A minimally invasive biomarker for sensitive and accurate diagnosis of Parkinson’s disease
Bibliographic record
Abstract
Seeding activities of disease-associated α-synuclein aggregates (αSynD), a hallmark of Parkinson’s disease (PD), are detectable by seed amplification assay (αSyn-SAA) and being developed as a diagnostic biomarker for PD. Sensitive and accurate αSyn-SAA for blood or saliva would greatly facilitate PD diagnosis. This prospective diagnostic study conducted αSyn-SAA analyses on serum and saliva samples collected from patients clinically diagnosed with PD or healthy controls (HC). 124 subjects (82 PD, 42 HC) donated blood and had extensive clinical assessments, of whom 74 subjects (48 PD, 26 HC) also donated saliva at the same visits. An additional 57 subjects (35 PD, 22 HC) donated saliva and had more limited clinical assessments. The mean ages were 69.21, 66.55, 69.58, and 64.71 years for PD serum donors, HC serum donors, PD saliva donors, and HC saliva donors, respectively. αSynD seeding activities in either sample type alone or both sample types together were evaluated for PD diagnosis. Serum αSyn-SAA data from 124 subjects showed 80.49% sensitivity, 90.48% specificity, and 0.9006 accuracy (AUC of ROC); saliva αSyn-SAA data from 131 subjects attained 74.70% sensitivity, 97.92% specificity, and 0.8966 accuracy. Remarkably, the combined serum and saliva αSyn-SAA from 74 subjects with both sample types achieved better diagnostic performance: 95.83% sensitivity, 96.15% specificity, and 0.98 accuracy. In addition, serum αSynD seeding activities correlated inversely with Montreal Cognitive Assessment in males and positively with Hamilton Depression Rating Scale in females and in the < 70 age group, whereas saliva αSynD seeding activities correlated inversely with age at diagnosis in males and in the < 70 age group. Our data indicate that serum and saliva αSyn-SAA together can achieve high diagnostic accuracy for PD comparable to that of CSF αSyn-SAA, suggesting their potential utility for highly sensitive, accurate, and minimally invasive diagnosis of PD in routine clinical practice and clinical studies.
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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 | 0.001 |
| 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) | 0.000 | 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".