Circulating Rather Than Intestinal PCSK9 (Proprotein Convertase Subtilisin Kexin Type 9) Regulates Postprandial Lipemia in Mice
Bibliographic record
Abstract
Objective: Increased postprandial lipemia (PPL) is an independent risk factor for atherosclerotic cardiovascular diseases. PCSK9 (Proprotein convertase subtilisin kexin type 9) is an endogenous inhibitor of the LDLR (low-density lipoprotein receptor) pathway. We previously showed that PCSK9 inhibition in mice reduces PPL. However, the relative contribution of intracellular intestinal PCSK9 or liver-derived circulating PCSK9 to this effect is still unclear. Approach and Results: To address this issue, we generated the first intestine-specific Pcsk9 -deficient (i- Pcsk9 −/− ) mouse model. PPL was measured in i- Pcsk9 −/− as well as in wild-type and streptozotocin-induced diabetic mice following treatment with a PCSK9 monoclonal antibody (alirocumab). Blocking the circulating form of PCSK9 with alirocumab significantly reduced PPL, while overexpressing human PCSK9 in the liver of full Pcsk9 −/− mice had the opposite effect. Alirocumab regulated PPL in a LDLR-dependent manner as this effect was abolished in Ldlr −/− mice. In contrast, i- Pcsk9 −/− mice did not exhibit alterations in plasma lipid parameters nor in PPL. Finally, PPL was highly exacerbated by streptozotocin-induced diabetes mellitus in Pcsk9 +/+ but not in Pcsk9 −/− mice, an effect that was mimicked by the use of alirocumab in streptozotocin-treated Pcsk9 +/+ mice. Conclusions: Taken together, our data demonstrate that PPL is significantly altered by full but not intestinal PCSK9 deficiency. Treatment with a PCSK9 monoclonal antibody mimics the effect of PCSK9 deficiency on PPL suggesting that circulating PCSK9 rather than intestinal PCSK9 is a critical regulator of PPL. These data validate the clinical relevance of PCSK9 inhibitors to reduce PPL, especially in patients with type 2 diabetes mellitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".