Clinically actionable genetic variation in patients with or at high-risk of cardiovascular diseases from the Montreal Heart Institute
Bibliographic record
Abstract
Aim Pharmacogenomics enables treatments to be tailored to individual genetic profiles, optimizing efficacy while reducing adverse effects. The Clinical Pharmacogenetics Implementation Consortium (CPIC) classifies gene-drug pairs by their level of evidence. Level A and B pairs are considered actionable, indicating that prescribers should (A) or could (B) modify therapy.Materials and methods This cross-sectional study aimed to assess the prevalence of actionable CPIC variants in the Montreal Heart Institute (MHI) Hospital Cohort. Genotyping was performed at the MHI Beaulieu-Saucier Pharmacogenomics Center using Agena’s MassARRAY and Illumina’s Global Screening Array. Genes ABCG2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, DPYD, HLA-A, HLA-B, SLCO1B1, TPMT, UGT1A1, and VKORC1 were analyzed in 10,082 participants.Results Participants had an average of 3.9 genes with actionable variants, and among the full cohort, 99.7% carried at least one actionable variant. Of the 65 CPIC level A or B gene-drug pairs evaluated, 57 involved medications used by at least one participant. Nearly 40% of participants had at least one actionable gene-drug pair - that is, they were taking a medication for which they carried an actionable variant.Conclusion This study confirms the high prevalence of actionable genetic variants in individuals with or at high risk of cardiovascular diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".