• Energy Research

To test whether youths who engage in vigorous physical activity are more likely to have lean bodies while ingesting relatively large amounts of energy. For this purpose, we studied the associations of both physical activity and adiposity with energy intake in adolescents.The study subjects were adolescents who participated in 1 of 2 cross-sectional studies, the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study (n = 1450; mean age, 14.6 years) or the European Youth Heart Study (EYHS; n = 321; mean age, 15.6 years). Physical activity was measured by accelerometry, and energy intake was measured by 24-hour recall. In the HELENA study, body composition was assessed by 2 or more of the following methods: skinfold thickness, bioelectrical impedance analysis, plus dual-energy X-ray absorptiometry or air-displacement plethysmography in a subsample. In the EYHS, body composition was assessed by skinfold thickness.Fat mass was inversely associated with energy intake in both studies and using 4 different measurement methods (P ≤ .006). Overall, fat-free mass was positively associated with energy intake in both studies, yet the results were not consistent across measurement methods in the HELENA study. Vigorous physical activity in the HELENA study (P < .05) and moderate physical activity in the EYHS (P < .01) were positively associated with energy intake. Overall, results remained unchanged after adjustment for potential confounding factors, after mutual adjustment among the main exposures (physical activity and fat mass), and after the elimination of obese subjects, who might tend to underreport energy intake, from the analyses.Our data are consistent with the hypothesis that more physically active and leaner adolescents have higher energy intake than less active adolescents with larger amounts of fat mass.

Since the discovery of active brown adipose tissue in human adults, non-shivering cold-induced thermogenesis (CIT) has been regarded as a promising tool to combat obesity. However, there is a lack of consensus regarding the method of choice to analyze indirect calorimetry data from a CIT study. We analyzed the impact of methods for data selection and methods for data analysis on measures of cold-induced energy expenditure (EE) and nutrient oxidation rates.Forty-four young healthy adults (22.1 ± 2.1 years old, 25.6 ± 5.2 kg/m2, 29 women) participated in the study. Resting metabolic rate (RMR), cold-induced thermogenesis (CIT), and cold-induced nutrient oxidation rates were estimated by indirect calorimetry under fasting conditions during 1 h of cold exposure combining air conditioning (19.5-20 °C) and a water perfused cooling vest set at a temperature of 4 °C above the individual shivering threshold. We applied three methods for data selection: (i) time intervals every 5 min (5min-TI), (ii) the most stable 5-min period of every forth part of the cold exposure (5min-SS-4P), and (iii) the most stable 5-min period of every half part of the cold exposure (5min-SS-2P). Lately we applied two methods for data analysis: (i) area under the curve as a percentage of the baseline RMR (AUC) and; (ii) the difference between EE at the end of the cold exposure and baseline RMR (Last-RMR).Mean overall CIT estimation ranged from 11.6 ± 10.0 to 20.1 ± 17.2 %RMR depending on the methods for data selection and analysis used. Regarding methods for data selection, 5min-SS-2P did not allow to observe physiologically relevant phenomena (e.g. metabolic shift in fuel oxidation; P = 0.547) due to a lack of resolution. The 5min-TI and 5min-SS-4P methods for data selection seemed to be accurate enough to observe physiologically relevant phenomena (all P < 0.014), but not comparable for estimating over-all CIT and cold-induced nutrient oxidation rates (P < 0.01). Regarding methods for data analysis, the AUC seemed to be less affected for data artefacts and to be more representative in participants with a non-stable energy expenditure during cold exposure.The methods for data selection and analysis can have a profound impact on CIT and cold-induced nutrient oxidation rates estimations, and therefore, it is mandatory to unify it across scientific community to allow inter-study comparisons. Based on our findings, 5min-TI should be considered the method of choice to study dynamics (i.e. changes across time) of CIT and cold-induced nutrient oxidation rates, while 5min-SS-4P and AUC should be the method of choice when computing CIT and cold-induced nutrient oxidation rates as a single value.

ObjectiveThis study aimed to describe the energy expenditure (EE) and macronutrient oxidation response to an individualized nonshivering cold exposure in young healthy adults.MethodsTwo different groups of 44 (study 1: 22.1 [SD 2.1] years old, 25.6 [SD 5.2] kg/m2, 34% men) and 13 young healthy adults (study 2: 25.6 [SD 3.0] years old, 23.6 [SD 2.4] kg/m2, 54% men) participated in this study. Resting metabolic rate (RMR) and macronutrient oxidation rates were measured by indirect calorimetry under fasting conditions in a warm environment (for 30 minutes) and in mild cold conditions (for 65 minutes, with the individual wearing a water‐perfused cooling vest set at an individualized temperature adjusted to the individual’s shivering threshold).ResultsIn study 1, EE increased in the initial stage of cold exposure and remained stable for the whole cold exposure (P < 0.001). Mean cold‐induced thermogenesis (9.56 ± 7.9 kcal/h) was 13.9% ± 11.6% of the RMR (range: −14.8% to 39.9% of the RMR). Carbohydrate oxidation decreased during the first 30 minutes of the cold exposure and later recovered up to the baseline values (P < 0.01) in parallel to opposite changes in fat oxidation (P < 0.01). Results were replicated in study 2.ConclusionsA 1‐hour mild cold exposure individually adjusted to elicit maximum nonshivering thermogenesis induces a very modest increase in EE and a shift of macronutrient oxidation that may underlie a shift in thermogenic tissue activity.

AbstractThe constrained total energy expenditure (TEE) model posits that progressive increases in physical activity (PA) lead to increases in TEE; but after certain PA threshold, TEE plateaus. Then, a compensatory reduction in the expenditure of non-essential activities constrains the TEE. We hypothesized that high PA levels as locomotion associate with a compensatory attenuation in arm movements. We included 209 adults (64% females, mean [SD] age 32.1 [15.0] years) and 105 children (40% females, age 10.0 [1.1] years). Subjects wore, simultaneously, one accelerometer in the non-dominant wrist and another in the hip for ≥ 4 days. We analyzed the association between wrist-measured (arm movements plus locomotion) and hip-measured PA (locomotion). We also analyzed how the capacity to dissociate arm movements from locomotion influences total PA. In adults, the association between wrist-measured and hip-measured PA was better described by a quadratic than a linear model (Quadratic-R2 = 0.54 vs. Linear-R2 = 0.52; P = 0.003). Above the 80th percentile of hip-measured PA, wrist-measured PA plateaued. In children, there was no evidence that a quadratic model fitted the association between wrist-measured and hip-measured PA better than a linear model (R2 = 0.58 in both models, P = 0.25). In adults and children, those with the highest capacity to dissociate arm movements from locomotion—i.e. higher arm movements for a given locomotion—reached the highest total PA. We conclude that, in adults, elevated locomotion associates with a compensatory reduction in arm movements (probably non-essential fidgeting) that partially explains the constrained TEE model. Subjects with the lowest arm compensation reach the highest total PA.

Several studies have explored the role of human brown adipose tissue (BAT) in energy expenditure. However, the link between BAT and appetite regulation needs to be more rigorously examined.We aimed to investigate the associations of BAT volume and 18F-fluordeoxyglucose (18F-FDG) uptake after a personalized cold exposure with energy intake and appetite-related sensations in young healthy humans.A total of 102 young adults (65 women; age: 22.08 ± 2.17 y; BMI: 25.05 ± 4.93 kg/m 2) took part in this cross-sectional study. BAT volume, BAT 18F-FDG uptake, and skeletal muscle 18F-FDG uptake were assessed by means of static 18F-FDG positron-emission tomography and computed tomography scans after a 2-h personalized exposure to cold. Energy intake was estimated via an objectively measured ad libitum meal and three nonconsecutive 24-h dietary recalls. Appetite-related sensations (i.e., hunger and fullness) were recorded by visual analog scales before and after a standardized breakfast (energy content = 50% of basal metabolic rate) and the ad libitum meal. Body composition was assessed by a whole-body DXA scan.BAT volume and 18F-FDG uptake were not associated with quantified ad libitum energy intake (all P > 0.088), nor with habitual energy intake estimated from the 24-h dietary recalls (all P > 0.683). Lean mass was positively associated with both the energy intake from the ad libitum meal (β: 17.612, R2 = 0.213; P  0.3).Neither BAT volume, nor BAT 18F-FDG uptake after cold stimulation, are related to appetite regulation in young adults. These results suggest BAT plays no important role in the regulation of energy intake in humans.This trial was registered at clinicaltrials.gov as NCT02365129.

Having valid and reliable resting energy expenditure (REE) estimations is crucial to establish reachable goals for dietary and exercise interventions. However, most of the REE predictive equations were developed some time ago and, as the body composition of the current population has changed, it is highly relevant to assess the validity of REE predictive equations in contemporary young adults. In addition, little is known about the role of sex and weight status on the validity of these predictive equations. Therefore, this study aimed to investigate the role of sex and weight status in congruent validity of REE predictive equations in young adults. A total of 132 young healthy adults (67.4% women, 18–26 years old) participated in the study. We measured REE by indirect calorimetry strictly following the standard procedures, and we compared it to 45 predictive equations. The most accurate equations were the following: (i) the Schofield and the “Food and Agriculture Organization of the United Nations/World Health Organization/United Nations” (FAO/WHO/UNU) equations in normal weight men; (ii) the Mifflin and FAO/WHO/UNU equations in normal weight women; (iii) the Livingston and Korth equations in overweight men; (iv) the Johnstone and Frankenfield equations in overweight women; (v) the Owen and Bernstein equations in obese men; and (vi) the Owen equation in obese women. In conclusion, the results of this study show that the best equation to estimate REE depends on sex and weight status in young healthy adults.