Test of a biobehavioral model linking weight suppression to binge-eating severity via leptin and glucagon-like peptide 1 in bulimia nervosa and related syndromes in women
Bibliographic record
Abstract
Abstract Background Weight suppression represents the difference between highest and current body weight and predicts maintenance of bulimia nervosa and related syndromes (BN-S). This study tested a biobehavioral model of binge-eating severity in which greater weight suppression links to reduced leptin, which links to reduced glucagon-like peptide 1 (GLP-1) release, which links to both decreased reward satiation and increased reward valuation, which link, respectively, to excessive food intake and loss of control while eating – the defining features of DSM-5 binge-eating episodes. Methods Women (N = 399) who met DSM-5 criteria for bulimia nervosa or another eating disorder with binge eating (n = 321) or had no lifetime eating disorder symptoms (n = 78) participated in a multi-visit protocol, including structured clinical interviews, height, weight, weight history, percent body fat, fasting leptin, post-prandial GLP-1 response to a fixed meal, and self-report and behavioral assessments of food reward satiation (ad lib meal) and food and nonfood reward valuation (progressive ratio tasks). Results A structural equation model (SEM) demonstrated excellent fit to data with significant pathways from greater weight suppression to lower leptin, to blunted GLP-1 response, to lower reward satiation, to larger eating/binge-eating episode size, with significant indirect paths through leptin, GLP-1, and reward satiation. SEM with paths via reward valuation to loss of control eating demonstrated inadequate fit. Conclusions Findings specifically link reduced GLP-1 response to severity of binge-episode size and support weight history assessment in eating disorders, DSM-5 over ICD-11 criteria for binge eating, and may inform future clinical trials of GLP-1 agonists for BN-S.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".