High Sensitivity C-Reactive Protein and Circulating Biomarkers of Endothelial Dysfunction in Patients with Chronic Myeloid Leukemia Receiving Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Abstract Tyrosine kinase inhibitors (TKIs) have revolutionized the management of patients with chronic myelogenous leukemia (CML); however, reports of cardiovascular (CV) toxicities caused by these drugs are concerning. While the role of CV risk factors and high sensitivity C-reactive protein (hsCRP) in the pathogenesis of CV events is known, limited data exist in CML patients. In this study, we investigated the relationship between hsCRP, CV risk factors, CML disease activity, and exposure to TKIs in 262 CML patients enrolled in a cross-sectional study. Additionally, we explored whether novel markers of vascular dysfunction were associated with exposure to specific TKIs. In multivariable analyses only body mass index (BMI) (OR: 1.15, 95% I: 1.108–1.246; P < 0.001) and CML duration (OR: 1.004, 95% CI: 1.001–1.008; P = 0.024) were independently associated with higher hsCRP. HsCRP level was not associated with CML disease activity or a specific TKI. In exploratory analyses, novel endothelial-centric markers (e.g., ET-1 and VCAM-1) were differential across the various TKIs, particularly amongst nilotinib- and ponatinib-treated patients. Using hsCRP to risk stratify CML patients may be a potential strategy for implementing aggressive CV risk factor modification in these patients while circulating markers of vascular dysfunction should be explored as potential markers of TKI-associated CV risk.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".