Modifiable Risk Factors Discriminate Memory Trajectories in Non-Demented Aging: Precision Factors and Targets for Promoting Healthier Brain Aging and Preventing Dementia
Bibliographic record
Abstract
BACKGROUND: Non-demented cognitive aging trajectories are characterized by vast level and slope differences and a spectrum of outcomes, including dementia. OBJECTIVE: The goal of AD risk management (and its corollary, promoting healthy brain aging) is aided by two converging objectives: 1) classifying dynamic distributions of non-demented cognitive trajectories, and 2) identifying modifiable risk-elevating and risk-reducing factors that discriminate stable or normal trajectory patterns from declining or pre-impairment patterns. METHOD: Using latent class growth analysis we classified three episodic memory aging trajectories for n = 882 older adults (baseline Mage=71.6, SD=8.9, range = 53-95, female=66%): Stable (SMA; above average level, sustained slope), Normal (NMA; average level, moderately declining slope), and Declining (DMA; below average level, substantially declining slope). Using random forest analyses, we simultaneously assessed 17 risk/protective factors from non-modifiable demographic, functional, psychological, and lifestyle domains. Within two age strata (Young-Old, Old-Old), three pairwise prediction analyses identified important discriminating factors. RESULTS: Prediction analyses revealed that different modifiable risk predictors, both shared and unique across age strata, discriminated SMA (i.e., education, depressive symptoms, living status, body mass index, heart rate, social activity) and DMA (i.e., lifestyle activities [cognitive, self-maintenance, social], grip strength, heart rate, gait) groups. CONCLUSION: Memory trajectory analyses produced empirical classes varying in level and slope. Prediction analyses revealed different predictors of SMA and DMA that also varied by age strata. Precision approaches for promoting healthier memory aging-and delaying memory impairment-may identify modifiable factors that constitute specific targets for intervention in the differential context of age and non-demented trajectory patterns.
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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 | 0.001 |
| 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".