Detection of early Alzheimer's disease‐like molecular alterations in a mouse model expressing human <scp>ApoE4</scp>
Bibliographic record
Abstract
The E4 allele of apolipoprotein E (ApoE4) is a key genetic risk factor for late-onset Alzheimer's disease (AD), increasing the risk of developing the disease by up to three-fold. However, the mechanisms by which ApoE4 contributes to AD pathogenesis are poorly understood. Here, we utilize a mouse model expressing either human ApoE3 or human ApoE4 to examine the effects of the E4 allele on a wide range of genetic and molecular pathways that are altered in early AD pathology. We demonstrate that ApoE4-expressing mice begin to show early differential expression of multiple genes, leading to alterations in downstream pathways related to neural cell maintenance, insulin signaling, amyloid processing and clearance, and synaptic plasticity. These alterations may result in the earlier accumulation of pathological proteins such as β-amyloid that may build up within cells, leading to the accelerated degeneration of neurons and astrocytes as observed in ApoE4-positive individuals. We also examine the metabolic effects associated with a high-fat diet (HFD) in male ApoE4-expressing mice compared with regular chow diet (RD) fed mice at different ages. We found that young ApoE4-expressing mice fed HFD developed metabolic disturbances, such as elevated weight gain, blood glucose, and plasma insulin levels that cumulatively have been observed to increase the risk of AD in humans. Taken together, our results reveal early pathways that could mediate ApoE4-related AD risk and may help identify more tractable therapeutic targets for treating ApoE4-associated AD.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".