Persistent functional impairments in cognitively normal older adults and risk of cognitive decline and dementia
Bibliographic record
Abstract
Abstract Background Maintaining functional independence is an essential aspect of healthy aging. In fact, functional dependence to perform activities of daily living (ADL) is a fundamental part of a dementia diagnosis. Newer diagnostic criteria for MCI also consider functional impairments (FI) but not to the extent of compromising functional independence. Therefore, accurate characterization of FI across the cognitive spectrum could help identify individuals who may be at a greater risk of cognitive decline and dementia, thereby improving treatment strategies. Here, we explored the utility of capturing persistent FI, in contrast to transient FI, to identify a higher‐risk group for cognitive decline and dementia. Method Data from cognitively normal (CN) older adults from the National Alzheimer’s Coordinating Center were analyzed. From the Functional Activities Questionnaire four items were used (assembling tax records, paying bills, shopping alone, and traveling). Persistent FI was operationalized as FI present at more than two‐thirds of all study visits (TTV) prior to cognitive decline and dementia. The two comparator groups either had transient FI not meeting TTV criteria or no FI in advance of cognitive decline and dementia. Kaplan‐Meier survival curves were generated for all FI groups. Cox proportional hazard models compared incidence rates for cognitive decline and dementia across FI groups, adjusted for age, sex, education, race, APOE‐e4 status, and presence of subjective cognitive decline and neuropsychiatric symptoms. Results The CN sample comprised 1,612 Persistent‐FI (age = 77.3±9.7; 61.7% female), 3,195 Transient‐FI (age = 72.9±8.8; 63.7% female), and 7,284 No‐FI participants (age = 69.7±8.4; 66.0% female) (Table 1). Persons with persistent FI had lower survival (p<0.0001) and a 3.89‐fold greater incidence rate for cognitive decline and dementia compared to No‐FI (CI:3.45‐4.38, p<0.001); persons with transient FI had a 2.04‐fold greater incidence rate than No‐FI (CI:1.84‐2.27, p<0.001) (Figure 1). Conclusions CN older adults with persistent FI had greater incidence of cognitive decline and dementia than transient FI or no FI. Operationalizing FI‐related risk based on the persistence of functional impairments improves the prognostication of cognitive decline and dementia and allows for the identification of individuals who are at a greater risk in the absence of objective cognitive impairments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".