Atorvastatin reduces <scp>T</scp>‐cell activation and exhaustion among <scp>HIV</scp>‐infected c<scp>ART</scp>‐treated suboptimal immune responders in <scp>U</scp>ganda: a randomised crossover placebo‐controlled trial
Bibliographic record
Abstract
OBJECTIVE: T-cell activation independently predicts mortality, poor immune recovery and non-AIDS illnesses during combination antiretroviral therapy (cART). Atorvastatin showed anti-immune activation effects among HIV-infected cART-naïve individuals. We investigated whether adjunct atorvastatin therapy reduces T-cell activation among cART-treated adults with suboptimal immune recovery. METHODS: A randomised double-blind placebo-controlled crossover trial, of atorvastatin 80 mg daily vs. placebo for 12 weeks, was conducted among individuals with CD4 increase <295 cells/μl after seven years of suppressive cART. Change in T-cell activation (CD3 + CD4 + /CD8 + CD38 + HLADR+) and in T-cell exhaustion (CD3 + CD4 + /CD8 + PD1 + ) was measured using flow cytometry. RESULTS: Thirty patients were randomised, 15 to each arm. Atorvastatin resulted in a 28% greater reduction in CD4 T-cell activation (60% reduction) than placebo (32% reduction); P = 0.001. Atorvastatin also resulted in a 35% greater reduction in CD8-T-cell activation than placebo (49% vs. 14%, P = 0.0009), CD4 T-cell exhaustion (27% vs. 17% in placebo), P = 0.001 and CD8 T-cell exhaustion (27% vs. 16%), P = 0.004. There was no carry-over/period effect. Expected adverse events were comparable in both groups, and no serious adverse events were reported. CONCLUSION: Atorvastatin reduced T-cell immune activation and exhaustion among cART-treated adults in a Ugandan cohort. Atorvastatin adjunct therapy should be explored as a strategy to improve HIV treatment outcomes among people living with HIV in sub-Saharan Africa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".