Associations of Alzheimer's-related plasma biomarkers with cognitive decline in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is associated with cognitive decline through multiple mechanisms, including Alzheimer's disease (AD) pathology and cortical Lewy body involvement. However, its underlying mechanisms remain unclear. Recently, AD-related plasma biomarkers have emerged as potential tools for predicting abnormal pathological protein accumulation. We aimed to investigate the association between AD-related plasma biomarkers and cognitive decline in PD patients. METHODS: Plasma biomarkers were measured in 70 PD patients (49 with nondemented Parkinson's disease (PDND) and 21 with Parkinson's disease dementia (PDD)) and 38 healthy controls (HCs) using a single-molecule array. The study evaluated (1) the correlation between plasma biomarkers and clinical parameters, (2) receiver operating characteristic curves and areas under the curve to evaluate the discrimination capacity of plasma biomarkers among groups, and (3) a generalized linear model to analyze associations with Addenbrooke's Cognitive Examination-Revised and Montreal Cognitive Assessment-Japanese version scores. RESULTS: Plasma glial fibrillary acidic protein significantly correlated with cognitive function tests, including all subdomains, with a notable increase in the PDD group compared with the HC and PDND groups, while plasma neurofilament light chain captured both cognitive decline and disease severity in the PDND and PDD groups. Plasma beta-amyloid 42/40 and pholphorylated-tau181 indicated AD pathology in the PDD group, but plasma beta-amyloid 42/40 was increased in the PDND group compared with HCs and decreased in the PDD group compared with the PDND group. CONCLUSIONS: AD-related plasma biomarkers may predict cognitive decline in PD and uncover underlying mechanisms suggesting astrocytic pathologies related to cognitive decline in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".