CREBH normalizes dyslipidemia and halts atherosclerosis in diabetes by decreasing circulating remnant lipoproteins
Bibliographic record
Abstract
Loss-of-function mutations in the transcription factor CREB3L3 (CREBH) associate with severe hypertriglyceridemia in humans. CREBH is believed to lower plasma triglycerides by augmenting the activity of lipoprotein lipase (LPL). However, by using a mouse model of type 1 diabetes mellitus (T1DM), we found that greater liver expression of active CREBH normalized both elevated plasma triglycerides and cholesterol. Residual triglyceride-rich lipoprotein (TRL) remnants were enriched in apolipoprotein E (APOE) and impoverished in APOC3, an apolipoprotein composition indicative of increased hepatic clearance. The underlying mechanism was independent of LPL, as CREBH reduced both triglycerides and cholesterol in LPL-deficient mice. Instead, APOE was critical for CREBH's ability to lower circulating remnant lipoproteins because it failed to reduce TRL cholesterol in Apoe-/- mice. Importantly, individuals with CREB3L3 loss-of-function mutations exhibited increased levels of remnant lipoproteins that were deprived of APOE. Recent evidence suggests that impaired clearance of TRL remnants promotes cardiovascular disease in patients with T1DM. Consistently, we found that hepatic expression of CREBH prevented the progression of diabetes-accelerated atherosclerosis. Our results support the proposal that CREBH acts through an APOE-dependent pathway to increase hepatic clearance of remnant lipoproteins. They also implicate elevated levels of remnants in the pathogenesis of atherosclerosis in T1DM.
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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 | 0.001 |
| 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".