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Background/objectives: Type 1 diabetes (T1D) is associated with an increase in resting metabolic rate (RMR), but the impact of T1D on other components of 24-h energy expenditure (24-h EE) is not known. Also, there is a lack of equations to estimate 24-h EE in patients with T1D. The aims of this analysis were to compare 24-h EE and its components in young adults with T1D and healthy controls across the spectrum of body mass index (BMI) and derive T1D-specific equations from clinical variables., Subjects/methods: Thirty-three young adults with T1D diagnosed ≥1 year prior and 33 healthy controls matched for sex, age and BMI were included in this analysis. We measured 24-h EE inside a whole room indirect calorimeter (WRIC) and body composition with dual x-ray absorptiometry., Results: Participants with T1D had significantly higher 24-h EE than healthy controls (T1D = 2047 ± 23 kcal/day vs control= 1908 ± 23 kcal/day; P < 0.01). We derived equations to estimate 24-h EE with both body composition (fat free mass + fat mass) and anthropometric (weight + height) models, which provided high coefficients of determination (R 2  = 0.912 for both). A clinical model that did not incorporate spontaneous physical activity yielded high coefficients of determination as well (R 2  = 0.897 and R 2  = 0.880 for body composition and anthropometric models, respectively)., Conclusion: These results confirm that young adults with established T1D have increased 24-h EE relative to controls without T1D. The derived equations from clinically available variables can assist clinicians with energy prescriptions for weight management in patients with T1D., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Objective: This analysis aimed to measure the intraparticipant reliability-the intraclass correlation coefficient-of all the components of daily energy expenditure (EE) (24-hour EE, sleep EE, resting EE, basal EE, and thermic effect of food) over a period of 3 consecutive days in 35 study participants., Methods: The components of daily EE and substrate use (respiratory exchange ratio) were measured over 3 consecutive days before and after a 3-week 1,000-kcal/d caloric restriction/weight-loss intervention., Results: There was a high degree of reliability for sleep EE (96.8%), 24-hour EE (97.8%), basal EE (90.6%), and resting EE (93.2%) during the run-in period. The intraclass correlation coefficient for the follow-up period after weight loss (3.67 ± 1.10 kg) remained high for sleep EE (95.6%), 24-hour EE (100%), basal EE (96.1%), and resting EE (92.5%). The minimal detectable differences in EE were reduced by 30% for both 24-hour EE and sleep EE when comparing 2 days versus 1 day spent in the whole-room indirect calorimeter., Conclusions: The reliability of the daily components of EE is very high both prior to and after a weight-loss intervention. We here provide instrumental data for investigators to adequately power studies investigating energy metabolism using whole-room indirect calorimetry., (© 2021 The Obesity Society.)

Background: Low-calorie diet (LCD)-induced weight loss demonstrates response heterogeneity. Physiologically, a decrease in energy expenditure lower than what is predicted based on body composition (metabolic adaptation) and/or an impaired capacity to increase fat oxidation may hinder weight loss. Understanding the metabolic components that characterize weight loss success is important for optimizing weight loss strategies., Objectives: We tested the hypothesis that overweight/obese individuals who had lower than expected weight loss in response to a 28-d LCD would be characterized by 1) impaired fat oxidation and 2) whole-body metabolic adaptation. We also characterized the molecular mechanisms associated with weight loss success/failure., Methods: This was a retrospective comparison of participants who met their predicted weight loss targets [overweight/obese diet sensitive (ODS), n = 23, females = 21, males = 2] and those that did not [overweight/obese diet resistant (ODR), n = 14, females = 12, males = 2] after a 28-d LCD (900-1000 kcal/d). We used whole-body (energy expenditure and fat oxidation) and tissue-specific measurements (metabolic proteins in skeletal muscle, gene expression in adipose tissue, and metabolites in serum) to detect metabolic properties and biomarkers associated with weight loss success., Results: The ODR group had greater mean ± SD metabolic adaptation (-175 ± 149 kcal/d; +119%) than the ODS group (-80 ± 108 kcal/d) after the LCD (P = 0.030). Mean ± SD fat oxidation increased similarly for both groups from baseline (0.0701 ± 0.0206 g/min) to day 28 (0.0869 ± 0.0269 g/min; P < 0.001). A principal component analysis factor comprised of serum 3-hydroxybutyric acid, citrate, leucine/isoleucine, acetyl-carnitine, and 3-hydroxylbutyrlcarnitine was associated with weight loss success at day 28 (std. β = 0.674, R2 = 0.479, P < 0.001)., Conclusions: Individuals who achieved predicted weight loss targets after a 28-d LCD were characterized by reduced metabolic adaptation. Accumulation of metabolites associated with acetyl-CoA excess and enhanced ketogenesis was identified in the ODS group.This trial was registered at clinicaltrials.gov as NCT01616082., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Human and microbial metabolism are distinct disciplines. Terminology, metrics, and methodologies have been developed separately. Therefore, combining the 2 fields to study energetic processes simultaneously is difficult., Objectives: When developing a mechanistic framework describing gut microbiome and human metabolism interactions, energy values of food and digestive materials that use consistent and compatible metrics are required. As an initial step toward this goal, we developed and validated a model to convert between chemical oxygen demand (COD) and gross energy (${E&#95;g}$) for >100 food items and ingredients., Methods: We developed linear regression models to relate (and be able to convert between) theoretical gross energy (${E&#95;g}^{\prime}$) and chemical oxygen demand (COD'); the latter is a measure of electron equivalents in the food's carbon. We developed an overall regression model for the food items as a whole and separate regression models for the carbohydrate, protein, and fat components. The models were validated using a sample set of computed ${E&#95;g}^{\prime}$ and COD' values, an experimental sample set using measured ${E&#95;g}$ and COD values, and robust statistical methods., Results: The overall linear regression model and the carbohydrate, protein, and fat regression models accurately converted between COD and ${E&#95;g}$, and the component models had smaller error. Because the ratios of COD per gram dry weight were greatest for fats and smallest for carbohydrates, foods with a high fat content also had higher ${E&#95;g}$ values in terms of kcal · g dry weight-1., Conclusion: Our models make it possible to analyze human and microbial energetic processes in concert using a single unit of measure, which fills an important need in the food-nutrition-metabolism-microbiome field. In addition, measuring COD and using the regressions to calculate ${E&#95;g}$ can be used instead of measuring ${E&#95;g}$ directly using bomb calorimetry, which saves time and money., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society for Nutrition.)