Identifying Subtypes of Alzheimer’s Disease: An analysis of possible cognitive subgroups through the life span
Bibliographic record
Abstract
Abstract Background Typical Alzheimer’s disease (AD) is linked to memory impairment and medial temporal lobe atrophy. However, different patterns of cognitive decline in individuals with AD have been described in the literature (e.g., Murray et al., 2011; Lam et al., 2013; Scheltens et al., 2015). The variability of AD is also observed throughout the life span, as aging processes develop (Ferreira et al., 2017), which highlights the importance of investigating changes over time and establishing possible stages and therapeutic windows. Method The Toronto Cognitive Assessment (TorCA) was used to provide a characterization of cognitive profiles including orientation, immediate recall, delayed recall, delayed recognition, visuospatial function, executive control and language in 609 individuals with possible AD. Normative data for the TorCA (Freedman et al., 2018) suggest different levels of impairment can be identified depending on age groups. The present study investigated 27 neuropsychological subtests within the TorCA. Scores were summed based on cognitive domains and transformed into z‐scores. Further, the dataset was split into 4 age groups (Group 1 = less than 59; Group 2 = 60 to 69; Group 3 = 70 to 79; and Group 4 = over 80 years of age). The scores were analyzed with latent profile analyses (LPA) to identify cluster memberships among subjects. Result Different AD subtypes were identified in each age group characterized by patterns of cognitive impairment as follows: a) Group 1: 1. Cluster 1: mild; 2. Cluster 2: amnestic; 3. Cluster 3: executive; 4. Cluster 4: moderate diffuse. b) Group 2: 1. Cluster 1: mild diffuse; 2. Cluster 2: executive / visuospatial; 3. Cluster 3: moderate with preserved orientation and visuospatial c) Group 3: 1. Cluster 1: moderate executive; 2. Cluster 2: mild diffuse; 3. Cluster 3: amnestic; 4. Cluster 4: moderate diffuse. d) Group 4: 1. Cluster 1: severe diffuse; 2. Cluster 2: mild diffuse. Conclusion These findings suggest that different subtypes of AD can be identified throughout the life span. Further investigation of these differences will aid in the development of clinical tools to diagnose and treat subgroups, in the development of protocols that stratify this population for better research recruitment, and establishing relevant neuropsychological and clinical tools.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".