An interaction effect between glucokinase gene variation and carbohydrate intakes modulate the plasma triglyceride response to a fish oil supplementation (818.3)
Bibliographic record
Abstract
A large inter‐individual variability in the plasma triglyceride (TG) response to fish oil consumption has been observed. A single‐nucleotide polymorphism (SNP) within glucokinase (GCK) gene has been associated with TG concentrations. Objective: To investigate the gene‐diet interaction effects between SNPs within GCK gene and dietary carbohydrate intakes on the plasma TG response to a fish oil supplementation. Methods: Two hundred and eight participants were recruited in the greater Quebec City area and completed a 6‐week fish oil supplementation (5g fish oil/day: 1.9‐2.2g EPA and 1.1g DHA). Thirteen SNPs within GCK gene were genotyped using TAQMAN methodology. Results: A gene‐diet interaction effect on the plasma TG response was observed between rs741038 and carbohydrate intake when age, sex and BMI were included in the model (p=0.008). In order to compare the plasma TG response between genotypes, participants were divided according to median carbohydrate intakes. Homozygotes of the minor C allele of rs741038 with carbohydrate intakes >48.6% had a greater decrease in their plasma TG concentrations than homozygotes C/C with carbohydrate intakes 蠄48.6% (p=0.002) and also than the other genotypes either with high or low CARB. Conclusion: The plasma TG response to a fish oil supplementation may be modulated by gene‐diet interaction effects involving GCK gene and carbohydrate intakes. Grant Funding Source : Supported by CIHR ‐ (MOP229488)
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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 | 0.000 |
| 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) | 0.002 | 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".