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Record W4321499026 · doi:10.1002/oby.23650

High‐fat diet‐induced elevation of body weight set point in male mice

2023· article· en· W4321499026 on OpenAlexafffund
Lisa Z. Fang, J Vidal, Oishi Hawlader, Michiru Hirasawa

Bibliographic record

VenueObesity · 2023
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsDietingEndocrinologyInternal medicineOvereatingObesityEnergy homeostasisWeight lossSet pointHomeostasisMedicineWeight gainBody weight

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.028
GPT teacher head0.265
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes2
Has abstractyes

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