Can Quantitative Gait Analysis Be Used to Guide Treatment of Patients with Different Subtypes of Parkinson's Disease?

Wearable sensor-based quantitative gait analysis in Parkinson's disease patients with different motor subtypes

Gait impairments are among the most common and disabling symptoms of Parkinson's disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.

A biomechanical analysis of turning during gait in individuals with different subtypes of Parkinson's disease

BACKGROUND

Turning while walking is a complex component of locomotor capacity, which can be challenging in the daily lives of people with Parkinson's disease. The aim of the study was to compare biomechanical strategies during turning of gait in individuals with Parkinson's disease and its different clinical subtypes.

METHODS

A cross-sectional study, comprising of 43 individuals with idiopathic Parkinson's disease, divided in subgroups: akineto-rigid, dominant tremor and mixed. Motor impairment was assessed using the Unified Parkinson's Disease Rating Scale. The gait biomechanical parameters (number of steps, step length, cadence, amplitude, velocity and radius of the turn) were analyzed during turning in a kinematics laboratory. In the statistical analysis, a comparison was made between subgroups, and correlations between biomechanical parameters.

FINDINGS

There was no statistically significant difference between the subgroups. In the correlation analysis, notable correlations were found between the anticipatory step length and the following variables: number of steps (r = -0.418), step length while turning (r = 0.805), step length after turning (r = 0.644), average velocity (r = 0.830), average velocity while turning (r = 0.755), and maximum velocity (rho = 0.835).

INTERPRETATION

The difficulties primarily occur during the anticipatory phase of the turn, which affects the entire task. The greater the length of the anticipatory step, the greater the length of the step taken to turn as well as the step taken after turning. And the greater the velocity, the greater the step length, and to fewer steps taken to perform the task.

Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics

Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.

Effects of deep brain stimulation on the kinematics of gait and balance in patients with idiopathic Parkinson's disease

BACKGROUND

Advanced stages of idiopathic Parkinson's disease are often characterised by gait alterations and postural instability. Despite improvements in patients' motor symptoms after deep brain stimulation of the subthalamic nucleus, its effects on gait and balance remain a matter of debate. This study investigated the effects of deep brain stimulation on balance and kinematic parameters of gait.

METHODS

The gait of 26 patients with advanced idiopathic Parkinson's disease was analysed before and after (between 3 and 6 months) after bilateral deep brain stimulation of the subthalamic nucleus. Computerised analysis was used to study cadence, number of cycles with the correct support sequence, number of cycles, duration of the cycle stages, and knee and ankle goniometry. Balance, postural instability, and mobility were assessed using the Tinetti and Timed Up and Go test.

FINDINGS

After stimulation, the following changes were significant (p < 0.01): number of cycles with the correct support sequence, number of total cycles, and foot contact. Patients improved significantly (p < 0.01) in the Tinetti and Timed Up and Go tests, the risk factors for falls changed from high (median 17) to low (median 25), and they improved from minor dependence (statistical median 14) to normality (statistical median 8.70).

INTERPRETATION

Deep brain stimulation to inhibit hyperactivity of the subthalamic nucleus was associated with an improvement in the space-time variables of gait and balance in patients with Parkinson's disease for up to 3-6 months. These results highlight the major role of the subthalamic nucleus in motor control mechanisms during locomotion and balance.

Consensus Paper: Ataxic Gait

The aim of this consensus paper is to discuss the roles of the cerebellum in human gait, as well as its assessment and therapy. Cerebellar vermis is critical for postural control. The cerebellum ensures the mapping of sensory information into temporally relevant motor commands. Mental imagery of gait involves intrinsically connected fronto-parietal networks comprising the cerebellum. Muscular activities in cerebellar patients show impaired timing of discharges, affecting the patterning of the synergies subserving locomotion. Ataxia of stance/gait is amongst the first cerebellar deficits in cerebellar disorders such as degenerative ataxias and is a disabling symptom with a high risk of falls. Prolonged discharges and increased muscle coactivation may be related to compensatory mechanisms and enhanced body sway, respectively. Essential tremor is frequently associated with mild gait ataxia. There is growing evidence for an important role of the cerebellar cortex in the pathogenesis of essential tremor. In multiple sclerosis, balance and gait are affected due to cerebellar and spinal cord involvement, as a result of disseminated demyelination and neurodegeneration impairing proprioception. In orthostatic tremor, patients often show mild-to-moderate limb and gait ataxia. The tremor generator is likely located in the posterior fossa. Tandem gait is impaired in the early stages of cerebellar disorders and may be particularly useful in the evaluation of pre-ataxic stages of progressive ataxias. Impaired inter-joint coordination and enhanced variability of gait temporal and kinetic parameters can be grasped by wearable devices such as accelerometers. Kinect is a promising low cost technology to obtain reliable measurements and remote assessments of gait. Deep learning methods are being developed in order to help clinicians in the diagnosis and decision-making process. Locomotor adaptation is impaired in cerebellar patients. Coordinative training aims to improve the coordinative strategy and foot placements across strides, cerebellar patients benefiting from intense rehabilitation therapies. Robotic training is a promising approach to complement conventional rehabilitation and neuromodulation of the cerebellum. Wearable dynamic orthoses represent a potential aid to assist gait. The panel of experts agree that the understanding of the cerebellar contribution to gait control will lead to a better management of cerebellar ataxias in general and will likely contribute to use gait parameters as robust biomarkers of future clinical trials.

Factors Contributing to Malnutrition in Parkinson's Disease Patients With Freezing of Gait

Background and Purpose

Little is known about the nutritional status and clinical characteristics of patients with Parkinson's disease with freezing of gait (PDFOG). The purpose of this study was to describe the relationship between nutritional status and characteristics of patients with PDFOG.

Methods

In this cross-sectional study, 178 PDFOG patients were recruited and classified as nutritionally normal or at risk of malnutrition/already malnourished based on their Mini Nutritional Assessment (MNA) scores. Each participant underwent a structured questionnaire, physical examination and routine serum biochemical tests.

Results

We found that 44.4 and 37.1% of PDFOG patients were malnourished [mini nutritional assessment (MNA) score &lt;17] and at risk of malnutrition (17 ≤ MNA score ≤ 23.5), respectively. Compared to patients with normal nutrition, PDFOG patients with malnutrition and at risk of malnutrition had longer duration of Parkinson's disease (PD) and freezing of gait (FOG), more levodopa equivalent daily doses (LEDD), lower body mass index (BMI), more motor symptoms according to the Unified PD Rating Scale-III (UPDRS-III) and non-motor symptoms according to the PD Non-motor Symptoms Questionnaire (PD-NMS) (P &lt; 0.05). Uric acid, albumin, prealbumin, and total cholesterol (TC) differed between the two groups (P &lt; 0.05). High Hoehn and Yahr (H-Y) stage, high Freezing of Gait Questionnaire (FOGQ) scores, low TC and low uric acid were risk factors for malnutrition in patients with PDFOG.

Conclusion

Our results showed disease severity, motor symptoms, TC levels and uric acid levels were all associated with nutritional status in patients with PDFOG. This study suggest early discovery of the nutritional status of PDFOG patients is important.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.