A review of the literature on wellbeing and modifiable dementia risk factors
Bibliographic record
Abstract
Wellbeing-defined broadly as experiencing one's life as enjoyable and fulfilling-has been associated with lower risk for Alzheimer's disease and related dementias. The mechanisms underlying this association are largely unknown. However, prior research and theory suggest that wellbeing impacts health behaviors and biological systems that are relevant to cognitive and brain health. Several of these factors have also been identified by the 2020 Lancet Commission on Dementia Prevention, Intervention, and Care as modifiable dementia risk factors. In the current review, we summarize and evaluate the evidence for associations between wellbeing and each of the 12 Lancet Commission risk factors. We found relatively consistent evidence for associations between higher wellbeing and lower levels of most of the risk factors: physical inactivity, social isolation, smoking, depression, hypertension, diabetes, hearing loss, traumatic brain injury, and air pollution. By contrast, we found evidence for only modest associations between wellbeing and education and mixed evidence for associations of wellbeing with alcohol use and body weight. Although most of the reviewed evidence was observational, longitudinal and experimental evidence suggests that many of the observed associations are likely bidirectional. These findings suggest that modifiable dementia risk factors may be mediators (i.e., intermediate steps in the causal chain) and/or confounders (i.e., variables that impact both wellbeing and dementia, and thus could induce a spurious association) of the association between wellbeing and dementia. We conclude by discussing next steps to test mediation hypotheses and to account for potential confounding in the relation between wellbeing and dementia.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".