Changes in Plasma Phospholipid Fatty Acid Patterns and their Impact on Plasma Triglyceride Levels Following Fish Oil Supplementation
Bibliographic record
Abstract
Any modifications in dietary fat intake affect the composition of cell membranes.Thus, principal component analysis (PCA) may be useful to show which variables are correlated with one another, possibly measuring the same construct.PCA allows reducing the number of observed variables, in the present study plasma phospholipid (PPL) FAs, into a smaller number of components that will account for most of the variance in comparison to one meas- AbstractThe objective of the present study was to test for associations between changes in fatty acids (FAs) and changes in plasma triglyceride (TG) levels after an n-3 FA supplementation and to test whether SNPs from the FADS gene cluster were associated with plasma FA levels or with specific FA patterns.A total of 210 subjects completed a 2-wk run-in period followed by 6-wk supplementation with 5g/d of fish oil.FA profiles of plasma phospholipids (PPLs) were obtained and 19 SNPs from the FADS gene cluster were genotyped.Principal component analysis was conducted and scores were calculated.There was an increase in EPA, DPA and DHA levels in PPLs as well as a decrease in ALA and all n-6 FA levels after the supplementation.Factor analysis suggested 4 post-n-3 FA supplementation patterns.Changes in AA, ALA, DGLA, as well as changes in total n-3 and omega-6 FAs in absolute quantities of FAs were all associated with a change in TG levels whereas the correlation remained significant only for AA and DGLA when FAs were expressed as percentage of total FAs.Several SNPs from the FADS gene cluster were associated with post-supplementation FA levels.These results suggest that FAs alone or regrouped in factors could play a role in modulating plasma TG levels after fish oil supplementation.SNPs from the FADS gene cluster interact with both FAs and/or factors to modulate TG levels.
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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 | 0.001 |
| 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".