Correlation Between Tenofovir Drug Levels and the Renal Biomarkers RBP-4 and ß2M in the ION-4 Study Cohort
Bibliographic record
Abstract
BACKGROUND: Concomitant dosing of ledipasvir (LDV) and tenofovir disoproxil fumarate (TDF) results in an increased tenofovir (TFV) area under the curve (AUC). The aim of this study was to examine whether there was a correlation between the renal biomarkers retinol binding protein-4 (RBP-4) and β2 microglobulin (β2M) and tenofovir AUC. METHODS: The ION-4 trial enrolled HIV/hepatitis C virus-coinfected patients on nonpharmacologically boosted antiretroviral regimens with TDF-containing backbones. We assessed for a correlation between tenofovir AUC and urinary biomarkers and also for changes in serologic biomarkers with respect to clinically relevant changes in renal function (creatinine clearance decrease >25%, change in creatinine >0.2 mg/dL, change in proteinuria from negative/trace to ≥1+). RESULTS: Three hundred thirty-five patients were enrolled in the ION-4 study; their demographic characteristics have been previously described. Both RBP-4 and β2M exhibited positive correlations with tenofovir AUC. Baseline and study levels of RBP-4 and β2M were higher for patients with increases in urine proteinuria and an absolute creatinine increase. CONCLUSIONS: TFV exposure is associated with increased proximal tubule urine biomarkers in participants on ledipasvir/sofosbuvir and nonpharmacologically boosted TDF-based antiretroviral regimens. Baseline proximal tubule biomarkers may predict nephrotoxicity risk if events are prevalent. Further studies assessing the predictive role of these urine biomarkers may help guide medical decision-making and risk/benefit assessments in patients with risk factors for renal dysfunction.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".