Category: Parkinson's Disease: Genetics
Objective: To evaluate in a group of asymptomatic carriers of the G2019S mutation of the LRRK2 gene if there are subclinical gait alterations, detectable with an inertial sensor system, before they are detected as a symptom by the patients or as a clinical sign in the neurological examination.
Background: There is a need for biomarkers to monitor the earliest phases of Parkinson’s disease (PD), especially in premotor stages. Here, we studied whether there are subclinical gait alterations in carriers of the G2019S mutation of LRRK2 that can be detected by means of an inertial sensor system.
Method: Twenty-one idiopathic PD patients, 20 LRRK2-G2019S PD, 27 asymptomatic carriers of LRRK2-G2019S mutation (AsG2019S) and 36 controls walked equipped with 16 lightweight inertial sensors in three different experiments: i/normal gait, ii/fast gait and iii/dual-task gait. In the AsG2019S group, DaT-SPECT (123I-ioflupane) with semi-quantitative analysis was carried out. Motor and cognitive performance were evaluated using MDS-UPDRSIII and MoCA scales. We employed neural network techniques to classify individuals based on their walking patterns.
Results: PD patients and controls showed differences in speed, stride length and arm swing amplitude, variability and asymmetry in all three tasks (p<0.01). In the AsG2019S group, differences were only detected during fast walking, with greater step time on the non-dominant side (p<0.05), lower step/stride time variability (p<0.01) and lower step time asymmetry (p<0.01). DaT uptake showed a significant correlation with step time during fast walking on the non-dominant side (r= -0.52; p<0.01). The neural network was able to differentiate between AsG2019S and healthy controls with an accuracy rate of 82.5%
Conclusion: By means of an inertial sensor system, it is possible to detect subclinical gait disturbances in presymptomatic LRRK2-PD subjects. The step or stride time during fast gait, given its correlation with striatal DaT binding, could be the earliest altered parameter.
To cite this abstract in AMA style:
A. Sánchez Rodríguez, C. Tirnauca, D. Salas-Gómez, M. Fernández-Gorgojo, I. Martínez Rodríguez, M. Sierra, I. González Aramburu, D. Stan, A. Gutierrez-González, J. M Meissner, J. Andrés Pacheco, M. Rivera Sánchez, M. Sánchez-Peláez, P. Sánchez Juan, J. Infante. Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson’s disease [abstract]. Mov Disord. 2022; 37 (suppl 2). https://www.mdsabstracts.org/abstract/sensor-based-gait-analysis-in-the-premotor-stage-of-lrrk2-g2019s-associated-parkinsons-disease/. Accessed May 16, 2025.« Back to 2022 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/sensor-based-gait-analysis-in-the-premotor-stage-of-lrrk2-g2019s-associated-parkinsons-disease/