Resilience to Alzheimer’s Disease in a pathologicaly confirmed cohort
Bibliographic record
Abstract
Abstract Background Why some individuals with significant Alzheimer’s disease(AD) lesions in their brain retain their cognitive abilities while others with the same level of pathology manifest symptoms of dementia remains incompletely understood. The term cognitive resilience refers to this phenomenon. With limited success in pharmacological interventions that prevent the onset of AD pathology, more efforts are aiming toward developing strategies to delay the clinical expression of AD; acetylcholinesterase inhibitors serve as an example, and compounds with similar pharmacodynamics such as nicotine may play a similar role. Here we sought to identify demographic, clinical, genetic, and neuropathological features associated with cognitive resilience in participants with severe AD neuropathology. Method Data for this retrospective cohort was collected from the datasets developed by the National Alzheimer’s Coordinating Centre (NACC). Individuals with severe AD pathology and no other primary neuropathology diagnoses who had their last visit within 2 years of their time of death were included. Severe AD pathology definition was based on National Institute on Aging–Reagan (NIA‐Reagan) criteria pathology, i.e., frequent neuritic plaques and Braak & Braak stage V/VI pathology. Cognition was assessed using the Mini‐Mental Status Examination (MMSE) score at their last visit and cases with scores ≥24 were defined as being cognitively intact, and thus resilient. Following bivariate analysis to compare resilient with non‐resilient groups, significantly different variables were adjusted for demographics using logistic regression analysis. Subsequently, statistically significant characteristics were entered in a multivariable model. Results We classified 59(9%) individuals as resilient and 595(91%) as non‐resilient. The binary logistic regression model showed that resilient subjects were older (odds ratio[OR] = 1.03;95% confidence interval[CI] = 1–1.07), had more years of education (OR = 1.16;95%CI = 1.04‐1.29), had lower BMI (OR = 0.91;95%CI = 0.85‐0.99), were more likely to be a smoker (OR = 2.78;95% CI = 1.45‐5.34), and were more likely to use an anticoagulant/antiplatelet at last visit compared with subjects with impaired cognition (OR = 1.87;95%CI = 1.01‐3.48). Conclusions Our results corroborated previous findings that lower BMI and higher education are associated with AD cognitive resilience, and additionally demonstrated that the level of smoking and usage of anticoagulant/antiplatelet medication have a direct relationship with cognitive resilience to AD severe pathology. Nicotine mimetics could be explored as a potential option for preventing AD clinical expression.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".