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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".