The contribution of afferent signals from the liver to metabolic regulation during exercise
Bibliographic record
Abstract
The crucial role of the liver as the only organ to produce glucose used by skeletal muscle during exercise is well known. Since hepatic glucose production is central to blood glucose homeostasis during exercise, it has been postulated that the liver may inform the central nervous system and other organs of its diminishing capacity to produce glucose from glycogen, before blood glucose falls. The sensory role of the liver during exercise would be similar to its role in the control of food intake. As a consequence, the experimental approaches used to test the hypothesis that afferent signals from the liver contribute to metabolic regulation during exercise are inspired by those used to test the same hypothesis in the regulation of food intake. In the present review, two questions are addressed. The existing evidence for the liver's sensory influence on metabolic adjustments to exercise is first reviewed; the nature of the initiating stimuli for the afferent contribution of the liver to physical exercise is discussed thereafter. The hypothetical construct upon which rests the contribution of the liver's afferent signals to metabolic regulation during exercise is that a decrease in liver glycogen or a related metabolic intermediate is sensed by the liver, and the signal is transduced to the central nervous system, most likely through the afferent activity of the hepatic vagus nerve, where it contributes to the orchestration of the metabolic and hormonal responses to exercise. Support in favour of this construct comes mainly from the demonstration that sectioning of the hepatic vagus nerve attenuates the normal hormonal response to exercise. It seems that the liver-glucagon axis is particularly responsive to this reflex activation. In other respects, the hepatic mechanism responsible for linking the metabolic activity in the liver to an afferent signal capable of regulating the metabolic response to exercise remains speculative. Substrates or derivatives of substrate oxidation, energy-related compounds (ATP and Pi), or changes in cell volume may all be related to changes in transmembrane potential in the liver cell, which according to the "potentiostatic" theory would determine the afferent vagal activity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".