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

TL;DR

This study combines blood biomarkers, clinical data, and brain imaging to better distinguish Parkinson’s disease from similar conditions, improving diagnostic accuracy.

Contribution

The first multidimensional model integrating plasma biomarkers, clinical parameters, and radiomic features for PD-APS differentiation with high precision.

Findings

Plasma GFAP and NFL levels showed a significant gradient across PD, APS, and healthy controls.

A combined model achieved an AUC of 0.874, outperforming single biomarkers like NFL alone.

The model was validated for stability with cross-validation yielding an AUC of 0.843.

Abstract

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.

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, 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.

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; 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).

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.

Figures