Measurements of auto‐antibodies to α‐synuclein in the serum and cerebral spinal fluids of patients with Parkinson's disease
Bibliographic record
Abstract
Abstract Biomarkers for α‐synuclein are needed for diagnosis and prognosis in Parkinson's disease (PD). Endogenous auto‐antibodies to α‐synuclein could serve as biomarkers for underlying synucleinopathy, but previous assessments of auto‐antibodies have shown variability and inconsistent clinical correlations. We hypothesized that auto‐antibodies to α‐synuclein could be diagnostic for PD and explain its clinical heterogeneity. To test this hypothesis, we developed an enzyme‐linked immunosorbent assay for measuring α‐synuclein auto‐antibodies in human samples. We evaluated 69 serum samples (16 healthy controls (HC) and 53 PD patients) and 145 CSF samples (52 HC and 93 PD patients) from our Institution. Both serum and CSF were available for 24 participants. Males had higher auto‐antibody levels than females in both fluids. CSF auto‐antibody levels were significantly higher in PD patients as compared with HC, whereas serum levels were not significantly different. CSF auto‐antibody levels did not associate with amyloid‐β1–42, total tau, or phosphorylated tau. CSF auto‐antibody levels correlated with performance on the Montreal Cognitive Assessment, even when controlled for CSF amyloidβ1–42. CSF hemoglobin levels, as a proxy for contamination of CSF by blood during lumbar puncture, did not influence these observations. Using recombinant α‐synuclein with N‐ and C‐terminal truncations, we found that CSF auto‐antibodies target amino acids 100 through 120 of α‐synuclein. We conclude that endogenous CSF auto‐antibodies are significantly higher in PD patients as compared with HC, suggesting that they could indicate the presence of underlying synucleinopathy. These auto‐antibodies associate with poor cognition, independently of CSF amyloidβ1–42, and target a select C‐terminal region of α‐synuclein. image Read the Editorial Highlight for this article on page 433 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".