scholarly journals Methodologic considerations for measuring energy expenditure differences between diets varying in carbohydrate using the doubly labeled water method

2019 ◽  
Vol 109 (5) ◽  
pp. 1328-1334 ◽  
Author(s):  
Kevin D Hall ◽  
Juen Guo ◽  
Kong Y Chen ◽  
Rudolph L Leibel ◽  
Marc L Reitman ◽  
...  

ABSTRACT Background Low-carbohydrate diets have been reported to significantly increase human energy expenditure when measured using doubly labeled water (DLW) but not by respiratory chambers. Although DLW may reveal true physiological differences undetected by respiratory chambers, an alternative possibility is that the expenditure differences resulted from failure to correctly estimate the respiratory quotient (RQ) used in the DLW calculations. Objective To examine energy expenditure differences between isocaloric diets varying widely in carbohydrate and to quantitatively compare DLW data with respiratory chamber and body composition measurements within an energy balance framework. Design DLW measurements were obtained during the final 2 wk of month-long baseline (BD; 50% carbohydrate, 35% fat, 15% protein) and isocaloric ketogenic diets (KD; 5% carbohydrate, 80% fat, 15% protein) in 17 men with a BMI of 25–35 kg/m2. Subjects resided 2 d/wk in respiratory chambers to measure energy expenditure (EEchamber). DLW expenditure was calculated using chamber-determined RQ either unadjusted (EEDLW) or adjusted (EEDLWΔRQ) for net energy imbalance using diet-specific coefficients. Accelerometers measured physical activity. Body composition changes were measured by dual-energy X-ray absorptiometry (DXA) which were combined with energy intake measurements to calculate energy expenditure by balance (EEbal). Results After transitioning from BD to KD, neither EEchamber nor EEbal were significantly changed (∆EEchamber = 24 ± 30 kcal/d; P = 0.43 and ∆EEbal = −141 ± 118 kcal/d; P = 0.25). Similarly, physical activity (−5.1 ± 4.8%; P = 0.3) and exercise efficiency (−1.6 ± 2.4%; P = 0.52) were not significantly changed. However, EEDLW was 209 ± 83 kcal/d higher during the KD (P = 0.023) but was not significantly increased when adjusted for energy balance (EEDLWΔRQ = 139 ± 89 kcal/d; P = 0.14). After removing 2 outliers whose EEDLW were incompatible with other data, EEDLW was marginally increased during the KD by 126 ± 62 kcal/d (P = 0.063) and EEDLW∆RQ was only 46 ± 65 kcal/d higher (P = 0.49). Conclusions DLW calculations failing to account for diet-specific energy imbalance effects on RQ erroneously suggest that low-carbohydrate diets substantially increase energy expenditure. This trial was registered at clinicaltrials.gov as NCT01967563.

2018 ◽  
Author(s):  
Kevin D. Hall ◽  
Juen Guo ◽  
Kong Y. Chen ◽  
Rudolph L. Leibel ◽  
Marc L. Reitman ◽  
...  

AbstractBackgroundVery low-carbohydrate diets have been reported to substantially increase human energy expenditure as measured by doubly labeled water (DLW) but not by respiratory chambers. Do the DLW data reflect true physiological differences that are undetected by respiratory chambers? Alternatively, are the apparent DLW energy expenditure a consequence of failure to fully account for respiratory quotient (RQ) differences between diets?ObjectiveTo examine energy expenditure differences between diets varying drastically in carbohydrate and to quantitatively compare DLW data with respiratory chamber and body composition measurements within an energy balance framework.DesignDLW measurements were obtained during the final two weeks of month-long baseline (BD; 50% carbohydrate, 35% fat, 15% protein) and isocaloric ketogenic diets (KD; 5% carbohydrate, 80% fat, 15% protein) in 17 men with BMI 25-35 kg/m2. Subjects resided 2d/week in respiratory chambers to measure energy expenditure (EEchamber). DLW expenditure was calculated using chamber-determined respiratory quotients (RQ) either unadjusted (EEDLW) or adjusted (EEDLWΔRQ) for net energy imbalance using diet-specific coefficients. Accelerometers measured physical activity. Body composition changes were measured by dual-energy X-ray absorptiometry which were combined with energy intake measurements to calculate energy expenditure by balance (EEbal).ResultsAfter transitioning from BD to KD, neither EEchamber nor EEbal were significantly changed (∆EEchamber=24±30 kcal/d; p=0.43 and ∆EEbal=-141±118 kcal/d; p=0.25). Similarly, physical activity (−5.1±4.8%; p=0.3) and exercise efficiency (−1.6±2.4%; p=0.52) were not significantly changed. However, EEDLW was 209±83 kcal/d higher during the KD (p=0.023) but was not significantly increased when adjusted for energy balance (EEDLWΔRQ =139±89 kcal/d; p=0.14). After removing 2 outliers whose EEDLW were incompatible with other data, EEDLW and EEDLW∆RQ were marginally increased during the KD by 126±62 kcal/d (p=0.063) and 46±65 kcal/d (p=0.49), respectively.ConclusionsDLW calculations failing to account for diet-specific energy imbalance effects on RQ erroneously suggest that very low carbohydrate diets substantially increase energy expenditure.


2004 ◽  
Vol 96 (4) ◽  
pp. 1357-1364 ◽  
Author(s):  
Louise C. Mâsse ◽  
Janet E. Fulton ◽  
Kathleen L. Watson ◽  
Matthew T. Mahar ◽  
Michael C. Meyers ◽  
...  

This study investigated the influence of two approaches (mathematical transformation and statistical procedures), used to account for body composition [body mass or fat-free mass (FFM)], on associations between two measures of physical activity and energy expenditure determined by doubly labeled water (DLW). Complete data for these analyses were available for 136 African American (44.1%) and Hispanic (55.9%) women (mean age 50 ± 7.3 yr). Total energy expenditure (TEE) by DLW was measured over 14 days. Physical activity energy expenditure (PAEE) was computed as 0.90 × TEE - resting metabolic rate. During week 2, participants wore an accelerometer for 7 consecutive days and completed a 7-day diary. Pearson's product-moment correlations and three statistical procedures (multiple regressions, partial correlations, and allometric scaling) were used to assess the effect of body composition on associations. The methods-comparison analysis was used to study the effect of body composition on agreement. The statistical procedures demonstrated that associations improved when body composition was included in the model. The accelerometer explained a small but meaningful portion of the variance in TEE and PAEE after body mass was accounted for. The methods-comparison analysis confirmed that agreement with DLW was affected by the transformation. Agreement between the diary (transformed with body mass) and TEE reflected the association that exists between body mass and TEE. These results suggest that the accelerometer and diary accounted for a small portion of TEE and PAEE. Most of the variance in DLW-measured energy expenditure was explained by body mass or FFM.


1995 ◽  
Vol 73 (3) ◽  
pp. 337-347 ◽  
Author(s):  
Klaas R. Westerterp ◽  
Jeroen H. H. L. M. Donkers ◽  
Elisabeth W. H. M. Fredrix ◽  
Piet oekhoudt

In adults, body mass (BM) and its components fat-free mass (FFM) and fat mass (FM) are normally regulated at a constant level. Changes in FM and FFM are dependent on energy intake (EI) and energy expenditure (EE). The body defends itself against an imbalance between EI and EE by adjusting, within limits, the one to the other. When, at a given EI or EE, energy balance cannot be reached, FM and FFM will change, eventually resulting in an energy balance at a new value. A model is described which simulates changes in FM and FFM using EI and physical activity (PA) as input variables. EI can be set at a chosen value or calculated from dietary intake with a database on the net energy of foods. PA can be set at a chosen multiple of basal metabolic rate (BMR) or calculated from the activity budget with a database on the energy cost of activities in multiples of BMR. BMR is calculated from FFM and FM and, if necessary, FFM is calculated from BM, height, sex and age, using empirical equations. The model uses existing knowledge on the adaptation of energy expenditure (EE) to an imbalance between EI and EE, and to resulting changes in FM and FFM. Mobilization and storage of energy as FM and FFM are functions of the relative size of the deficit (EI/EE) and of the body composition. The model was validated with three recent studies measuring EE at a fixed EI during an interval with energy restriction, overfeeding and exercise training respectively. Discrepancies between observed and simulated changes in energy stores were within the measurement precision of EI, EE and body composition. Thus the consequences of a change in dietary intake or a change in physical activity on body weight and body composition can be simulated.


1998 ◽  
Vol 85 (3) ◽  
pp. 1063-1069 ◽  
Author(s):  
Raymond D. Starling ◽  
Michael J. Toth ◽  
William H. Carpenter ◽  
Dwight E. Matthews ◽  
Eric T. Poehlman

Determinants of daily energy needs and physical activity are unknown in free-living elderly. This study examined determinants of daily total energy expenditure (TEE) and free-living physical activity in older women ( n = 51; age = 67 ± 6 yr) and men ( n = 48; age = 70 ± 7 yr) by using doubly labeled water and indirect calorimetry. Using multiple-regression analyses, we predicted TEE by using anthropometric, physiological, and physical activity indexes. Data were collected on resting metabolic rate (RMR), body composition, peak oxygen consumption (V˙o 2 peak), leisure time activity, and plasma thyroid hormone. Data adjusted for body composition were not different between older women and men, respectively (in kcal/day): TEE, 2,306 ± 647 vs. 2,456 ± 666; RMR, 1,463 ± 244 vs. 1,378 ± 249; and physical activity energy expenditure, 612 ± 570 vs. 832 ± 581. In a subgroup of 70 women and men, RMR andV˙o 2 peakexplained approximately two-thirds of the variance in TEE ( R 2 = 0.62; standard error of the estimate = ±348 kcal/day). Crossvalidation of this equation in the remaining 29 women and men was successful, with no difference between predicted and measured TEE (2,364 ± 398 and 2,406 ± 571 kcal/day, respectively). The strongest predictors of physical activity energy expenditure ( P < 0.05) for women and men were V˙o 2 peak( r = 0.43), fat-free mass ( r = 0.39), and body mass ( r = 0.34). In summary, RMR andV˙o 2 peak are important independent predictors of energy requirements in the elderly. Furthermore, cardiovascular fitness and fat-free mass are moderate predictors of physical activity in free-living elderly.


Medicina ◽  
2018 ◽  
Vol 55 (1) ◽  
pp. 2 ◽  
Author(s):  
Marja Leppänen ◽  
Pontus Henriksson ◽  
Hanna Henriksson ◽  
Christine Delisle Nyström ◽  
Francisco Llorente-Cantarero ◽  
...  

Background and objectives: There is a lack of studies investigating associations of physical activity level (PAL) and activity energy expenditure (AEE) using the doubly-labeled water (DLW) method with body composition and physical fitness in young children. Thus, we aimed to examine cross-sectional associations of PAL and AEE with body composition indices and physical fitness components in Swedish preschool children. Materials and methods: PAL was calculated as total energy expenditure measured using DLW divided by the predicted basal metabolic rate in 40 children aged 5.5 (standard deviation 0.2) years. AEE was calculated as total energy expenditure minus basal metabolic rate and the thermic effect of food, and divided by fat-free mass. Body composition was assessed using the 3-component model by combining measurements based on isotope dilution and air-displacement plethysmography. Physical fitness (muscular strength, motor fitness, and cardiorespiratory fitness) was evaluated using the PREFIT test battery. Multiple linear regression models were conducted. Results: PAL and AEE were negatively associated with body mass index, percent body fat, and fat mass index (PAL: standardized β −0.35, −0.41, and −0.45, all p < 0.036; AEE: standardized β −0.44, −0.44, and −0.47, all p < 0.006, respectively). Furthermore, PAL and AEE were positively associated with the standing long jump test (PAL: standardized β 0.37, p = 0.017; AEE: standardized β 0.38, p = 0.014). There were no statistically significant associations found regarding PAL or AEE with fat-free mass index or any other physical fitness test. Conclusions: Greater PAL and AEE at the age 5.5 were significantly associated with body fatness and improved lower-body muscular strength. Therefore, increasing physical activity, and thus energy expenditure, at young ages may be beneficial for preventing overweight/obesity. However, further studies with larger sample sizes are needed to confirm the results.


Dietary assessment 34 Individual assessment 38 Body composition 44 Anthropometry 50 This chapter considers dietary assessment of populations and groups and nutrition assessment of the individual. Assessment of physical activity and energy expenditure can be found in Chapter 5, ‘Macronutrients and energy balance’. Dietary assessment is an imprecise procedure; the imprecision can be minimized by using the appropriate technique and by an understanding of the errors implicit in the methodology. Dietary assessment is further hampered by the fact that, in assessing diet, it will change. Precision varies from very precise techniques, such as metabolic balance studies to the broad estimates of population studies. The methodology chosen must be appropriate for the nutrient/s that are being assessed and for the individual or population being assessed. The timing of the assessment is also important and must consider cultural variations, such as differences in the week (week day vs. weekend day), seasons (wet vs. dry season), and special occasions, e.g. Ramadan, Christmas....


Author(s):  
Mallika S. Sarma ◽  
Cara J. Ocobock ◽  
Sarah Martin ◽  
Shannon Rochelle ◽  
Brendan P. Croom ◽  
...  

2021 ◽  
Author(s):  
Patrick Mullie ◽  
Pieter Maes ◽  
Laurens van Veelen ◽  
Damien Van Tiggelen ◽  
Peter Clarys

ABSTRACT Introduction Adequate energy supply is a prerequisite for optimal performances and recovery. The aims of the present study were to estimate energy balance and energy availability during a selection course for Belgian paratroopers. Methods Energy expenditure by physical activity was measured with accelerometer (ActiGraph GT3X+, ActiGraph LLC, Pensacola, FL, USA) and rest metabolic rate in Cal.d−1 with Tinsley et al.’s equation based on fat-free mass = 25.9 × fat-free mass in kg + 284. Participants had only access to the French individual combat rations of 3,600 Cal.d−1, and body fat mass was measured with quadripolar impedance (Omron BF508, Omron, Osaka, Japan). Energy availability was calculated by the formula: ([energy intake in foods and beverages] − [energy expenditure physical activity])/kg FFM−1.d−1, with FFM = fat-free mass. Results Mean (SD) age of the 35 participants was 25.1 (4.18) years, and mean (SD) percentage fat mass was 12.0% (3.82). Mean (SD) total energy expenditure, i.e., the sum of rest metabolic rate, dietary-induced thermogenesis, and physical activity, was 5,262 Cal.d−1 (621.2), with percentile 25 at 4,791 Cal.d−1 and percentile 75 at 5,647 Cal.d−1, a difference of 856 Cal.d−1. Mean daily energy intake was 3,600 Cal.d−1, giving a negative energy balance of 1,662 (621.2) Cal.d−1. Mean energy availability was 9.3 Cal.kg FFM−1.d−1. Eleven of the 35 participants performed with a negative energy balance of 2,000 Cal.d−1, and only five participants out of 35 participants performed at a less than 1,000 Cal.d−1 negative energy balance level. Conclusions Energy intake is not optimal as indicated by the negative energy balance and the low energy availability, which means that the participants to this selection course had to perform in suboptimal conditions.


2020 ◽  
Vol 2 (3) ◽  
pp. 64-67
Author(s):  
Bando H

Adequate nutritional therapy and research have been crucial for diabetes and obesity. Recent topics include Calorie restriction (CR) and Low Carbohydrate Diet (LCD). It is rather difficult to calculate energy intake in person, and also to calculate the energy of the meal. There are some methods for investigating these factors, such as the total energy expenditure (TEE), physical-activity-related energy expenditure (PAEE), metabolic equivalent (MET) values, and the doubly-labeled water (DLW) method. Multi factors would be involved in the study. Further investigation would be expected for the determination of an appropriate amount of energy intake and meal energy in the future.


Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3394
Author(s):  
Sarah A. Purcell ◽  
Ryan J. Marker ◽  
Marc-Andre Cornier ◽  
Edward L. Melanson

Many breast cancer survivors (BCS) gain fat mass and lose fat-free mass during treatment (chemotherapy, radiation, surgery) and estrogen suppression therapy, which increases the risk of developing comorbidities. Whether these body composition alterations are a result of changes in dietary intake, energy expenditure, or both is unclear. Thus, we reviewed studies that have measured components of energy balance in BCS who have completed treatment. Longitudinal studies suggest that BCS reduce self-reported energy intake and increase fruit and vegetable consumption. Although some evidence suggests that resting metabolic rate is higher in BCS than in age-matched controls, no study has measured total daily energy expenditure (TDEE) in this population. Whether physical activity levels are altered in BCS is unclear, but evidence suggests that light-intensity physical activity is lower in BCS compared to age-matched controls. We also discuss the mechanisms through which estrogen suppression may impact energy balance and develop a theoretical framework of dietary intake and TDEE interactions in BCS. Preclinical and human experimental studies indicate that estrogen suppression likely elicits increased energy intake and decreased TDEE, although this has not been systematically investigated in BCS specifically. Estrogen suppression may modulate energy balance via alterations in appetite, fat-free mass, resting metabolic rate, and physical activity. There are several potential areas for future mechanistic energetic research in BCS (e.g., characterizing predictors of intervention response, appetite, dynamic changes in energy balance, and differences in cancer sub-types) that would ultimately support the development of more targeted and personalized behavioral interventions.


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