Genetic Polymorphisms of TYMS, MTHFR, ATIC, MTR, and MTRR Are Related to the Outcome of Methotrexate Therapy for Rheumatoid Arthritis in a Chinese Population
Bibliographic record
Abstract
Objective: Analysis of the relationship between single nucleotide polymorphisms (SNPs) and outcomes of methotrexate (MTX) therapy for rheumatoid arthritis (RA) in China. Patients and methods: TYMS 28 bp VNTR (rs34743033), MTHFR (677C>T (rs1801133) and 1298A>C (rs1801131)), ATIC 347C>G (rs2372536), MTR A2756G (rs1805087) and MTRR 66A>G (rs1801394) enzyme proteins may be related to the outcomes of MTX therapy, according to our previous meta-analysis. A total of 162 patients with RA were included in our study. SNPs were evaluated using polymerase chain reaction (PCR). Disease Activity Score 28 (DAS28) was used to evaluate the clinical response, and adverse drug reactions (ADRs) were collected after physical examinations of the patients. Results: The MTHFR 677C>T gene showed a relationship with the ADRs of MTX in the Recessive model (TT vs (CC+CT)) (p=0.04, OR=2.20, 95% CI: 1.01, 4.77). In the Codominant model (CT vs (CC+TT)), the MTHFR 677C>T gene also showed a trend of association with ADRs (p=0.08, OR=0.52, 95% CI: 0.25, 1.08). No significant difference was found between TYMS, MTHFR, ATIC, MTR and MTRR gene polymorphisms and the RA response or ADRs related to MTX in our study. Conclusion: Our results showed that the MTHFR (677C>T (rs1801133) TT genotype is associated with ADRs to MTX in Chinese RA patients. Other SNPs, including TYMS 28bp VNTR (rs34743033), MTHFR (677C>T (rs1801133) and 1298A>C (rs1801131)), ATIC 347C>G (rs2372536), MTR A2756G (rs1805087) and MTRR 66A>G (rs1801394) gene polymorphisms, were not associated with MTX treatment outcomes. Further studies are required to validate these findings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".