Use of glucagon‐like peptide‐1 receptor agonists in eating disorder populations
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1As) are being used as approved or off-label treatments for weight loss. As such, there has been increasing concern about the potential for GLP-1As to impact eating disorder (ED) symptomatology. This article seeks to (1) review the current state of knowledge regarding GLP-1As and ED symptomatology; (2) provide recommendations for future research; and (3) guide ED clinicians in how to discuss GLP-1As in clinical practice. Although evidence is limited, it is possible that GLP-1As could exacerbate, or contribute to the development of, ED pathology and negatively impact ED treatment. Preliminary research on the use of GLP-1As to treat binge eating has been conducted; however, studies have design limitations and additional research is needed. Therefore, at the current time there is not sufficient evidence to support the use of GLP-1s to treat ED symptoms. In summary, more research is required before negative or positive conclusions can be drawn about the impact of GLP-1As on EDs psychopathology. Herein, we provide specific recommendations for future research and a guide to help clinicians navigate discussions with their clients about GLP-1As. A client handout is also provided. PUBLIC SIGNIFICANCE: Despite glucagon-like peptide-1 receptor agonists (GLP-1As; e.g., semaglutide) increasingly being the topic of clinical and public discourse, little is known about their potential impact on ED symptoms. It is possible that GLP-1As could maintain, worsen, or improve ED symptoms. This article reviews the limited literature on GLP-1As and ED symptoms, recommends future research, and provides clinicians with a guide for discussing GLP-1As with ED clients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".