Impact of Tenofovir-Based Pre-exposure Prophylaxis on Biomarkers of Bone Formation, Bone Resorption, and Bone Mineral Metabolism in HIV-Negative Adults
Bibliographic record
Abstract
Abstract Background Pre-exposure prophylaxis (PrEP) with emtricitabine (FTC)/tenofovir disoproxil fumarate (TDF) reduces the risk of HIV seroconversion but may promote bone mineral density (BMD) decline. The mechanisms of BMD decline with FTC/TDF remain unclear, and studies in HIV-positive individuals have been confounded by the effects of HIV and concomitant antiretroviral medications. We evaluated the impact of FTC/TDF on biomarkers of bone remodeling and bone mineral metabolism in HIV-negative men and women enrolled in the Partners PrEP Study. Methods In a random sample of HIV-negative participants randomized to FTC/TDF PrEP (n = 50) or placebo (n = 50), serum parathyroid hormone (PTH), bone biomarkers (C-telopeptide, procollagen 1 intact N-terminal propeptide, and sclerostin), and plasma fibroblast growth factor 23 were measured at baseline and month 24, and the percentage change was compared between groups. In a complementary analysis, we compared the change in biomarkers between participants with and without a 25% decline in glomerular filtration rate (GFR) on FTC/TDF. Results Baseline characteristics were similar between the groups (median age, 38 years; 40% women). Vitamin D insufficiency was common, but baseline GFR and PTH were in the normal range. We observed a significantly greater percent increase in serum C-telopeptide in participants randomized to FTC/TDF vs placebo (P = .03), suggesting an increase in bone remodeling. We observed no differences in the other biomarkers, or in a separate analysis comparing participants with and without a decline in GFR. Conclusions Increased bone remodeling may mediate the BMD decline observed with tenofovir-containing PrEP and antiretroviral therapy, independent of a TDF-mediated decrease in kidney function.
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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 | 0.000 |
| 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".