Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau S.A.R., China.
J Parkinsons Dis. 2023;13(4):473-484. doi: 10.3233/JPD-225080.
Few efficient and simple models for the early prediction of Parkinson's disease (PD) exists.
To develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators.
Expression levels of blood-based miRNAs and clinical variables from 1,284 individuals were downloaded from the Parkinson's Progression Marker Initiative database on June 1, 2022. Initially, the generalized estimating equation was used to screen candidate biomarkers of PD progression in the discovery phase. Then, the elastic net model was utilized for variable selection and a logistics regression model was constructed to establish a nomogram. Additionally, the receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were utilized to evaluate the performance of the nomogram.
An accurate and externally validated nomogram was constructed for predicting prodromal and early PD. The nomogram is easy to utilize in a clinical setting since it consists of age, gender, education level, and transcriptional score (calculated by 10 miRNA profiles). Compared with the independent clinical model or 10 miRNA panel separately, the nomogram was reliable and satisfactory because the area under the ROC curve achieved 0.72 (95% confidence interval, 0.68-0.77) and obtained a superior clinical net benefit in DCA based on external datasets. Moreover, calibration curves also revealed its excellent prediction power.
The constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.
目前很少有高效且简单的模型可以用于早期预测帕金森病（PD）。
通过整合 microRNA（miRNA）表达谱和临床指标，开发并验证一种新的列线图，用于早期识别 PD。
于 2022 年 6 月 1 日从帕金森进展标志物倡议数据库中下载了 1284 名个体的血液 miRNA 表达水平和临床变量。首先，在发现阶段使用广义估计方程筛选 PD 进展的候选生物标志物。然后，使用弹性网络模型进行变量选择，并构建逻辑回归模型来建立列线图。此外，还使用受试者工作特征（ROC）曲线、决策曲线分析（DCA）和校准曲线来评估列线图的性能。
构建了一个用于预测前驱期和早期 PD 的准确且经过外部验证的列线图。该列线图易于在临床环境中使用，因为它由年龄、性别、教育水平和转录评分（由 10 个 miRNA 谱计算得出）组成。与独立的临床模型或 10 个 miRNA 谱分别相比，该列线图具有可靠性和满意度，因为 ROC 曲线下面积达到 0.72（95%置信区间，0.68-0.77），并且在基于外部数据集的 DCA 中获得了更高的临床净收益。此外，校准曲线也显示了其出色的预测能力。
所构建的列线图具有基于实用性和准确性进行大规模 PD 早期筛查的潜力。
