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Record W4416936031 · doi:10.3389/fnut.2025.1620538

Common genetic polymorphisms define one-carbon metabolite responses to different forms of choline in healthy adult males

2025· article· en· W4416936031 on OpenAlexafffund
Jianzhang Dong, Gia V. Shelp, Elizabeth M. Poole, Justine Keathley, Marica Bakovic, Clara E. Cho

Bibliographic record

VenueFrontiers in Nutrition · 2025
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaBalchemUtah Agricultural Experiment Station
KeywordsMetaboliteCholineMetabolomicsAlleleGenetic variationMetabolismGenetic variants

Abstract

fetched live from OpenAlex

Background Responsiveness to nutrients can be determined by many types of variations, such as single-nucleotide polymorphisms (SNPs). Choline is an essential nutrient critical for proper organ function and exists in different forms, such as free choline or as derivatives, including phosphatidylcholine (PC). Although genetic variations in genes encoding enzymes that influence choline metabolism have been identified, little is known regarding individual responses to free choline and PC in relation to SNPs. Here, we determined the effect of different forms of choline, genotype, and their interaction on one-carbon metabolite concentrations in urine, which has utility in capturing the overall change in choline metabolism. Methods A randomized, double-blinded, crossover study was conducted in healthy adult males ( n = 37) who were provided with a standardized meal containing 600 mg choline, either as choline bitartrate (CB) or PC, or no choline (NC). Urine was collected at study baseline and pooled throughout the 6-h study duration. Choline dehydrogenase ( CHDH ) rs12676, betaine-homocysteine S-methyltransferase ( BHMT ) rs3733890, choline kinase alpha ( CHKA ) rs10791957, and phosphatidylethanolamine N-methyltransferase ( PEMT ) rs4646343 were genotyped. Results There was a main treatment effect for urinary choline change from baseline, reflective of differences in absorption by free choline versus PC ( p < 0.01). A reduction in responsiveness to CB was found with genetic variation in CHDH rs12676, manifested as lower choline oxidation ( p < 0.05), and downstream pathways in the methionine cycle ( p < 0.01), whereas a reduction in responsiveness to PC occurred with genetic variation in BHMT rs3733890 ( p < 0.05). Genetic variations in CHKA rs10791957 and PEMT rs4646343 reflected differences in the partitioning of choline in response to CB and PC ( p < 0.01). Multivariate analysis showed that groups with an accumulated number of effect alleles across all SNPs have contrasting responses to CB and PC that deviate from the patterns derived from treatment effect alone ( p < 0.05). Conclusion Unique metabolite signatures in one-carbon metabolism arise in response to supplemental intake of different forms of choline, driven by genetic variations that regulate choline homeostasis. Our findings highlight the importance of nutrient–gene interactions in deciphering the complexity of individual metabolic responses, supporting the emerging concept of precision nutrition.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.307
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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