Can switching to doravirine/lamivudine/tenofovir DF halt or reverse INSTI-associated weight gain?
Bibliographic record
Abstract
Background: In randomized trials, doravirine (DOR) appears relatively weight-neutral, while tenofovir DF (TDF) may be weight-suppressive. Here we designed a prospective, observational pilot study to assess the impact of switching to DOR/3TC/TDF on weight trajectory in patients with significant integrase inhibitor (INSTI)-associated weight gain. Methods: Adults with a ≥10% increase in body weight while on an INSTI (± tenofovir alafenamide) regimen and HIV RNA <50 copies/mL were switched to DOR/3TC/TDF for 12 months. Weight, waist circumference, and routine bloodwork were measured at baseline and every 12 weeks. Total body fat (DXA scan) and body image/self-esteem (B-WISE, modified Fat Redistribution and Metabolic Change in HIV questionnaires) were assessed at baseline and week 48. The target enrolment was 25 participants. Results: ), waist circumference (-0.8/-1.1/+1 cm), total body fat (-1.5%/-2.9%/-3.5%), blood pressure, fasting glucose, and lipids were observed. Body image/self-esteem improved; all maintained HIV RNA <50 copies/mL with no proteinuria/significant eGFR change. The study was closed due to futility in attaining timely target enrolment. Conclusions: Two out of three Black women with a ≥10% INSTI-associated increase in body weight had weight loss of >5% after switching to DOR/3TC/TDF for 48 weeks. All three participants experienced small improvements in waist circumference, body fat, and metabolism, and all reported improvements in body image/self-esteem. Pandemic challenges significantly impacted the ability to enrol participants.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".