Polymorphisms in Genes Involved in Fatty Acid β-Oxidation Interact with Dietary Fat Intakes to Modulate the Plasma TG Response to a Fish Oil Supplementation
Bibliographic record
Abstract
A large inter-individual variability in the plasma triglyceride (TG) response to an omega-3 polyunsaturated fatty acid (n-3 PUFA) supplementation has been observed. The objective was to examine gene-diet interaction effects on the plasma TG response after a fish oil supplementation, between single-nucleotide polymorphisms (SNPs) within genes involved in fatty acid β-oxidation and dietary fat intakes. Two hundred and eight (208) participants were recruited in the greater Quebec City area. The participants completed a six-week fish oil supplementation (5 g fish oil/day: 1.9-2.2 g EPA and 1.1 g DHA). Dietary fat intakes were measured using three-day food records. SNPs within RXRA, CPT1A, ACADVL, ACAA2, ABCD2, ACOX1 and ACAA1 genes were genotyped using TAQMAN methodology. Gene-diet interaction effects on the plasma TG response were observed for SNPs within RXRA (rs11185660, rs10881576 and rs12339187) and ACOX1 (rs17583163) genes. For rs11185660, fold changes in RXRA gene expression levels were different depending on SFA intakes for homozygotes T/T. Gene-diet interaction effects of SNPs within genes involved in fatty acid β-oxidation and dietary fat intakes may be important in understanding the inter-individual variability in plasma TG levels and in the plasma TG response to a fish oil supplementation.
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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 | 0.000 |
| 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) | 0.001 | 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".