Predicting the onset of freezing of gait in Parkinson's disease

Assessment tools for cognitive performance in Parkinson's disease and its genetic contributors

Background

We have shown that genetic factors associating with motor progression of Parkinson's disease (PD), but their roles in cognitive function is poorly understood. One reason is that while cognitive performance in PD can be evaluated by various cognitive scales, there is no definitive guide indicating which tool performs better.

Methods

Data were obtained from the Parkinson's Progression Markers Initiative, where cognitive performance was assessed using five cognitive screening tools, including Symbol Digit Modalities Test (SDMT), Montreal Cognitive Assessment, Benton Judgment of Line Orientation, Modified Semantic Fluency Test, and Letter Number Sequencing Test, at baseline and subsequent annual follow-up visit for 5 years. Genetic data including ApoE and other PD risk genetic information were also obtained. We used SPSS-receiver operating characteristic and ANOVA repeated measures to evaluate which cognitive assessment is the best reflecting cognitive performance in PD at early stage and over time. Logistic regression analyses were used to determine the genetic associations with the rapidity of cognitive decline in PD.

Results

SDMT performed better in detecting mild cognitive impairment at baseline (AUC = 0.763), and SDMT was the only tool showing a steady cognitive decline during longitudinal observation. Multigenetic factors significantly associated with cognitive impairment at early stage of the disease (AUC = 0.950) with IP6K2 rs12497850 more evident, and a significantly faster decline (AUC = 0.831) within 5 years after motor onset, particularly in those carrying FGF20 rs591323.

Conclusion

SDMT is a preferable cognitive assessment tool for PD and genetic factors synergistically contribute to the cognitive dysfunction in PD.

Prodromal Cognitive Changes as a Prognostic Indicator of Forthcoming Huntington's Disease Severity: A Retrospective Longitudinal Study

BACKGROUND

Cognitive changes in Huntington's disease (HD) precede motor manifestations. ENROLL-HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions.

OBJECTIVES

Our study aimed to test whether cognitive scores in prodromal ENROLL-HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested.

METHODS

From the global ENROLL-HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow-up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206).

RESULTS

The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed-up patients after phenoconversion.

CONCLUSIONS

CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD.

Comparison of sleep characteristics between Parkinson's disease with and without freezing of gait: A systematic review

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Among the motor complaints, freezing of gait (FOG) is a common and disabling phenomenon that episodically hinders patients' ability to produce efficient steps. Concurrently, sleep disorders are prevalent in PD and significantly impact the quality of life of affected individuals. Numerous studies have suggested a bidirectional relationship between FOG and sleep disorders. Therefore, our objective was to systematically review the literature and compare sleep outcomes in PD patients with FOG (PD + FOG) and those without FOG (PD-FOG). By conducting a comprehensive search of the PubMed and Web of Science databases, we identified 20 eligible studies for inclusion in our analysis. Our review revealed that compared to PD-FOG, PD + FOG patients exhibited more severe symptoms of rapid eye movement sleep behavior disorder in nine studies, increased daytime sleepiness in eight studies, decreased sleep quality in four studies, and more frequent and severe sleep disturbances in four studies. These findings indicate that PD + FOG patients generally experience worse sleep quality, higher levels of daytime sleepiness, and more disruptive sleep disturbances compared to those without FOG (PD-FOG). The association between sleep disturbances and FOG highlights the importance of evaluating and monitoring these symptoms in PD patients and open the possibility for future studies to assess the impact of managing sleep disturbances on the severity and occurrence of FOG, and vice versa.

Cortical thickness and white matter microstructure predict freezing of gait development in Parkinson's disease

The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.

A meta-analysis identifies factors predicting the future development of freezing of gait in Parkinson's disease

Freezing of gait (FOG) is a debilitating problem that is common among many, but not all, people with Parkinson's disease (PD). Numerous attempts have been made at treating FOG to reduce its negative impact on fall risk, functional independence, and health-related quality of life. However, optimal treatment remains elusive. Observational studies have recently investigated factors that differ among patients with PD who later develop FOG, compared to those who do not. With prediction and prevention in mind, we conducted a systematic review and meta-analysis of publications through 31.12.2022 to identify risk factors. Studies were included if they used a cohort design, included patients with PD without FOG at baseline, data on possible FOG predictors were measured at baseline, and incident FOG was assessed at follow-up. 1068 original papers were identified, 38 met a-priori criteria, and 35 studies were included in the meta-analysis (n = 8973; mean follow-up: 4.1 ± 2.7 years). Factors significantly associated with a risk of incident FOG included: higher age at onset of PD, greater severity of motor symptoms, depression, anxiety, poorer cognitive status, and use of levodopa and COMT inhibitors. Most results were robust in four subgroup analyses. These findings indicate that changes associated with FOG incidence can be detected in a subset of patients with PD, sometimes as long as 12 years before FOG manifests, supporting the possibility of predicting FOG incidence. Intriguingly, some of these factors may be modifiable, suggesting that steps can be taken to lower the risk and possibly even prevent the future development of FOG.

Long-term levodopa ameliorates sequence effect in simple, but not complex walking in early Parkinson's disease patients

BACKGROUND

The sequence effect (SE) is characterized by the progressive decrement of movements and is often observed in Parkinson's disease (PD) patients. While acute effect of levodopa does not ameliorate the SE, the effect of long-term levodopa treatment for the SE remains unknown.

OBJECTIVE

We aimed to elucidate the SEs during various gait conditions and their response to long-term levodopa treatment in drug-naïve PD patients.

METHODS

Nineteen drug-naïve PD patients and 21 healthy controls were enrolled. Gait parameters were measured via wearable inertial sensors in the following conditions:1) straight walking, 2) circular walking: walking a circle of 1 m diameter in a clock-wise direction for 3 laps, 3) straight or circular walking under cognitive-motor dual-task (serial 7s subtractions). PD patients were evaluated at baseline, within 1 h after intravenous administration of levodopa, and after one, three, and six months treatment with levodopa. The SE was measured by a linear regression slope by plotting consecutive stride lengths over steps. Patients were also separately analyzed depending on laterality of symptoms.

RESULTS

Long-term levodopa treatment ameliorated the SE only during single-task straight walking. The SE during circular walking was exacerbated after long-term levodopa treatment for right-side dominant patients. During dual-task straight walking, the SE at baseline was greater in right-side dominant PD patients.

CONCLUSIONS

The SE only during single-task straight walking can be ameliorated by long-term levodopa treatment. However, the SE may be exaggerated by cognitive motor interference or by asymmetrical stride length with/without long-term levodopa treatment, depending on the laterality of symptoms.

Impact of GBA variants on longitudinal freezing of gait progression in early Parkinson's disease

BACKGROUND

Freezing of gait (FOG) is a common disabling gait disturbance among patients with Parkinson's disease (PD), but the influence of genetic variants on the incidence of FOG has been poorly studied to date.

OBJECTIVES

We aimed to evaluate the association of GBA variants with the risk of FOG development in a large early PD cohort.

METHODS

This study included 371 early PD patients from the Parkinson's Progression Markers Initiative (PPMI) who were divided into a GBA variant carrier group (GBA-PD group, n = 44) and an idiopathic PD group without GBA variants (iPD group, n = 327). They were followed up for up to 5 years to examine the progression of FOG. The cumulative incidence of FOG and risk factors for FOG were assessed using Kaplan‒Meier and Cox regression analyses.

RESULTS

At baseline, the GBA-PD group had lower CSF β-amyloid 1-42 (Aβ₄₂) levels and more severe motor and nonmotor symptoms than the iPD group. During the 5-year follow-up, the GBA-PD group had a higher incidence of FOG than the iPD group, and the FOG progression rate was related to GBA variant severity. In the multivariable Cox model without CSF Aβ₄₂, GBA variants were significant predictors of future FOG, and the association remained significant after adding CSF Aβ₄₂ to the model. In the subgroup analyses, the effect of GBA variants was not observed in the "low-level" group. However, in the "high-level" group, GBA variants independently increased the risk of FOG, and this association was stronger than the association with CSF Aβ₄₂.

CONCLUSION

GBA variants are novel genetic risk factors for future FOG development in early PD patients. This association seemed to be mediated by both Aβ-dependent pathways and Aβ-independent pathways.

Gait Declines Differentially in, and Improves Prediction of, People with Parkinson's Disease Converting to a Freezing of Gait Phenotype

BACKGROUND

Freezing of gait (FOG) is a debilitating, variably expressed motor symptom in people with Parkinson's disease (PwPD) with limited treatments.

OBJECTIVE

To determine if the rate of progression in spatiotemporal gait parameters in people converting from a noFOG to a FOG phenotype (FOGConv) was faster than non-convertors, and determine if gait parameters can help predict this conversion.

METHODS

PwPD were objectively monitored longitudinally, approximately every 6 months. Non-motor assessments were performed at the initial visit. Steady-state gait in the levodopa ON-state was collected using a gait mat (Protokinetics) at each visit. The rate of progression in 8 spatiotemporal gait parameters was calculated. FOG convertors (FOGConv) were classified if they did not have FOG at initial visit and developed FOG at a subsequent visit.

RESULTS

Thirty freezers (FOG) and 30 non-freezers were monitored an average of 3.5 years, with 10 non-freezers developing FOG (FOGConv). FOGConv and FOG had faster decline in mean stride-length, swing-phase-percent, and increase in mean total-double-support percent, coefficient of variability (CV) foot-strike-length and CV swing-phase-percent than the remaining non-freezers (noFOG). On univariate modeling, progression rates of mean stride-length, stride-velocity, swing-phase-percent, total-double-support-percent and of CV swing-phase-percent had high discriminative power (AUC > 0.83) for classification of the FOGConv and noFOG groups.

CONCLUSION

FOGConv had a faster temporal decline in objectively quantified gait than noFOG, and progression rates of spatiotemporal gait parameters were more predictive of FOG phenotype conversion than initial (static) parameters Objectively monitoring gait in disease prediction models may help define FOG prone groups for testing putative treatments.