Angiopoietin-2 is associated with sickle cell complications, including stroke risk, and decreases with hydroxyurea therapy
Bibliographic record
Abstract
Hydroxyurea reduces morbidity and mortality in children with sickle cell anemia (SCA). The endothelium is central to SCA-related complications including stroke. However, hydroxyurea’s impact on the endothelium is not well described. To address this gap, we measured plasma levels of endothelial activation markers (angiopoietin-2, P-Selectin, sE-Selectin, sICAM-1, sVCAM-1) by ELISA following initiation of hydroxyurea therapy. Samples were collected from Ugandan children with SCA enrolled in a clinical trial evaluating hydroxyurea versus placebo (NOHARM, ClinicalTrials.gov: NCT01976416). Samples were collected at enrollment, and then after 2-, 4- and 12-months follow-up. Longitudinal changes in biomarkers levels were evaluated using linear mixed effects models. Transcranial Doppler (TCD) velocities were measured at 10-12 months follow-up to assess cerebral blood flow and primary stroke risk. Mediation analysis was used to explore causal pathways of hydroxyurea-mediated effects on TCD velocities. In total, 798 plasma samples were tested from 205 children (mean enrollment age: 2.2 years). At enrollment, higher levels of angiopoietin-2 were associated with a previous medical history of dactylitis, vaso-occlusive crises, acute chest syndrome, and transfusion (p<0.05 for all). Hydroxyurea therapy at a fixed dose of 20 mg/kg/day decreased plasma angiopoietin-2, P-Selectin, and sE-Selectin levels over the study period (p<0.05 for all). Angiopoietin-2 and sE-Selectin were associated with higher TCD velocities. Mediation analysis suggested that hydroxyurea decreases TCD velocities through an increase in fetal and total hemoglobin. Increased fetal and total hemoglobin, and decreased white blood cell count may decrease TCD velocity in part through an angiopoiten-2 mediated pathway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".