A Low‐Frequency Variant in <i>MAPK14</i> Provides Mechanistic Evidence of a Link With Myeloperoxidase: A Prognostic Cardiovascular Risk Marker
Bibliographic record
Abstract
BACKGROUND: Genetics can be used to predict drug effects and generate hypotheses around alternative indications. To support Losmapimod, a p38 mitogen-activated protein kinase inhibitor in development for acute coronary syndrome, we characterized gene variation in MAPK11/14 genes by exome sequencing and follow-up genotyping or imputation in participants well-phenotyped for cardiovascular and metabolic traits. METHODS AND RESULTS: Investigation of genetic variation in MAPK11 and MAPK14 genes using additive genetic models in linear or logistic regression with cardiovascular, metabolic, and biomarker phenotypes highlighted an association of RS2859144 in MAPK14 with myeloperoxidase in a dyslipidemic population (Genetic Epidemiology of Metabolic Syndrome Study), P=2.3×10(-6)). This variant (or proxy) was consistently associated with myeloperoxidase in the Framingham Heart Study and Cardiovascular Health Study studies (replication meta-P=0.003), leading to a meta-P value of 9.96×10(-7) in the 3 dyslipidemic groups. The variant or its proxy was then profiled in additional population-based cohorts (up to a total of 58 930 subjects) including Cohorte Lausannoise, Ely, Fenland, European Prospective Investigation of Cancer, London Life Sciences Prospective Population Study, and the Genetics of Obesity Associations study obesity case-control for up to 40 cardiovascular and metabolic traits. Overall analysis identified the same single nucleotide polymorphisms to be nominally associated consistently with glomerular filtration rate (P=0.002) and risk of obesity (body mass index ≥30 kg/m(2), P=0.004). CONCLUSIONS: As myeloperoxidase is a prognostic marker of coronary events, the MAPK14 variant may provide a mechanistic link between p38 map kinase and these events, providing information consistent with current indication of Losmapimod for acute coronary syndrome. If replicated, the association with glomerular filtration rate, along with previous biological findings, also provides support for kidney diseases as alternative indications.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".