A simple question about falls to distinguish balance and gait difficulties in Parkinson's disease

Genetic analysis and natural history of Parkinson's disease due to the LRRK2 G2019S variant

The LRRK2 G2019S variant is the most common cause of monogenic Parkinson's disease (PD); however, questions remain regarding the penetrance, clinical phenotype and natural history of carriers. We performed a 3.5-year prospective longitudinal online study in a large number of 1286 genotyped LRRK2 G2019S carriers and 109 154 controls, with and without PD, recruited from the 23andMe Research Cohort. We collected self-reported motor and non-motor symptoms every 6 months, as well as demographics, family histories and environmental risk factors. Incident cases of PD (phenoconverters) were identified at follow-up. We determined lifetime risk of PD using accelerated failure time modelling and explored the impact of polygenic risk on penetrance. We also computed the genetic ancestry of all LRRK2 G2019S carriers in the 23andMe database and identified regions of the world where carrier frequencies are highest. We observed that despite a 1 year longer disease duration (P = 0.016), LRRK2 G2019S carriers with PD had similar burden of motor symptoms, yet significantly fewer non-motor symptoms including cognitive difficulties, REM sleep behaviour disorder (RBD) and hyposmia (all P-values ≤ 0.0002). The cumulative incidence of PD in G2019S carriers by age 80 was 49%. G2019S carriers had a 10-fold risk of developing PD versus non-carriers. This rose to a 27-fold risk in G2019S carriers with a PD polygenic risk score in the top 25% versus non-carriers in the bottom 25%. In addition to identifying ancient founding events in people of North African and Ashkenazi descent, our genetic ancestry analyses infer that the G2019S variant was later introduced to Spanish colonial territories in the Americas. Our results suggest LRRK2 G2019S PD appears to be a slowly progressive predominantly motor subtype of PD with a lower prevalence of hyposmia, RBD and cognitive impairment. This suggests that the current prodromal criteria, which are based on idiopathic PD, may lack sensitivity to detect the early phases of LRRK2 PD in G2019S carriers. We show that polygenic burden may contribute to the development of PD in the LRRK2 G2019S carrier population. Collectively, the results should help support screening programmes and candidate enrichment strategies for upcoming trials of LRRK2 inhibitors in early-stage disease.

Feasibility and positive effects of scalp acupuncture for modulating motor and cerebral activity in Parkinson's disease: A pilot study

BACKGROUND

A variety of acupuncture therapies have shown efficacy in Parkinson's disease (PD).

OBJECTIVE

To evaluate scalp acupuncture (SA) effects on motor and cerebral activity by using gait equipment and resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Twelve patients with PD received SA. They underwent the first functional-imaging scan after tactile stimulation and the second scan following needle removal. Gait test and local sensation assessment were performed immediately after each functional scan. Gait parameter differences between pre- and post-SA were analyzed using a paired t-test and altered brain areas in degree centrality (DC) and fractional amplitude of low-frequency fluctuation (fALFF) were identified between the two scans.

RESULTS

Eight patients completed the experiment. Stride length, maximum ankle height, maximum ankle horizontal displacement, gait speed, and range of shank motion significantly increased post-treatment (P <  0.05). fALFF in left middle frontal gyrus and DC in left cerebellum (corrected) increased, while fALFF in left inferior parietal lobule (corrected) during SA decreased, compared with those in tactile stimulation. A positive correlation was observed between right limb swings and both fALFF areas.

CONCLUSIONS

Differences in gait and brain analyses presented modulation to motor and brain activity in PD, thus, providing preliminary evidence for SA efficacy.

K-D Balance: An objective measure of balance in tandem and double leg stances

BACKGROUND AND OBJECTIVE

Subjective grade-based scoring balance assessments tend to be lengthy and have demonstrated poor repeatability and reliability. This study examined the reliability of a mobile balance assessment tool and differences in balance measurements between individuals at risk for a balance deficit secondary to a diagnosed neurological or musculoskeletal condition and a control group of healthy individuals.

METHODS

Objective balance testing was measured using K-D Balance on a compatible iPhone. Seventy-seven participants were enrolled (control group, n = 44; group at risk for balance deficits, n = 33). Mean and standard deviation of K-D Balance were recorded for each stance. Intra-rater reliability was calculated by repeating the trial.

RESULTS

Overall balance scores were superior for the control group compared with the group at risk for balance deficits in double leg stance (mean (SD): 0.15 (0.12) versus 0.18 (0.13), p = 0.260), tandem stance right leg (mean (SD): 0.27 (0.17) versus 0.45 (0.49), p = 0.028), and tandem stance left leg (mean (SD): 0.26 (0.17) versus 0.35 (0.35), p = 0.136). Intra-rater reliability was good to excellent for K-D Balance double leg stance (intra-class correlation coefficient (ICC) = 0.80, 95% confidence interval (CI) 0.58-1.03), tandem stance right leg (ICC = 0.96, 95% CI 0.86-1.06) and tandem stance left leg (ICC = 0.98, 95% CI 0.95-1.0).

CONCLUSIONS

K-D Balance revealed differences in balance performance between healthy individuals compared with individuals with neurological or musculoskeletal impairment. Objective balance measures may improve the accuracy and reliability of clinical balance assessment by detecting subtle differences in balance and aid in early detection of diseases that impair balance.

Instrumental or Physical-Exercise Rehabilitation of Balance Improves Both Balance and Gait in Parkinson's Disease

We hypothesised that rehabilitation specifically addressing balance in Parkinson's disease patients might improve not only balance but locomotion as well. Two balance-training protocols (standing on a moving platform and traditional balance exercises) were assessed by assigning patients to two groups (Platform, n = 15, and Exercises, n = 17). The platform moved periodically in the anteroposterior, laterolateral, and oblique direction, with and without vision in different trials. Balance exercises were based on the Otago Exercise Program. Both platform and exercise sessions were administered from easy to difficult. Outcome measures were (a) balancing behaviour, assessed by both Index of Stability (IS) on platform and Mini-BESTest, and (b) gait, assessed by both baropodometry and Timed Up and Go (TUG) test. Falls Efficacy Scale-International (FES-I) and Parkinson's Disease Questionnaire (PDQ-8) were administered. Both groups exhibited better balance control, as assessed both by IS and by Mini-BESTest. Gait speed at baropodometry also improved in both groups, while TUG was less sensitive to improvement. Scores of FES-I and PDQ-8 showed a marginal improvement. A four-week treatment featuring no gait training but focused on challenging balance tasks produces considerable gait enhancement in mildly to moderately affected patients. Walking problems in PD depend on postural instability and are successfully relieved by appropriate balance rehabilitation. This trial is registered with ClinicalTrials.gov NCT03314597.

A Prospective Evaluation of an Outpatient Assessment of Postural Instability to Predict Risk of Falls in Patients with Parkinson's Disease Presenting for Deep Brain Stimulation

Background

Postural instability (PI) and falls, major causes of morbidity in patients with PD, are often overlooked. DBS is a mainstay therapy for Parkinson's disease (PD) and has been purported to both worsen and improve PI. An effective PI evaluation that can predict fall risk in patients with PD presenting for DBS is needed.

Methods

Forty-nine consecutive patients with PD were enrolled. Self-reported falls were the gold standard. Tests evaluated were the Berg Balance Scale (BBS), Timed-Up-and-Go (TUG), Pull Test, and Biodex Balance System Sway Index on firm (SI-FIRM) and soft (SI-SOFT) surfaces.

Results

The best single tests for fall risk were the BBS and SI-FIRM, each with sensitivities of 79% and specificities of 60% and 65%, respectively. When the evaluation was combined into a composite measure requiring four positive tests out of five, the sensitivity was 72% and specificity was 80%.

Conclusions

A simple, efficient outpatient physical therapy assessment is effective in diagnosing fall risk in patients with PD.

A mobile cloud-based Parkinson's disease assessment system for home-based monitoring

BACKGROUND

Parkinson's disease (PD) is the most prevalent movement disorder of the central nervous system, and affects more than 6.3 million people in the world. The characteristic motor features include tremor, bradykinesia, rigidity, and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced adverse effects, such as dyskinesia and motor fluctuation. Clinicians must regularly monitor patients in order to identify these effects and other declines in motor function as soon as possible. Current clinical assessment for Parkinson's is subjective and mostly conducted by brief observations made during patient visits. Changes in patients' motor function between visits are hard to track and clinicians are not able to make the most informed decisions about the course of therapy without frequent visits. Frequent clinic visits increase the physical and economic burden on patients and their families.

OBJECTIVE

In this project, we sought to design, develop, and evaluate a prototype mobile cloud-based mHealth app, "PD Dr", which collects quantitative and objective information about PD and would enable home-based assessment and monitoring of major PD symptoms.

METHODS

We designed and developed a mobile app on the Android platform to collect PD-related motion data using the smartphone 3D accelerometer and to send the data to a cloud service for storage, data processing, and PD symptoms severity estimation. To evaluate this system, data from the system were collected from 40 patients with PD and compared with experts' rating on standardized rating scales.

RESULTS

The evaluation showed that PD Dr could effectively capture important motion features that differentiate PD severity and identify critical symptoms. For hand resting tremor detection, the sensitivity was .77 and accuracy was .82. For gait difficulty detection, the sensitivity was .89 and accuracy was .81. In PD severity estimation, the captured motion features also demonstrated strong correlation with PD severity stage, hand resting tremor severity, and gait difficulty. The system is simple to use, user friendly, and economically affordable.

CONCLUSIONS

The key contribution of this study was building a mobile PD assessment and monitoring system to extend current PD assessment based in the clinic setting to the home-based environment. The results of this study proved feasibility and a promising future for utilizing mobile technology in PD management.