A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment
Bibliographic record
Abstract
Background Thrombolytic recombinant tissue-plasminogen activator (r-tPA) treatment is the only pharmacological intervention available in the ischemic stroke acute phase. This treatment is associated with an increased risk of intracerebral hemorrhages, known as hemorrhagic transformations (HT), which worsen patient's prognosis. Objectives To investigate the association between genetically-determined natural hemostatic factors' levels, with increased risk of HT after r-tPA treatment. Patients/Methods Using genome-wide association studies data on risk of HT after r-tPA treatment, and from seven hemostatic factors (VII, VIII, von-Willebrand factor [VWF], XI, fibrinogen, plasminogen activator inhibitor-1 [PAI-1], and tissue plasminogen activator [tPA]), we performed local and global genetic correlations estimation multi-trait analyses and colocalization, and two-sample Mendelian Randomization (MR) analyses between hemostatic factors and HT. Results Local correlations identified a genomic region on chromosome 16 with shared covariance: fibrinogen-HT, p-value=2.45×10 -11 . Multi-trait analysis between fibrinogen-HT revealed 3 loci that simultaneously regulate circulating levels of fibrinogen and risk of HT: rs56026866 ( PLXND1 ), p-value=8.80×10 -10 , rs1421067 ( CHD9 ), p-value=1.81×10 -14 , and rs34780449, near ROBO1 gene, p-value=1.64×10 -08 . Multi-trait analysis between VWF-HT showed a novel common association regulating VWF and risk of HT after r-tPA at rs10942300 ( ZNF366 ), p-value=1.81×10 -14 . MR analysis did not find significant causal associations, although a nominal association was observed for FXI-HT (inverse variance weighted estimate [SE], 0.07[-0.29–0.00]; OR 0.87[0.75–1.00], raw-p-value=0.05). Conclusions We identified 4 shared loci between hemostatic factors and HT after r-tPA treatment, suggesting common regulatory mechanisms between fibrinogen and VWF levels and HT. Further research to determine a possible mediating effect of fibrinogen on HT risk are needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".