Repurposing Metformin in Nondiabetic People With HIV: Influence on Weight and Gut Microbiota
Bibliographic record
Abstract
BACKGROUND: People with HIV (PWH) taking antiretroviral therapy (ART) may experience weight gain, dyslipidemia, increased risk of non-AIDS comorbidities, and long-term alteration of the gut microbiota. Both low CD4/CD8 ratio and chronic inflammation have been associated with changes in the gut microbiota of PWH. The antidiabetic drug metformin has been shown to improve gut microbiota composition while decreasing weight and inflammation in diabetes and polycystic ovary syndrome. Nevertheless, it remains unknown whether metformin may benefit PWH receiving ART, especially those with a low CD4/CD8 ratio. METHODS: In the Lilac pilot trial, we recruited 23 nondiabetic PWH receiving ART for more than 2 years with a low CD4/CD8 ratio (<0.7). Blood and stool samples were collected during study visits at baseline, after a 12-week metformin treatment, and 12 weeks after discontinuation. Microbiota composition was analyzed by 16S rDNA gene sequencing, and markers of inflammation were assessed in plasma. RESULTS: Metformin decreased weight in PWH, and weight loss was inversely correlated with plasma levels of the satiety factor GDF-15. Furthermore, metformin changed the gut microbiota composition by increasing the abundance of anti-inflammatory bacteria such as butyrate-producing species and the protective CONCLUSIONS: Our study provides the first evidence that a 12-week metformin treatment decreased weight and favored anti-inflammatory bacteria abundance in the microbiota of nondiabetic ART-treated PWH. Larger randomized placebo-controlled clinical trials with longer metformin treatment will be needed to further investigate the role of metformin in reducing inflammation and the risk of non-AIDS comorbidities in ART-treated PWH.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".