Gender-Specific Association Between FGFR4 Gly388Arg Gene Variants and Hypertension
Bibliographic record
Abstract
AIMS: Variations in fibroblast growth factor (FGF) levels have been associated with alterations in blood pressure. FGFs mediate their function through binding to their FGF receptor (FGFR). The FGFR4 Gly388Arg polymorphism is associated with cancer and cardiovascular diseases, but its association with hypertension is unclear. Here, we aimed to investigate the association between the FGFR4 Gly388Arg polymorphism and hypertension. MATERIALS AND METHODS: Three hundred Saudi individuals (150 normotensive controls and 150 hypertensive subjects) were genotyped for the FGFR4 Gly388Arg (G/A) polymorphism using polymerase chain reaction-restriction fragment length polymorphism method. Anthropometrics, glucose and lipid profiles were measured for all subjects. The frequency of the FGFR4 Arg388 (A) allele was significantly higher in hypertensive subjects (36%) than controls (24.3%) (odds ratio [OR] 2.4, 95% confidence interval [CI] 1.5-3.83, p < 0.001). In addition, GA (OR 2.51, 95% CI 1.3-4.85, p = 0.006), AA (OR 5.58, 95% CI 1.79-11.8, p = 0.003), and GA + AA (OR 2.91, 95% CI 1.55-5.46, p = 0.001) genotypes were significantly associated with the risk of hypertension, even after adjusting for age, body mass index, and glucose. Gender stratification showed a significant association only in female subjects (p < 0.001). Furthermore, subjects with GA and AA genotypes showed significantly higher diastolic blood pressure than those with GG genotype (p = 0.004). CONCLUSION: The FGFR4 Arg388 allele is associated with an increased risk of hypertension in Saudi female subjects. The lack of association in men needs to be further investigated.
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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 | 0.000 |
| 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) | 0.003 | 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".