy balance framework using a multi-level experimental system on a single cohort of overweight and obese human subjects. This has disclosed relationships between variables in the domains of body composition [fat-free mass (FFM), fat mass (FM)], metabolism, gastrointestinal hormones, hunger and energy intake. In this Commentary, we review our own and other data, and discuss a new formulation whereby appetite control and energy intake are regulated by energy expenditure. Specifically, we propose that FFM (the largest contributor to resting metabolic rate), but not body mass index or FM, is closely associated with self-determined meal size and daily energy intake. This formulation has implications for understanding weight regulation and the management of obesity.

Resting metabolic rate is associated with hunger, self-determined meal size, and daily energy intake and may represent a marker for appetite

Background: There are strong logical reasons why energy expended in metabolism should influence the energy acquired in food-intake behavior. However, the relation has never been established, and it is not known why certain people experience hunger in the presence of large amounts of body energy. Objective: We investigated the effect of the resting metabolic rate (RMR) on objective measures of whole-day food intake and hunger. Design: We carried out a 12-wk intervention that involved 41 overweight and obese men and women [mean ± SD age: 43.1 ± 7.5 y; BMI (in kg/m2): 30.7 ± 3.9] who were tested under conditions of physical activity (sedentary or active) and dietary energy density (17 or 10 kJ/g). RMR, daily energy intake, meal size, and hunger were assessed within the same day and across each condition. Results: We obtained evidence that RMR is correlated with meal size and daily energy intake in overweight and obese individuals. Participants with high RMRs showed increased levels of hunger across the day (P < 0.0001) and greater food intake (P < 0.00001) than did individuals with lower RMRs. These effects were independent of sex and food energy density. The change in RMR was also related to energy intake (P < 0.0001). Conclusions: We propose that RMR (largely determined by fat-free mass) may be a marker of energy intake and could represent a physiologic signal for hunger. These results may have implications for additional research possibilities in appetite, energy homeostasis, and obesity. This trial was registered under international standard identification for controlled trials as ISRCTN47291569.

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life : evidence of a bias changing with increasing metabolic rate

Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0-3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland-Altman statistical test. Results: The mean measured SMR was 1.48 MJ/day. The mean predicted BMR values were 1.66 and 1.47 MJ/day for the weight only and weight and height equations, respectively. The Bland-Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ/day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ/day (1%). However the 95% limits of agreement were wide: -0.64 to + 0.28 MJ/day (28%) for the former equation and -0.39 to + 0.41 MJ/day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.

The Pandolf load carriage equation is a poor predictor of metabolic rate while wearing explosive ordnance disposal protective clothing

This investigation aimed to quantify metabolic rate when wearing an explosive ordnance disposal (EOD) ensemble (~33kg) during standing and locomotion; and determine whether the Pandolf load carriage equation accurately predicts metabolic rate when wearing an EOD ensemble during standing and locomotion. Ten males completed 8 trials with metabolic rate measured through indirect calorimetry. Walking in EOD at 2.5, 4.0 and 5.5km·h−1 was significantly (p < 0.05) greater than matched trials without the EOD ensemble by 49% (127W), 65% (213W) and 78% (345W), respectively. Mean bias (95% limits of agreement) between predicted and measured metabolism during standing, 2.5, 4 and 5.5km·h−1 were 47W (19 to 75W); −111W (−172 to −49W); −122W (−189 to −54W) and −158W (−245 to −72W), respectively. The Pandolf equation significantly underestimated measured metabolic rate during locomotion. These findings have practical implications for EOD technicians during training and operation and should be considered when developing maximum workload duration models and work-rest schedules.

Does metabolic compensation explain the majority of less-than-expected weight loss in obese adults during a short-term severe diet and exercise intervention?

Objective: We investigated to what extent changes in metabolic rate and composition of weight loss explained the less-than-expected weight loss in obese men and women during a diet-plus-exercise intervention. Design: 16 obese men and women (41 ± 9 years; BMI 39 ± 6 kg/m2) were investigated in energy balance before, after and twice during a 12-week VLED (565–650 kcal/day) plus exercise (aerobic plus resistance training) intervention. The relative energy deficit (EDef) from baseline requirements was severe (74-87%). Body composition was measured by deuterium dilution and DXA and resting metabolic rate (RMR) by indirect calorimetry. Fat mass (FM) and fat-free mass (FFM) were converted into energy equivalents using constants: 9.45 kcal/gFM and 1.13 kcal/gFFM. Predicted weight loss was calculated from the energy deficit using the '7700 kcal/kg rule'. Results: Changes in weight (-18.6 ± 5.0 kg), FM (-15.5 ± 4.3 kg), and FFM (-3.1 ± 1.9 kg) did not differ between genders. Measured weight loss was on average 67% of the predicted value, but ranged from 39 to 94%. Relative EDef was correlated with the decrease in RMR (R=0.70, P<0.01) and the decrease in RMR correlated with the difference between actual and expected weight loss (R=0.51, P<0.01). Changes in metabolic rate explained on average 67% of the less-than-expected weight loss, and variability in the proportion of weight lost as FM accounted for a further 5%. On average, after adjustment for changes in metabolic rate and body composition of weight lost, actual weight loss reached 90% of predicted values. Conclusion: Although weight loss was 33% lower than predicted at baseline from standard energy equivalents, the majority of this differential was explained by physiological variables. While lower-than-expected weight loss is often attributed to incomplete adherence to prescribed interventions, the influence of baseline calculation errors and metabolic down-regulation should not be discounted.

Comparison of the Cosmed K4 b2 and the Deltatrac II™ metabolic cart in measuring resting energy expenditure in adults

Background and Aims: The objective of the study was to compare data obtained from the Cosmed K4 b2 and the Deltatrac II™ metabolic cart for the purpose of determining the validity of the Cosmed K4 b2 in measuring resting energy expenditure. Methods: Nine adult subjects (four male, five female) were measured. Resting energy expenditure was measured in consecutive sessions using the Cosmed K4 b2, the Deltatrac II™ metabolic cart separately and the Cosmed K4 b2 and Deltatrac II™ metabolic cart simultaneously, performed in random order. Resting energy expenditure (REE) data from both devices were then compared with values obtained from predictive equations. Results: Bland and Altman analysis revealed a mean bias for the four variables, REE, respiratory quotient (RQ), VCO2, VO2 between data obtained from Cosmed K4 b2 and Deltatrac II™ metabolic cart of 268 ± 702 kcal/day, -0.0±0.2, 26.4±118.2 and 51.6±126.5 ml/min, respectively. Corresponding limits of agreement for the same four variables were all large. Also, Bland and Altman analysis revealed a larger mean bias between predicted REE and measured REE using Cosmed K4 b2 data (-194±603 kcal/day) than using Deltatrac™ metabolic cart data (73±197 kcal/day). Conclusions: Variability between the two devices was very high and a degree of measurement error was detected. Data from the Cosmed K4 b2 provided variable results on comparison with predicted values, thus, would seem an invalid device for measuring adults. © 2002 Elsevier Science Ltd. All rights reserved.

Visual impairment, postural stability and falls among older adults with glaucoma

Purpose: To investigate the impact of glaucomatous visual impairment on postural sway and falls among older adults.Methods: The sample comprised 72 community-dwelling older adults with open-angle glaucoma, aged 74.0 5.8 years (range 62 to 90 years). Measures of visual function included binocular visual acuity (high-contrast), binocular contrast sensitivity (Pelli- Robson) and binocular visual fields (merged monocular HFA 24-2 SITA-Std). Postural stability was assessed under four conditions: eyes open and closed, on a firm and on a foam surface. Falls were monitored for six months with prospective falls diaries. Regression models, adjusting for age and gender, examined the association between vision measures and postural stability (linear regression) and the number of falls (negative binomial regression). Results: Greater visual field loss was significantly associated with poorer postural stability with eyes open, both on firm (r = 0.34, p < 0.01) and foam (r = 0.45, p < 0.001) surfaces. Eighteen (25 per cent) participants experienced at least one fall: 12 (17 per cent) participants fell only once and six (eight per cent) participants fell two or more times (up to five falls). Visual field loss was significantly associated with falling; the rate of falls doubled for every 10 dB reduction in field sensitivity (rate ratio = 1.08, 95% CI = 1.02–1.13). Importantly, in a model comprising upper and lower field sensitivity, only lower field loss was significantly associated with the number of falls (rate ratio = 1.17, 95% CI = 1.04–1.33). Conclusions: Binocular visual field loss was significantly associated with postural instability and falls among older adults with glaucoma. These findings provide valuable directions for developing falls risk assessment and falls prevention strategies for this population.

The effect of graded levels of exercise on energy intake and balance in free-living men, consuming their normal diet

Objective: To assess the effect of graded increases in exercised-induced energy expenditure (EE) on appetite, energy intake (EI), total daily EE and body weight in men living in their normal environment and consuming their usual diets. Design: Within-subject, repeated measures design. Six men (mean (s.d.) age 31.0 (5.0) y; weight 75.1 (15.96) kg; height 1.79 (0.10) m; body mass index (BMI) 23.3(2.4) kg/m2), were each studied three times during a 9 day protocol, corresponding to prescriptions of no exercise, (control) (Nex; 0 MJ/day), medium exercise level (Mex; ~1.6 MJ/day) and high exercise level (Hex; ~3.2 MJ/day). On days 1-2 subjects were given a medium fat (MF) maintenance diet (1.6 ´ resting metabolic rate (RMR)). Measurements: On days 3-9 subjects self-recorded dietary intake using a food diary and self-weighed intake. EE was assessed by continual heart rate monitoring, using the modified FLEX method. Subjects' HR (heart rate) was individually calibrated against submaximal VO2 during incremental exercise tests at the beginning and end of each 9 day study period. Respiratory exchange was measured by indirect calorimetry. Subjects completed hourly hunger ratings during waking hours to record subjective sensations of hunger and appetite. Body weight was measured daily. Results: EE amounted to 11.7, 12.9 and 16.8 MJ/day (F(2,10)=48.26; P<0.001 (s.e.d=0.55)) on the Nex, Mex and Hex treatments, respectively. The corresponding values for EI were 11.6, 11.8 and 11.8 MJ/day (F(2,10)=0.10; P=0.910 (s.e.d.=0.10)), respectively. There were no treatment effects on hunger, appetite or body weight, but there was evidence of weight loss on the Hex treatment. Conclusion: Increasing EE did not lead to compensation of EI over 7 days. However, total daily EE tended to decrease over time on the two exercise treatments. Lean men appear able to tolerate a considerable negative energy balance, induced by exercise, over 7 days without invoking compensatory increases in EI.

Differences in post-prandial responses to fat and carbohydrate loads in habitual high and low fat consumers (phenotypes).

The present study investigated metabolic responses to fat and carbohydrate ingestion in lean male individuals consuming an habitual diet high or low in fat. Twelve high-fat phenotypes (HF) and twelve low-fat phenotypes (LF) participated in the study. Energy intake and macronutrient intake variables were assessed using a food frequency questionnaire. Resting (RMR) and postprandial metabolic rate and substrate oxidation (respiratory quotient; RQ) were measured by indirect calorimetry. HF had a significantly higher RMR and higher resting heart rate than LF. These variables remained higher in HF following the macronutrient challenge. In all subjects the carbohydrate load increased metabolic rate and heart rate significantly more than the fat load. Fat oxidation (indicated by a low RQ) was significantly higher in HF than in LF following the fat load; the ability to oxidise a high carbohydrate load did not differ between the groups. Lean male subjects consuming a diet high in fat were associated with increased energy expenditure at rest and a relatively higher fat oxidation in response to a high fat load; these observations may be partly responsible for maintaining energy balance on a high-fat (high-energy) diet. In contrast, a low consumer of fat is associated with relatively lower energy expenditure at rest and lower fat oxidation, which has implications for weight gain if high-fat foods or meals are periodically introduced to the diet.

Description and evaluation of a Newton-based electronic appetite rating system for temporal tracking of appetite in human subjects

This study assessed the reliability and validity of a palm-top-based electronic appetite rating system (EARS) in relation to the traditional paper and pen method. Twenty healthy subjects [10 male (M) and 10 female (F)] — mean age M=31 years (S.D.=8), F=27 years (S.D.=5); mean BMI M=24 (S.D.=2), F=21 (S.D.=5) — participated in a 4-day protocol. Measurements were made on days 1 and 4. Subjects were given paper and an EARS to log hourly subjective motivation to eat during waking hours. Food intake and meal times were fixed. Subjects were given a maintenance diet (comprising 40% fat, 47% carbohydrate and 13% protein by energy) calculated at 1.6×Resting Metabolic Rate (RMR), as three isoenergetic meals. Bland and Altman's test for bias between two measurement techniques found significant differences between EARS and paper and pen for two of eight responses (hunger and fullness). Regression analysis confirmed that there were no day, sex or order effects between ratings obtained using either technique. For 15 subjects, there was no significant difference between results, with a linear relationship between the two methods that explained most of the variance (r2 ranged from 62.6 to 98.6). The slope for all subjects was less than 1, which was partly explained by a tendency for bias at the extreme end of results on the EARS technique. These data suggest that the EARS is a useful and reliable technique for real-time data collection in appetite research but that it should not be used interchangeably with paper and pen techniques.

Exercise-induced energy expenditure : implications for exercise prescription and obesity

Objective: Walking is commonly recommended to help with weight management. We measured total energy expenditure (TEE) and its components to quantify the impact of increasing exercise-induced energy expenditure (ExEE) on other components of TEE. Methods: Thirteen obese women underwent an 8-week walking group intervention. TEE was quantified using doubly labeled water, ExEE was quantified using heart rate monitors, daily movement was assessed by accelerometry and resting metabolic rate was measured using indirect calorimetry. Results: Four of the 13 participants achieved the target of 1500 kcal wk−1 of ExEE and all achieved 1000 kcal wk−1. The average ExEE achieved by the group across the 8 weeks was 1434 ± 237 kcal wk−1. Vigorous physical activity, as assessed by accelerometry, increased during the intervention by an average of 30 min per day. Non-exercise activity thermogenesis (NEAT) decreased, on average, by 175 kcal d−1 (−22%) from baseline to the intervention and baseline fitness was correlated with change in NEAT. Conclusions: Potential alterations in non-exercise activity should be considered when exercise is prescribed. The provision of appropriate education on how to self-monitor daily activity levels may improve intervention outcomes in groups who are new to exercise. Practice implications: Strategies to sustain incidental and light physical activity should be offered to help empower individuals as they develop and maintain healthy and long-lasting lifestyle habits.

Interactions between energy intake and expenditure in the development and treatment of obesity.

Summary There are four interactions to consider between energy intake (EI) and energy expenditure (EE) in the development and treatment of obesity. (1) Does sedentariness alter levels of EI or subsequent EE? and (2) Do high levels of EI alter physical activity or exercise? (3) Do exercise-induced increases in EE drive EI upwards and undermine dietary approaches to weight management and (4) Do low levels of EI elevate or decrease EE? There is little evidence that sedentariness alters levels of EI. This lack of cross-talk between altered EE and EI appears to promote a positive EB. Lifestyle studies also suggest that a sedentary routine actually offers the opportunity for over-consumption. Substantive changes in non exercise activity thermogenesis are feasible, but not clearly demonstrated. Cross talk between elevated EE and EI is initially too weak and takes too long to activate, to seriously threaten dietary approaches to weight management. It appears that substantial fat loss is possible before intake begins to track a sustained elevation of EE. There is more evidence that low levels of EI does lower physical activity levels, in relatively lean men under conditions of acute or prolonged semi-starvation and in dieting obese subjects. During altered EB there are a number of small but significant changes in the components of EE, including (i) sleeping and basal metabolic rate, (ii) energy cost of weight change alters as weight is gained or lost, (iii) exercise efficiency, (iv) energy cost of weight bearing activities, (v) during substantive overfeeding diet composition (fat versus carbohydrate) will influence the energy cost of nutrient storage by ~ 15%. The responses (i-v) above are all “obligatory” responses. Altered EB can also stimulate facultative behavioural responses, as a consequence of cross-talk between EI and EE. Altered EB will lead to changes in the mode duration and intensity of physical activities. Feeding behaviour can also change. The degree of inter-individual variability in these responses will define the scope within which various mechanisms of EB compensation can operate. The relative importance of “obligatory” versus facultative, behavioural responses -as components of EB control- need to be defined.

Changes in resting and walking energy expenditure and walking speed during pregnancy in obese women

Background Energy conserving processes reported in undernourished women during pregnancy are a recognised strategy to provide energy required to support fetal development. Women who are obese before conceiving arguably have sufficient fat stores to support the energy demands of pregnancy without the need to provoke energy conserving mechanisms. Objective We tested the hypothesis that obese women would demonstrate behavioural adaptation (i.e. decrease in self-selected walking (SSW) speed) but not metabolic compensation (i.e. decrease in resting metabolic rate (RMR) or metabolic cost of walking) during gestation. Design RMR, SSW speed, metabolic cost of walking, and anthropometry were measured in 23 women (BMI: 33.6 ± 2.5 kg/m2; 31 ± 4 years) at approximately weeks 15 (wk 15) and 30 (wk 30) of gestation. RMR was also measured in two cohorts of non-pregnant controls matched for age, weight and height of the pregnant cohort at wk 15 (N=23) and wk 30 (N=23). Results GWG varied widely (11.3 ± 5.4 kg) and 52% of women gained more weight than is recommended. RMR increased significantly by an average 177 ± 176 kcal/d (11±12%; P<0.0001); however the within-group variability was large. Both the metabolic cost of walking and SSW speed decreased significantly (P<0.01). While RMR increased in >80% of the cohort, the net oxygen cost of walking decreased in the same proportion of women. Conclusions While the increase in RMR was greater than was explained by weight gain, there was evidence of both behavioural and biological compensation in the metabolic cost of walking in obese women during gestation.