Interleukin-17 receptor-A signalling: atheroprotective role in JAK2 clonal haematopoiesis
Bibliographic record
Abstract
Clonal haematopoiesis of indeterminate potential (CHIP) involves age-related acquisition and expansion of genes frequently mutated in haematologic malignancies (e.g. DNMT3A, TET2, or JAK2).1,2 JAK2 heightens cardiovascular disease (CVD) risk.1,3 The mechanism, although incompletely understood, involves pyroptosis and plasma membrane rupture, mediated by Ninjurin-1 (NINJ1), followed by release of damage-associated molecular patterns and cytokines.2,4 Moreover, individuals with JAK2-CHIP have elevated levels of circulating interleukin-17 receptor-A (IL-17RA).5 IL-17RA signalling, implicated in autoimmunity, paradoxically may have a protective role in atherogenesis.6 IL-17A produced by T helper 17 (Th17) cells binding to IL-17RA in myeloid cells may induce a TREM2 macrophage response to create a feedback loop and dampen further inflammation.7 The proteomic association between JAK2-CHIP and IL-17RA, along with the potential role of IL-17RA signalling in atherosclerosis, led us to hypothesize that IL-17RA signalling modifies CVD risk among individuals with JAK2-CHIP. We conducted our discovery study in 487 409 participants in the UK Biobank (UKB), of whom 192 had JAK2-CHIP. We validated our findings in 179 individuals between 40 and 70 years old with JAK2-CHIP at Vanderbilt BioVU using whole-genome sequencing on 250 391 participants. Clonal haematopoiesis of indeterminate potential detection across 74 canonical genes was performed using the Mutect2 somatic variant caller, with CHIP defined as mutations with a variant allele fraction (VAF) ≥ 0.02.1,8 We calculated gIL-17RA using 76 variants from a model trained with Somalogic protein measurements from the INTERVAL study (OPGS000019).9 To test how IL-17RA modifies the JAK2-CHIP and incident CVD association, we stratified participants by gIL-17RA into high, intermediate, and low tertiles. Cardiovascular disease was defined as a composite of coronary artery disease, heart failure, stroke, and peripheral vascular disease by International Classification of Diseases codes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".