Prevalence of Actionable Pharmacogenetic Genotype Frequencies, Cautionary Medication Use, and Polypharmacy in Community‐Dwelling Older Adults
Bibliographic record
Abstract
Older adults (65 years and over) frequently manage complex medication regimens and are vulnerable to adverse drug reactions and treatment inefficacies, some of which could be preventable with pharmacogenetics (PGx)-guided prescribing. This study examined the prevalence of actionable PGx genotypes (i.e., those linked to a guideline that recommends a change to standard prescribing), the use of cautionary medications (i.e., those associated with an actionable PGx genotype), polypharmacy (i.e., ≥ 5 medications simultaneously), and cytochrome P450 enzyme inhibitor and inducer use among 13,670 older adults enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Genotyping was conducted for 10 pharmacogenes with actionable PGx-based prescribing guidelines. Medication data were collected annually and assessed to identify cautionary medication use in the cohort. Most participants (98.8%) carried at least one actionable PGx genotype, with an average of three actionable genotypes per participant. VKORC1 (61.1%) and CYP2C19 (59.6%) were the most frequently observed genes with actionable genotypes. Statins (29.3%), nonsteroidal anti-inflammatory drugs (14.2%), and proton-pump inhibitors (7.9%) were the most used cautionary medications, with 27.5% of participants taking at least one medication for which PGx guidelines recommended a deviation from standard prescribing. Most (83.9%) participants reported taking a polypharmacy regimen, and 68.2% reported use of at least one cytochrome P450 enzyme inhibitor or inducer during the trial. Our findings underscore the high prevalence of actionable PGx genotypes, polypharmacy, and use of inhibitors and inducers in older adults, which collectively have the potential to inform safer and more effective prescribing practices.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".