Severity Of Parkinsonian Symptoms Research Articles

Reinforcement Learning-Based Adaptive Classification for Medication State Monitoring in Parkinson's Disease.

Parkinson's Disease (PD) patients frequently transition between the 'ON' state, where medication is effective, and the 'OFF' state, affecting their quality of life. Monitoring these transitions is vital for personalized therapy. We introduced a framework based on Reinforcement Learning (RL) to detect transitions between medication states by learning from continuous movement data. Unlike traditional approaches that typically identify each state based on static data patterns, our approach focuses on understanding the dynamic patterns of change throughout the transitions, providing a more generalizable medication state monitoring method. We integrated a deep Long Short-Term Memory (LSTM) neural network and three one-class unsupervised classifiers to implement an RL-based adaptive classifier. We tested on two PD datasets: Dataset PD1 with 12 subjects (14-minute average recording) and Dataset PD2 with seven subjects (120-minute average recording). Data from wrist and ankle wearables captured transitions during 2 to 4-hour daily activities. The algorithm demonstrated its effectiveness in detecting medication states, achieving an average weighted F1-score of 82.94% when trained and tested on Dataset PD1. It performed well when trained on Dataset PD1 and tested on Dataset PD2, with a weighted F1-score of 76.67%. It surpassed other models, was resilient to severe PD symptoms, and performed well with imbalanced data. Notably, prior work has not addressed the generalizability from one dataset to another, essential for real-world applications with varied sensors. Our innovative framework revolutionizes PD monitoring, setting the stage for advanced therapeutic methods and greatly enhancing the life quality of PD patients.

PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment

This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.

Early dopaminergic replacement treatment initiation benefits motor symptoms in patients with Parkinson's disease.

Parkinson's disease (PD) generally progresses slowly, but it is controversial whether delaying treatment accelerates the progression. Determine the correlation between the time of dopaminergic replacement treatment initiation and the severity of clinical symptoms in PD, including motor and non-motor symptoms. PD patients were divided between 155 people who were diagnosed de novo and 165 PD patients receiving dopamine replacement therapy. Basic patient characteristics included gender, age, age at onset, disease duration, and the time of dopaminergic replacement treatment initiation. We used MDS-UPDRS scores to evaluate the severity of motor symptoms and we also used the scale to assess the severity of non-motor symptoms such as cognition, mood, sleep, and quality of life. The mean time between symptom onset and the initiation of drug treatment was 31.0 (22.5) months. After adjusting for age, sex, age at onset, and disease duration, we found that the MDS-Unified Parkinson's Disease Rating Scale (UPDRS)-III score increased faster in the de novo group with a similar disease duration (F = 8.7, p = 0.0034) than the treatment group. The cumulative incidence of progression to H-Y score 3 in de novo PD group over disease duration was 39.7% in 50months and 92.2% in 100 months, while in treated group such cumulative incidence was 15.5% in 50 months, 51.4% in 100 months and 81.5% in 150 months. The cumulative incidence of patients in the de novo PD group was higher than that in the treated group (p = 0.001), suggesting that untreated patients were more likely to progress to the advanced stages. Symptoms onset, the time between symptom onset and treatment initiation, age, sex, and disease duration explained 28.95% of the total variation in the MDS-UPDRS-III score for motor symptoms. In drug-naïve patients, the time between symptom onset and treatment initiation explained 20.1% of the total variation in the MDS-UPDRS-III score for motor symptoms (t = 6.15, p < 0.001). These data in our study showed that early dopaminergic replacement treatment have played a positive role in PD patients, while dopaminergic replacement delayed treatment might be detrimental to motor symptoms and non-motor state of PD patient. Recognizing early stage symptoms of PD and early diagnosis are of great significance to treatment.

Amantadine use in the French prospective NS-Park cohort.

To assess amantadine use and associated factors in the patients with Parkinson's disease (PD). Immediate-release amantadine is approved for the treatment of PD and is largely used in clinical practice to treat "levodopa-induced dyskinesia (LIDs). Its use varies according to countries and PD stages. The prospective NS-Park cohort collects features of PD patients followed by 26 French PD Expert Centres. Variables used for the analyses included demographics, motor and non-motor PD symptoms and motor complications [motor fluctuations (MFs), LIDs)], antiparkinsonian pharmacological classes and levodopa equivalent daily dose (LEDD). We evaluated: (i) prevalence of amantadine use and compared clinical features of amantadine users vs. non-users (cross-sectional analysis); (ii) factors associated with amantadine initiation (longitudinal analysis); (iii) amantadine effect on LIDs, MFs, apathy, impulse control disorders and freezing of gait (Fog) (longitudinal analysis). Amantadine use prevalence was 12.6% (1,585/12,542, median dose = 200mg). Amantadine users were significantly younger, with longer and more severe PD symptoms, greater LEDD and more frequent use of device-aided/surgical treatment. Factors independently associated with amantadine initiation were younger age, longer PD duration, more frequent LIDs, MFs and FoG, higher LEDD and better cognitive function. 9 of the 658 patients on amantadine had stopped it at the following visit, after 12-18months (1.3%). New users of amantadine presented a higher improvement in LIDs and MF compared to amantadine never users. About 12% of PD patients within the French NS-Park cohort used amantadine, mostly those with younger age and more severe PD. Amantadine initiation was associated with a subsequent reduction in LIDs and MFs.

Study on the correlation between cardiac function indices measured by velocity vector imaging and disease severity in patients with Parkinson disease.

This study aimed to evaluate cardiac function, particularly left ventricular systolic function, in patients with Parkinson disease (PD) using velocity vector imaging (VVI), and to determine whether a correlation exists between left ventricular global systolic function and PD severity. A case-control study design was used to select 56 PD patients and 30 healthy controls from January 2019 to December 2019. The characteristics of age, sex, BMI and course of disease were collected. The Hoehn-Yahr (H-Y) score was collected to record the grading of PD. The left ventricular systolic function of all patients was evaluated by variable vapor injection (VVI). The left ventricular systolic function was compared between the case group and the control group, and the correlation between cardiac dysfunction and the severity of PD symptoms was assessed using the modified H-Y scale. Compared with control group, left ventricular global systolic function18.22 (17.08, 19.12) vs 18.88 (18.12, 20.01) was lower in PD patients as indicated by left ventricular global longitudinal strain (GLS), and the difference was statistically significant (P = 0.039). Additionally, H-Y scores (r = -0.404) and PD duration(r = -0.323) were significantly correlated with reduced left ventricular ejection fraction (P < 0.01), GLS (P < 0.001), left ventricular global radial strain (GRS; P < 0.001), and left ventricular global circumferential strain (GCS; P < 0.001), along with their associated peak strain rates (GLSr, GRSr, and GCSr; P < 0.001). Subclinical left ventricular global systolic dysfunction in patients with PD can be detected using VVI, and reduced left ventricular systolic function correlates with the modified H-Y score and duration of the disease.

Bradykinesia and rigidity modulated by functional connectivity between the primary motor cortex and globus pallidus in Parkinson's disease.

The mechanisms underlying motor fluctuations in patients with Parkinson's disease (PD) are currently unclear. Regional brain stimulation reported the changing of motor symptoms, but the correlation with functional connectivity (FC) in the brain network is not fully understood. Hence, our study aimed to explore the relationship between motor symptom severity and FC using resting-state functional magnetic resonance imaging (rsfMRI) in the "on" and "off" states of PD. In 26 patients with sporadic PD, FC was assessed using rsfMRI, and clinical severity was analyzed using the motor part of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS Part III) in the on and off states. Correlations between FC values and MDS-UPDRS Part III scores were assessed using Pearson's correlation coefficient. The correlation between FC and motor symptoms differed in the on and off states. FC between the ipsilateral precentral gyrus (PreCG) and globus pallidus (GP) correlated with the total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Lateralization analysis indicated that FC between the PreCG and GP correlated with the contralateral total MDS-UPDRS Part III scores and those for bradykinesia/rigidity in the off state. Aberrant FC in cortico-striatal circuits correlated with the severity of motor symptoms in PD. Cortico-striatal hyperconnectivity, particularly in motor pathways involving PreCG and GP, is related to motor impairments in PD. These findings may facilitate our understanding of the mechanisms underlying motor symptoms in PD and aid in developing treatment strategies such as brain stimulation for motor impairment.

Treatment Detection and Movement Disorder Society-Unified Parkinson's Disease Rating Scale, Part III Estimation Using Finger Tapping Tasks.

The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A local dynamic feature selection fusion method for voice diagnosis of Parkinson's disease

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). The existing PD voice feature analysis methods generally reduce dimensionality by selecting features with high relevance and low redundancy, ignoring the complementarity between features. To fully explore the intrinsic connection between PD features, this paper proposes a local dynamic feature selection fusion method (LDFSF) for PD identification and symptom severity prediction. First, the maximal information coefficient is used to calculate the relevance between features and the target value, and removes low relevant features. Then, a dynamic feature selection strategy based on self-organizing map network clustering is designed. This strategy dynamically updates the feature subset in a forward manner, and combines the relevance, redundancy and complementarity for feature selection. Among them, self-organizing map network clustering reduces the computational cost by reducing the number of candidate features, and effectively improves the accuracy of the model. Finally, the selected features are fed into the appropriate classifier or regressor to achieve accurate PD identification and severity progress prediction. Experiments on public OPDD and PTDS datasets show that the classification accuracy, sensitivity and specificity of LDFSF method on OPDD are 98.20%, 92.00% and 99.50% respectively, and the mean absolute error of predicting PD severity (motor- and total-UPDRS) on PTDS are 1.62 and 1.99 respectively, which are better than the compared methods for the selection of five and eight features respectively, thus indicating that the proposed LDFSF method can be effectively used in PD voice diagnosis.

Real-World Driving Data Indexes Dopaminergic Treatment Effects in Parkinson's Disease.

Driving is a complex, everyday task that impacts patient agency, safety, mobility, social connections, and quality of life. Digital tools can provide comprehensive real-world (RW) data on driver behavior in patients with Parkinson's disease (PD), providing critical data on disease status and treatment efficacy in the patient's own environment. This pilot study examined the use of driving data as a RW digital biomarker of PD symptom severity and dopaminergic therapy effectiveness. Naturalistic driving data (3974 drives) were collected for 1 month from 30 idiopathic PD drivers treated with dopaminergic medications. Prescriptions data were used to calculate levodopa equivalent daily dose (LEDD). The association between LEDD and driver mobility (number of drives) was assessed across PD severity, measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PD drivers with worse motor symptoms based on self-report (Part II: P = 0.02) and clinical examination (Part III: P < 0.001) showed greater decrements in driver mobility. LEDD levels >400 mg/day were associated with higher driver mobility than those with worse PD symptoms (Part I: P = 0.02, Part II: P < 0.001, Part III: P < 0.001). Results suggest that comprehensive RW driving data on PD patients may index disease status and treatment effectiveness to improve patient symptoms, safety, mobility, and independence. Higher dopaminergic treatment may enhance safe driver mobility in PD patients with worse symptom severity.

Analyzing the effect of the COVID-19 vaccine on Parkinson's disease symptoms.

Parkinson's Disease (PD) is one of the most common neurodegenerative diseases. PD has recently received more attention by researchers in the midst of the COVID-19 pandemic. Yet to be researched is the effect of the COVID-19 vaccines on PD patients. Several PD patients are still hesitant to the vaccine due to this unaddressed fear. The purpose of this study is to address this gap. Surveys were administered to PD patients 50 years and older at UF Fixel Institute who received at least one dose of the COVID-19 vaccine. Survey questions included patients' severity of PD symptoms before and after the vaccine and extent of worsening PD symptoms post-vaccination. After three weeks of collecting responses, the data was analyzed. 34 respondents were eligible for data consideration because they fell within the age range being studied. A total of 14 respondents out of 34 (41%, p=0. 0001) reported that their PD symptoms worsened after the COVID-19 vaccine to some extent. There was strong evidence of worsening of PD symptoms post COVID-19 vaccination, however it was mostly mild and limited to a couple of days. The worsening had statistically significant moderate positive correlation with vaccine hesitancy and post-vaccine general side effects. A possible causative mechanism of PD symptom worsening using existing scientific knowledge would be stress and anxiety associated with vaccine hesitancy and the extent of post-vaccine general side effects (fever, chills, pain), likely via simulating a mild systemic infection/inflammation the latter already established causes of PD symptom worsening.

Complement system changes in blood in Parkinson's disease and progressive Supranuclear Palsy/Corticobasal Syndrome

Parkinson's Disease (PD) is diagnosed clinically, and early PD is often challenging to differentiate from atypical parkinsonian disorders such as the Four-repeat (4R-) Tauopathies Progressive Supranuclear Palsy and Corticobasal Syndrome. Diagnostic biomarkers are needed, and proteomic studies have suggested that the plasma complement system is altered in PD, but validation studies are lacking.In this study, plasma from 148 individuals (PD, 4R-Tauopathies, and healthy controls (HC)) were used to quantify 12 complement proteins with immunoassays, and CH50 classical pathway complement activity was quantified in sera from further 78 individuals (PD and HC).Complement factors C1q and C3 in plasma were lower in individuals with 4R-Tauopathies (ANOVA, p = 0.0041, p = 0.0057 respectively) compared to both PD and HC. None of the complement proteins were altered between PD and HC, however a few proteins correlated with clinical parameters within the PD group. Notably, levels of C3 correlated with non-motor symptoms in female patients. Classical pathway complement activity was not altered in PD serum, but did correlate with mental fatigue.In conclusion, individuals with 4R-Tauopathies showed lower plasma C1q and C3 compared PD and HC. Neither complement levels nor CH50 activity were significantly altered in PD versus HC but may associate with PD symptom severity.

Type 2 diabetes mellitus augments Parkinson's disease risk or the other way around: Facts, challenges and future possibilities.

About 10% of the adult population is living with type 2 diabetes mellitus (T2DM) and 1% of the population over 60 years of age is suffering from Parkinson's disease (PD). A school of thought firmly believes that T2DM, an age-related disease, augments PD risk. Such relationship is reflected from the severity of PD symptoms in drug naive subjects possessing T2DM. Onset of Parkinsonian feature in case controls possessing T2DM corroborates the role of hyperglycemia in PD. A few cohort, meta-analysis and animal studies have shown an increased PD risk owing to insulin resistance. High fat diet and role of insulin signaling in the regulation of sugar metabolism, oxidative stress, α-synuclein aggregation and accumulation, inflammatory response and mitochondrial function in PD models and sporadic PD further connect the two. Although little is reported about the implication of PD in hyperglycemia and T2DM, a few studies have also contradicted. Ameliorative effect of anti-diabetic drugs on Parkinsonian symptoms and vague outcome of anti-PD medications in T2DM patients also suggest a link. The article reviews the literature supporting augmented risk of one by the other, analysis of proof of the concept, facts, challenges, future possibilities and standpoint on the subject.

Worse Sleep Quality Aggravates the Motor and Non-Motor Symptoms in Parkinson's Disease.

BackgroundsSleep disorders are the most common and disabling symptoms in patients with Parkinson's disease (PD). Understanding the associations between sleep characteristics and motor and non-motor symptoms (NMSs) in PD can provide evidence to guide therapeutic interventions and nursing strategies. We aimed to investigate the association between sleep characteristics and motor function and NMSs in PD using multiple approaches.MethodsA total of 328 participants were included, and all participants underwent Pittsburgh Sleep Quality Index (PSQI) evaluation and clinical assessments of PD symptoms. We conducted Spearman's correlation to evaluate the associations between sleep and PD symptoms, nonlinear regression to assess the relationships between sleep habits and PD, and mediated analyses to test the effects of NMSs on global PSQI and PD severity, quality of life, and motor symptoms.ResultsPoor sleep was associated with more severe PD symptoms. In addition, the reflection point for bedtime was around 21:52, associated with motor symptoms, and insufficient and excessive total time spent in bed and nocturnal sleep duration were correlated with higher NMS burdens. The optimal points were 8–9.2 and 6.2–6.9 h, respectively. It was also discovered that NMSs played the mediating roles in global sleep with the quality of life, PD stages, and motor symptoms to a varying range of 6.8–95.4%.ConclusionsSleep disorders have a significant effect on the burden of PD symptoms. The current findings provide new insights into the monitoring and management of sleep and PD and need to be further explored in the future studies.

Assessment of Parkinson's Disease Severity From Videos Using Deep Architectures.

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

Characteristics of Objective Sleep and Its Related Risk Factors Among Parkinson's Disease Patients With and Without Restless Legs Syndrome.

Objective: This study aimed to investigate the objective sleep characteristics and their related risk factors among Parkinson's disease (PD) patients with and without restless legs syndrome (RLS).Methods: A total of 125 patients with PD who underwent overnight polysomnography (PSG) were recruited consecutively. Eighty-one patients, including 27 PD with RLS (PD-RLS) and 54 PD without RLS (PD-NRLS), were included in the final analysis after 1:2 propensity score matching. Demographic, clinical, and polysomnographic data were compared between PD patients with and without RLS. The risk factors for sleep quality were examined using a multiple linear regression model.Results: The prevalence of RLS among PD patients was 28.0% (35/125). The PD-RLS group exhibited a higher score for the Unified Parkinson Disease Rating Scale (UPDRS) III than the PD-NRLS group. Also, the PD-RLS patients displayed significantly shorter total sleep times, worse sleep quality, decreased stage 3 duration, a longer wake time after sleep onset, and a higher arousal index than those without RLS (all p < 0.05). In the multiple linear regression model, PD duration (β = −0.363, 95% CI: −0.652 to −0.074; p = 0.016), UPDRS-III (β = −0.356, 95% CI: −0.641 to −0.071; p = 0.016), and periodic limb movement index (PLMI) (β = −0.472, 95% CI: −0.757 to −0.187; p = 0.002) were determined to be the risk factors influencing sleep quality in PD-RLS patients. The UPDRS-III (β = −0.347, 95% CI: −0.590 to −0.104; p = 0.006) and HAMD scores (β = −0.343, 95% CI: −0.586 to −0.100; p = 0.007) were significantly associated with sleep quality after adjusting for confounding factors in PD-NRLS patients, respectively.Conclusions: PD-RLS patients exhibited more disturbed and fragmented sleep in objective sleep architecture than PD-NRLS patients. The severity of motor symptoms in PD was significantly associated with poor sleep quality in both PD-RLS and PD-NRLS patients. Notably, our findings indicated that periodic limb movements during sleep (PLMS) was the risk factor that influenced the objective sleep quality in PD patients with RLS.

Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network.

Telemonitoring of Parkinson’s Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters’ assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to within less than 3.5 points difference from the clinical raters’ assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4–5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PD symptom telemonitoring applications.

Emerging role of S100B protein implication in Parkinson's disease pathogenesis.

The exact etiology of Parkinson's disease (PD) remains obscure, lacking effective diagnostic and prognostic biomarkers. In search of novel molecular factors that may contribute to PD pathogenesis, emerging evidence highlights the multifunctional role of the calcium-binding protein S100B that is widely expressed in the brain and predominantly in astrocytes. Preclinical evidence points towards the possible time-specific contributing role of S100B in the pathogenesis of neurodegenerative disorders including PD, mainly by regulating neuroinflammation and dopamine metabolism. Although existing clinical evidence presents some contradictions, estimation of S100B in the serum and cerebrospinal fluid seems to hold a great promise as a potential PD biomarker, particularly regarding the severity of motor and non-motor PD symptoms. Furthermore, given the recent development of S100B inhibitors that are able to cross the blood brain barrier, novel opportunities are arising in the research field of PD therapeutics. In this review, we provide an update on recent advances in the implication of S100B protein in the pathogenesis of PD and discuss relevant studies investigating the biomarker potential of S100B in PD, aiming to shed more light on clinical targeting approaches related to this incurable disorder.

Pharmacological Treatment of Early Motor Manifestations of Parkinson Disease (PD).

Parkinson disease (PD), as a slowly progressive neurodegenerative disorder, undergoes six neuropathological stages. The earliest clinical manifestation presents in the middle stage of the disorder pathologically, when 50% or more of the dopaminergic neurons have degenerated in the substantia nigra. This discrepancy between the early stage clinically and that pathologically has, in part, spurred the debate as to when it is best to initiate symptomatic therapy. The most well-studied monotherapeutic agents for PD in its early course include levodopa (the cornerstone of PD therapy), dopamine agonists, and monoamine oxidase inhibitors (MAOIs). With several options for initiating pharmacologic therapy, along with the heterogenous presentation of the disorder, an individualized approach is warranted. Careful deliberation must be done to optimize risk reduction while providing effective symptom control, taking the chronological age, comorbidities, social and financial disposition, work status, and both immediate- and long-term goals into consideration. Generally, treatment can be delayed in patients with mild symptoms and minimal functional impairment at any age. If treatment must be initiated, dopamine agonists and monoamine oxidase type B inhibitors can be used, especially in younger patients with milder disease. However, for older patients, those with moderate to severe PD symptoms, regardless of age, or for patients with greater comorbidities, levodopa generally remains the better choice. Eventually, regardless of initial therapy, studies have shown that most will eventually require levodopa therapy when symptoms become more disabling.

The Relationship Between Social Anxiety Disorder and Motor Symptoms of Parkinson Disease: A Pilot Study

BackgroundIn patients with Parkinson disease (PD), motor symptoms coexist with several nonmotor neuropsychiatric symptoms. Various anxiety subtypes (generalized anxiety disorder, panic disorder, and social anxiety disorder [SAD]) are more prevalent in patients with PD than in the general population. ObjectiveWe estimated the prevalence of SAD in early patients with PD and the relationship between severity of SAD and PD symptoms. MethodsThe Liebowitz Social Anxiety Scale (LSAS) and Unified Parkinson's Disease Rating Scale (UPDRS) III, which assess function impairment, were used to grade symptom severity among 41 patients with early PD. Ratings were compared and analyzed in relation to UPDRS subdivisions. ResultsUPDRS III and LSAS scores were not significantly correlated (r = 0.23, P = 0.14), but LSAS and UPDRS I, which evaluate nonanxiety psychiatric symptoms, were significantly correlated (r = 0.44; P = 0.004) and were stronger in the group not treated for PD (r = 0.82) but were in the group treated for PD (r = 0.28), although this difference did not reach statistical significance (P = 0.07 using the Fisher r-to-z transformation). LSAS also correlated with total UPDRS and UPDRS II (P ≤ 0.04). ConclusionsResults suggest that SAD symptoms in patients with PD correlate with PD symptoms as evaluated by the total UPDRS and UPDRS I and II. In our pilot study, this correlation was higher in levodopa-untreated patients with PD but was not statistically significant. Because the UPDRS III and LSAS were not statistically significantly correlated, a direct motor correlation with SAD symptoms cannot be suggested. Further investigation is needed to clarify the relationship of SAD in patients with PD and potential treatment options.

Examining the Reserve Hypothesis in Parkinson's Disease: A Longitudinal Study.

Whether reserve plays a role in Parkinson's disease (PD) patients has received less attention than in dementia and has been mainly examined in relation with cognitive function. To investigate whether reserve plays a role in the severity and progression of motor, cognitive, and nonmotor PD symptoms by examining whether education level (proxy of reserve) is associated with baseline performance and rate of progression. We used data from a longitudinal cohort of PD patients (≤5-year disease duration at baseline) annually followed up to 5 years (n = 393; 41% women; mean age = 62.3 years, standard deviation = 10.0; mean disease duration = 2.6 years, standard deviation = 1.5). We examined the relationship of education with time to reach Hoehn and Yahr stage ≥3 using Cox regression and with baseline severity and progression of motor (Movement Disorder Society-Unified Parkinson's Disease Rating Scale parts II and III, gait speed), cognitive (Mini-Mental State Examination), and nonmotor (depression, anxiety, nonmotor symptoms scale, quality of life) symptoms using mixed models. Education level was not associated with age at onset or diagnosis. Compared with the low-education group, the incidence of Hoehn and Yahr ≥3.0 was 0.42 times lower (95% confidence interval, 0.22-0.82, P = 0.012) in the high-education group. Higher education was associated with better baseline motor function (P < 0.001), but not with the rate of motor decline (P > 0.15). Similar results were observed for cognition. Education was not associated with nonmotor symptoms. Higher education is associated with better baseline motor/cognitive function in PD, but not with rate of decline, and with a lower risk of reaching Hoehn and Yahr ≥3 during the follow-up. Our observations are consistent with a passive reserve hypothesis for motor/cognitive symptoms. © 2019 International Parkinson and Movement Disorder Society.