Dual glucagon-like peptide-1 and glucagon receptor agonism reduces energy intake in type 2 diabetes with obesity.

AIMS: To establish which components of energy balance mediate the clinically significant weight loss demonstrated with use of cotadutide, a glucagon-like peptide-1 (GLP-1)/glucagon receptor dual agonist, in early-phase studies. MATERIALS AND METHODS: We conducted a phase 2a, single-centre, randomized, placebo-controlled trial in overweight and obese adults with type 2 diabetes. Following a 16-day single-blind placebo run-in, participants were randomized 2:1 to double-blind 42-day subcutaneous treatment with cotadutide (100-300 µg daily) or placebo. The primary outcome was percentage weight change. Secondary outcomes included change in energy intake (EI) and energy expenditure (EE). RESULTS: A total of 12 participants (63%) in the cotadutide group and seven (78%) in the placebo group completed the study. The mean (90% confidence interval [CI]) weight change was -4.0% (-4.9%, -3.1%) and -1.4% (-2.7%, -0.1%) for the cotadutide and placebo groups, respectively (p = 0.011). EI was lower with cotadutide versus placebo (-41.3% [-66.7, -15.9]; p = 0.011). Difference in EE (per kJ/kg lean body mass) for cotadutide versus placebo was 1.0% (90% CI -8.4, 10.4; p = 0.784), assessed by doubly labelled water, and -6.5% (90% CI -9.3, -3.7; p < 0.001), assessed by indirect calorimetry. CONCLUSION: Weight loss with cotadutide is primarily driven by reduced EI, with relatively small compensatory changes in EE.

Nutrition and Physical Activity During British Army Officer Cadet Training: Part 1-Energy Balance and Energy Availability.

Military training is characterized by high daily energy expenditures which are difficult to match with energy intake, potentially resulting in negative energy balance (EB) and low energy availability (EA). The aim of this study was to quantify EB and EA during British Army Officer Cadet training. Thirteen (seven women) Officer Cadets (mean ± SD: age 24 ± 3 years) volunteered to participate. EB and EA were estimated from energy intake (weighing of food and food diaries) and energy expenditure (doubly labeled water) measured in three periods of training: 9 days on-camp (CAMP), a 5-day field exercise (FEX), and a 9-day mixture of both CAMP and field-based training (MIX). Variables were compared by condition and gender with a repeated-measures analysis of variance. Negative EB was greatest during FEX (-2,197 ± 455 kcal/day) compared with CAMP (-692 ± 506 kcal/day; p < .001) and MIX (-1,280 ± 309 kcal/day; p < .001). EA was greatest in CAMP (23 ± 10 kcal·kg free-fat mass [FFM]-1·day-1) compared with FEX (1 ± 16 kcal·kg FFM-1·day-1; p = .002) and MIX (10 ± 7 kcal·kg FFM-1·day-1; p = .003), with no apparent difference between FEX and MIX (p = .071). Irrespective of condition, there were no apparent differences between gender in EB (p = .375) or EA (p = .385). These data can be used to inform evidenced-based strategies to manage EA and EB during military training, and enhance the health and performance of military personnel.

The Importance of Hydration in Body Composition Assessment in Children Aged 6-16 Years.

Body composition is associated with many noncommunicable diseases. The accuracy of many simple techniques used for the assessment of body composition is influenced by the fact that they do not take into account tissue hydration and this can be particularly problematic in paediatric populations. The aims of this study were: (1) to assess the agreement of two dual energy X-ray absorptiometry (DXA) systems for determining total and regional (arms, legs, trunk) fat, lean, and bone mass and (2) to compare lean soft tissue (LST) hydration correction methods in children. One hundred and twenty four healthy children aged between 6 and 16 years old underwent DXA scans using 2 GE healthcare Lunar systems (iDXA and Prodigy). Tissue hydration was either calculated by dividing total body water (TBW), by 4-component model derived fat free mass (HFFMTBW) or by using the age and sex specific coefficients of Lohman, 1986 (HFFMLohman) and used to correct LST. Regression analysis was performed to develop cross-calibration equations between DXA systems and a paired samples t-test was conducted to assess the difference between LST hydration correction methods. iDXA resulted in significantly lower estimates of total and regional fat and lean mass, compared to Prodigy. HFFMTBW showed a much larger age/sex related variability than HFFMLohman. A 2.0 % difference in LST was observed in the boys (34.5 kg vs 33.8 kg respectively, p < 0.05) and a 2.5% difference in the girls (28.2 kg vs 27.5 kg respectively, p < 0.05) when corrected using either HFFMTBW or HFFMLohman. Care needs to be exercised when combining data from iDXA and Prodigy, as total and regional estimates of body composition can differ significantly. Furthermore, tissue hydration should be taken into account when assessing body composition as it can vary considerably within a healthy paediatric population even within specific age and/or sex groups.

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study.

BACKGROUND: Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion. METHODS: Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation. RESULTS: Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90). CONCLUSIONS: In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Validity of energy expenditure estimation methods during 10 days of military training.

Wearable physical activity (PA) monitors have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established research methods has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors and a PA log against doubly labeled water (DLW) during British Army Officer Cadet (OC) training. For 10 days of training, twenty (10 male and 10 female) OCs (mean ± SD: age 23 ± 2 years, height 1.74 ± 0.09 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK) on the dominant wrist, wore one commercially available monitor (Fitbit SURGE, USA) on the non-dominant wrist, and completed a self-report PA log. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analyzed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement (LoA) were employed to compare TEE from each estimation method to DLW. Average daily TEE from DLW was 4112 ± 652 kcal·day-1 against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal. day-1 ) while Fitbit tended to underestimate (-656 ± 683 kcal·day-1 ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day-1 ). Wearable physical activity monitors provide a cheaper and more practical method for estimating free-living TEE than DLW in military settings. The GENEActiv accelerometer demonstrated good validity for assessing daily TEE and would appear suitable for use in large-scale, longitudinal military studies.

Implications of the variation in biological 18 O natural abundance in body water to inform use of Bayesian methods for modelling total energy expenditure when using doubly labelled water.

RATIONALE: Variation in 18 O natural abundance can lead to errors in the calculation of total energy expenditure (TEE) when using the doubly labelled water (DLW) method. The use of Bayesian statistics allows a distribution to be assigned to 18 O natural abundance, thus allowing a best-fit value to be used in the calculation. The aim of this study was to calculate within-subject variation in 18 O natural abundance and apply this to our original working model for TEE calculation. METHODS: Urine samples from a cohort of 99 women, dosed with 50 g of 20% 2 H2 O, undertaking a 14-day breast milk intake protocol, were analysed for 18 O. The within-subject variance was calculated and applied to a Bayesian model for the calculation of TEE in a separate cohort of 36 women. This cohort of 36 women had taken part in a DLW study and had been dosed with 80 mg/kg body weight 2 H2 O and 150 mg/kg body weight H2 18 O. RESULTS: The average change in the δ18 O value from the 99 women was 1.14‰ (0.77) [0.99, 1.29], with the average within-subject 18 O natural abundance variance being 0.13‰2 (0.25) [0.08, 0.18]. There were no significant differences in TEE (9745 (1414), 9804 (1460) and 9789 (1455) kJ/day, non-Bayesian, Bluck Bayesian and modified Bayesian models, respectively) between methods. CONCLUSIONS: Our findings demonstrate that using a reduced natural variation in 18 O as calculated from a population does not impact significantly on the calculation of TEE in our model. It may therefore be more conservative to allow a larger variance to account for individual extremes.

Application of Bayesian analysis to the doubly labelled water method for total energy expenditure in humans.

RATIONALE: The doubly labelled water (DLW) method is the reference method for the estimation of free-living total energy expenditure (TEE). In this method, where both 2 H and 18 O are employed, different approaches have been adopted to deal with the non-conformity observed regarding the distribution space for the labels being non-coincident with total body water. However, the method adopted can have a significant effect on the estimated TEE. METHODS: We proposed a Bayesian reasoning approach to modify an assumed prior distribution for the space ratio using experimental data to derive the TEE. A Bayesian hierarchical approach was also investigated. The dataset was obtained from 59 adults (37 women) who underwent a DLW experiment during which the 2 H and 18 O enrichments were measured using isotope ratio mass spectrometry (IRMS). RESULTS: TEE was estimated at 9925 (9106-11236) [median and interquartile range], 9646 (9167-10540), and 9,638 (9220-10340) kJ·day-1 for women and at 13961 (12851-15347), 13353 (12651-15088) and 13211 (12653-14238) kJ·day-1 for men, using normalized non-Bayesian, independent Bayesian and hierarchical Bayesian approaches, respectively. A comparison of hierarchical Bayesian with normalized non-Bayesian methods indicated a marked difference in behaviour between genders. The median difference was -287 kJ·day-1 for women, and -750 kJ·day-1 for men. In men there is an appreciable compression of the TEE distribution obtained from the hierarchical model compared with the normalized non-Bayesian methods (range of TEE 11234-15431 kJ·day-1 vs 10786-18221 kJ·day-1 ). An analogous, yet smaller, compression is seen in women (7081-12287 kJ·day-1 vs 6989-13775 kJ·day-1 ). CONCLUSIONS: The Bayesian analysis is an appealing method to estimate TEE during DLW experiments. The principal advantages over those obtained using the classical least-squares method is the generation of potentially more useful estimates of TEE, and improved handling of outliers and missing data scenarios, particularly if a hierarchical model is used.

An Investigation Into the Differences in Bone Density and Body Composition Measurements Between 2 GE Lunar Densitometers and Their Comparison to a 4-Component Model.

We describe a study to assess the precision of the GE Lunar iDXA and the agreement between the iDXA and GE Lunar Prodigy densitometers for the measurement of regional- and total-body bone and body composition in normal to obese healthy adults. We compare the whole-body fat mass by dual-energy X-ray absorptiometry (DXA) to measurements by a 4-component (4-C) model. Sixty-nine participants, aged 37 ± 12 yr, with a body mass index of 26.2 ± 5.1 kg/cm2, were measured once on the Prodigy and twice on the iDXA. The 4-C model estimated fat mass from body mass, total body water by deuterium dilution, body volume by air displacement plethysmography, and bone mass by DXA. Agreements between measurements made on the 2 instruments and by the 4-C model were analyzed by Bland-Altman and linear regression analyses. Where appropriate, translational cross-calibration equations were derived. Differences between DXA software versions were investigated. iDXA precision was less than 2% of the measured value for all regional- and whole-body bone and body composition measurements with the exception of arm fat mass (2.28%). We found significant differences between iDXA and Prodigy (p < 0.05) whole-body and regional bone, fat mass (FM), and lean mass, with the exception of hip bone mass, area and density, and spine area. Compared to iDXA, Prodigy overestimated FM and underestimated lean mass. However, compared to 4-C, iDXA showed a smaller bias and narrower limits of agreement than Prodigy. No significant differences between software versions in FM estimations existed. Our results demonstrate excellent iDXA precision. However, significant differences exist between the 2 GE Lunar instruments, Prodigy and iDXA measurement values. A divergence from the reference 4-C observations remains in FM estimations made by DXA even following the recent advances in technology. Further studies are particularly warranted in individuals with large FM contents.

Probiotic supplementation prevents high-fat, overfeeding-induced insulin resistance in human subjects.

The purpose of the present study was to determine whether probiotic supplementation (Lactobacillus casei Shirota (LcS)) prevents diet-induced insulin resistance in human subjects. A total of seventeen healthy subjects were randomised to either a probiotic (n 8) or a control (n 9) group. The probiotic group consumed a LcS-fermented milk drink twice daily for 4 weeks, whereas the control group received no supplementation. Subjects maintained their normal diet for the first 3 weeks of the study, after which they consumed a high-fat (65 % of energy), high-energy (50 % increase in energy intake) diet for 7 d. Whole-body insulin sensitivity was assessed by an oral glucose tolerance test conducted before and after overfeeding. Body mass increased by 0·6 (SE 0·2) kg in the control group (P< 0·05) and by 0·3 (SE 0·2) kg in the probiotic group (P>0·05). Fasting plasma glucose concentrations increased following 7 d of overeating (control group: 5·3 (SE 0·1) v. 5·6 (SE 0·2) mmol/l before and after overfeeding, respectively, P< 0·05), whereas fasting serum insulin concentrations were maintained in both groups. Glucose AUC values increased by 10 % (from 817 (SE 45) to 899 (SE 39) mmol/l per 120 min, P< 0·05) and whole-body insulin sensitivity decreased by 27 % (from 5·3 (SE 1·4) to 3·9 (SE 0·9), P< 0·05) in the control group, whereas normal insulin sensitivity was maintained in the probiotic group (4·4 (SE 0·8) and 4·5 (SE 0·9) before and after overeating, respectively (P>0·05). These results suggest that probiotic supplementation may be useful in the prevention of diet-induced metabolic diseases such as type 2 diabetes.

Sex differences in energy balance, body composition, and metabolic and endocrine markers during prolonged arduous military training.

This study investigated sex differences in energy balance, body composition, and metabolic and endocrine markers during prolonged military training. Twenty-three trainees (14 women) completed 44-wk military training (three terms of 14 wk with 2-wk adventurous training). Dietary intake and total energy expenditure were measured over 10 days during each term by weighed food and doubly labeled water. Body composition was measured by dual-energy X-ray absorptiometry (DXA) at baseline and at the end of each term. Circulating metabolic and endocrine markers were measured at baseline and at the end of terms 2 and 3. Absolute energy intake and total energy expenditure were higher, and energy balance was lower, for men than women (P ≤ 0.008). Absolute energy intake and balance were lower, and total energy expenditure was higher, during term 2 than terms 1 and 3 (P < 0.001). Lean mass did not change with training (P = 0.081). Fat mass and body fat increased from term 1 to terms 2 and 3 (P ≤ 0.045). Leptin increased from baseline to terms 2 and 3 in women (P ≤ 0.002) but not in men (P ≥ 0.251). Testosterone and free androgen index increased from baseline to term 3 (P ≤ 0.018). Free thyroxine (T4) decreased and thyroid-stimulating hormone (TSH) increased from baseline to term 2 and term 3 (P ≤ 0.031). Cortisol decreased from baseline to term 3 (P = 0.030). IGF-I and total triiodothyronine (T3) did not change with training (P ≥ 0.148). Men experienced greater energy deficits than women during military training due to higher total energy expenditure.NEW & NOTEWORTHY Energy deficits are common in military training and can result in endocrine and metabolic disturbances. This study provides first investigation of sex differences in energy balance, body composition, and endocrine and metabolic markers in response to prolonged and arduous military training. Men experienced greater energy deficits than women due to higher energy expenditure, which was not compensated for by increased energy intake. These energy deficits were not associated with decreases in fat or lean mass or metabolic or endocrine function.

A randomized, controlled trial of a Nordic, protein-reduced complementary diet in infants: effects on body composition, growth, biomarkers, and dietary intake at 12 and 18 months.

BACKGROUND: High intake of protein and low intake of plant-based foods during complementary feeding can contribute to negative long-term health effects. OBJECTIVES: To investigate the effects of a protein-reduced, Nordic complementary diet on body composition, growth, biomarkers, and dietary intake, compared with current Swedish dietary recommendations for infants at 12 and 18 mo. METHODS: Healthy, term infants (n = 250) were randomly allocated to either a Nordic group (NG) or a conventional group (CG). From 4 to 6 mo, NG participants received repeated exposures of Nordic taste portions. From 6 to 18 mo, NG was supplied with Nordic homemade baby food recipes, protein-reduced baby food products, and parental support. CG followed the current Swedish dietary recommendations. Measurements of body composition, anthropometry, biomarkers, and dietary intake were collected from baseline and at 12 and 18 mo. RESULTS: Of the 250 infants, 82% (n = 206) completed the study. There were no group differences in body composition or growth. In NG, protein intake, blood urea nitrogen and plasma IGF-1 were lower compared to CG at 12 and 18 mo. Infants in NG consumed 42% to 45% more fruits and vegetables compared to CG at 12 and 18 mo, which was reflected in a higher plasma folate at 12 and 18 mo. There were no between-group differences in EI or iron status. CONCLUSIONS: Introduction of a predominantly plant-based, protein-reduced diet as part of complementary feeding is feasible and can increase fruit and vegetable intake. This trial was registered at clinicaltrials.gov as NCT02634749.

Centile reference chart for resting metabolic rate through the life course.

OBJECTIVE: Reference centile charts are widely used for the assessment of growth and have progressed from describing height and weight to include body composition variables such as fat and lean mass. Here, we present centile charts for an index of resting energy expenditure (REE) or metabolic rate, adjusted for lean mass versus age, including both children and adults across the life course. DESIGN, PARTICIPANTS AND INTERVENTION: Measurements of REE by indirect calorimetry and body composition using dual-energy X-ray absorptiometry were made in 411 healthy children and adults (age range 6-64 years) and serially in a patient with resistance to thyroid hormone α (RTHα) between age 15 and 21 years during thyroxine therapy. SETTING: NIHR Cambridge Clinical Research Facility, UK. RESULTS: The centile chart indicates substantial variability, with the REE index ranging between 0.41 and 0.59 units at age 6 years, and 0.28 and 0.40 units at age 25 years (2nd and 98th centile, respectively). The 50th centile of the index ranged from 0.49 units (age 6 years) to 0.34 units (age 25 years). Over 6 years, the REE index of the patient with RTHα varied from 0.35 units (25th centile) to 0.28 units (<2nd centile), depending on changes in lean mass and adherence to treatment. CONCLUSION: We have developed a reference centile chart for an index of resting metabolic rate in childhood and adults, and shown its clinical utility in assessing response to therapy of an endocrine disorder during a patient's transition from childhood to adult.

Measuring the Exercise Component of Energy Availability during Arduous Training in Women.

INTRODUCTION: Low energy availability (EA) may impede adaptation to exercise, suppressing reproductive function and bone turnover. Exercise energy expenditure (EEE) measurements lack definition and consistency. This study aimed to compare EA measured from moderate and vigorous physical activity from accelerometry (EEEmpva) with EA from total physical activity (EEEtpa) from doubly labeled water in women. The secondary aim was to determine the relationship of EA with physical fitness, body composition by dual-energy x-ray absorptiometry, heart rate variability (HRV), and eating behavior (Brief Eating Disorder in Athletes Questionnaire [BEDA-Q]). METHODS: This was a prospective, repeated-measures study, assessing EA measures and training adaptation during 11-month basic military training. Forty-seven women (23.9 ± 2.6 yr) completed three consecutive 10-d assessments of EEEmvpa, EEEtpa, and energy intake (EI). EA measures were compared using linear regression and Bland-Altman analyses; relationships of EA with fat mass, HRV, 1.5-mile run times, and BEDA-Q were evaluated using partial correlations. RESULTS: EA from EEEmvpa demonstrated strong agreement with EA from EEEtpa across the measurement range (R2 = 0.76, r = 0.87, P < 0.001) and was higher by 10 kcal·kg-1 FFM·d-1. However, EA was low in absolute terms because of underreported EI. Higher EA was associated with improved 1.5-mile run time (r = 0.28, P < 0.001), fat mass loss (r = 0.38, P < 0.001), and lower BEDA-Q score (r = -0.37, P < 0.001) but not HRV (all P > 0.10). CONCLUSION: Accelerometry-based EEE demonstrated validity against doubly labeled water during multistressor training, the difference representing 10 kcal·kg-1 FFM·d-1 EEE from nonexercise activity. Beneficial physical but not autonomic adaptations were associated with higher EA. EAmvpa and BEDA-Q warrant consideration for low EA assessment and screening.

Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y.

BACKGROUND & AIMS: The aim was to generate a predictive equation to assess body composition (BC) in children with obesity using bioimpedance (BIA), and avoid bias produced by different density levels of fat free mass (FFM) in this population. METHODS: This was a cross-sectional validation study using baseline data from a randomized intervention trial to treat childhood obesity. Participants were 8 to 14y (n = 315), underwent assessments on anthropometry and BC through Air Displacement Plethysmography (ADP), Dual X-Ray Absorptiometry and BIA. They were divided into a training (n = 249) and a testing subset (n = 66). In addition, the testing subset underwent a total body water assessment using deuterium dilution, and thus obtained results for the 4-compartment model (4C). A new equation to estimate FFM was created from the BIA outputs by comparison to a validated model of ADP adjusted by FFM density in the training subset. The equation was validated against 4C in the testing subset. As reference, the outputs from the BIA device were also compared to 4C. RESULTS: The predictive equation reduced the bias from the BIA outputs from 14.1% (95%CI: 12.7, 15.4) to 4.6% (95%CI: 3.8, 5.4) for FFM and from 18.4% (95%CI: 16.9, 19.9) to 6.4% (95% CI: 5.3, 7.4) for FM. Bland-Altman plots revealed that the new equation significantly improved the agreement with 4C; furthermore, the observed trend to increase the degree of bias with increasing FM and FFM also disappeared. CONCLUSION: The new predictive equation increases the precision of BC assessment using BIA in children with obesity.

A novel approach to assess body composition in children with obesity from density of the fat-free mass.

BACKGROUND & AIMS: Assessment of Fat Mass (FM) and fat-free mass (FFM) using Air-displacement plethysmography (ADP) technique assumes constant density of FFM (DFFM) by age and sex. It has been recently shown that DFFM further varies according to body mass index (BMI), meaning that ADP body composition assessments of children with obesity could be biased if DFFM is assumed to be constant. The aim of this study was to validate the use of the calculations of DFFM (rather than constant density of the FFM) to improve accuracy of body composition assessment in children with obesity. METHODS: cross-sectional validation study in 66 children with obesity (aged 8-14 years) where ADP assessments of body composition assuming constant density (FFMBODPOD and FMBODPOD) were compared to those where DFFM was adjusted in relation to BMI (FFMadjusted and FMadjusted), and both compared to the gold standard reference, the 4-component model (FFM4C and FM4C). RESULTS: FFMBODPOD was overestimated by 1.50 kg (95%CI -0.68 kg, 3.63 kg) while FFMadjusted was 0.71 kg (-1.08 kg, 2.51 kg) (percentage differences compared to FFM4C were 4.9% (±2.9%) and 2.8% (±2.1%), respectively (p < 0.001)). Consistently, FM was underestimated by both methods, representing a mean difference between methods of 4.0% (±2.9%) and 6.8% (±3.8%), respectively, when compared to the reference method. The agreement and reliability of body composition assessments were improved when adjusted using calculations (adjusted models) rather than assuming constant DFFM. CONCLUSIONS: The use of constant values for fat-free mass properties may increase bias when assessing body composition (FM and FFM) in children with obesity by two-component techniques such as ADP. Using adjusted corrections as proposed in the present work may reduce the bias by half.

Descriptive epidemiology of energy expenditure in the UK: findings from the National Diet and Nutrition Survey 2008-15.

BACKGROUND: Little is known about population levels of energy expenditure, as national surveillance systems typically employ only crude measures. The National Diet and Nutrition Survey (NDNS) in the UK measured energy expenditure in a 10% subsample by gold-standard doubly labelled water (DLW). METHODS: DLW-subsample participants from the NDNS (383 males, 387 females) aged 4-91 years were recruited between 2008 and 2015 (rolling programme). Height and weight were measured and body-fat percentage estimated by deuterium dilution. RESULTS: Absolute total energy expenditure (TEE) increased steadily throughout childhood, ranging from 6.2 and 7.2 MJ/day in 4- to 7-year-olds to 9.7 and 11.7 MJ/day for 14- to 16-year-old girls and boys, respectively. TEE peaked in 17- to 27-year-old women (10.7 MJ/day) and 28- to 43-year-old men (14.4 MJ/day), before decreasing gradually in old age. Physical-activity energy expenditure (PAEE) declined steadily with age from childhood (87 kJ/day/kg in 4- to 7-year-olds) through to old age (38 kJ/day/kg in 71- to 91-year-olds). No differences were observed by time, region and macronutrient composition. Body-fat percentage was strongly inversely associated with PAEE throughout life, irrespective of expressing PAEE relative to body mass or fat-free mass. Compared with females with <30% body fat, females with >40% recorded 29 kJ/day/kg body mass and 18 kJ/day/kg fat-free mass less PAEE in analyses adjusted for age, geographical region and time of assessment. Similarly, compared with males with <25% body fat, males with >35% recorded 26 kJ/day/kg body mass and 10 kJ/day/kg fat-free mass less PAEE. CONCLUSIONS: This first nationally representative study reports levels of human-energy expenditure as measured by gold-standard methodology; values may serve as a reference for other population studies. Age, sex and body composition are the main determinants of energy expenditure. Key Messages This is the first nationally representative study of human energy expenditure, covering the UK in the period 2008-2015. Total energy expenditure (MJ/day) increases steadily with age throughout childhood and adolescence, peaks in the 3rd decade of life in women and 4th decade of life in men, before decreasing gradually in old age. Physical activity energy expenditure (kJ/day/kg or kJ/day/kg fat-free mass) declines steadily with age from childhood to old age, more steeply so in males. Body-fat percentage is strongly inversely associated with physical activity energy expenditure. We found little evidence that energy expenditure varied by geographical region, over time, or by dietary macronutrient composition.

Physical inactivity and obesity: links with insulin resistance and type 2 diabetes mellitus.

Data from the health survey for England 2006, showed that the prevalence of type 2 diabetes mellitus (T2DM) has more than doubled in men and women since 1991. In the USA certain States have a prevalence of T2DM of greater than 10%. Globally it has been reported that this increase is by no means slowing down and that the number of individuals with the disease is expected to rise from 171 million cases reported in 2000 to 366 million by the year 2030. Physical inactivity and obesity are two major risk factors for the development of T2DM. In this review we will discuss evidence of an association between physical inactivity, obesity and T2DM from prospective cohort studies and clinical trials. We will also discuss some of the potential mechanisms that are thought to link obesity and physical inactivity with the major pathophysiological precursor of T2DM, insulin resistance.

Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders.

BACKGROUND: Energy expenditure prediction equations are used to estimate energy intake based on general population measures. However, when using equations to compare with a disease cohort with known metabolic abnormalities, it is important to derive one's own equations based on measurement conditions matching the disease cohort. OBJECTIVE: We aimed to use newly developed prediction equations based on a healthy pediatric population to describe and predict resting energy expenditure (REE) in a cohort of pediatric patients with thyroid disorders. METHODS: Body composition was measured by DXA and REE was assessed by indirect calorimetry in 201 healthy participants. A prediction equation for REE was derived in 100 healthy participants using multiple linear regression and z scores were calculated. The equation was validated in 101 healthy participants. This method was applied to participants with resistance to thyroid hormone (RTH) disorders, due to mutations in either thyroid hormone receptor ß or α (ß: female n = 17, male n = 9; α: female n = 1, male n = 1), with deviation of REE in patients compared with the healthy population presented by the difference in z scores. RESULTS: The prediction equation for REE = 0.061 * Lean soft tissue (kg) - 0.138 * Sex (0 male, 1 female) + 2.41 (R2 = 0.816). The mean ± SD of the residuals is -0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTHß patients are -0.02 ± 1.26. z Scores of -1.69 and -2.05 were recorded in male (n = 1) and female ( n = 1) RTHα patients. CONCLUSIONS: We have described methodology whereby differences in REE between patients with a metabolic disorder and healthy participants can be expressed as a z score. This approach also enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTHα) to be monitored.

Validity and reliability of an online self-report 24-h dietary recall method (Intake24): a doubly labelled water study and repeated-measures analysis.

Online self-reported 24-h dietary recall systems promise increased feasibility of dietary assessment. Comparison against interviewer-led recalls established their convergent validity; however, reliability and criterion-validity information is lacking. The validity of energy intakes (EI) reported using Intake24, an online 24-h recall system, was assessed against concurrent measurement of total energy expenditure (TEE) using doubly labelled water in ninety-eight UK adults (40-65 years). Accuracy and precision of EI were assessed using correlation and Bland-Altman analysis. Test-retest reliability of energy and nutrient intakes was assessed using data from three further UK studies where participants (11-88 years) completed Intake24 at least four times; reliability was assessed using intra-class correlations (ICC). Compared with TEE, participants under-reported EI by 25 % (95 % limits of agreement -73 % to +68 %) in the first recall, 22 % (-61 % to +41 %) for average of first two, and 25 % (-60 % to +28 %) for first three recalls. Correlations between EI and TEE were 0·31 (first), 0·47 (first two) and 0·39 (first three recalls), respectively. ICC for a single recall was 0·35 for EI and ranged from 0·31 for Fe to 0·43 for non-milk extrinsic sugars (NMES). Considering pairs of recalls (first two v. third and fourth recalls), ICC was 0·52 for EI and ranged from 0·37 for fat to 0·63 for NMES. EI reported with Intake24 was moderately correlated with objectively measured TEE and underestimated on average to the same extent as seen with interviewer-led 24-h recalls and estimated weight food diaries. Online 24-h recall systems may offer low-cost, low-burden alternatives for collecting dietary information.