Tesamorelin improves fat quality independent of changes in fat quantity
Bibliographic record
Abstract
OBJECTIVES: Fat quality and quantity may affect health similarly or differently. Fat quality can be assessed by measuring fat density on CT scan (greater density = smaller, higher quality adipocytes). We assessed the effects of tesamorelin, a growth hormone-releasing hormone analogue that reduces visceral fat (VAT) quantity in some people living with HIV (PWH), on fat density. DESIGN: Participants from two completed, placebo-controlled, randomized trials of tesamorelin for central adiposity treatment in PWH were included if they had either a clinical response to tesamorelin (VAT decrease ≥8%, ≈70% of participants) or were placebo-treated. METHODS: CT VAT and subcutaneous fat (SAT) density (Hounsfield Units, HU) were measured by a central blinded reader. RESULTS: Participants (193 responders, 148 placebo) were 87% male and 83% white. Baseline characteristics were similar across arms, including VAT (-91 HU both arms, P = 0.80) and SAT density (-94 HU tesamorelin, -95 HU placebo, P = 0.29). Over 26 weeks, mean (SD) VAT and SAT density increased in tesamorelin-treated participants only [VAT: +6.2 (8.7) HU tesamorelin, +0.3 (4.2) HU placebo, P < 0.0001; SAT: +4.0 (8.7) HU tesamorelin, +0.3 (4.8) HU placebo, P < 0.0001]. The tesamorelin effects persisted after controlling for baseline VAT or SAT HU and area, and VAT [+2.3 HU, 95% confidence interval (4.5-7.3), P = 0.001) or SAT (+3.5 HU, 95% confidence interval (2.3-4.7), P < 0.001] area change. CONCLUSION: In PWH with central adiposity who experienced VAT quantity reductions on tesamorelin, VAT and SAT density increased independent of changes in fat quantity, suggesting that tesamorelin also improves VAT and SAT quality in this group.
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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) | 0.000 | 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) | 0.004 | 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".