The VKORC1 and CYP2C9 Genotypes Significantly Affect Vitamin K Antagonist Dosing Only in Patients Aged 20 Years or Older
Bibliographic record
Abstract
Abstract Abstract 1243 INTRODUCTION: Anticoagulation with Vitamin K antagonists (VKA) is problematic due to difficulties in safely managing dosing. Polymorphisms in cytochrome P450 C9 (CYP2C9) and vitamin K epoxide reductase genes (VKORC1) have been shown to affect VKA dosing in adults. Recently we reported that, in children, the VKORC1 and CYP2C9 genotypes play insignificant roles in explaining variation in VKA dosing (Nowak-Gottl et al. Blood 2010 116:61–1–6105). Given the qualitative differences in the role these polymorphisms play in VKA dosing variation between adults and children, we were interested in determining at what age these polymorphism begin to play a role in variation in VKA dosing. Understanding at which age these genotypes begin to play a significant role will lead to only screening patients in whom there would be potential benefit from the knowledge of their VKA dose related genotypes. METHODS: We performed a prospective cohort study of patient's 1–30 years of age, who were receiving VKA and were considered to be on stable anticoagulation. Stable anticoagulation was defined as a VKA requirement remaining constant for 3 consecutive measurements after achieving the target INR (target INR 2.0–3.0). Blood samples were collected for DNA with which VKORC1 and CYP2C9 genotyping were performed. Patient demographics and data on VKA dose (mg/kg) were collected. The VKA dose (mg/kg) was transformed by taking the square root of the dose values to produce an approximate symmetric distribution. Multiple linear regression of the transformed VKA dose was used to assess its relationship with genetic and clinical/demographic variables. VKORC1 genotypes were categorized into three groups (AA, GA, and GG) and CYP2C9 genotype into two groups (any mutation 1.2, 1.3 or 2.2, and wild type 1.1). Gender and INR were not associated with VKA dose and were removed from the model. The final model used weight, VKORC1 and CYP2C9 genotypes as explanatory variables which were fit for the following age groups: 1– 13 years, 14–19 years, 20–30 years. Weight was used in the model but was highly positively correlated with age and body mass index. A summary of the fitted models (partial R2 and p value) is reported in Table I. RESULTS: A total of 91 patients were recruited (1–13 years n=18, 14–19 years n=44, 20–30 years n=29). The distribution of genotypes in the study population were consistent with previous reports in the literature (VKORC1 AA (12%) GA (43%) GG (45%); CYP2C9 1.1 (68%), 1.2 (20%), 1.3 (14%) 2.2/2.2/3.3 (2%). In the regression model in patients 1–13 years of age and 14–19 years of age, *weight explained the dosing variation significantly and far more than the two genes. The polymorphisms in VKORC1 or CYP2C9 were not significantly associated with VKA dosing in this age range and there was no significant difference in dosing among differing genotypes. In contrast, in the age group 20–30 years, weight was no longer significantly associated with variation in VKA dosing. However, ** VKORC1 and **CYP2C9 were significantly associated with variation in VKA dosing in patients over the age of 20 years. Also, in the 20–30 year age group carriers of VKORC1 AA genotype required significantly lower daily doses than GG genotypes (p-value=0.036). CONCLUSION: Assessing genotypes in patients under the age of 20 has little clinical relevance explaining no more than 12.7% of variation in VKA dosing. Weight or age have a far greater effect on dosing variation under the age of 20 years. However, in patients 20 years of age or older, the VKORC1 and CYP2C9 genotypes play a significant role, explaining 42.5% of the variation in VKA dosing. Designing models based on the differences in the various age groups is important for optimizing VKA dosing. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".