<i>Apolipoprotein E</i>ε4 Does Not Modulate Amyloid-β–Associated Neurodegeneration in Preclinical Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Among cognitively healthy older individuals, the relationship among the 2 hallmark proteins of AD (Aβ and τ APOE ε4) and neurodegeneration is not well-understood. Here, we investigated the relationship between Aβ, p-τ, and APOE ε4 on longitudinal brain atrophy in preclinical AD. MATERIALS AND METHODS: We examined 107 cognitively healthy older adults who underwent longitudinal MR imaging and baseline lumbar puncture. Within the same linear mixed-effects model, we concurrently investigated main and interactive effects between the APOE ε4 genotype and CSF Aβ(1-42), CSF p-τ and CSF Aβ(1-42), and the APOE ε4 genotype and CSF p-τ on entorhinal cortex atrophy rate. We also examined the relationship of APOE ε4, CSF p-τ, and CSF Aβ(1-42) on the atrophy rate of other AD-vulnerable neuroanatomic regions. RESULTS: The full model with main and interactive effects demonstrated a significant interaction only between CSF p-τ and CSF Aβ(1-42) on entorhinal cortex atrophy rate, indicating elevated atrophy with time in individuals with increased CSF p-τ and decreased CSF Aβ(1-42). The APOE ε4 genotype was significantly and specifically associated with CSF Aβ(1-42). However, the interaction between the APOE ε4 genotype and either CSF Aβ(1-42) or CSF p-τ on entorhinal cortex atrophy rate was not significant. We found similar results in other AD-vulnerable regions. CONCLUSIONS: On the basis of our findings and building on prior experimental evidence, we propose a model of the pathogenic cascade underlying preclinical AD in which APOE ε4 primarily influences the pathology of Alzheimer disease via Aβ-related mechanisms, and in turn, Aβ-associated neurodegeneration occurs only in the presence of p-τ.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".