Factors associated with weight loss response to GLP-1 analogues for obesity treatment: a retrospective cohort analysis
Bibliographic record
Abstract
OBJECTIVES: The response to glucagon-like peptide-1 (GLP-1) analogues for weight loss varies significantly. We investigated the anthropometric, demographic and clinical characteristics associated with total body weight loss (TBWL) from subcutaneous GLP-1 analogue therapy in patients with obesity in a real-world setting. DESIGN: Retrospective cohort analysis. SETTING: An urban, multidisciplinary obesity community clinic in Vancouver, Canada, from November 2018 to April 2021. PARTICIPANTS: who had filled a new prescription for subcutaneous semaglutide or liraglutide, with at least 6-month follow-up, were included (mean follow-up: 17.3 months). Individuals with prior bariatric surgery were excluded. OUTCOMES: The primary outcome was the %TBWL over a mean follow-up period of 520 days. Participant's TWBL was categorised as non-response (<5% TBWL), moderate response (5%-15% TBWL) or hyper-response (>15% TBWL). RESULTS: The average %TBWL in the cohort was 12.2%. Among the participants, 17.8% had a non-response, 48.4% had a moderate response and 33.8% had a hyper-response. In the multivariable regression analysis, being a woman was associated with hyper-response (adjusted OR 1.92, CI 1.01 to 3.65, p=0.048). Age, diabetes status, baseline BMI, being sedentary, anxiety and depression were not independently associated with TBWL in response to GLP-1 analogue therapy. CONCLUSIONS: In a real-world setting, female sex was found to be associated with a hyper-response to GLP-1 analogue therapy for obesity management. Other clinical factors evaluated, including diabetes status, were not associated with the response. Future research should assess additional variables and support the development of novel biomarkers that are associated with weight loss response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".