Attitudes to aging are associated with cognition and biomarker status in older adults at risk for Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Attitudes toward aging influence longevity as well as physical and psychological health, yet their relationship with cognition and Alzheimer's disease (AD) remains largely unknown. We hypothesized that positive attitudes toward aging would predict better cognition and diminished risk on AD blood-based biomarker profiles in older adults at risk for AD. Understanding the influence of subjective outlook on AD risk and cognitive resilience could provide valuable insights into disease mechanisms. METHOD: A subsample of older adults (n = 54; 39 women) with a family history of AD from the PREVENT-AD longitudinal cohort participated in this study. Participants completed the Attitudes to Ageing Questionnaire (AAQ-24), which provided three scores: psychosocial loss, psychological growth, and physical change. Cognitive assessments included episodic memory (Rey Auditory Verbal Learning Test, RAVLT) and executive functions (Delis-Kaplan Executive Function System-Color Word Interference Test, D-KEFS-CWIT). The AD blood-based biomarkers p-tau217 and Aβ42/40 ratios were also measured. A Weighted Least Squares regression model was used to correct for heteroscedasticity and to test whether attitudes toward aging predicted cognitive performance and plasma biomarker profiles. All analyses were corrected for multiple comparisons using the Bonferroni method, with covariates of non-interest, including age, sex, education, depression, and APOE4 carriership status, incorporated in the model. RESULT: Positive attitudes to aging were associated with better recall and recognition (RAVLT) and improved executive function including switching and inhibitory control (D-KEFS-CWIT) (p <0.00077, Bonferroni corrected), while negative attitudes showed the opposite pattern. In addition, negative attitudes to aging were associated with a lower Aβ42/40 ratio, while positive attitudes were linked to lower p-tau217 (p <0.0167, Bonferroni corrected). CONCLUSION: These findings indicate that positive attitudes toward aging are significantly linked to both enhanced cognitive performance and a lower risk profile in AD blood-based biomarkers. Our results suggests that life outlook could either be an early indicator of disease or, alternatively, a modifiable AD 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 | 0.003 |
| 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 | 0.001 | 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".