Identifying the optimal combinations of dementia risk factors to be targeted in multidomain lifestyle intervention in Canada
Bibliographic record
Abstract
Abstract Background The optimal combinations of modifiable risk factors to be targeted in preventive dementia trials may vary across countries and settings. We aimed to identify the combinations of modifiable risk factors associated with cognitive change in Canadian adults. Method Population Attributable Fraction analyses on 30,097 participants from the Canadian Longitudinal Study on Aging and prevalence of 2, 3 and 4 risk combinations of the 12 modifiable risk factors identified in the 2020 Dementia Lancet report were estimated to note the ten most prevalent combinations. Association between the identified combinations and 3‐year cognitive changes was examined with linear mixed models. Result Risk factor combinations were associated with greater change in memory than executive function (Table 1). Among the ten most prevalent dyad combinations, hearing loss and physical inactivity showed the largest adjusted effect on global cognition (β = ‐0.08, 95% CI ‐0.09 to ‐0.06), memory (β = ‐0.1, ‐0.18 to ‐0.14), and executive function (β = ‐0.03, ‐0.04 to ‐0.02). The triad of hearing loss, physical inactivity, and sleep disturbance was associated with the largest adjusted effect across all domains (global: β = ‐0.06, ‐0.07 to ‐0.04; memory: β = ‐0.12, ‐0.15 to ‐0.09; Executive Function: β = ‐0.03, ‐0.04 to ‐0.01). The tetrad of hearing loss, depression, physical inactivity, and sleep disturbance was associated with the largest adjusted effect on memory (β = ‐0.11; ‐0.18 to ‐0.05). The dyad that was associated with greater memory change than their individual effect was hearing loss and physical inactivity, while it was depression, physical inactivity, and sleep disturbance for the triad combination (Figure 1). The tetrad combination included obesity, depression, physical inactivity, and sleep disturbance. Conclusion Identifying risk factor clusters with the highest prevalence and potential effect size can optimize efficiency of trial design. Intervention programs should consider including hearing loss and physical inactivity or obesity, depression, physical inactivity, and sleep disturbance. These findings may help strategically tailor multidomain dementia intervention programs to have the greatest impact in the Canadian population.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".