Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets
Bibliographic record
Abstract
ABSTRACT Background Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. Methods We performed two-sample Mendelian randomization (MR) and genetic colocalization to prioritize anticoagulant targets with the strongest efficacy (venous thromboembolism [VTE] prevention) and safety (low bleeding risk) profiles. We leveraged three large-scale plasma protein datasets (deCODE, n=35,559; Fenland n = 10,708; ARIC n= 7,213) and one liver gene expression dataset (n =246) to evaluate evidence for a causal effect of 26 coagulation cascade plasma proteins on VTE from a new genome-wide association meta-analysis of 44,232 VTE cases and 847,152 controls (from the UK Biobank, FinnGen and Estonian Biobank), stroke subtypes (from UK Biobank and International Stroke Genetics consortium 73,652 cases and 1,234,808 controls), bleeding outcomes (FinnGen, n=309,154) and over one million parental lifespans (UK Biobank and LifeGen consortium). Results Genetically predicted reductions in F2 blood levels were associated with lower VTE risk (OR [odds ratio] per 1 standard deviation [SD] lower F2=0.44, 95% CI=0.38-0.51, p=2.6E-28) and cardioembolic stroke risk (OR = 0.55, 95% CI=0.39-0.76, p=4.2e-04) but not with bleeding (OR = 1.13, 95% CI=0.93-1.36, p=2.2e-01). Genetically predicted F11 reduction were associated with lower risk of VTE (OR = 0.61, 95% CI=0.58-0.64, p=4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI=0.69-0.86, p=4.1e-06), but not with bleeding (OR = 1.01, 95% CI=0.95-1.08, p=7.5e-01) (Figure 3). These MR associations were concordant across the three blood protein datasets and the hepatic gene expression dataset as well as three different MR and colocalization analyses. Conclusion These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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) | 0.002 | 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".