Omega-3 fatty acid regulation of lipoprotein lipase and FAT/CD36 and its impact on white adipose tissue lipid uptake
Bibliographic record
Abstract
Lipid uptake by white adipose tissue (WAT) is critically important for storage of excess energy and to protect peripheral tissues from ectopic lipid deposition. When WAT becomes dysfunctional (i.e., with obesity), it is characterized by impaired lipid uptake and increased lipolysis which, together, promote whole-body dyslipidemia. Omega-3 polyunsaturated fatty acids (N-3 PUFA) are widely studied for their triacylglycerol (TAG)-lowering properties and cardiometabolic health benefits. One potential mechanism underlying these benefits is the modification of WAT lipid uptake; however, there are gaps in our understanding regarding the specific mechanisms by which N-3 PUFA function. Evidence to date suggests that N-3 PUFA promote TAG clearance by increasing lipoprotein lipase (LPL) activity and the abundance of fatty acid transporters. Specifically, N-3 PUFA have been shown to increase LPL activity through increased gene transcription and modifications of endogenously produced LPL regulators such as apolipoprotein C-II/III and angiopoietin-like proteins. This review presents and discusses the available in vitro and in vivo research to provide a comprehensive overview of N-3 PUFA regulation of WAT lipid uptake in healthy and obese contexts. Additionally, we highlight areas where more research is necessary to better understand the contribution of increased WAT lipid uptake in relation to the TAG-lowering properties associated with N-3 PUFA.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".