Association of education with Aβ burden in preclinical familial and sporadic Alzheimer disease
Bibliographic record
Abstract
Objective To determine whether years of education and the ε4 risk allele at APOE influence β-amyloid (Aβ) pathology similarly in asymptomatic individuals with a family history of sporadic Alzheimer disease (AD) and presymptomatic autosomal dominant AD mutation carriers. Methods We analyzed cross-sectional data from 106 asymptomatic individuals with a parental history of sporadic AD (PREVENT-AD cohort; age 67.28 ± 4.72 years) and 117 presymptomatic autosomal dominant AD mutation carriers (DIAN cohort; age 35.04 ± 9.43 years). All participants underwent structural MRI and Aβ-PET imaging. In each cohort we investigated the influence of years of education, APOE ε4 status, and their interaction on Aβ-PET. Results Asymptomatic individuals with a parental history of sporadic AD showed increased Aβ burden associated with APOE ε4 carriage and lower level of education, but no interaction between these. Presymptomatic mutation carriers of autosomal dominant AD showed no relation between APOE ε4 and Aβ burden, but increasing level of education was associated with reduced Aβ burden. The association between educational attainment and Aβ burden was similar in the 2 cohorts. Conclusions While the APOE ε4 allele confers increased tendency toward Aβ accumulation in sporadic AD only, protective environmental factors, like increased education, may promote brain resistance against Aβ pathology in both sporadic and autosomal dominant AD.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".