The association between leptin and weight maintenance outcome in anorexia nervosa
Bibliographic record
Abstract
Abstract Objective Relapse after weight restoration in anorexia nervosa (AN) is a critical problem. Higher body fat percentage after weight gain has been shown to predict better weight maintenance outcome. Leptin, a fat‐derived hormone, has been associated with progress during weight gain, but its association with weight maintenance is unknown. This study aims to determine whether leptin levels after weight restoration in AN are associated with weight maintenance. Method Participants were 41 women with AN hospitalized for inpatient treatment. Participants were evaluated 2–4 weeks after weight restoration to body mass index (BMI) ≥ 19.5 kg/m2 for plasma leptin and body composition. Weight maintenance outcome was defined by whether a participant maintained a BMI of at least 18.5 kg/m2 at the end of 1 year following hospital discharge. Results Twenty (48.8%) out of 41 patients maintained their weight at 1 year. Percent body fat and leptin were significantly higher in the group who maintained weight (body fat, p = .004, Hedges' g = 0.944; log‐leptin, p = .010, Hedges' g = 0.821), but there were no differences in predischarge BMI, duration of illness, and duration of amenorrhea. Using regression modeling, only higher log‐leptin (pWald = .021) and percent body fat (pWald = .010), as well as fat‐adjusted leptin (pWald = .029), independently predicted weight maintenance at 1 year. Discussions Our findings suggest that for acutely‐weight restored women with AN, higher predischarge leptin measurements are associated with better outcome in the year following treatment. Prospective studies examining leptin as well as other parameters of metabolic health could offer insights into biomarkers that may improve clinical outcomes.
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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 | 0.003 |
| 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 | 0.000 | 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 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".