P.003 Assessing the impact of attitudes toward aging on cognition and Alzheimer’s Disease biomarkers in older adults
Bibliographic record
Abstract
Background: Attitudes toward aging influence many health outcomes, yet their relationship with cognition and Alzheimer’s disease (AD) remains unknown. To better understand their impact on cognition and AD risk, we examined whether positive attitudes predict better cognition and diminished risk on AD biomarkers. Methods: A subsample of older adults with a family history of AD (n=54; women=39) from the McGill PREVENT-AD cohort participated in this study. Participants completed the Attitudes to Ageing Questionnaire (AAQ-24), providing three scores: psychosocial loss, psychological growth and physical change. Participants underwent cognitive testing (Rey Auditory Verbal Learning Test, RAVLT; Delis-Kaplan Executive Function System-Color Word Interference Test, D-KEFS-CWIT), and AD blood-based biomarker assessments (p-tau217, Aβ42/40). Regression models tested associations, adjusting for covariates (age, sex, education, depression, APOE4), and were Bonferroni corrected. Results: Positive attitudes were associated with better recall and recognition (RAVLT) and improved word reading, colour naming, switching, and inhibition (D-KEFS-CWIT) (p<0.00077), while negative attitudes showed the opposite pattern. Negative attitudes were correlated with lower Aβ42/40 ratios, while positive attitudes were linked to lower p-tau217 (p<0.0167). Conclusions: These findings demonstrate that positive attitudes predict better cognition and a lower risk profile for AD biomarkers, suggesting that life outlook may be an early disease feature or a risk factor.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".