Predicting CSF α-Synuclein Seed Amplification Assay Status From Demographics and Clinical Data.

Area of Science:

• Neurology
• Biomarker Discovery
• Diagnostic Development

Background:

• Alpha-synuclein (α-syn) cerebrospinal fluid seed amplification assay (CSF SAA) is a promising diagnostic tool for Parkinson's disease (PD) and other synucleinopathies.
• Accurate prediction of α-syn SAA status can improve early diagnosis and management of PD.

Purpose of the Study:

• To develop and externally validate predictive models for α-syn positive or negative CSF SAA status.
• To utilize easily accessible clinical predictors for in vivo diagnosis in a mixed population with and without PD.

Main Methods:

• Logistic regression models (uni- and multi-variable) were developed using data from the Parkinson Progression Marker Initiative (PPMI) study.
• Models were externally validated in the Systemic Synuclein Sampling Study (S4) cohort, both using CSF α-syn SAA measurements.

Main Results:

• The multivariable model, incorporating age- and sex-specific University of Pennsylvania Smell Identification Test (UPSIT) percentiles, sex, constipation, LRRK2, and GBA status, achieved high internal performance (AUROC 0.921, sensitivity 0.858, specificity 0.868).
• External validation in the S4 cohort demonstrated excellent performance (AUROC 0.978, sensitivity 0.958, specificity 0.870), confirming the model's robustness.
• The University of Pennsylvania Smell Identification Test (UPSIT) scores were highly significant predictors of CSF α-syn SAA status.

Conclusions:

• Data-driven models utilizing non-invasive clinical features can accurately predict CSF α-syn SAA status in individuals with and without PD.
• These models offer a valuable tool for improving the diagnostic accuracy of synucleinopathies.