Dos Santos Vanessa Brzoskowski, Ravazio Rafaela, Teixeira-Dos-Santos Daniel, Schumacher Schuh Artur Francisco, Mattjie Christian, Pasquali Joana M, de Borba Mauricia Denise, Barros Rodrigo C, Olchik Maira Rozenfeld
Postgraduate Program in Medicine, Medical Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
Machine Learning Theory and Applications Lab, School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil.
Clin Park Relat Disord. 2025 Jul 21;13:100373. doi: 10.1016/j.prdoa.2025.100373. eCollection 2025.
Parkinson's disease (PD) is a heterogeneous disorder, suggesting the presence of distinct subtypes. Speech data, though easy to collect, remains underutilized in subtyping PD.
Cross-sectional study with PD patients recruited from the Movement Disorders Outpatient Clinic of the Neurology Service at the University Hospital in Porto Alegre, Brazil. We included participants diagnosed with idiopathic PD and excluded participants with other disorders that could affect speech. Clinical and sociodemographic data were collected alongside MDS-UPDRS Parts II and III motor assessments. Tremor and gait-posture scores were derived from specific MDS-UPDRS items, with additional data on Deep Brain Stimulation (DBS) status and Levodopa Equivalent Daily Dose (LEDD). The tasks diadochokinesis (DDK) and monologue were recorded and acoustically analyzed using software. We compared our identified clusters using clinical data through an analysis of covariance adjusted for age, sex, and disease duration.
Ninety individuals with PD were included, with 61.2 (± 9.4) years old, 13.6 (± 6.6) disease duration, and 47.6 (± 10) age at onset. We identified three speech groups with strong separation between them, comprising 49 (mild), 13 (moderate), and 29 (severe) patients. Tremor and postural-gait stability scores differed significantly across the three clusters, with cluster 3 exhibiting higher tremor (13.42 ± 10.66 vs. 7.09 ± 6.62, p = 0.020) and greater postural-gait instability (10.25 ± 6.69 vs. 5.46 ± 4.91, p = 0.009) than cluster 1. These differences weren't explainable by distinct age, sex, or disease duration.
Our speech-based clustering algorithm effectively differentiated Parkinson's disease subtypes in this sample, identifying distinct groups based on tremor and axial symptoms.
帕金森病(PD)是一种异质性疾病,提示存在不同的亚型。语音数据虽然易于收集,但在PD亚型分类中仍未得到充分利用。
对从巴西阿雷格里港大学医院神经科运动障碍门诊招募的PD患者进行横断面研究。我们纳入了被诊断为特发性PD的参与者,排除了可能影响语音的其他疾病患者。收集临床和社会人口统计学数据以及MDS-UPDRS第二部分和第三部分的运动评估数据。震颤和步态-姿势评分来自特定的MDS-UPDRS项目,还有关于深部脑刺激(DBS)状态和左旋多巴等效日剂量(LEDD)的额外数据。记录了轮替运动(DDK)和独白任务,并使用软件进行声学分析。我们通过对年龄、性别和病程进行协方差调整后的临床数据分析来比较我们识别出的聚类。
纳入了90例PD患者,年龄为61.2(±9.4)岁,病程为13.6(±6.6)年,发病年龄为47.6(±10)岁。我们识别出三个语音组,它们之间有明显区分,分别包括49例(轻度)、13例(中度)和29例(重度)患者。震颤和姿势-步态稳定性评分在三个聚类之间有显著差异,聚类3的震颤(13.42±10.66对7.09±6.62,p = 0.020)和姿势-步态不稳定性(10.25±6.69对5.46±4.91,p = 0.009)高于聚类1。这些差异不能用不同的年龄、性别或病程来解释。
我们基于语音的聚类算法在本样本中有效地区分了帕金森病亚型,根据震颤和轴性症状识别出了不同的组。