Downregulation of adipose LPL by PAR2 contributes to the development of hypertriglyceridemia
Bibliographic record
Abstract
Lipoprotein lipase (LPL) hydrolyzes circulating triglycerides (TGs), releasing fatty acids (FA) and promoting lipid storage in white adipose tissue (WAT). However, the mechanisms regulating adipose LPL and its relationship with the development of hypertriglyceridemia are largely unknown. WAT from obese humans exhibited high PAR2 expression, which was inversely correlated with the LPL gene. Decreased LPL expression was also inversely correlated with elevated plasma TG levels, suggesting that adipose PAR2 might regulate hypertriglyceridemia by downregulating LPL. In mice, aging and high palmitic acid diet (PD) increased PAR2 expression in WAT, which was associated with a high level of macrophage migration inhibitory factor (MIF). MIF downregulated LPL expression and activity in adipocytes by binding with CXCR2/4 receptors and inhibiting Akt phosphorylation. In a MIF overexpression model, high-circulating MIF levels suppressed adipose LPL, and this suppression was associated with increased plasma TGs but not FA. Following PD feeding, adipose LPL expression and activity were significantly reduced, and this reduction was reversed in Par2-/- mice. Recombinant MIF infusion restored high plasma MIF levels in Par2-/- mice, and the levels decreased LPL and attenuated adipocyte lipid storage, leading to hypertriglyceridemia. These data collectively suggest that downregulation of adipose LPL by PAR2/MIF may contribute to the development of hypertriglyceridemia.
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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) | 0.001 | 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".