• 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.

Indirect calorimetry (IC) is considered the reference method to determine the resting energy expenditure (REE), but its use in a clinical context is limited. Alternatively, there is a number of REE predictive equations to estimate the REE. However, it has been shown that the available REE predictive equations could either overestimate or underestimate the REE as measured by IC. Moreover, the role of the weight status in the accuracy and validity of the REE predictive equations requires further attention. Therefore, this study aimed to determine the accuracy and validity of REE predictive equations in normal-weight, overweight, and obese sedentary middle-aged adults. A total of 73 sedentary middle-aged adults (53% women, 40–65 years old) participated in the study. We measured REE by indirect calorimetry, strictly following the standard procedures, and we compared it with the values obtained from 33 predictive equations. The most accurate predictive equations in middle-aged sedentary adults were: (i) the equation of FAO/WHO/UNU in normal-weight individuals (50.0% of prediction accuracy), (ii) the equation of Livingston in overweight individuals (46.9% of prediction accuracy), and (iii) the equation of Owen in individuals with obesity (52.9% of prediction accuracy). Our study shows that the weight status plays an important role in the accuracy and validity of different REE predictive equations in middle-aged adults.

High-intensity interval training (HIIT) is promoted as a time-efficient strategy to improve body composition but concomitant beer intake, which is common among physically active individuals, may interfere with these effects. The primary aim of this study is to determine the effects of a 10-week (2 days/week) HIIT program on anthropometric and body composition measurements, and to assess whether those effects are influenced by the moderate consumption of beer (at least 5 days/week), or its alcohol equivalent. Young (24 ± 6 years old) healthy adults (n = 72, 35 females) volunteered for a non-training group (Non-Training group) or for HIIT training. Those going for training choose whether they preferred to receive alcohol or not. Those choosing alcohol were randomly allocated for receiving beer (5.4%; T-Beer group) or the equivalent amount of alcohol (vodka; T-Ethanol group) in sparkling water. Those choosing no-alcohol were randomly allocated for receiving alcohol-free beer (0.0%; T-0.0Beer group) or sparkling water (T-Water group). From Monday through Friday, men ingested 330 mL of the beverage with lunch and 330 mL with dinner; women ingested 330 mL with dinner. Before and after the intervention, anthropometry and body composition, through dual-emission X-ray absorptiometry, were measured. No changes in body mass, waist circumference, waist/hip ratio, visceral adipose tissue or bone mineral density occurred in any of the groups. By contrast, in all the training groups, significant decreases in fat mass together with increases in lean mass (all p < 0.05) occurred. These positive effects were not influenced by the regular intake of beer or alcohol. In conclusion, a moderate beer intake does not blunt the positive effect of 10-week HIIT on body composition in young healthy adults.

This cross-sectional study aimed to examine the association of basal metabolic rate (BMR) and basal fat and carbohydrate oxidation (BFox and BCHox, respectively) with cardiometabolic risk factors and insulin sensitivity in sedentary middle-aged adults. A total of 71 healthy sedentary adults (37 women) aged 40–65 years participated in the current study. Data were collected during the baseline assessments of the FIT-AGEING randomized controlled trial. BMR was measured via indirect calorimetry, and BFox and BCHox estimated by stoichiometric equations. Blood pressure, glucose, insulin, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides plasma levels were selected as cardiometabolic risk factors and assessed following standard procedures. We observed positive associations of BMR with plasma insulin and the homeostatic model assessment of insulin resistance index (HOMA; all p < 0.05) which were attenuated or disappeared after controlling by sex, age, and/or lean mass. There were positive associations between BFox and the quantitative insulin sensitivity check index (QUICKI; p < 0.015), while negative associations were noted between BFox and plasma insulin and HOMA (p < 0.015). There was a significant negative association between BCHox with QUICKI (p < 0.01), whereas significant positive relationships were obtained when BCHox was associated with plasma insulin and HOMA (p < 0.01). These associations persisted in almost all cases when controlling by sex, age and/or lean mass. No further relationships were found when BMR, BFox, and BCHox were associated with other cardiometabolic risk factors. In conclusion, our study findings support that greater BFox and lower BCHox are related to improved insulin sensitivity, whereas BMR seems to be not associated with neither cardiometabolic risk nor insulin sensitivity in sedentary middle-aged adults. Further intervention studies are necessary to well-understand the physiological mechanism implied in this relationship.