Active Image-Assisted Food Records in Comparison to Regular Food Records: A Validation Study against Doubly Labeled Water in 12-Month-Old Infants.

Overreporting of dietary intake in infants is a problem when using food records (FR), distorting possible relationships between diet and health outcomes. Image-assisted dietary assessment may improve the accuracy, but to date, evaluation in the pediatric setting is limited. The aim of the study was to compare macronutrient and energy intake by using an active image-assisted five-day FR against a regular five-day FR, and to validate image-assistance with total energy expenditure (TEE), was measured using doubly labeled water. Participants in this validation study were 22 healthy infants randomly selected from the control group of a larger, randomized intervention trial. The parents reported the infants' dietary intake, and supplied images of main course meals taken from standardized flat-surfaced plates before and after eating episodes. Energy and nutrient intakes were calculated separately using regular FR and image-assisted FRs. The mean (± standard deviations) energy intake (EI) was 3902 ± 476 kJ/day from the regular FR, and 3905 ± 476 kJ/day from the FR using active image-assistance. The mean EI from main-course meals when image-assistance was used did not differ (1.7 ± 55 kJ, p = 0.89) compared to regular FRs nor did the intake of macronutrients. Compared to TEE, image-assisted FR overestimated EI by 10%. Without validation, commercially available software to aid in the volume estimations, food item identification, and automation of the image processing, image-assisted methods remain a more costly and burdensome alternative to regular FRs in infants. The image-assisted method did, however, identify leftovers better than did regular FR, where such information is usually not readily available

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 (P0·05). 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, P0·05). These results suggest that probiotic supplementation may be useful in the prevention of diet-induced metabolic diseases such as type 2 diabetes

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

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.Medical Research Council (http://www.mrc.ac.uk/) grants MC_UU_12015/1 and MC_UU_12015/3 to NW and SB, studentship from MedImmune to TW. Medical Research Council, UK Biobank, MedImmune and Newcastle University strategic funding for Digital Civics covered the costs of the field work

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

Evidence of Zavora Bay as a critical site for reef manta rays, Mobula alfredi, in southern Mozambique

The largest known reef manta ray (Mobula alfredi) population in Africa has been monitored for more than 20 years at several locations on the coast of the Inhambane Province in southern Mozambique. Nonetheless, before this study, little had been reported on the population dynamics of M. alfredi from Zavora, a remote bay in the region. Photographic mark-recapture was used to investigate the size and structure of M. alfredi that aggregate at "Red Sands," a reef cleaning station in Zavora Bay. An 11 year photographic data set was used to identify 583 M. alfredi individuals between 2010 and 2021. More than half of M. alfredi individuals were resighted at least once, with most encounters (up to 18 for one individual) occurring during the peak sighting period in July-November each year. An even sex ratio was observed, 44% females and 50% males, with no significant difference in resightings between the sexes. Pollock's robust design population models were used to estimate annual abundance, emigration, annual apparent survival and capture probability at Red Sands from July to November over a 6 year period (2016-2021). Abundance estimates varied year to year, ranging from 35 (95% c.i. [30, 45]) up to 233 (95% c.i. [224, 249]) M. alfredi individuals. Given the seasonal affinity of M. alfredi observed at Red Sands, this study highlights the importance of understanding fine-scale site use within the larger home range of this population to develop local management strategies

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 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 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.The authors were supported by the UK Medical Research Council (unit programme numbers. MC_UU_12015/1, MC_UU_12015/3, U105960371) and the NIHR Biomedical Research Centre in Cambridge (IS-BRC-1215-20014). TL was funded by the Cambridge Trust

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

Training with low muscle glycogen enhances fat metabolism in well-trained cyclists

Purpose: To determine the effects of training with low muscle glycogen on exercise performance, substrate metabolism, and skeletal muscle adaptation. Methods: Fourteen well-trained cyclists were pair-matched and randomly assigned to HIGH-or LOW-glycogen training groups. Subjects performed nine aerobic training (AT; 90 min at 70% (V) over dotO(2max)) and nine high-intensity interval training sessions (HIT; 8 x 5-min efforts, 1-min recovery) during a 3-wk period. HIGH trained once daily, alternating between AT on day 1 and HIT the following day, whereas LOW trained twice every second day, first performing AT and then, 1 h later, performing HIT. Pretraining and posttraining measures were a resting muscle biopsy, metabolic measures during steady-state cycling, and a time trial. Results: Power output during HIT was 297 +/- 8 W in LOW compared with 323 +/- 9 W in HIGH (P < 0.05); however, time trial performance improved by similar to 10% in both groups (P < 0.05). Fat oxidation during steady-state cycling increased after training in LOW (from 26 +/- 2 to 34 +/- 2 mu mol.kg(-1).min(-1), P < 0.01). Plasma free fatty acid oxidation was similar before and after training in both groups, but muscle-derived triacylglycerol oxidation increased after training in LOW (from 16 +/- 1 to 23 +/- 1 mu mol.kg(-1).min(-1), P < 0.05). Training with low muscle glycogen also increased beta-hydroxyacyl-CoA-dehydrogenase protein content (P < 0.01). Conclusions: Training with low muscle glycogen reduced training intensity and, in performance, was no more effective than training with high muscle glycogen. However, fat oxidation was increased after training with low muscle glycogen, which may have been due to the enhanced metabolic adaptations in skeletal muscle

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-labelled 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 analysed 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 against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal day ) while Fitbit tended to underestimate (-656 ± 683 kcal·day ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day ). 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