Neurocircuitry underlying the actions of glucagon-like peptide 1 and peptide YY3–36 in the suppression of food, drug-seeking, and anxiogenesis
Bibliographic record
Abstract
Obesity is a critical health condition worldwide that increases the risks of comorbid chronic diseases, but it can be managed with weight loss. However, conventional interventions relying on diet and exercise are inadequate for achieving and maintaining weight loss, thus there is significant market interest for pharmaceutical anti-obesity agents. For decades, receptor agonists for the gut peptide glucagon-like peptide 1 (GLP-1) featured prominently in anti-obesity medications by suppressing appetite and food reward to elicit rapid weight loss. As the neurocircuitry underlying food motivation overlaps with that for drugs of abuse, GLP-1 receptor agonism has also been shown to decrease substance use and relapse, thus its therapeutic potential may extend beyond weight management to treat addictions. However, as prolonged use of anti-obesity drugs may increase the risk of mood-related disorders like anxiety and depression, and individuals taking GLP-1-based medication commonly report feeling demotivated, the long-term safety of such drugs is an ongoing concern. Interestingly, current research now focuses on dual agonist approaches that include GLP-1 receptor agonism to enable synergistic effects on weight loss or associated functions. GLP-1 is secreted from the same intestinal cells as the anorectic gut peptide, Peptide YY3–36 (PYY3–36), thus this review assessed the therapeutic potential and underlying neural circuits targeted by PYY3–36 when administered independently or in combination with GLP-1 to curb the appetite for food or drugs of abuse like opiates, alcohol, and nicotine. Additionally, we also reviewed animal and human studies to assess the impact, if any, for GLP-1 and/or PYY3–36 on mood-related behaviors in relation to anxiety and depression. As dual agonists targeting GLP-1 and PYY3–36 may produce synergistic effects, they can be effective at lower doses and offer an alternative approach for therapeutic benefits while mitigating undesirable side effects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".