Associations of <i>MTHFR</i> and <i>MTRR</i> Polymorphisms With Serum Lipid Levels in Chinese Hypertensive Patients
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of the methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) gene polymorphisms and their interactions with environmental factors on serum lipid levels. METHODS: We investigated totally 340 patients with essential hypertension, from Dongzhi community, Anhui, China. High-throughput TaqMan allelic discrimination assay was used for the genotyping of MTHFR C677T (Ala222Val), MTHFR A1298C (Glu429Ala), MTRR A66G (Ile22Met), and MTRR His595Tyr. RESULTS: Compared with the MTRR 66AA genotype carriers, the GG genotype carriers had lower serum total cholesterol (TC) levels (adjusted β ± standard error [SE]: -0.5 ± 0.2 mmol/L; P = .003) and low-density lipoprotein cholesterol (LDL-C) levels (adjusted β ± SE: -0.4 ± 0.2 mmol/L; P = .005). Their false discovery rate (FDR)-adjusted P values were 0.056 and 0.056, respectively. We further found that there was a statistically significant interaction between 677TT genotype and sex in their associations with LDL levels (P interaction = .020), and significant interaction between 677TT genotype and smoking on LDL levels (P interaction = .036). A similar pattern of interaction was found between 66GG and drinking on levels of TC (P interaction = .034) and LDL (P interaction = .020). However, there were no significant interactions observed after FDR adjustment. CONCLUSION: Both MTHFR and MTRR gene polymorphisms could be important genetic determinants of serum lipid levels in Chinese patients with hypertension. These findings need to be replicated in a larger sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".