Metaanalysis of Methylenetetrahydrofolate Reductase (MTHFR) Polymorphisms Affecting Methotrexate Toxicity
Bibliographic record
Abstract
OBJECTIVE: Methotrexate (MTX) is an effective therapy for rheumatoid arthritis (RA) but it is also associated with toxicity. Pharmacogenetics is the systematic evaluation of the role of genetic differences in the efficacy and toxicity of therapeutic interventions. Because the results of small pharmacogenetic studies are often misleading, we undertook a metaanalysis of published studies to determine the role of polymorphisms in the therapeutic efficacy and toxicity of MTX. METHODS: A search of PubMed produced 55 publications, which were then reviewed for relevance to MTX toxicity and efficacy in patients with RA. To ensure that no study was missed, each polymorphism found was then entered as an independent search string and all results were reviewed again. RESULTS: Only 2 polymorphisms [C677T and A1298C in methylenetetrahydrofolate reductase (MTHFR); total 8 studies] relevant to MTX metabolism and efficacy had sufficient data to allow a metaanalysis of their association with toxicity; there was no polymorphism with sufficient data to perform a metaanalysis of efficacy. In a fixed-effects model, the C677T polymorphism was associated with increased toxicity (OR 1.71, 95% CI 1.32-2.21, p < 0.001). The A1298C polymorphism was not associated with increased toxicity (OR 1.12, 95% CI 0.79-1.6, p = 0.626). CONCLUSION: As pharmacogenetics evolves, more data are needed to assess the role of various polymorphisms for drug efficacy and toxicity. These results illustrate the paucity of reliable pharmacogenetic data on a commonly used antirheumatic drug and the potential role of pharmacogenetics in tailoring drug therapy for an individual patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.066 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".