Persistent Functional Impairment as an Early Indicator of Alzheimer Disease Pathology and Progression
Bibliographic record
Abstract
BACKGROUND: Functional impairment (FI) is a key criterion for diagnosing dementia. However, subtle functional changes may occur during preclinical and prodromal phases but may not be accurately characterized. Furthermore, research linking FI to Alzheimer disease (AD) biofluid biomarkers is limited. Here we examined cross-sectional associations between cerebrospinal fluid (CSF) AD biomarkers and persistent versus transient FI in dementia-free older adults, and the longitudinal association of FI with incident dementia. METHODS: Data from 1000 participants (age 72.9 ± 7.0; 45.2% female; 62.8% MCI) from the Alzheimer's Disease Neuroimaging Initiative were analyzed. CSF biomarkers included p-tau181, Aβ42, and ptau-181/Aβ42 ratio. Three Functional Activities Questionnaire items of "preparing a hot beverage," "preparing a balanced meal," and "shopping alone" were identified by factor analysis as assessing function rather than cognition directly. Persistent-FI was operationalized as FI present at> two-thirds of pre-dementia visits. Comparator groups included Transient-FI and No-FI. Linear regression modeled the association between FI status and baseline biomarker levels, while Cox regression assessed the association between FI and incident dementia. Models adjusted for age, sex, education, APOE-ε4 status, and MMSE. RESULTS: Compared to No-FI, Persistent-FI was associated with lower Aβ42 (Beta = -8.93; 95% CI: -13.56 to -4.03; p < 0.001), higher p-tau181 (Beta = 10.81; 95% CI: 0.44-22.26; p = 0.041), and ptau181/Aβ42 ratio (Beta = 21.66; 95% CI: 7.02-38.31; p = 0.003). In contrast, Transient-FI showed no significant associations. APOE-ε4 carrier status was more prevalent in the Persistent-FI group compared to No-FI (p = 0.009), but not in Transient-FI (p = 0.931). Compared to No-FI, Persistent-FI had a 6.66-fold greater dementia incidence rate (95% CI: 4.98-8.91, p < 0.001), while Transient-FI had a 1.72-fold greater incidence rate (95% CI: 1.09-2.72, p = 0.021). CONCLUSIONS: Findings extend the limited research on the association of FI with CSF AD biomarkers in dementia-free populations. Operationalizing FI-related risk by persistence enhances prognostication, identifying individuals with greater AD pathology and progression risk. This approach could enhance screening, early detection, and risk stratification, informing timely interventions before dementia onset.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".