Polymorphisms, de novo lipogenesis, and plasma triglyceride response following fish oil supplementation
Bibliographic record
Abstract
Interindividual variability in the response of plasma triglyceride concentrations (TG) following fish oil consumption has been observed. Our objective was to examine the associations between single-nucleotide polymorphisms (SNPs) within genes encoding proteins involved in de novo lipogenesis and the relative change in plasma TG levels following a fish oil supplementation. Two hundred and eight 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 eicosapentaenoic acid and 1.1 g docosahexaenoic acid. SNPs within SREBF1, ACLY, and ACACA genes were genotyped using TAQMAN methodology. After correction for multiple comparison, only two SNPs, rs8071753 (ACLY) and rs1714987 (ACACA), were associated with the relative change in plasma TG concentrations (P = 0.004 and P = 0.005, respectively). These two SNPs explained 7.73% of the variance in plasma TG relative change following fish oil consumption. Genotype frequencies of rs8071753 according to the TG response groups (responders versus nonresponders) were different (P = 0.02). We conclude that the presence of certain SNPs within genes, such as ACLY and ACACA, encoding proteins involved in de novo lipogenesis seem to influence the plasma TG response following fish oil consumption.
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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 | 0.001 | 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".