Impaired intestinal afferent nerve satiety signalling and vagal afferent excitability in diet induced obesity in the mouse
Bibliographic record
Abstract
Gastrointestinal vagal afferents transmit satiety signals to the brain via both chemical and mechanical mechanisms. There is indirect evidence that these signals may be attenuated in obesity. We hypothesized that responses to satiety mediators and distension of the gut would be attenuated after induction of diet induced obesity. Obesity was induced by feeding a high fat diet (60% kcal from fat). Low fat fed mice (10% kcal from fat) served as a control. High fat fed mice were obese, with increased visceral fat, but were not hyperglycaemic. Recordings from jejunal afferents demonstrated attenuated responses to the satiety mediators cholecystokinin (CCK, 100 nm) and 5-hydroxytryptamine (5-HT, 10 μm), as was the response to low intensity jejunal distension, while responses to higher distension pressures were preserved. We performed whole cell patch clamp recordings on nodose ganglion neurons, both unlabelled, and those labelled by fast blue injection into the wall of the jejunum. The cell membrane of both labelled and unlabelled nodose ganglion neurons was less excitable in HFF mice, with an elevated rheobase and decreased number of action potentials at twice rheobase. Input resistance of HFF neurons was also significantly decreased. Calcium imaging experiments revealed reduced proportion of nodose ganglion neurons responding to CCK and 5-HT in obese mice. These results demonstrate a marked reduction in afferent sensitivity to satiety related stimuli after a chronic high fat diet. A major mechanism underlying this change is reduced excitability of the neuronal cell membrane. This may explain the development of hyperphagia when a high fat diet is consumed. Improving sensitivity of gastrointestinal afferent nerves may prove useful to limit food intake in 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".