背景:预测帕金森病(PD)可以为患者提供靶向治疗。然而,在临床实践中,可以根据患者的症状和体征大致评估疾病的严重程度。
目的:本研究试图探索与PD严重程度相关的因素并构建预测模型。
方法:从我们的研究中心招募PD患者和健康对照,同时记录他们的基本人口统计信息。血清炎症标志物水平,例如胱抑素C(CysC),C反应蛋白(CRP),RANTES(受激活管制,正常T细胞表达和分泌),白细胞介素-10(IL-10),并测定了所有参与者的白细胞介素-6(IL-6)。根据Hoehn和Yahr(H-Y)量表将PD患者分为早期和中期组,并使用PD相关量表进行评估。LASSO逻辑回归分析(模型C)帮助选择基于临床量表评估的变量,血清炎症因子水平,和经颅超声测量。通过10倍交叉验证确定了最优和谐模型系数λ。此外,将模型C与多变量(模型A)和逐步(模型B)逻辑回归进行比较。接受者操作特征(ROC)的曲线下面积(AUC),Brier分数,校正曲线,和决策曲线分析(DCA)有助于确定预测模型的判别和校准,然后配置森林图和柱状图。
结果:该研究包括113名健康个体和102名PD患者,26名早期和76名中晚期患者。单变量方差分析筛选出炎症标志物CysC和RANTES之间的统计学显著差异。中晚期的平均CysC水平显着高于早期(p<0.001),而RANTES则没有(p=0.740)。与UPDRS-I相关的LASSO逻辑回归模型(λ.1se=0.061),UPDRS-II,UPDRS-III,HAMA,PDQ-39和CysC作为包含的独立变量表明,模型C的辨别和校准(AUC=0.968,Brier=0.049)优于模型A(AUC=0.926,Brier=0.079)和模型B(AUC=0.929,Brier=0.071)模型。
结论:研究结果显示多种因素与PD评估相关。此外,炎症标志物CysC和经颅超声测量可以客观预测PD症状的严重程度,帮助医生监测患者的PD演变,同时有针对性的干预措施。
Predicting 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.
The current study attempted to explore the factors linked with PD severity and construct a predictive model.
The 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.
The 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.
The 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.