Peripheral and central signals in the control of eating in normal, obese and binge-eating human subjects
Bibliographic record
Abstract
The worldwide increase in the incidence of obesity is a consequence of a positive energy balance, with energy intake exceeding expenditure. The signalling systems that underlie appetite control are complex, and the present review highlights our current understanding of key components of these systems. The pattern of eating in obesity ranges from over-eating associated with binge-eating disorder to the absence of binge-eating. The present review also examines evidence of defects in signalling that differentiate these sub-types. The signalling network underlying hunger, satiety and metabolic status includes the hormonal signals leptin and insulin from energy stores, and cholecystokinin, glucagon-like peptide-1, ghrelin and peptide YY3-36 from the gastrointestinal tract, as well as neuronal influences via the vagus nerve from the digestive tract. This information is routed to specific nuclei of the hypothalamus and brain stem, such as the arcuate nucleus and the solitary tract nucleus respectively, which in turn activate distinct neuronal networks. Of the numerous neuropeptides in the brain, neuropeptide Y, agouti gene-related peptide and orexin stimulate appetite, while melanocortins and alpha-melanocortin-stimulating hormone are involved in satiety. Of the many gastrointestinal peptides, ghrelin is the only appetite-stimulating hormone, whereas cholecystokinin, glucagon-like peptide-1 and peptide YY3-36 promote satiety. Adipose tissue provides signals about energy storage levels to the brain through leptin, adiponectin and resistin. Binge-eating has been related to a dysfunction in the ghrelin signalling system. Moreover, changes in gastric capacity are observed, and as gastric capacity is increased, so satiety signals arising from gastric and post-gastric cues are reduced. Understanding the host of neuropeptides and peptide hormones through which hunger and satiety operate should lead to novel therapeutic approaches for obesity; potential therapeutic strategies are highlighted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".