Effects of tesamorelin on inflammatory markers in HIV patients with excess abdominal fat: relationship with visceral adipose reduction
Bibliographic record
Abstract
OBJECTIVE: To report the effects of tesamorelin, a growth hormone-releasing hormone analogue, on inflammatory and fibrinolytic markers and to relate these effects to changes in visceral adipose tissue (VAT). DESIGN AND METHODS: Four hundred and ten HIV-infected patients with abdominal adiposity were randomized to 2 mg tesamorelin (n = 273) or placebo (n = 137) subcutaneously daily for 26 weeks. Circulating plasminogen activator inhibitor-1 (PAI-1) antigen, tissue plasminogen activator (tPA) antigen, C-reactive protein (CRP), and adiponectin were assessed. RESULTS: At baseline, VAT was significantly associated with PAI-1 antigen (ρ = 0.36, P < 0.001), tPA antigen (ρ = 0.29, P < 0.001), CRP (ρ = 0.18, P < 0.001), and adiponectin (ρ = -0.22, P < 0.001). Treatment with tesamorelin resulted in a significant decrease from baseline in tPA antigen (-2.2 ± 2.5 vs. -1.6 ± 2.9 ng/ml, tesamorelin vs. placebo, P < 0.05). Changes in PAI-1 antigen were not significant in the tesamorelin group compared to placebo. Among patients receiving tesamorelin, changes in inflammatory markers were associated with change in VAT (PAI-1 antigen: ρ = 0.16, P = 0.02; tPA antigen: ρ = 0.16, P = 0.02; adiponectin: ρ = -0.27, P < 0.001), and these associations remained significant when controlling for changes in insulin-like growth factor-1. CONCLUSION: In HIV patients with abdominal adiposity, tesamorelin may have a modest beneficial effect on adiponectin and fibrinolytic markers in association with changes in VAT. Further studies are needed to determine the clinical significance of these changes. These data further highlight the deleterious role of excessive VAT and the utility of strategies to improve VAT in this 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".