Alzheimer’s Environmental and Genetic Risk Scores are Differentially Associated With General Cognitive Ability and Dementia Severity
Bibliographic record
Abstract
PURPOSE: We investigated the association of the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) and an Alzheimer disease (AD) genetic risk score (GRS) with cognitive performance. METHODS: The ANU-ADRI (composed of 12 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modeling was used to evaluate the association of the ANU-ADRI and GRS with: (1) general cognitive ability (g), (2) dementia-related variance in cognitive performance (δ), and (3) verbal ability (VA), episodic memory (EM), executive function (EF), and processing speed (PS). RESULTS: A worse ANU-ADRI score was associated with poorer performance in "g" [β (SE)=-0.40 (0.02), P<0.001], δ [-0.40 (0.04), P<0.001], and each cognitive domain [VA=-0.29 (0.04), P<0.001; EM=-0.34 (0.03), P<0.001; EF=-0.38 (0.03), P<0.001; and PS=-0.40 (0.03), P<0.001]. A worse GRS was associated with poorer performance in δ [-0.08 (0.03), P=0.041] and EM [-0.10 (0.03), P=0.035]. CONCLUSIONS: The ANU-ADRI was broadly associated with worse cognitive performance, including general ability and dementia severity, validating its further use in early dementia risk assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".