Molecular determinants of thrombosis recurrence risk across venous thromboembolism subtypes
Bibliographic record
Abstract
ABSTRACT: Venous thromboembolism (VTE) is a frequent (annual incidence of 1-2 per 1000 individuals) and potentially life-threatening (case-fatality rate up to 10%) disease. VTE is associated with serious short-term and long-term complications, including a recurrence rate approaching 20% within 5 years. Anticoagulant therapy, the mainstay of VTE treatment, drastically reduces the risk of early VTE recurrence, but it exposes patients to a substantial risk of bleeding. We analyzed the genomic architecture of VTE recurrence using data from 6355 patients across 8 cohorts (including 1775 recurrences), enriched by subgroup analyses, according to sex and clinical manifestation of first VTE, which led to the identification of 28 molecular markers. Through genome-wide association studies, we identified 1 locus associated with VTE recurrence, GPR149/MME. Among all variants known to be associated with first VTE, KNG1, and FGG were associated with recurrence. Additionally, Mendelian randomization analyses identified 7 proteins as risk factors for recurrence: elevated plasma levels of coagulation factor XI, coagulation factor VIII, von Willebrand factor, histo-blood group ABO system transferase, and Golgi membrane protein 2; and decreased levels of proprotein convertase subtilisin/kexin 9 and pro-interleukin-16. Subgroup analyses revealed 18 molecular determinants associated with VTE recurrence, with notable differences between subgroups. For example, the exonic variant SLC4A1 p.Glu40Lys was significantly associated in patients who experienced pulmonary embolism but showed no effect in those with deep vein thrombosis. These findings emphasize the role of specific genetic loci and protein pathways in influencing VTE recurrence and provide valuable insights into potential therapeutic targets. Further research is needed to clarify the biological mechanisms driving these associations.
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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".