Centre for Human Drug Research (CHDR), Leiden, The Netherlands.
Leiden University Medical Centre (LUMC), Leiden, The Netherlands.
Mov Disord. 2023 Oct;38(10):1795-1805. doi: 10.1002/mds.29520. Epub 2023 Jul 4.
The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
能够监测帕金森病（PD）患者中快速作用药物效果的客观且易于实施的生物标志物的验证将有益于抗帕金森病药物的开发。我们开发了综合生物标志物来检测左旋多巴/卡比多巴的效果并估计 PD 症状的严重程度。为此，我们训练了机器学习算法来选择最佳的指击任务特征组合，以预测治疗效果和疾病严重程度。数据是在一项有 20 名 PD 患者参与的安慰剂对照交叉研究中收集的。在治疗期间进行交替索引和中指敲击（IMFT）、交替食指敲击（IFT）和拇指-食指敲击（TIFT）任务以及运动障碍协会统一帕金森病评定量表（MDS-UPDRS）III。我们训练分类算法来选择包含 MDS-UPDRS III 项目评分的特征；单独的 IMFT、IFT 和 TIFT；以及所有三个敲击任务的综合特征，以对治疗效果进行分类。此外，我们训练回归算法使用敲击任务特征单独和综合来估计 MDS-UPDRS III 总分。IFT 综合生物标志物具有最佳的分类性能（83.50%的准确率，93.95%的精度），优于 MDS-UPDRS III 综合生物标志物（75.75%的准确率，73.93%的精度）。当估计 MDS-UPDRS III 总分时，它也取得了最佳的性能（平均绝对误差：7.87，皮尔逊相关系数：0.69）。我们证明了 IFT 综合生物标志物在检测治疗效果方面优于综合敲击任务和 MDS-UPDRS III 综合生物标志物。这为在临床试验中采用 IFT 综合生物标志物来检测抗帕金森病治疗效果提供了证据。© 2023 作者。运动障碍由 Wiley 期刊出版公司代表国际帕金森病和运动障碍协会出版。
