Acute Adaptation of Energy Expenditure Predicts Diet-Induced Weight Loss: Revisiting the Thrifty Phenotype
Bibliographic record
Abstract
Energy expenditure decreases in response to weight loss. Although this phenomenon is likely driven, at least in part, by neuroendocrine adaptations to caloric imbalance, its underlying molecular mechanisms are not yet fully elucidated (1). Because adaptive thermogenesis intensifies and is sustained over time as weight loss increases (2,3), it is considered to mitigate weight loss. The variability of adaptive thermogenesis in response to weight changes has also been suggested to contribute to an individual’s relative susceptibility to obesity and associated complications, such as type 2 diabetes. Rooted in a gene-centered view of evolution and natural selection, this notion was coined the “thrifty gene” hypothesis (4). Its genetic basis having been largely refuted (5), this theory has now been supplanted by the “thrifty phenotype” hypothesis. The thrifty phenotype, originally applied to the susceptibility toward type 2 diabetes and later extended to obesity and its other complications, is thought to result from the complex interplay of environmental cues with the genome known as epigenetic mechanisms (6). In this issue of Diabetes , Reinhardt et al. (7) report on testing their hypothesis that individuals who display greater reduction in energy expenditure during fasting or weaker increases during overfeeding (i.e., a thrifty phenotype) are resistant to weight loss. After an initial weight-maintenance period, the authors submitted 12 obese volunteers …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".