Genomic Landscape of Thrombosis Recurrence Risk Across Venous Thromboembolism Subtypes
Bibliographic record
Abstract
Abstract Venous thromboembolism (VT) is a frequent (annual incidence of 1 to 2 per 1,000) and potentially life-threatening (case-fatality rate up to 10%) disease. VT is associated with serious short-term and long-term complications including a recurrence rate of approximately 20% within five years. Anticoagulant therapy, the mainstay of VT treatment, drastically reduces the risk of early VT recurrence, but it exposes patients to a substantial risk of bleeding. We analysed the genomic architecture of VT recurrence using data from 6,571 patients across eight cohorts, 1,816 of whom experienced recurrence, with a particular focus on the clinical manifestation of the type of first VT event. Through genome-wide association studies (GWAS), we identified three loci significantly associated (P<5×10 -8 ) with VT recurrence in the general VT population: GPR149/MME , L3MBTL4 , and THSD7B . Protein Quantitative Trait Locus and Mendelian Randomization analyses further identified elevated plasma levels of coagulation factor XI and GOLM2 as risk factors for recurrence, while decreased levels of PCSK9 and pro-IL16 were linked to reduced VT recurrence risk. Subgroup analyses revealed 18 loci associated with VT recurrence, with notable differences between pulmonary embolism (PE) and deep vein thrombosis (DVT). For example, the exonic variant SLC4A1 p.Glu40Lys was significantly associated with recurrence in PE patients (Hazard Ratio (HR)=3.23, P=9.7×10 -12 ) but showed no effect in DVT (HR=1.00, P=0.98). These findings emphasize the role of specific genetic loci and protein pathways in influencing VT recurrence and provide valuable insights into potential therapeutic targets. Further research is needed to clarify the biological mechanisms driving these associations. Key points 29 loci/proteins associated with VT recurrence risk. The genomic architecture of VT recurrence risk varies based on the initial clinical presentation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".