Fasting inhibits excitatory synaptic input on paraventricular oxytocin neurons via neuropeptide Y and Y1 receptor, inducing rebound hyperphagia, and weight gain
Bibliographic record
Abstract
Fasting with varying intensities is used to treat obesity-related diseases. Re-feeding after fasting exhibits hyperphagia and often rebound weight gain. However, the mechanisms underlying the hyperphagia and rebound remain elusive. Here we show that 24 h food restriction (24 h FR) and milder 50% FR, both depress synaptic transmission in the hypothalamic paraventricular nucleus (PVN) and induce acute hyperphagia in rats. 24 h FR is followed by weight rebound but 50% FR is not. Orexigenic neuropeptide Y (NPY) via the Y1 receptor (Y1R) inhibited the miniature excitatory postsynaptic current (mEPSC) on anorexigenic oxytocin neurons in the PVN. 24 h FR and 50% FR activated this neuronal pathway to induce acute hyperphagia on Days 1–3 and Days 1–2 after FR, respectively. 24 h FR induced large mEPSC depression, recurrent hyperphagia on Days 9–12 and rebound weight gain on Days 12–17, whereas 50% FR induced moderate mEPSC depression and sustained weight reduction. Transverse data analysis on Day 1 after 24 h FR and 50% FR demonstrated saturation kinetics for the mEPSC depression-hyperphagiacurve, implying hysteresis. The results reveal FR-driven synaptic plasticity in the NPY-Y1R-oxytocin neurocircuit that drives acute hyperphagia. FR with the intensity that regulates the synapse-feeding relay without hysteresis is the key for successful dieting.
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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) | 0.000 | 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".