Genetic determinants of VWF clearance and FVIII binding modify FVIII pharmacokinetics in pediatric hemophilia A patients
Bibliographic record
Abstract
Abstract Factor VIII (FVIII) pharmacokinetic (PK) properties show high interpatient variability in hemophilia A patients. Although previous studies have determined that age, body mass index, von Willebrand factor antigen (VWF:Ag) levels, and ABO blood group status can influence FVIII PK, they do not account for all observed variability. In this study, we aim to describe the genetic determinants that modify the FVIII PK profile in a population of 43 pediatric hemophilia A patients. We observed that VWF:Ag and VWF propeptide (VWFpp)/VWF:Ag, but not VWFpp, were associated with FVIII half-life. VWFpp/VWF:Ag negatively correlated with FVIII half-life in patients with non-O blood type, but no correlation was observed for type O patients, suggesting that von Willebrand factor (VWF) half-life, as modified by the ABO blood group, is a strong regulator of FVIII PK. The FVIII-binding activity of VWF positively correlated with FVIII half-life, and the rare or low-frequency nonsynonymous VWF variants p.(Arg826Lys) and p.(Arg852Glu) were identified in patients with reduced VWF:FVIIIB but not VWF:Ag. Common variants at the VWF, CLEC4M, and STAB2 loci, which have been previously associated with plasma levels of VWF and FVIII, were associated with the FVIII PK profile. Together, these studies characterize the mechanistic basis by which VWF clearance and ABO glycosylation modify FVIII PK in a pediatric population. Moreover, this study is the first to identify non-VWF and non-ABO variants that modify FVIII PK in pediatric hemophilia A patients.
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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 | 0.001 | 0.001 |
| 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".