Meta-Analysis of NUDT15 Genetic Polymorphism on Thiopurine-Induced Myelosuppression in Asian Populations
Bibliographic record
Abstract
Backgound: The high incidence of thiopurine-induced myelosuppression in Asians is known to be attributable to genetic variation in thiopurine metabolism. A quantitative synthesis to summarize the genetic association with thiopurine-induced myelosuppression in Asians was therefore conducted. Methods: A Literature search was performed from January 2016 to May 2021 in the following databases: PubMed, Web of Science, and Embase and addition search included the studies from Zhang et al. Two reviewers independently extracted the following data: the author’s name, year of publication, ethnicity, drugs, diseases, genetic polymorphisms, onset, type of myelosuppression and results of Hardy-Weinberg equilibrium. The Newcastle-Ottawa Scale was used to assess the quality of the studies. The pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated to evaluate the associations of NUDT15 and the risk of thiopurine-induced myelosuppression stratified by onset and type of myelosuppressive. Subgroup analysis by NUDT15 genetic polymorphisms was performed. Results: A total of 30 studies was included in this meta-analysis. The overall OR for the relationship between NUDT15 genetic polymorphisms and thiopurine-induced early onset of leukopenia and neutropenia in Asian populations were 11.43 (95% CI 7.11–18.35) and 16.35 (95% CI 10.20–26.22). Among NUDT15 polymorphisms, NUDT15*3 showed a significantly increased risk of early leukopenia (OR 15.31; 95% CI 9.65–24.27) and early neutropenia (OR 15.85; 95% CI 8.80–28.53). A significantly higher thiopurine-induced early neutropenic risk was also found for NUDT15*2 (OR 37.51; 95% CI 1.99–708.69). Whereas, NUDT15*5 and NUDT15*6 variants showed a lower risk of leukopenia. Conclusion: This study suggests that NUDT15*3 and NUDT15*2 are important genetic markers of thiopurine-induced early onset of myelotoxicity in Asians, therefore, early detection of these variants before initiating thiopurine therapy is necessary.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".