Cognitive reserve and resilience: Possible mechanisms of dementia prevention
Bibliographic record
Abstract
Abstract Background Predictions about future dementia prevalence vary but usually suggest large increases in numbers of people with dementia as the population ages. However, in some countries, for example, the US, UK and Netherlands, while overall numbers of people with dementia are growing as predicted, the age‐specific incidence rates of dementia have decreased substantially. This is probably due to educational, socio‐economic, health and lifestyle changes. Method We reviewed known risk factors listed in the 2017 Lancet commission: education, hypertension, hearing impairment, smoking, obesity, depression, exercise, diabetes and social contact; literature about cognitive reserve and considered effective interventions for these risks. Result Cognitive reserve is the brain resilience which allows for cognition maintenance despite neuropathological damage. Early, mid and late life factors are all important. Early‐life factors, such as education, are important for cognitive reserve and those with more education build greater cognitive reserve. Lifelong higher educational attainment also reduces dementia risk. Cognitive reserve is not static and is affected by a number of factors. Quantifying it uses proxy measures such as education, residual approaches (the variance of cognition not explained by demographic variables and brain measures) or identifying underlying brain functional. People in more cognitively demanding jobs tend to show less cognitive deterioration before, and sometimes after retirement than those in less demanding jobs. Hypertension and obesity in mid‐life are risk factors for dementia, as is hearing impairment. More frequent social contacts at age 60 years is associated with lower dementia risk over 15 years of follow‐up. Smoking in late life increases the risk of dementia. In later life people’s physical health may moderate the susceptibility to neuropathology. Those who are frail may develop Alzheimer’s disease with a lesser burden of neuropathology. Older people otherwise in good physical health can sustain a higher burden of neuropathology without cognitive impairment. There are however relatively few evidence based interventions and we will discuss what interventions we found in systematic review and what developments are still needed. Conclusion There are clinically and economically effective interventions. It is important to consider the stage of the life course when thinking about possible effective interventions to prevent 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".