Alzheimer’s environmental and genetic risk scores are differentially associated with ‘g’ and δ
Bibliographic record
Abstract
Abstract Introduction We investigated the association of the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) and an AD genetic risk score (GRS) with cognitive performance. Methods The ANU-ADRI (composed of 11 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1,061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modelling 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, episodic memory, executive function and processing speed. Results A worse ANU-ADRI score was associated with poorer performance in ‘g’, δ, and each cognitive domain. A worse GRS was associated with poorer performance in δ and episodic memory. Discussion The ANU-ADRI was broadly associated with worse cognitive performance, validating its further use in early dementia risk assessment. Highlights An environmental/lifestyle dementia risk index is broadly associated with cognitive performance An Alzheimer’s genetic risk score is associated with dementia severity and episodic memory The environmental risk index is more strongly associated with dementia severity than genetic risk Research in Context Systematic Review The authors reviewed the literature using online databases (e.g. PubMed). Previous research has highlighted the need for dementia risk assessment tools to be evaluated on outcomes prior to dementia onset, such as cognitive performance. The relevant citations have been appropriately cited. Interpretation The Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) was more broadly associated with cognitive performance than Alzheimer’s genetic risk. For the ANU-ADRI, stronger effects were observed for dementia-related variance in cognitive task performance that for variance in general cognitive function. This suggests that ANU-ADRI is more specifically associated with dementia-related processes and further validates its use in early risk assessment for dementia. Future Directions Accordingly, future studies should seek to evaluate the association of the ANU-ADRI and genetic risk with AD biomarkers and longitudinal cognitive performance to evaluate differential trajectories in ‘g’ and δ.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".