Plasma microRNA predict cognitive decline in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease, is currently diagnosed clinically by impairments in motor control. PD, however, includes a diversity of non-motor symptoms, such as cognitive decline. Thus, it is imperative to establish a diagnostic framework for PD which reflects this heterogeneous phenotype. While misfolded α-synuclein is a cellular hallmark of PD and candidate biofluid marker, microRNA are an important class of biomarkers that are stable and easily detectable in blood, and are dysregulated at post-mortem in PD patients. This study aimed to establish PD plasma microRNA biomarkers that reflect cognitive abilities, as determined by the Montreal Cognitive Assessment (MoCA). METHODS: Using custom-designed low-density TaqMan arrays we assessed plasma levels of 187 neurodegeneration-related microRNA, in cross-sectional (n = 102), and longitudinal cohorts (n = 26) as well as in post-mortem brain tissue (n = 16). RESULTS: We found numerous microRNA were altered with increasing cognitive decline in PD and that the overall direction of change moved towards downregulation. A notable exception was miR-192-5p which was consistently upregulated in plasma and was found to be downregulated at postmortem in the superior frontal gyrus. Overall, microRNA identified were largely distinct from those known to be regulated in Alzheimer's disease. Focusing on a longitudinal cohort, controlled for disease progression and age we showed that miR-151-3p and miR-192-5p provided the best predictive model for separating cognitively normal PD patients from those who decline cognitively. CONCLUSION: Plasma microRNA are altered in PD patients, can predict cognitive decline and therefore may be clinically useful biomarkers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".