The cholesteryl ester transfer protein (CETP) raises cholesterol levels in the brain
Bibliographic record
Abstract
The cholesteryl ester transfer protein (CETP) is a lipid transfer protein responsible for the exchange of cholesteryl esters and triglycerides between lipoproteins. Decreased CETP activity is associated with longevity, cardiovascular health, and maintenance of good cognitive performance. Interestingly, mice lack the CETP-encoding gene and have very low levels of LDL particles compared with humans. Currently, the molecular mechanisms induced because of CETP activity are not clear. To understand how CETP activity affects the brain, we utilized CETP transgenic (CETPtg) mice that show elevated LDL levels upon induction of CETP expression through a high-cholesterol diet. CETPtg mice on a high-cholesterol diet showed up to 22% higher cholesterol levels in the brain. Using a microarray on mostly astrocyte-derived mRNA, we found that this cholesterol increase is likely not because of elevated de novo synthesis of cholesterol. However, cholesterol efflux is decreased in CETPtg mice along with an upregulation of the complement factor C1Q, which plays a role in neuronal cholesterol clearance. Our data suggest that CETP activity affects brain health through modulating cholesterol distribution and clearance. Therefore, we propose that CETPtg mice constitute a valuable research tool to investigate the impact of cholesterol metabolism on brain function.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".