Frailty trajectory related to Alzheimer’s dementia after controlling for neuropathological burden
Bibliographic record
Abstract
Abstract Background Frailty is an established risk factor for cognitive decline and Alzheimer’s disease. Changes in frailty have been associated with adverse health outcomes including mortality, health service use, institutionalization, and disability. Few studies have examined the longitudinal relationship between frailty and cognition. Here, we use data from a longitudinal clinical‐pathologic cohort study to extend this work and examine longitudinal change in frailty, how it is influenced by sex, neuropathology, and Alzheimer’s dementia. Method Participants of the Rush Memory and Aging project (n=443, 69.3% female, 83.1±5.9 years of age at baseline) underwent annual clinical evaluations (average 5.3±3.5 years of follow‐up) followed by post‐mortem neuropathologic assessment. A frailty index was constructed from 41 health variables at each annual study evaluation. Alzheimer’s dementia was determined (blinded to neuropathological data) at time of death. Age, sex, and education, and neuropathological burden (10‐item index including amyloid load, neurofibrillary tangle density, TDP‐43, hippocampal sclerosis, cerebral amyloid angiopathy, gross infarcts, gross chronic infarcts, atherosclerosis, arteriolosclerosis, and presence of Lewy bodies) were evaluated as covariates. Frailty trajectories were calculated using a mixed effects model. Result Frailty increased linearly at a rate of 3% per year on average. Higher baseline frailty was associated with being female (F(1)=22.45, p<0.0001), but not with neuropathological burden (p=0.25) or having Alzheimer’s dementia at death (p=0.142). Frailty trajectories differed as a function of sex, neuropathological index, and Alzheimer’s dementia status; specifically, female sex (p=0.04), higher neuropathological burden (p<0.0001), and Alzheimer’s dementia (p<0.0001) were associated with accumulating deficits at a significantly faster rate. Conclusion Findings suggest a strong link between health status as measured by a frailty index and Alzheimer’s dementia, after controlling for neuropathology. Frailty trajectories over 18 years in older adults predicted dementia risk, underscoring the importance of frailty intervention and management in later life.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".