Risk factor profiles and cognitive function in older adults: the Brain Health Support Program Study
Bibliographic record
Abstract
BACKGROUND: Dementia risk factors coexist and interact in meaningful ways. Identifying risk profiles can inform prevention strategies. METHODS: This study examined profiles using seven modifiable risk factors and their associations with cognition and sex among older adults at risk of dementia. Risk profiles were identified using principal component analysis, and linear regressions assessed their associations with cognition. RESULTS: Three profiles emerged: (1) better sleep and social/psychological health; (2) better diet, physical activity, and vascular health; and (3) better cognitive engagement. Similar profiles were observed in women, except that better cognitive engagement grouped with poorer vascular health. In men, better cognitive engagement grouped with better vascular health and vision/hearing, while lower cognitive engagement was grouped with better sleep and social/psychological health. The lower cognitive engagement profile in men was associated with poorer cognition. DISCUSSION: Prevention strategies should target meaningful combinations of risk factors to improve dementia prevention. Further research is needed to validate and refine these profiles. Highlights: This study evaluated risk profiles using a broad range of modifiable risk factors.This is the first study to analyze such profiles in a Canadian population, stratified by sex.Risk profiles differed by sex, especially for the cognitive engagement risk factor.In men, lower cognitive engagement profile was associated with poorer cognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".