Glutathione S‐transferase gene polymorphisms and vitamin C
Bibliographic record
Abstract
Genetic polymorphisms of glutathione S ‐transferases (GSTs) have been shown to affect fasting serum ascorbic acid (vitamin C) concentrations. The objective of this study was to determine whether three common polymorphisms in the GSTM1, GSTT1 and GSTP1 genes modify the serum ascorbic acid response to dietary vitamin C. Non‐smoking men and women (n= 1016) aged 20‐29 completed a 196‐item food frequency questionnaire that was used to estimate vitamin C intake, and provided a fasting blood sample for genotyping and determining serum ascorbic acid concentrations by HPLC. A significant diet‐gene interaction was observed for the GSTT1 polymorphism (p = 0.04). After adjusting for caloric intake, sex, ethnicity, season, c‐reactive protein and BMI, the Spearman correlation between dietary vitamin C and serum ascorbic acid was 0.36 (p<0.0001) for the GSTT1*0/*0 genotype and 0.14 (p=0.01) for the GSTT1*1/*1 + *1/*0 genotypes). Average serum ascorbic acid concentrations (mean ± SE) were higher among individuals with the GSTT1*1/*1 + *1/*0 than those with the GSTT1*0/*0 genotype (28.8 ± 1.1 versus 25.4 ± 1.5μmol/L) (p= 0.04, adjusted for caloric intake, sex, race, season, c‐reactive protein and BMI). No significant diet‐gene interactions were observed for GSTM1 (p=0.59) or GSTP1 (p=0.72). Our findings suggest that GSTT1 genotypes modify the serum ascorbic acid response to vitamin C intake. Grant Funding Source Advanced Foods & Materials Network
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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) | 0.002 | 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".