The Effect of Tenofovir Disoproxil Fumarate on Bone Mineral Density: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background We conducted a systematic review and meta-analysis (CRD#42017070552) to quantify the impact of oral tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) on bone mineral density (BMD) and the risk of osteoporosis, low bone mass and fractures, among people taking it as pre-exposure prophylaxis (PrEP), HIV treatment and HBV treatment. Methods We searched MEDLINE and EMBASE for randomized controlled trials published from 1997–2018 reporting BMD, osteoporosis, low bone mass and/or fractures in treatment-naive patients taking compared with not taking TDF for 48 ±4 weeks. We pooled outcomes using DerSimonian random-effects models. Results Our search yielded 5,178 abstracts, representing 3,865 articles, with 25 meeting the inclusion criteria. TDF was associated with greater BMD decline when taken as PrEP (lumbar spine: mean difference [MD]=-0.82%, 95% CI=-1.28, -0.37%, I 2 =38%; total hip: MD=-0.81%, 95% CI=-1.22, -0.40%, I 2 =48%) and HIV treatment (lumbar spine: MD=-1.62%, 95% CI=-2.30, -0.95%, I 2 =93%; total hip: MD=-1.75%, 95% CI=-2.08, -1.42%, I 2 =83%; femoral neck: MD=-1.26%, 95% CI=-2.15, -0.38%, I 2 =43%) in comparison to those not taking TDF. Eight studies reported on incident osteoporosis or low bone mass, with variable results. Pooled results from five PrEP studies showed that TDF was not associated with increased fractures compared with no PrEP (RR=1.12, 95% CI=0.752, 1.74, I 2 =26%). Conclusions TDF caused greater decreases in BMD than did comparators when used for all three indications and the magnitude of this decrease was larger for HIV treatment compared with PrEP. Fractures were not increased among PrEP patients. The clinically significant BMD decline caused by TDF and current expansion of PrEP use suggest attention to the adverse bone effects of TDF will increase in importance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".