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Record W2016645059 · doi:10.1159/000314597

Vitamin C Transporter Gene Polymorphisms, Dietary Vitamin C and Serum Ascorbic Acid

2009· article· en· W2016645059 on OpenAlexafffundabout
Leah E. Cahill, Ahmed El‐Sohemy

Bibliographic record

VenueLifestyle Genomics · 2009
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaConnaught FundUniversity of TorontoAdvanced Foods and Materials Network
KeywordsAscorbic acidVitamin CGenotypeVitaminInternal medicineBiologyEndocrinologyPolymorphism (computer science)BiochemistryGeneMedicineFood science

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Vitamin C transporter proteins SVCT1 and SVCT2 are required for the absorption and transport of vitamin C in humans. This study aims to determine whether common SVCT genotypes modify the association between dietary vitamin C and serum ascorbic acid. METHODS: Non-smoking men and women (n=1,046) aged 20-29 were participants of the Toronto Nutrigenomics and Health Study. Overnight fasting blood samples were collected to determine serum ascorbic acid concentrations by HPLC and to genotype for two SVCT1 (rs4257763 and rs6596473) and two SVCT2 (rs6139591 and rs2681116) polymorphisms. RESULTS: No diet-gene interactions were observed for the vitamin C transporter polymorphisms, however, the average (mean+/-SE) serum ascorbic acid concentrations differed between rs4257763 genotypes (GG: 24.4+/-1.3, GA: 26.8+/-1.1, AA: 29.7+/-1.4 micromol/l; p=0.002). For this polymorphism, the correlation between dietary vitamin C and serum ascorbic acid was only significant in subjects with a G allele. The SVCT2 polymorphisms also appeared to modify the strength of the diet-serum correlation. CONCLUSIONS: Our findings demonstrate that genetic variation in SVCT1 can influence serum ascorbic acid concentrations and that SVCT1 and SVCT2 genotypes modify the strength of the correlation between dietary vitamin C and serum ascorbic acid.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.254
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations66
Published2009
Admission routes3
Has abstractyes

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