Self‐ and study partner–reported cognitive decline in older adults without dementia: The role of α‐synuclein and amyloid biomarkers in the Alzheimer's Disease Neuroimaging Initiative
Bibliographic record
Abstract
INTRODUCTION: Subjective cognitive decline (SCD) may be an early marker of Alzheimer's disease (AD) pathology. Until recently, it was impossible to measure biomarkers specific for α-synuclein pathology; therefore, its association with subjective reports of cognitive decline is unknown. METHODS: Alzheimer's Disease Neuroimaging Initiative participants without dementia (n = 918) were classified as positive or negative for amyloid beta (Aβ+ or Aβ-) and α-synuclein (α-syn+ or α-syn-) biomarkers. Self- and study partner-reported cognitive decline was measured with the Everyday Cognition (ECog) questionnaire. RESULTS: Per self-report, Aβ+/α-syn+ had the greatest cognitive decline. Aβ-/α-syn+ did not differ from Aβ-/α-syn- across ECog scores. Study partner-reported results had a similar pattern, but Aβ+/α-syn- and Aβ+/α-syn+ did not differ across ECog scores. Mild cognitive impairment classification moderated the study partner-reported memory score. DISCUSSION: While α-syn+ alone did not increase subjective reports of cognitive decline, Aβ+/α-syn+ had the most self- and study partner-rated cognitive decline. Therefore, the presence of multiple pathologies was associated with greater SCD. HIGHLIGHTS: Cerebrospinal fluid α-synuclein (α-syn) seed amplification assay was used to determine α-syn positivity. Amyloid beta (Aβ)-/α-syn-, Aβ-/α-syn+, Aβ+/α-syn-, and Aβ+/α-syn+ biomarker groups were created. Aβ+/α-syn+ had greater subjective cognitive decline (SCD) than the other biomarker groups. Aβ-/α-syn+ did not differ from Aβ-/α-syn- across self- or study-partner reported SCD scores. Study partner-reported subjective memory results were largely driven by participants with mild cognitive impairment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".