Frailty and mild behavioral impairment: A longitudinal study of pre‐dementia markers
Bibliographic record
Abstract
Abstract Background Frailty and mild behavioral impairment (MBI) are non‐cognitive markers of dementia and have been associated with one another cross‐sectionally. Better understanding the association between frailty, MBI, and dementia is necessary to determine whether dementia prognostication could be improved by assessing for both frailty and MBI. This study investigated the longitudinal associations between frailty, MBI, and incident dementia. Method We analyzed data from 20,010 dementia‐free older adults from the National Alzheimer’s Coordinating Center. Frailty was operationalized using a previously published 44‐item frailty index (FI). MBI was derived from the Neuropsychiatric Inventory Questionnaire using a published algorithm; participants were classified as MBI+ if MBI symptoms were present for ≥2 consecutive visits. We used Cox proportional hazards regression to model the association between: (1) baseline FI and incident MBI; (2) baseline MBI and the incident severe frailty (FI≥0.20). We also examined whether MBI+ older adults with higher FI were more likely to develop dementia than those with lower FI. All models were adjusted for age, sex, education, and cognitive status (cognitively normal or mild cognitive impairment). Result Participant characteristics are described in Table 1. The hazard of developing MBI rose by 45% for every 0.1 increase in FI (adjusted hazard ratio; aHR = 1.45, 95%CI: 1.36‐1.55, p<.001) [Table 2]. Likewise, the hazard of developing severe frailty (FI≥0.20) was 97% greater in MBI+ participants compared to MBI‐ participants (aHR = 1.97, 95%CI: 1.85‐2.10, p<.001). Finally, although MBI alone was associated with incident dementia (aHR = 2.02, 95%CI:1.89‐2.17, p<.001), the hazard developing dementia further rose by 1.17 (95%CI: 1.08‐1.26, p <.001) for every 0.1 increase in FI within a sample of MBI+ participants. Conclusion In dementia‐free older adults, frailty is associated with the development of MBI. Further, MBI is associated with the development of severe frailty. Understanding these relationships identifies the potential to use frailty screening as an indicator of individuals who could develop MBI, and MBI screening as an indicator of individuals at risk of accumulating more health deficits. Although, MBI alone predicts incident dementia, our findings suggest that including general health measures, i.e., frailty, in modeling may improve dementia prognostication.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".