Potentially Modifiable Risk Factors for Low Cognition and Dementia in Canada
Bibliographic record
Abstract
Cognitive impairment and dementia are a multifactorial process involving several pathological mechanisms beyond neurodegeneration. Dementia is characterized by severe and progressive cognitive impairment affecting daily livings. In Canada, over 600,000 people live with dementia, and its consequences are catastrophic. However, estimates suggest that 35% to 45% of dementia cases worldwide are attributable to 9 to 14 modifiable risk factors. The primary objective of this dissertation was to estimate the dementia prevention potential in Canada associated with well-established modifiable risk factors in Canada using the Canadian Longitudinal Study on Aging, a nation-wide longitudinal cohort of 30,097 adults. We found that 50% of dementia cases in Canada were attributable to 12 risk factors, with physical inactivity, obesity, hypertension, and hearing loss being the most prominent. Recent lifestyle intervention trials showed that it is possible to improve cognition by combating combinations of risk factors. To help identify risk profiles of individuals most likely to benefit from these interventions, the second objective was to identify risk factor combinations that are both highly prevalent and strongly associated with low cognition. The dyadic combination of hearing loss and physical inactivity (Cohen’s effect size d = -0.21) was most strongly associated with low cognition, followed by adding hypertension for the triad (d = -0.25), and adding obesity for the tetrad (d = -0.27). The synergistic interaction effect observed between hypertension and physical inactivity supports the rationale of joint prevention efforts on both vascular and brain health. The third objective was to identify combinations that are both highly prevalent and have the largest cognitive detrimental effect over 3 years. The combination of hearing loss with physical inactivity was the most prevalent dyad with the largest effect size (d = -0.28) and a synergistic effect. For the triad, the combination of hearing loss, physical inactivity, and hypertension had the greatest effect (d = -0.28), while the tetrad included hearing loss, physical inactivity, hypertension, and sleep disturbance (d = -0.20). Altogether, this dissertation highlights, for the first time, that up to 50% of dementia cases in Canada could be prevented by targeting specific risk factors, with hearing loss and physical inactivity being those that would yield the largest effect size if addressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".