High‐fat diet‐induced elevation of body weight set point in male mice
Bibliographic record
Abstract
OBJECTIVE: High-fat diets (HFD) are thought to disrupt energy homeostasis to drive overeating and obesity. However, weight loss resistance in individuals with obesity suggests that homeostasis is intact. This study aimed to reconcile this difference by systematically assessing body weight (BW) regulation under HFD. METHODS: Male C57BL/6 N mice were fed diets with varying fat and sugar in different durations and patterns. BW and food intake were monitored. RESULTS: BW gain was transiently accelerated by HFD (≥40%) prior to plateauing. The plateau was consistent regardless of starting age, HFD duration, or fat/sugar content. Reverting to a low-fat diet (LFD) caused transiently accelerated weight loss, which correlated with how heavy mice were before the diet relative to LFD-only controls. Chronic HFD attenuated the efficacy of single or repetitive dieting, revealing a defended BW higher than that of LFD-only controls. CONCLUSIONS: This study suggests that dietary fat modulates the BW set point immediately upon switching from LFD to HFD. Mice defend a new elevated set point by increasing caloric intake and efficiency. This response is consistent and controlled, suggesting that hedonic mechanisms contribute to rather than disrupt energy homeostasis. An elevated floor of the BW set point after chronic HFD could explain weight loss resistance in individuals with obesity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".