Combining plasma biomarkers, clinical parameters, and neuroimaging features for differential diagnosis of Parkinson's disease and atypical parkinsonian syndromes: a multidimensional modeling approach.
Combining plasma biomarkers, clinical parameters, and neuroimaging features for differential diagnosis of Parkinson's disease and atypical parkinsonian syndromes: a multidimensional modeling approach.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Jinan, CN · Author affiliation
Department of Neurology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, Guangdong, China.Location evidence
Guangzhou, CN · Author affiliation
Department of Neurology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, Guangdong, China.Location evidence
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Original abstract
BackgroundThe 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.MethodsA 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, α-syn, and tau), clinical indicators (e.g., disease duration and UPDRS-III scores), and radiomic features (1,316 IBSI-standardized features) from magnetic resonance imaging scans of the midbrain and pons were collected. Core variables were screened using LASSO regression and random forest algorithms, and a multivariate logistic regression model was constructed. The performance of the model was evaluated using receiver operating characteristic curves, calibration curves, and cross-validation.ResultsPlasma 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; p < 0.05). The levels of α-syn and tau in the PD and APS groups were significantly higher than those in the HC group (p < 0.001), but there was no difference between the two groups. Four core variables (NFL, GFAP, disease duration, and pontine voxel volume) were selected. The area under the curve (AUC) of the combined model for identifying PD and APS was 0.874 (95% confidence interval: 0.801-0.946), which was significantly higher than that of single variables (such as NFL alone AUC = 0.653). Cross-validation confirmed the stability of the model (AUC = 0.843).ConclusionThis 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.