<i>PPARα</i> L162V polymorphism alters the potential of n‐3 fatty acids to increase lipoprotein lipase activity
Bibliographic record
Abstract
Omega-3 fatty acids (FAs) may accelerate plasma triglyceride (TG) clearance by altering lipoprotein lipase (LPL) activity. Yet, the ability of n-3 FAs to increase LPL activity is dependent on transcription factors such as peroxisome proliferator-activated receptor alpha (PPARalpha). The objective was to examine the effects of n-3 FAs on LPL activity considering the occurrence of PPARalpha L162V polymorphism. First, 14 pairs of men either L162 homozygotes or carriers of the V162 allele were supplemented with n-3 FAs. Second, transient transfections in HepG2 cells, for the L162- and V162-PPARalpha variants with the peroxisome proliferator-response element from the human LPL gene, were transactivated with n-3 FAs. In vivo results demonstrate that the LPL activity increased non-significantly by 14.4% in L162 homozygotes compared with 6.6% in carriers of the PPARalpha-V162 allele, after n-3 FA supplementation. Additionally, the L162 homozygotes tended towards an inverse correlation between LPL activities and plasma TG levels. Conversely, carriers of the V162 allele showed no such relationship. In vitro data demonstrates that transcription rates of LPL tended to be higher for the L162-PPARalpha than V162-PPARalpha after n-3 FAs activation. Overall, these results indicate that n-3 FA supplementation increases the transcription rate of LPL to a greater extent in L162-PPARalpha than V162-PPARalpha.
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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) | 0.004 | 0.001 |
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".