Identifying frailty‐related clusters of Alzheimer’s and Vascular dementia: A multi‐modal data approach
Bibliographic record
Abstract
Abstract Background The proportion of people living with dementia (PLWD) is increasing due to aging global populations. Frailty is a complex, multifactorial, and age‐related state of increased vulnerability commonly co‐occurring with Alzheimer’s disease (AD) and vascular dementia (VaD). Variability in frailty levels of PLWD motivates deeper investigation into dementia’s presenting characteristics. Machine learning techniques applied to large medical datasets can identify characterize disease phenotypes. Therefore, the aim of this study is to use unsupervised machine learning to discover frailty‐related clusters in AD and VaD. Method We will use data from the United Kingdom Biobank (UKBB), a large‐scale prospective cohort database which includes more than 500,000 participants aged 40‐69 years at baseline. The data include electronic health records, brain magnetic resonance imaging, polygenic risk scores, and frailty index scores. We will select features for clustering using principal component analysis. Clustering algorithms will include k‐means, affinity propagation, and latent class analysis. After cluster characterization, we will evaluate the association between clusters and prevalent AD (n = 2,634) and VaD (n = 1,664) using logistic regression models. Regression models will be stratified by sex and adjusted for age. Receiver‐operating curve will be plotted, and area‐under the curve will be compared using C‐statistics. This study is supported by the Canadian Consortium on Neurodegeneration in Aging Trainee Synapse Project Funds. Our data application has been approved by UKBB. Result We expect that the clustering algorithms will reveal several clusters of similar PLWD and allow evaluations based on shared patterns of regional brain atrophy, the degree of frailty, and polygenic risk. In addition, we also expect that clusters characterized by high frailty levels will be associated with prevalent AD and VaD. Conclusion Frailty is an important factor in the clinical evaluation of PLWD. The use of unsupervised machine learning techniques may reveal distinct high‐risk clusters that are related to prevalent AD and VaD. This work may improve early identification of PLWD and aid in the development of personalized care and therapy for people at risk of AD and VaD.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".