FRAILTY EFFECTS ON COGNITIVE CHANGES IN AGING ARE MODERATED BY DOMAIN, GENETIC RISK, AND SEX
Bibliographic record
Abstract
Introduction: Age-related frailty reflects cumulative multisystem physiological and health decline. Frailty increases risk of adverse brain and cognitive outcomes, including differential decline and dementia. In a longitudinal sample of non-demented older adults, we examine whether (a) frailty predicts trajectories across three cognitive domains (memory, executive function (EF), and speed) and (b) prediction patterns are modified by Alzheimer’s genetic risk (Apolipoprotein E (APOE)) or sex. Methods: Participants (n = 655; M age = 70.7, range 53–95; 3 waves) were from the Victoria Longitudinal Study. After computing a frailty index, we used latent growth modeling and path analysis to test frailty effects on level and change in three latent cognitive variables. We tested two potential moderators by stratifying by APOE risk (e4+, e4-) and sex. Results: First, frailty levels predicted speed and EF performance levels, and differential memory change slopes. Second, change in frailty predicted rate of decline for both speed and EF. Third, genetic moderation analyses showed that APOE risk (e4+) carriers were selectively sensitive to frailty effects on memory change. Fourth, sex moderation analyses showed that females were selectively sensitive to (a) frailty effects on memory change and (b) frailty change effects on speed change. In contrast, the frailty effects on EF change were stronger in males. Conclusion: In non-demented older adults, increasing frailty is associated with differential decline in cognitive trajectories. These effects vary by cognitive domain and are moderated by both genetic risk and sex.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".