Potentially modifiable risk factors for dementia putting the evidence together: Total population attributable fraction
Bibliographic record
Abstract
Abstract Background Our authors from around the world met to summarise the available knowledge, decide which potentially modifiable risk factors for dementia have compelling evidence and create the most comprehensive analysis to date for potentially modifiable risk factors to inform policy, give individuals the opportunity to control their risks and generate research. Method We incorporated all risk factors for which we judged there was strong enough evidence. We used the largest recent worldwide meta‐analyses for risk factor prevalence and relative risk and if not available the best data. We performed new meta‐analyses for depression and hearing loss relative risks. We used all 37,000 participants aged ≥ 45 years from HUNT, Norwegian longitudinal population‐based study, to estimate communalities (risk factors clustering) as people frequently have multiple risk factors. Four principal components explained 51% of total risk factors variance. We then calculated weighted population attributable fraction (PAF) estimates. Result We will present each potentially modifiable dementia risk factor’s prevalence, communality, relative risk, unweighted and weighted PAFs and our new lifecourse infographic. Conclusion Our results give hope suggesting many dementias can be prevented or delayed. Many risk factors are linked to deprivation, for example, where people live and exposure to air pollution, or finding affordable healthy food within walking distance and having the resources and skills to prepare it. We have more evidence that longer exposure to a risk has more effect, for example in diabetes, and that risks have more effect in otherwise vulnerable people, for example air pollution. Thus, it is important to redouble efforts to treat existing conditions in communities and people with multiple risks where approaches beyond individual treatment or behaviour change have potentially larger impact. While association is not causation, the effect on cognition of multicomponent, hearing aid and hypertensions RCTs, and naturalistic changes with reduction in air pollution, cigarette smoking, social contact, hearing and vision corrections and work cognitive stimulation, continue to suggest causal relationships with dementia. Socially disadvantaged groups in all countries are more at risk and should be prioritised for intervention. There is more evidence that risks are also modifiable for people at increased genetic risk.
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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 | 0.045 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".