Fibrinogen and clot-related phenotypes determined by fibrinogen polymorphisms: Independent and IL-6-interactive associations
Bibliographic record
Abstract
Interleukin-6 (IL-6) induces the expression of fibrinogen, and polymorphic variation within the fibrinogen genes is believed to alter the magnitude of this expression. The identification of the functional relevance of individual fibrinogen single nucleotide polymorphisms (SNPs) has been hindered by the high linkage disequilibrium (LD) reported in the European fibrinogen gene locus. This study investigated two novel and 12 known fibrinogen SNPs of potential functional relevance, in 2010 Tswana individuals known to have low LD. We aimed to identify functional polymorphisms that contribute to clot-related phenotypes and total and γ' fibrinogen concentrations independently and through their interaction with IL-6, by taking advantage of the high fibrinogen and IL-6 concentrations and the low LD reported in black South Africans. Fibrinogen was significantly associated with IL-6, thereby mediating associations of IL-6 with clot formation and structure, although attenuating the association of IL-6 with clot lysis time. None of the common European fibrinogen haplotypes was present in this study population. Putative functional fibrinogen SNPs FGB-rs7439150, rs1800789 (-1420G/A) and rs1800787 (-148C/T) were significantly associated with fibrinogen concentration and altered clot properties, with several associations significantly influenced by IL-6 concentrations. The impact of harbouring several minor fibrinogen SNP alleles on the association of IL-6 and fibrinogen concentration was cumulative, with possession of each additional minor allele showing a stronger relationship of IL-6 with fibrinogen. This was also reflected in differences in clot properties, suggesting potential clinical relevance. Therefore, when investigating the effect of fibrinogen genetics on fibrinogen concentrations and CVD outcome, the possible interactions with modulating factors and the fact that SNP effects seem to be additive should be taken into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".