The protective effects of the methylenetetrahydrofolate reductase rs1801131 variant among Saudi smokers
Bibliographic record
Abstract
Methylenetetrahydrofolate reductase (MTHFR) polymorphism plays a fundamental role in susceptibility to various diseases, including cancers and autoimmune diseases. In the current study, we aimed to compare genotype and allele frequency variations of rs1801131, one of the most common variants found in the MTHFR gene, among Saudi smokers and non-smokers. We hypothesized that genetic variations of this gene are responsible for many diseases, particularly those caused by cigarette smoking (CS) such as pulmonary diseases, oral cancer and lung cancer. We performed a case-control study on a sample of 235 healthy smokers and 239 healthy non-smokers in Saudi Arabia. The rs1801131 SNP genotypes were determined using a genotyping assay and multiple in silico algorithmic software programs were used to identify the effects and structural functions of the rs1801131 (Glu429Ala) mutation. Using chi-squared tests, we found that, among smokers, TG and GG genotype carriers had 0.209-fold (OR = 0.209, P < 0.005) and 0.427-fold (OR = 0.427, P = 0.003) lower risks of CS-related disease compared to TT reference genotypes. In addition, this protective effect was observed in Saudi smokers independent of age, gender, types of smoking, duration, and average daily smoking consumption. Filling a research gap by exploring this topic in the Saudi population, the current findings indicate that genotype and allele distributions of MTHFR rs1801131 polymorphism present fundamental protective effects against the risk of CS-related disease. These findings should be verified in future studies with larger sample sizes, different ethnicities, and patients suffering from CS-related diseases, such as oral cancer and lung cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".