Diagnostic biomarkers for α-synucleinopathies- state of the art and future developments: a systematic review
Bibliographic record
Abstract
BACKGROUND: Alpha-synucleinopathies are common disorders that are expected to become increasingly prevalent in the future along with the longer life expectancy. However, their diagnosis is problematic as they are mainly based on clinical criteria without the support of disease-specific biomarkers. This leads to frequent misdiagnoses, as underlined by autopsy studies, and an imprecise selection of patients for clinical trials, preventing progress in the development of disease-modifying treatments. In recent years important advances have been made regarding the development of specific biomarkers for the detection of pathological α-synuclein (α-syn), which may improve the diagnosis of patients affected by α-synucleinopathies. RESULTS: In this review, we describe in detail the most promising techniques to detect pathological α-syn in patient-derived samples. In particular, we describe the diagnostic accuracy of each individual cerebrospinal fluid (CSF), plasma and skin α-syn biomarker in differentiating α-synucleinopathies from controls, from other neurodegenerative disorders and between different α-synucleinopathies. Furthermore, we underline the main advantages and limitations of these techniques for clinical practice. Finally, we provide our suggestions for further development considering both technical aspects and large-scale standardization. CONCLUSIONS AND RELEVANCE: We conclude that immunofluorescence on biopsied skin tissue and the seed amplification assay on CSF show the best diagnostic accuracy and reliability in the studies that have been performed to date. We discuss the opportunities of these techniques as well as the main current limitations and technical problems that need to be considered before they can be adopted for clinical use.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".