P2–101: Amyloid and vascular cognitive impairment: A pilot study of frequency and impact
Bibliographic record
Abstract
Traditionally, Alzheimer's disease (AD) and vascular cognitive impairment (VCI) were considered to be distinct and unrelated; however, increasing evidence is demonstrating an overlap between AD and VCI pathology. As such, the current criteria for the clinical diagnosis of VCI may not distinguish those with cognitive impairment due to subcortical ischemic small vessel disease from those with cognitive impairment due to mixed vascular and AD pathology (mixed dementia - MD). Furthermore, it is unclear how co-existing amyloid pathology may affect cognitive function in people with VCI. The purpose of this pilot study was to determine the frequency of MD in patients diagnosed with VCI. In addition, we investigated how co-existing amyloid pathology may affect cognitive function in people with VCI. We conducted a planned sub-analysis of a randomized controlled trial investigating the effect of targeted aerobic exercise training on cognitive function in people with VCI. Elevan participants - 8 participants with VCI and 3 normal controls - underwent a PiB-PET scan to estimate amyloid burden. Participants with VCI who exhibited PiB uptake 2 standard deviations above the mean of controls were considered to be PiB-positive and to have MD. To determine the effect of co-existing amyloid pathology on cognitive function we collected the following measures: 1) ADAS-Cog; 2) EXIT-25; and 3) a) Digits Forward and Backwards Test, b) Stroop-Colour Word Test, and c) Trail Making Test (Part B-Part A). To determine the associations between PiB uptake and cognitive function we conducted correlational analysis using Pearson correlation coefficients. Five (62.5%) participants with VCI were PiB-positive and 3 (37.5%) participants were PiB-negative. Increased PiB retention was significantly correlated with reduced cognitive performance in the ADAS-Cog (r=0.849, p=0.008) and the Trail Making Test (r=0.861, p=0.006). Neuroimaging with PiB-PET showed 62.5% of those with a clinical diagnosis of VCI have co-existing amyloid pathology. Critically, increased amyloid binding was associated with increased cognitive deficits in domains typically affected by both AD and VCI. Thus, our results suggest that those with VCI and co-existing amyloid pathology demonstrate more diverse cognitive deficits. M ore research is needed to develop reliable and valid measures for the diagnosis of MD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".