Genetic determinants of tissue factor pathway inhibitor plasma levels
Bibliographic record
Abstract
Tissue factor pathway inhibitor (TFPI) impedes early stages of the blood coagulation response, and low TFPI plasma levels increase the risk of thrombosis. TFPI plasma levels are heritable, but specific genetic determinants are unclear. We conducted a comprehensive review of genetic risk factors for TFPI plasma levels and identified 26 studies. We included 16 studies, as well as results from two unpublished genome-wide studies, in random effects meta-analyses of four commonly reported genetic variants in TFPI and its promoter (rs5940, rs7586970/rs8176592, rs10931292, and rs10153820) and 10 studies were summarised narratively. rs5940 was associated with all measures of TFPI (free, total, and activity), and rs7586970 was associated with total TFPI. Neither rs10931292 nor rs10153820 showed evidence of association. The narrative summary included 6 genes and genetic variants (P151L mutation in TFPI, PROS1, F5, APOE, GLA, and V617F mutation in JAK2) as well as a genome-wide linkage study, and suggested future research directions. A limitation of the systematic review was the heterogeneous measurement of TFPI. Nonetheless, our review found robust evidence that rs5940 and rs7586970 moderate TFPI plasma levels and are candidate risk factors for thrombosis, and that the regulation of TFPI plasma levels involves genetic factors beyond the TFPI gene.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".