Vascular risk and cognitive trajectories in cognitively normal older adults: A Bayesian approach
Bibliographic record
Abstract
BACKGROUND: Cognitive decline poses a significant challenge to aging populations, with vascular risk factors-such as hypertension, diabetes, smoking, and hypercholesterolemia-playing a key role in its onset and progression. While prior studies using traditional statistical approaches, like linear mixed models, have demonstrated associations between vascular risk and cognitive decline, they often fail to fully account for uncertainty and variability in individual trajectories. This study aimed to estimate the influence of vascular risk factors on rates of cognitive decline in cognitively normal adults using a Bayesian statistical approach that incorporates prior knowledge and probabilistic reasoning. METHOD: We analyzed a cohort of cognitively normal adults aged 60 or older (N = 4,528) from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS v3), followed for a median of 3.8 years. We constructed a measure of vascular risk based on individuals' Framingham Risk Score (FRS), which incorporates factors such as age, systolic blood pressure, diabetes, smoking, and hypercholesterolemia. Cognitive decline was assessed as the change in Montreal Cognitive Assessment (MoCA) scores over time. Bayesian hierarchical models with random slopes and intercepts were fitted to estimate the relationship between vascular risk (high vs. low) and rates of cognitive decline, accounting for repeated measures within individuals. RESULT: Preliminary unadjusted findings indicate that individuals with high vascular risk (FRS ≥ 15 for males, ≥ 18 for females) had lower baseline MoCA scores (β = -1.12, 95% credible interval [-1.33, -0.91]) compared to those with low risk (FRS ≤ 12 for males, ≤ 10 for females). High-risk individuals also experienced a decline in MoCA scores over 6-month intervals (β = -0.03, 95% credible interval [-0.06, -0.00]), while the low-risk group exhibited stable cognitive trajectories. Most variability in cognitive trajectories was explained by differences in baseline MoCA scores, with random slopes capturing modest variability in rates of change across individuals. CONCLUSION: These findings highlight the probabilistic relationship between vascular risk and cognitive decline, underscoring variability in individual trajectories. The Bayesian approach provides a robust framework for incorporating uncertainty and may help identify early risk phenotypes to better target interventions.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".