Effect of genetic and vascular risk factors on rates of cognitive decline in early-onset and late-onset Alzheimer's disease
Bibliographic record
Abstract
Background Although previous studies have shown that cognitive decline in Alzheimer's disease (AD) is associated with various risk factors, they primarily focused on late-onset AD (LOAD). Objective We aim to evaluate the differential impact of risk factors on the cognitive decline between early-onset AD (EOAD, onset < 65 years) and LOAD (onset ≥ 65 years) and explore the longitudinal effect of Apolipoprotein E allele 4 ( APOE ε4) on cortical atrophy in both cohorts. Methods Using data from 212 EOAD and 1101 LOAD participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), we conducted multivariable mixed-effect models to evaluate the impact of APOE ε4, education, hypertension, diabetes, dyslipidemia, and body mass index on cognitive performance. Preprocessed MRI data were utilized for longitudinal parametric mapping. Results APOE ε4 carriers in both groups showed significantly accelerated declines in language, verbal memory, executive function, and general cognition. By controlling other significant risk factors, APOE ε4 carriers showed faster declines in language and verbal memory in both groups. Females exhibited accelerated declines in Language and verbal memory in the EOAD and LOAD cohorts respectively. LOAD individuals with hypertension showed faster declines while overweight and obese participants displayed slower declines in both cohorts across all domains except visuospatial. Notably, APOE ε4 status was associated with longitudinal cortical atrophy in the LOAD cohort but not in the EOAD cohort. Conclusions Known risk factors for AD were associated with cognitive decline in both EOAD and LOAD cohorts.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".