Rare Variation in Drug Metabolism and Long QT Genes and the Genetic Susceptibility to Acquired Long QT Syndrome
Bibliographic record
Abstract
BACKGROUND: Acquired long QT syndrome (aLQTS) is a serious unpredictable adverse drug reaction. Pharmacogenomic markers may predict risk. METHODS: Among 153 aLQTS patients (mean age 58 years [range, 14-88], 98.7% White, 85.6% symptomatic), computational methods identified proteins interacting most significantly with 216 QT-prolonging drugs. All cases underwent sequencing of 31 candidate genes arising from this analysis or associating with congenital LQTS. Variants were filtered using a minor allele frequency <1% and classified for susceptibility for aLQTS. Gene-burden analyses were then performed comparing the primary cohort to control exomes (n=452) and an independent replication aLQTS exome sequencing cohort. RESULTS: In 25.5% of cases, at least one rare variant was identified: 22.2% of cases carried a rare variant in a gene associated with congenital LQTS, and in 4% of cases that variant was known to be pathogenic or likely pathogenic for congenital LQTS; 7.8% cases carried a cytochrome-P450 (CYP) gene variant. Of 12 identified CYP variants, 11 (92%) were in an enzyme known to metabolize at least one culprit drug to which the subject had been exposed. Drug-drug interactions that affected culprit drug metabolism were found in 19% of cases. More than one congenital LQTS variant, CYP gene variant, or drug interaction was present in 7.8% of cases. Gene-burden analyses of the primary cohort compared to control exomes (n=452), and an independent replication aLQTS exome sequencing cohort (n=67) and drug-tolerant controls (n=148) demonstrated an increased burden of rare (minor allele frequency<0.01) variants in CYP genes but not LQTS genes. CONCLUSIONS: Rare susceptibility variants in CYP genes are emerging as potentially important pharmacogenomic risk markers for aLQTS and could form part of personalized medicine approaches in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".