Weight loss associated with semaglutide treatment among people with HIV
Bibliographic record
Abstract
OBJECTIVE: There is limited real-world evidence about the effectiveness of semaglutide for weight loss among people with HIV (PWH). We aimed to investigate weight change in a US cohort of PWH who initiated semaglutide treatment. DESIGN: Observational study using the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. METHODS: We identified adult PWH who initiated semaglutide between 2018 and 2022 and with at least two weight measurements. The primary outcome was within-person bodyweight change in kilograms at 1 year. The secondary outcome was within-person Hemoglobin A1c percentage (HbA1c) change. Both outcomes were estimated using multivariable linear mixed model. RESULTS: In total, 222 new users of semaglutide met inclusion criteria. Mean follow-up was 1.1 years. Approximately 75% of new semaglutide users were men, and at baseline, mean age was 53 years [standard deviation (SD): 10], average weight was 108 kg (SD: 23), mean BMI was 35.5 kg/m 2 , mean HbA1c was 7.7% and 77% had clinically recognized diabetes. At baseline, 97% were on ART and 89% were virally suppressed (viral load < 50 copies/ml). In the adjusted mixed model analysis, treatment with semaglutide was associated with an average weight loss of 6.47 kg at 1 year (95% CI -7.67 to -5.18) and with a reduction in HbA1c of 1.07% at 1 year (95% CI -1.64 to -0.50) among the 157 PWH with a postindex HbA1c value. CONCLUSION: Semaglutide was associated with significant weight loss and HbA1c reduction among PWH, comparable to results of previous studies from the general population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".