The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography

Abstract

BackgroundPredicting Parkinson's disease (PD) can provide patients with targeted therapies. However, disease severity can be roughly evaluated in clinical practice based on the patient's symptoms and signs.ObjectiveThe current study attempted to explore the factors linked with PD severity and construct a predictive model.MethodThe PD patients and healthy controls were recruited from our study center while recording their basic demographic information. The serum inflammatory markers levels, such as Cystatin C (Cys C), C‐reactive protein (CRP), RANTES (regulated on activation, normal T cell expressed and secreted), Interleukin‐10 (IL‐10), and Interleukin‐6 (IL‐6) were determined for all the participants. PD patients were categorized into early and mid‐advanced groups based on the Hoehn and Yahr (H‐Y) scale and evaluated using PD‐related scales. LASSO logistic regression analysis (Model C) helped select variables based on clinical scale evaluations, serum inflammatory factor levels, and transcranial sonography measurements. The optimal harmonious model coefficient λ was determined via 10‐fold cross‐validation. Moreover, Model C was compared with multivariate (Model A) and stepwise (Model B) logistic regression. The area under the curve (AUC) of a receiver operator characteristic (ROC), brier score, calibration curve, and decision curve analysis (DCA) helped determine the discrimination and calibration of the predictive model, followed by configuring a forest plot and column chart.ResultsThe study included 113 healthy individuals and 102 PD patients, with 26 early and 76 mid‐advanced patients. Univariate analysis of variance screened out statistically significant differences among inflammatory markers Cys C and RANTES. The average Cys C level in the mid‐advanced stage was significantly higher than in the early stage (p < 0.001) but not for RANTES (p = 0.740). The LASSO logistic regression model (λ.1se = 0.061) associated with UPDRS‐I, UPDRS‐II, UPDRS‐III, HAMA, PDQ‐39, and Cys C as the included independent variables revealed that the Model C discrimination and calibration (AUC = 0.968, Brier = 0.049) were superior to Model A (AUC = 0.926, Brier = 0.079) and Model B (AUC = 0.929, Brier = 0.071) models.ConclusionThe study results show multiple factors are linked with PD assessment. Moreover, the inflammatory marker Cys C and transcranial sonography measurement could objectively predict PD symptom severity, helping doctors monitor PD evolution in patients while targeting interventions.