HDL from an Alzheimer's disease perspective
Bibliographic record
Abstract
PURPOSE OF REVIEW: We review current knowledge regarding HDL and Alzheimer's disease, focusing on HDL's vasoprotective functions and potential as a biomarker and therapeutic target for the vascular contributions of Alzheimer's disease. RECENT FINDINGS: Many epidemiological studies have observed that circulating HDL levels associate with decreased Alzheimer's disease risk. However, it is now understood that the functions of HDL may be more informative than levels of HDL cholesterol (HDL-C). Animal model studies demonstrate that HDL protects against memory deficits, neuroinflammation, and cerebral amyloid angiopathy (CAA). In-vitro studies using state-of-the-art 3D models of the human blood-brain barrier (BBB) confirm that HDL reduces vascular Aβ accumulation and attenuates Aβ-induced endothelial inflammation. Although HDL-based therapeutics have not been tested in clinical trials for Alzheimer's disease , several HDL formulations are in advanced phase clinical trials for coronary artery disease and atherosclerosis and could be leveraged toward Alzheimer's disease . SUMMARY: Evidence from human studies, animal models, and bioengineered arteries supports the hypothesis that HDL protects against cerebrovascular dysfunction in Alzheimer's disease. Assays of HDL functions relevant to Alzheimer's disease may be desirable biomarkers of cerebrovascular health. HDL-based therapeutics may also be of interest for Alzheimer's disease, using stand-alone or combination therapy approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".