Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease

Sánchez-Rodríguez, Antonio; Tîrnăucă, Cristina; Salas-Gómez, Diana; Fernández-Gorgojo, Mario; Martínez-Rodríguez, I.; Sierra, María; González‐Aramburu, Isabel; Stan, Diana; Gutiérrez-González, Angela; Meissner, Johannes Mario; Andrés-Pacheco, Javier; Rivera-Sánchez, María; Sánchez-Peláez, María Victoria; Sánchez‐Juan, Pascual; Infante, Jon

doi:10.1016/j.parkreldis.2022.03.020

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…However, since these abnormalities were found in variant carriers, regardless of disease status, they could simply mirror the presence of LRRK2-G2019S, and not represent a true prodromal sign suggesting phenoconversion [34]. A subsequent study, conducted on a small cohort of individuals (27 LRRK2-G2019S-NMC and 36 HC), also found some differences in step time on the non-dominant side during fast-walking in NMC vs. controls and a negative correlation with dopamine transporter (DAT) uptake [36].…”

Section: Motor Profilementioning

confidence: 94%

Who is at Risk of Parkinson Disease? Refining the Preclinical Phase of GBA1 and LRRK2 Variant Carriers: a Clinical, Biochemical, and Imaging Approach

Menozzi

Schapira

Blandini

et al. 2023

Curr Neurol Neurosci Rep

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Purpose of Review Genetic variants in GBA1 and LRRK2 genes are the commonest genetic risk factor for Parkinson disease (PD); however, the preclinical profile of GBA1 and LRRK2 variant carriers who will develop PD is unclear. This review aims to highlight the more sensitive markers that can stratify PD risk in non-manifesting GBA1 and LRRK2 variant carriers. Recent Findings Several case–control and a few longitudinal studies evaluated clinical, biochemical, and neuroimaging markers within cohorts of non-manifesting carriers of GBA1 and LRRK2 variants. Summary Despite similar levels of penetrance of PD in GBA1 and LRRK2 variant carriers (10–30%), these individuals have distinct preclinical profiles. GBA1 variant carriers at higher risk of PD can present with prodromal symptoms suggestive of PD (hyposmia), display increased α-synuclein levels in peripheral blood mononuclear cells, and show dopamine transporter abnormalities. LRRK2 variant carriers at higher risk of PD might show subtle motor abnormalities, but no prodromal symptoms, higher exposure to some environmental factors (non-steroid anti-inflammatory drugs), and peripheral inflammatory profile. This information will help clinicians tailor appropriate screening tests and counseling and facilitate researchers in the development of predictive markers, disease-modifying treatments, and selection of healthy individuals who might benefit from preventive interventions.

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Section: Motor Profilementioning

confidence: 94%

Who is at Risk of Parkinson Disease? Refining the Preclinical Phase of GBA1 and LRRK2 Variant Carriers: a Clinical, Biochemical, and Imaging Approach

Menozzi

Schapira

Blandini

et al. 2023

Curr Neurol Neurosci Rep

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“…Participants in the HC group had a mean age of 64 ± 10.1 years. We refer the reader to (page 23, Table 1) [22] for more detailed statistics on other anthropomorphic parameters and MoCA (Montreal Cognitive Assessment) scores.…”

Section: Methodsmentioning

confidence: 99%

“…Secondly, one could opt to select only those predictors that were shown to be relevant in [22]: "PD patients and controls showed differences in speed, stride length and arm swing amplitude, variability and asymmetry in all three tasks. [.…”

Section: Data Visualizationmentioning

confidence: 99%

“…This selection can be made either manually (by an expert) or automatically (by means of algorithms). In this case, we opt for the first one, since we already have a list of predictors that were identified as relevant in distinguishing between a PD patient and a participant from the HC group in a previous study [22,33], as mentioned in Section 2.4. Whenever we refer to a dataset with selected features, we add a * to its name: OrigD * will denote the original dataset from which we only keep 19 selected columns.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Apart from the 3 × 42 predictors mentioned above and detailed in Tables A1-A3 from Appendix B, we also used the age and the average turning time as predictors (the values are extracted from the first and third experiment, since in the second one, the participant only walks the corridor once, without turning around) in the previous study [22]. We decided to drop them here so as to only keep predictors specific to the three experiments (age was not one of these), which are present in all of them (the second experiment had no "turning time").…”

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confidence: 99%

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A Machine Learning Approach to Detect Parkinson’s Disease by Looking at Gait Alterations

et al. 2022

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Parkinson’s disease (PD) is often detected only in later stages, when about 50% of nigrostriatal dopaminergic projections have already been lost. Thus, there is a need for biomarkers to monitor the earliest phases, especially for those that are at higher risk. In this work, we explore the use of machine learning methods to diagnose PD by analyzing gait alterations via an inertial sensors system that participants in the study wear while walking down a 15 m long corridor in three different scenarios. To achieve this goal, we have trained six well-known machine learning models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees and random forest. We thoroughly explored several ways to mitigate the problems derived from the small amount of available data. We found that, while achieving accuracy rates of over 70% is quite common, the accuracy of the best model trained is only slightly above the 80% mark. This model has high precision and specificity (over 90%), but lower sensitivity (only 71%). We believe that these results are promising, especially given the size of the population sample (41 PD patients and 36 healthy controls), and that this research venue should be further explored.

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Wearables for diagnosis and predicting clinical milestones

Paul,

Mancini

2024

Handbook of Digital Technologies in Movement Disorders

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Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease

Cited by 6 publications

References 24 publications

Who is at Risk of Parkinson Disease? Refining the Preclinical Phase of GBA1 and LRRK2 Variant Carriers: a Clinical, Biochemical, and Imaging Approach

Who is at Risk of Parkinson Disease? Refining the Preclinical Phase of GBA1 and LRRK2 Variant Carriers: a Clinical, Biochemical, and Imaging Approach

A Machine Learning Approach to Detect Parkinson’s Disease by Looking at Gait Alterations

Wearables for diagnosis and predicting clinical milestones

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