SVCT1 and SVCT2 Genotypes Modify the Association between Dietary Vitamin C and Serum Ascorbic Acid Concentrations in Men
Bibliographic record
Abstract
Clinical trials have established that individuals have different serum ascorbic acid (vitamin C) responses to dietary vitamin C. This may be due, in part, to genetic differences. In humans, vitamin C transport proteins 1 and 2 (SVCT1 and SVCT2) are required for vitamin C absorption and uptake, respectively. Our objective was to determine if polymorphisms in the SVCT1 and SVCT2 genes alter fasting serum ascorbic acid concentrations in response to dietary vitamin C. We recruited 533 women and 235 men between the ages of 20â29 yrs who were all non‐smokers. Dietary, lifestyle and anthropometric data were obtained and blood samples collected to determine fasting serum ascorbic acid concentrations by HPLC and to isolate DNA. Real‐time PCR was used to genotype for two polymorphisms in SVCT1 (rs4257763 G>A and rs6596473 G>C); and two polymorphisms in SVCT2 (rs6139591 G>A and rs2681116 C>T). Overall, the diet‐serum correlation was significant for men (r=0.21, p<0.01), but significant correlations were only observed for the following genotypes: rs4257763GG (r=0.32, p<0.01), rs6596473CC (r=0.44, p<0.01), rs6139591GG (r=0.39, p<0.01) and rs2681116 TT (r=0.66, p<0.001). No significant associations were observed for women in any of the genotype groups. Our data suggest that the correlation between dietary vitamin C and serum ascorbic acid concentrations in men differs between the genotypes of common polymorphisms that affect the absorption and uptake of ascorbic acid. Supported by the Advanced Foods & Materials Network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".