Adaptive thermogenesis in response to weight loss and weight regain: first evidence in adolescents with severe obesity
Bibliographic record
Abstract
Abstract While the effects of multidisciplinary weight loss (WL) on resting energy expenditure remain unclear in adolescents with obesity, the potential presence of adaptive thermogenesis (AT) has never been explored, which was the objective of the present work. Twenty-six adolescents (14·1 ( sd 1·5) years) with severe obesity completed a 9-month inpatient multidisciplinary intervention followed by a 4-month follow-up. Anthropometric measurements, body composition (dual X-ray absorptiometry) and resting energy expenditure (REE, indirect calorimetry) were assessed before (T0) and after 9 months of WL intervention (T1) and after a 4-month follow-up (T2). AT, at the level of REE, was defined as a significantly lower measured v . predicted (using regression models with baseline data) REE. Two pre-cited REE equations were used, using both fat mass and fat-free mass (FFM) (predicted REE using equation 1) or FFM only (predicted REE using equation 2). Measured and predicted REE significantly decreased between T0 and T1 ( P < 0·001) and remained lower at T2 compared with T0 (measured REE: P = 0·017; predicted REE: P < 0·001). Predicted REE using equation 2 was significantly higher than measured REE at T1 ( P = 0·012), suggesting the presence of AT. FFM at T0 was negatively correlated with ATp1T1 (Rho = –0·428; P = 0·033) and ATp2T1 (Rho = –0·485; P = 0·014). The variation of FFM between T0 and T1 was negatively correlated with AT at T1 and T2. These preliminary results suggest the existence of AT in response to WL in adolescents with obesity, independently of the degree of WL. AT was associated with subsequent body weight and fat regain, suggesting AT may represent a damper to WL attempts while increasing the adolescents’ risks for subsequent weight and adiposity rebounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".