Retrospective Data Analysis of the Influence of Age and Sex on TPMT Activity and Its Phenotype–Genotype Correlation
Bibliographic record
Abstract
Abstract Background Therapeutic efficacy and toxicity of thiopurine drugs (used as anticancer and immunosuppressant agents) are affected by thiopurine S-methyltransferase (TPMT) enzyme activity. TPMT genotype and/or phenotype is used to predict the risk for adverse effects before drug administration. Inosine triphosphate pyrophosphatase (ITPA) is another enzyme involved in thiopurine metabolism. In this study, we aimed to evaluate (a) frequency of various TPMT phenotypes and genotypes, (b) correlations between them, (c) influence of age and sex on TPMT activity, and (d) distribution of ITPA variants among various TPMT subgroups. Methods TPMT enzyme activity was determined by LC-MS/MS. TPMT (*2,*3A–C) and ITPA (rs1127354, rs7270101) genotypes were determined using a customized TaqMan® OpenArray®. Results TPMT enzyme activity varied largely (6.3–90 U/mL). The frequency of low, intermediate, normal, and high activity was 0.5% (n = 230), 13.1% (n = 5998), 86.1% (n = 39448), and 0.28% (n = 126), respectively. No significant difference in TPMT activity in relation to age and sex was found. Genotype analysis revealed the frequency of variant TPMT alleles was 6.73% (*3A, n = 344), 0.05% (*3B, n = 2), 2.22% (*3C, n = 95), and 0.42% (*2, n = 19). Analysis of paired phenotype and genotype showed that TPMT activity in samples with variant allele(s) was significantly lower than those without variant alleles. Lastly, an equal distribution of ITPA variants was found among normal and abnormal TPMT activity. Conclusions This retrospective data analysis demonstrated a clustering of variant TPMT genotypes with phenotypes, no significant influence of age and sex on TPMT activity, and an equal distribution of ITPA variants among various TPMT subgroups.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".