Appetite control after weight loss: what is the role of bloodborne peptides?
Bibliographic record
Abstract
The literature presented in this paper argues that our limited ability to maintain energy balance in a weight-reduced state is the product of our difficulty in compensating for the weight loss-induced reduction in total energy expenditure. The end result, translated into the overwhelming complexity of preserving long-term weight loss, is presented as being a consequence of compromised appetite control. Given the present-day food landscape and the resultant susceptibility to passive overconsumption, the focus of this review will be on the peripheral ("bottom-up") signals (leptin, PYY, ghrelin, and GLP-1) and the evidence highlighting their influence on feeding behaviour. As we continue studying paradigms of body mass reduction, specifically the data emerging from patients of bariatric surgery, it is becoming clearer that counter-regulatory adaptations, possibly through down-(leptin, PYY, and GLP-1) or upregulation (ghrelin) of peptides, have an impact on energy balance. In itself, food deprivation influences some of the peptides that ultimately provide the physiological input for the overt expression of feeding behaviour; these peripheral adaptations are expected to serve as feeding cues--cues that, in the end, can serve to compromise the maintenance of energy balance. In a potentially novel intervention to increase compliance to long-term reductions in energy intake, it is proposed that manipulating the pattern of food intake to favourably alter the profile of gastrointestinal peptides would lead to better dietary control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".