Changes in Inflammation and Immune Activation With Atazanavir-, Raltegravir-, Darunavir-Based Initial Antiviral Therapy: ACTG 5260s
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the integrase inhibitor raltegravir (RAL) reduces inflammation and immune activation compared with ritonavir-boosted protease inhibitors (PIs). METHODS: In a prospective, randomized, multicenter clinical trial that included 328 human immunodeficiency type 1 (HIV-1)-infected, treatment-naive participants were randomized to receive tenofovir disoproxil fumarate-emtricitabine (TDF/FTC) plus atazanavir/ritonavir (ATV/r), darunavir/ritonavir (DRV/r), or RAL. A total of 234 participants (71%) with HIV-1 RNA levels <50 copies/mL by week 24 were included. Plasma biomarkers of inflammation and coagulation that were analysed included high-sensitivity C-reactive protein, interleukin-6 (IL-6), GlycA, D-dimer, soluble CD14 (sCD14), sCD163, and sIL-2r; blood cellular markers included %CD38+DR+ of T-cell subsets and %CD14+CD16+ and%CD14(dim)CD16+ monocyte subsets. Changes from baseline were examined at earlier (24 or 48 weeks) and later (96 weeks) time points, with 95% confidence intervals on fold-change. Pairwise treatment groups were compared using Wilcoxon rank sum tests, with P values adjusted for false discovery rate control. RESULTS: Changes in biomarkers varied by regimen during the 96 weeks of follow-up as follows: hsCRP declined with ATV/r and RAL, IL-6 declined only with RAL, and GLycA decreased in all groups. D-dimer declined with ATV/r and DRV/r and was unchanged with RAL. Markers of T-cell activation and sCD163 (but not sCD14 and CD14-+CD16+) declined in all groups. CONCLUSIONS: Despite some differences in specific markers of inflammation and immune activation between the antiretroviral therapy (ART) regimens, we found no consistent evidence that the reduction of inflammation and immune activation with ART initiation was different between RAL and PI-based regimens. CLINICAL TRIALS REGISTRATION: NCT00811954 and NCT00851799.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".