Profiles of β-Amyloid Peptides and Key Secretases in Brain Autopsy Samples Differ with Sex and APOE ε4 Status: Impact for Risk and Progression of Alzheimer Disease
Bibliographic record
Abstract
The APOE ε4 allele was originally reported to contribute to risk of Alzheimer's disease (AD) in women, yet male and female AD patient-derived data are routinely pooled. Histopathological hallmarks of AD include neurofibrillary tangles centered on hyperphosphorylated Tau and plaques composed of the β-amyloid (Aβ) peptide that is derived by sequential secretase-mediated cleavage of the Amyloid Protein Precursor (APP). We chose to examine profiles of Aβ(1-40), Aβ(1-42), and N-truncated (i.e., p3-related) fragments in the plaque-associated fraction of autopsied cortical and corresponding hippocampal samples from donors with a diagnosis of early-onset (EOAD) and late-onset (LOAD) AD. Levels of Aβ(1-40), Aβ(1-42), and the p3 fragment-enriched pool were increased in EOAD and LOAD samples, and correlated well within -but not between- regions. Counterintuitively, these increases were similar regardless of the AD donor's APOE ε4 status. Focusing on the donor's sex and APOE ε4 status as nominal variables (i.e., omitting diagnosis from the stratification) revealed that increases in Aβ peptides were specific to female carriers of the ε4 allele and correlated with the proportional expression of BACE1/β-secretase and ADAM10/α-secretase in the cortex and with nicastrin (γ-secretase) expression in the hippocampus. These data preliminarily support the possibility that AD follows distinct amyloidogenic processes in males and females, and that the APOE ε4 allele exerts a major influence on the disease process, particularly in women. This knowledge could significantly impact the (re)interpretation of unsuccessful outcomes of clinical interventions targeting either Aβ peptides directly or the secretases implicated in APP processing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".