Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

帕金森病 (PD) 具有多种症状和合并症，使其诊断和治疗变得复杂。这项横断面、单中心研究的主要目的是评估数字步态传感器数据在监测和诊断帕金森病运动和步态障碍方面的效用。作为次要目标，对于检测合并症、非运动结果和疾病进展亚组等更具挑战性的任务，我们首次评估了数字标记与代谢组学和临床数据的整合。使用鞋上数字传感器，我们在单次访问中收集了 162 名患者和 129 名对照者的步态测量数据。机器学习模型显示出显着的诊断能力，与对照组相比，PD 的 AUC 分数为 83-92%，运动严重程度分类的 AUC 分数高达 75%。将步态数据与代谢组学和临床数据相结合，可以改进对难以检测的合并症（例如幻觉）的预测。总体而言，这种使用数字生物标志物和多模式数据集成的方法可以帮助客观的疾病监测、诊断和合并症检测。