Proglucagon-Derived Peptides, Glucose-Dependent Insulinotropic Polypeptide, and Dipeptidyl Peptidase-4-Mechanisms of Action in Adipose Tissue
Bibliographic record
Abstract
Proglucagon-derived peptides (PGDPs) and related gut hormones exemplified by glucose-dependent insulinotropic polypeptide (GIP) regulate energy disposal and storage through actions on metabolically sensitive organs, including adipose tissue. The actions of glucagon, glucagon-like peptide (GLP)-1, GLP-2, GIP, and their rate-limiting enzyme dipeptidyl peptidase-4, include direct and indirect regulation of islet hormone secretion, food intake, body weight, all contributing to control of white and brown adipose tissue activity. Moreover, agents mimicking actions of these peptides are in use for the therapy of metabolic disorders with disordered energy homeostasis such as diabetes, obesity, and intestinal failure. Here we highlight current concepts and mechanisms for direct and indirect actions of these peptides on adipose tissue depots. The available data highlight the importance of indirect peptide actions for control of adipose tissue biology, consistent with the very low level of endogenous peptide receptor expression within white and brown adipose tissue depots. Finally, we discuss limitations and challenges for the interpretation of available experimental observations, coupled to identification of enduring concepts supported by more robust evidence.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".