Cardiometabolic Profiles and Gene Expression Following Fish Oil Supplementation: Is There a Role for FADS Genotypes?
Bibliographic record
Abstract
Consumption of omega‐3 fatty acids (FAs) modifies cardiometabolic risk factors in hyperlipidemic and diabetic individuals; however, their effects in young healthy adults are unclear. We examined the impact of fish oil supplements on blood cardiometabolic markers, FA composition, eicosanoid profiles and gene expression. We then examined whether these endpoints varied with an individual's fatty acid desaturase (FADS) genotype. Young male adults (18 – 25 yrs) consumed daily fish oil supplements (up to 3g omega‐3 FAs) for 12 weeks. Significant reductions in blood triglycerides were seen by week 12. Paradoxically, two omega‐6 derived eicosanoids (PGF2α and TXB2) were increased with supplementation. Whole blood gene expression revealed no differences in key genes regulating eicosanoid production, i.e., ALOX5, ALOX12, ALOX15, COX‐2 and iPLA2. FA analysis showed increases in eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in serum and red blood cells (RBCs), and decreases in arachidonic acid (AA). Compared to major allele carriers, individuals with FADS minor allele variants experienced greater increases in EPA levels in serum and RBCs during supplementation. There were no genotype differences for DHA or AA levels. Collectively, our results showed that fish oil supplements alter the eicosanoid profile independent of changes in gene expression. Additionally, we demonstrated that changes in FA levels after supplementation are influenced by an individual's FADS genotype. Further investigations regarding the relationship between fish oil supplements, eicosanoid profiles, and FADS genotype are warranted. Grant Funding Source: Supported by NSERC
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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) | 0.001 | 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".