Cognitive reserve, presynaptic proteins and dementia in the elderly
Bibliographic record
Abstract
Differences in cognitive reserve may contribute to the wide range of likelihood of dementia in people with similar amounts of age-related neuropathology. The amounts and interactions of presynaptic proteins could be molecular components of cognitive reserve, contributing resistance to the expression of pathology as cognitive impairment. We carried out a prospective study with yearly assessments of N = 253 participants without dementia at study entry. Six distinct presynaptic proteins, and the protein-protein interaction between synaptosomal-associated protein 25 (SNAP-25) and syntaxin, were measured in post-mortem brains. We assessed the contributions of Alzheimer's disease (AD) pathology, cerebral infarcts and presynaptic proteins to odds of dementia, level of cognitive function and cortical atrophy. Clinical dementia was present in N = 97 (38.3%), a pathologic diagnosis of AD in N = 142 (56.1%) and cerebral infarcts in N = 77 (30.4%). After accounting for AD pathology and infarcts, greater amounts of vesicle-associated membrane protein, complexins I and II and the SNAP-25/syntaxin interaction were associated with lower odds of dementia (odds ratio = 0.36-0.68, P < 0.001 to P = 0.03) and better cognitive function (P < 0.001 to P = 0.03). Greater cortical atrophy, a putative dementia biomarker, was not associated with AD pathology, but was associated with lower complexin-II (P = 0.01) and lower SNAP-25/syntaxin interaction (P < 0.001). In conclusion, greater amounts of specific presynaptic proteins and distinct protein-protein interactions may be structural or functional components of cognitive reserve that reduce the risk of dementia with aging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".