Discontinuing semaglutide after weight loss: strategy for weight maintenance and a possible new side effect
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) facilitate weight loss. Weight regain off therapy is concerning. We reported the case of a 35-year-old male prescribed oral semaglutide with 22.7 kg weight loss over 120 days. Herein, we describe the clinical course when discontinuing GLP-1 RA therapy, one approach to maintaining weight loss after discontinuation, and a possible new side effect. At day 120, we continued oral semaglutide 7 mg daily, down from 14 mg, for weight maintenance with subsequent weight regain. We re-increased semaglutide to 14 mg/day with weight re-loss within 1 month and weight maintance for a year. We then discontinued semaglutide; weight loss was maintained for 6 months. The patient reported lactose intolerance ∼13 months before starting semaglutide. During semaglutide therapy, the patient reported worsened lactose intolerance and new gluten intolerance. Food allergy/celiac testing were negative. Intolerances did not improve with semaglutide discontinuation. Six months after semaglutide discontinuation, the patient was diagnosed with small intestinal bacterial overgrowth, possibly worsened by semaglutide. Factors potentially supporting weight maintenance were early drug treatment for new-onset obesity, non-geriatric age, strength training, and diet modification. The case highlights tailoring approaches to maintain weight loss without GLP-1 RAs. Trials are needed to optimize weight maintenance strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".