Pathologies Underlying Longitudinal Cognitive Decline in the Oldest Old
Bibliographic record
Abstract
BACKGROUND: Understanding contributions of different brain pathologies to domain-specific cognitive trajectories in the oldest old is crucial to guide future intervention studies. METHODS: Two-hundred-twenty Oregon Alzheimer's Disease Center research participants who were cognitively intact at entry were followed on average for 7.3 years with annual neuropsychological testing until death (mean age, 93.7 y) and autopsy. Mixed effects models examined the relationship between trajectories in memory, verbal fluency, and mini-mental state examination (MMSE) and pathology (neurofibrillary tangles, neuritic plaques, gross infarcts, hippocampal sclerosis, Lewy bodies, APOE genotype, age at death, and years of education). The association between the MMSE trajectory and pathologic variables were examined using a Poisson model with MMSE errors as outcomes given the nonlinear distribution of MMSE scores. RESULTS: Memory trajectory was associated with the APOε4 allele (P=0.006). Verbal fluency trajectory was associated with gross infarcts (P=0.008). MMSE trajectory was associated with high Braak scores (P=0.03), gross infarcts (P<0.0001), hippocampal sclerosis (P=0.003), moderate neuritic plaques (P=0.04), and the APOε4 allele (P=0.02). CONCLUSIONS: The association between trajectory of decline in global cognitive scores and multiple brain pathologies highlights the importance of accounting for comorbid pathologies in therapeutic trials aimed at one specific pathology in the oldest old. Only the APOε4 allele showed an association with memory decline, despite accounting for Alzheimer's disease pathology, suggesting that APOE may be involved in mechanisms beyond amyloid metabolism in its role in memory. Further studies are needed to examine the role of APOE in brain aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".