Combining plasma biomarkers, clinical parameters, and neuroimaging features for differential diagnosis of Parkinson's disease and atypical parkinsonian syndromes: a multidimensional modeling approach.

Background: The early differential diagnosis of Parkinson's disease (PD) and atypical parkinsonian syndromes (APSs) poses challenges. The current methods, which rely on clinical assessments and single-modal biomarkers, lack sufficient sensitivity and specificity. This study aims to develop a multidimensional model integrating plasma biomarkers, clinical parameters, and neuroimaging radiomic features to improve the accuracy of differentiating PD from APS. Methods: A total of 150 participants were enrolled in the study, including 56 healthy controls (HC), 54 patients with PD, and 40 patients with APSs. Plasma biomarkers (NFL, GFAP, Results: Plasma GFAP and NFL levels showed a significant gradient change: APS group (GFAP: 89.9 pg./mL; NFL: 77.3 pg./mL) > PD group (GFAP: 47.1 pg./mL; NFL: 50.0 pg./mL) > HC group (GFAP: 22.1 pg./mL; NFL: 37.5 pg./mL; Conclusion: This was the first study to integrate plasma NFL/GFAP, clinical disease duration, and pontine radiomic features to construct a high-precision PD-APS differential model (AUC > 0.87), addressing the limitations of traditional single-mode approaches. The gradient changes in GFAP and the APS-specificity of NFL serve as key biomarkers. Thus, this multidimensional framework provides a practical diagnostic tool for resource-limited scenarios.