Parkinson's Disease Severity Research Articles

Differences in subthalamic oscillatory activity in the two hemispheres associated with severity of Parkinson's disease.

It is well known that motor features of Parkinson's disease (PD) commonly begin on one side of the body and extend to the other side with disease progression. The onset side generally remains more severely affected over the course of the disease. However, the pathophysiology underlying the asymmetry of motor manifestations remains unclear. The purpose of the present study is to examine whether alterations in neuronal activity in the subthalamic nucleus (STN) associate with PD severity. Microelectrode recording was performed in the STN during targeting for 30 patients in the treatment of deep brain stimulation. The mean spontaneous firing rate (MSFR), power density spectral analysis, and correlations were calculated. Characteristics of subthalamic oscillatory activity were compared between two hemispheres. UPDRS III scores during "Off" and "On" states were obtained for the body side of initial symptoms (BSIS) and the body side of extended symptoms (BSES). There were significant differences of MSFR (41.3 ± 11.0 Hz vs 35.2 ± 10.0 Hz) and percentage of ß frequency oscillatory neurons (51.3% vs 34.9%) between BSIS and BSES. The percentage of ß frequency oscillatory neurons correlated with the bradykinesia/rigidity scores for both sides (p < 0.05). In contrast, the percentage of tremor frequency oscillatory neurons was significantly higher in the BSES than that in the BSIS. In particular, these neurons only correlated with the tremor scores of the BSES (p < 0.05). The results suggest that increased neuronal firing rate and ß frequency oscillatory neurons in the STN are associated with contralateral side motor severity and its progression. Tremor frequency oscillatory neurons are less observed in the STN of the BSIS suggesting that ß oscillatory activity dominates and tremor frequency oscillatory activity reciprocally declines.

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.

Oculometric measures as a tool for assessment of clinical symptoms and severity of Parkinson’s disease

Abnormalities of oculometric measures (OM) are widely described in people with Parkinson's disease (PD). However, knowledge of correlations between abnormal OM, disease severity and clinical assessment in PD patients is still lacking. To evaluate these correlations, PD patients (215 patients, mean age 69 ± 9.1 years, 79 females) with severe (H&Y > 3) and mild to moderate (H&Y ≤ 2) disease, and 215 age-matched healthy subjects were enrolled. All patients were evaluated using MDS-UPDRS and an oculometric test using computer vision and deep learning algorithms. Comparisons of OM between groups and correlations between OM and MDS-UPDRS scores were calculated. Saccadic latency (ms) was prolonged in patients with severe compared with mild to moderate disease (pro-saccades: 267 ± 69 vs. 238 ± 53, p = 0.0011; anti-saccades: 386 ± 119 vs. 352 ± 106, p = 0.0393) and in patients with mild to moderate disease versus healthy subjects (pro-saccades: 238 ± 53 vs. 220 ± 45, p = 0.0003; anti-saccades: 352 ± 106 vs. 289 ± 71, p < 0.0001). Error rate (%) was higher among patients with severe (64.06 ± 23.08) versus mild to moderate disease (49.84 ± 24.81, p = 0.0001), and versus healthy subjects (49.84 ± 24.81 vs. 28.31 ± 21.72, p = 0.00001). Response accuracy (%) was lower for patients with severe (75.66 ± 13.11) versus mild to moderate disease (79.66 ± 13.56, p = 0.0462), and versus healthy subjects (79.66 ± 13.56 vs. 90.27 ± 8.79, p < 0.0001). Pro- and anti-saccadic latency, error rate and accuracy were correlated with MDS-UPDRS scores (r = 0.32, 0.28, 0.36 and -0.30, respectively, p < 0.0001) and similar correlations were found with its axial subscore (R = 0.38, 0.29, 0.44, and -0.30, respectively, p < 0.0001). Several OM were different in patients under levodopa treatment. OM worsened as PD severity increases, and were correlated with MDS-UPDRS scores. Using OM can be implemented for PD patients’ assessment as a tool to follow disease progression.

Mitochondrial sensitive probe with aggregation‐induced emission characteristics for early brain diagnosis of Parkinson's disease

AbstractThe early diagnosis of Parkinson's disease (PD) provides opportunities for early intervention to slow the progression of neurological degeneration in patients, particularly as the aging population increases in our society. Among a series of pathological features of PD, mitochondria abnormalities have been identified as central event that occurs at the early stage of PD. However, the method for detecting mitochondrial abnormalities‐associated early PD has not been fully developed. We herein report a specifically mitochondrial targeting probe (named TPA‐BT‐SCP) that is able to characterize mitochondria abnormalities for early diagnosis of PD and monitor PD neurodegenerative progress. The probe is an aggregation‐induced emission (AIE) probe with a strong positive charge, a 3D distorted molecular structure, and a separated HOMO‐LUMO distribution, designed with unique molecular design guidelines. Our research demonstrated that TPA‐BT‐SCP could emit stable and strong fluorescence, and rapidly accumulate in mitochondria due to the negative charge. After intranasal administration of 1‐methy‐4‐phenyl‐1,2,3,6‐tetrahydropyridine (MPTP)‐induced PD mice, TPA‐BT‐SCP successfully bypassed the blood−brain barrier to light up the brain, allowing the grading of PD severity based on its high sensitivity. Taken together, this work develops a novel AIE probe that exhibits dramatically high sensitivity to mitochondrial changes and enables noninvasive diagnosis of early PD in the brain.

AI Computing as Ubiquitous Healthcare Solution: Predict Parkinson’s for Large Masses in Society

Parkinson’s disease (PD) is one of the macroscale-effected neurological disorders that have adverse effects on the quality of life as they influence the proper functioning of organs. To misfortune, PD has limited diagnosing techniques and lacks treatment to cure at advanced stages. Therefore, there is a prime requirement to diagnose PD at the initial stage with the assistance of advanced technologies. This article focuses on the use of revolutionary computing technology, which is Artificial Intelligence (AI) to early diagnose this fatal disorder and provide an efficient healthcare solution. The compact Two-Dimensional Convolution Neural Network (2-D CNN)-based computing algorithm is developed to diagnose PD from the gait variation. The proposed system is able to provide promising accuracy of 98% in classifying PD that surpasses the current state-of-the-art performance. To provide a ubiquitous healthcare solution for diagnosing PD, the proposed 2-D CNN model will be a quantum leap breakthrough. Consequently, the progressive PD spread, growth, and severity can be efficiently controlled. Therefore, discern ubiquitous healthcare services for large masses in society.

Role of the functional head impulse test in evaluating vestibulo-ocular reflex abnormalities in individuals with Parkinson's disease.

To assess vestibular-ocular reflex (VOR) function in patients with Parkinson's disease (PD) using a new method called functional head impulse test (fHIT). Case-control study. Tertiary medical center. Twenty individuals with PD were recruited for PD group, and twenty healthy individuals for the control group. Ages of both groups ranged from 47 to 76 years. According to the modified Hoehn-Yahr score, PD group was classified as an early stage with a range of 1-2.5 and a mid-late stage with a range of 3-5. Percentage of correct answers (%CA) was calculated using fHIT for lateral, anterior and posterior semicircular canal (SCC) planes. PD group had statistically lower fHIT %CA in the right lateral, left lateral, and left posterior SCC planes compared to control group (p < .05). There was no statistical difference between two PD severity groups and functional VOR abnormality. It was concluded that the functional VOR in the right lateral and left lateral and left posterior SCC plane was affected in individuals with PD. Our results show that impaired VOR and reduced dynamic visual acuity should be considered in individuals with PD for vestibular rehabilitation exercises.

What Dietary Vitamins and Minerals Might Be Protective against Parkinson's Disease?

Dietary constituents may affect the progression of Parkinson's disease (PD). This study aimed to assess the contribution of dietary intake of vitamins and minerals to the severity, motor and non-motor symptoms, and risk of PD. In this case-control study, 120 patients with PD and 50 healthy participants participated. Dietary intake of vitamins and minerals was determined using a 147-item food frequency questionnaire. The severity of PD was determined by the Unified Parkinson's Disease Rating Scale (UPDRS). Patients with PD had lower intake of several vitamins and minerals including lycopene, thiamine, vitamin B6, vitamin B12, pantothenic acid, magnesium, zinc, manganese, selenium, chromium, and phosphorus, but had higher intake of α-tocopherol. High dietary intake of vitamin A, α-carotene, β-cryptoxanthin, vitamin C, and α-tocopherol were correlated with increased odds of PD. High intake of lycopene, thiamin, vitamin B6, pantothenic acid, magnesium, zinc, manganese, chromium, and phosphorous correlated with reduced odds of PD. The predictive power of α-tocopherol concerning the risk of PD was stronger relative to other vitamins. Dietary intake of pantothenic acid was negatively correlated with PD severity and symptoms of motor examination and complication. The severity and motor symptoms of PD were also negatively correlated with β-carotene, vitamin C, riboflavin, vitamin B6, and biotin intake. The UPDRS total score and motor symptoms in PD patients were negatively correlated with phosphorus, magnesium, zinc, manganese, and chromium, and strongly with potassium intake. The findings indicate that adequate dietary intake of vitamins and minerals may have a preventive effect on developing PD and progression of motor decline.

A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease.

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

The Association of the Glymphatic Function with Parkinson's Disease Symptoms: Neuroimaging Evidence from Longitudinal and Cross-Sectional Studies.

Emerging pathological evidence suggests that there is an association between glymphatic dysfunction and the progression of Parkinson's disease (PD). However, the clinical evidence of this association remains lacking. In this study, the index for diffusion tensor image analysis along the perivascular space (ALPS index) was calculated to evaluate glymphatic function. Overall, 289 patients with PD were enrolled in the cross-sectional study. The ALPS index was found to be negatively correlated with age, disease severity, and dyskinesia. In the longitudinal study, the information on a total of 95 PD patients with 5-year follow-up examinations was collected from the Parkinson's Progression Marker Initiative, 33 of which were classified into the low ALPS index group, and all others were classified into the mid-high ALPS index group based on the first tertile of the baseline ALPS index. The results of longitudinal regression indicated that there was a significant main group effect on autonomic dysfunction, as well as on activities of daily living. In addition, the low ALPS index group had faster deterioration in MDS-UPDRS part III and part II, Symbol Digit Modalities Test and Hopkins Verbal Learning Test. Path analysis showed that ALPS index acted as a significant mediator between tTau/ Aβ1-42 and cognitive change in the Symbol Digit Modalities Test score at year 4 and year 5. The ALPS index, an neuroimaging marker of glymphatic function, is correlated with PD disease severity, motor symptoms, and autonomic function, and is predictive of faster deterioration in motor symptoms and cognitive function. Additionally, glymphatic function may mediate the pathological role of toxic protein in cognitive decline. ANN NEUROL 2023;94:672-683.

Automatic classification of the severity level of Parkinson’s disease: A comparison of speaking tasks, features, and classifiers

Automatic speech-based severity level classification of Parkinson’s disease (PD) enables objective assessment and earlier diagnosis. While many studies have been conducted on the binary classification task to distinguish speakers in PD from healthy controls (HCs), clearly fewer studies have addressed multi-class PD severity level classification problems. Furthermore, in studying the three main issues of speech-based classification systems – speaking tasks, features, and classifiers – previous investigations on the severity level classification have yielded inconclusive results due to the use of only a few, and sometimes just one, type of speaking task, feature, or classifier in each study. Hence, a systematic comparison is conducted in this study between different speaking tasks, features, and classifiers. Five speaking tasks (vowel task, sentence task, diadochokinetic (DDK) task, read text task, and monologue task), four features (phonation, articulation, prosody, and their fusion), and four classifier architectures (support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and AdaBoost) were compared. The classification task studied was a 3-class problem to classify PD severity level as healthy vs. mild vs. severe. Two MDS-UPDRS scales (MDS-UPDRS-III and MDS-UPDRS-S) were used for the ground truth severity level labels. The results showed that the use of the monologue task and the articulation and fusion of features improved classification accuracy significantly compared to the use of the other speaking tasks and features. The best classification systems resulted in a rate of accuracy of 58% (using the monologue task with the articulation features) for the MDS-UPDR-III scale and 56% (using the monologue task with fusion of features) for the MDS-UPDRS-S scale.

Predicting Parkinson's Disease Severity using Telemonitoring Data and Machine Learning Models: A Principal Component Analysis-based Approach for Remote Healthcare Services during COVID-19 Pandemic

Parkinson's disease (PD) is a progressive and chronic neurological condition that affects about 1% of the world's over-60 population. The COVID-19 pandemic has emphasized the significance of remote healthcare services, such as telemedicine, in managing chronic diseases such as PD. This research intends to construct machine learning (ML) models to predict PD severity utilizing vocal data derived from the UCI database for motor and total Unified Parkinson's disease rating scale (UPDRS) ratings. ML was used to study the association between voice vibration and PD, and PCA and ML models were utilized to minimize model complexity and compare the predictive performance of various statistical models for PD regression. The dataset included 5,875 medical voice records from 42 patients with early-stage PD who participated in a six-month clinical trial. The proposed PCA model simplified the model and achieved a root-mean-square error of 1.78 with an R-squared value of 0.95 for predicting the motor UPDRS and 1.78 with an R-squared value of 0.97 for predicting the total UPDRS. This work can give a framework for developing remote healthcare services for Parkinson's disease and other chronic conditions, which can be helpful during pandemics and other situations where access to in-person care is limited.

Substantia nigra nigrosome-1 imaging correlates with the severity of motor symptoms in Parkinson's disease

BackgroundNigrosome-1 imaging has been used for assisting the diagnosis of Parkinson's disease (PD). We aimed to examine the diagnostic performance of loss of nigrosome-1 in PD and the correlation between the size of the nigrosome-1 and motor severity of PD. MethodsWe included 237 patients with PD and 165 controls. The motor severity of PD was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) part III score and Hoehn-Yahr staging. The 3 or 1.5 Tesla susceptibility-weighted imaging combined with a deep-learning algorithm was applied for detecting the loss and the size of nigrosome-1. Clinical correlations and diagnostic performance of size of nigrosome-1 were also investigated. ResultsThe mean nigrosome-1 size was significantly smaller in PD patients than in controls (0.06 ± 0.07 cm2 vs. 0.20 ± 0.05 cm2, P < 0.001). The area under the receiver operating characteristic curve (AUC) of the established model showed 0.94 accuracy (95% confidence interval [CI]: 0.87, 1.01, P < 0.01) in differentiating between the PD and control groups. Moreover, the partial loss of nigrosome-1 detected with SWI had an AUC of 0.96 in discriminating early-stage PD from controls (95% CI: 0.88, 1.02, P < 0.001). After adjusting for age, sex, disease duration, and levodopa equivalent daily dose, the estimated size of nigrosome-1 was negatively associated with the UPDRS part III motor score (ρ = −0.433, P < 0.001), but not with Mini-Mental State Examination scores (ρ = 0.006, P = 0.894). ConclusionsThe extent of loss and the size of nigrosome-1 may potentially assist in the diagnosis of PD. Nigrosome-1 size reflects the motor severity of PD.

Association of depression with disease duration, quality of life and adherence in Parkinson's disease: A cross sectional study.

Parkinson's disease (PD) is a progressive motor disorder often accompanied by non-motor symptoms such as depression. The objective was to estimate the prevalence of depression in PD patients, and assess its association with disease duration, quality of life and adherence to treatment. This cross-sectional study was conducted in a tertiary care centre for patients diagnosed with PD. Depression was diagnosed using Hamilton Depression Rating Scale. The Chi-square test was used to assess the difference in proportions of depression in various types and severity of PD. Depression was also correlated with disease duration, quality of life (QOL) and adherence to treatment using the Pearson correlation test. A P value of <0.05 was considered statistically significant. Among 51 patients, 20 (39.22%) patients were found to have depression. The mean duration of disease in depressed patients was significantly longer compared to that in non-depressed patients (7.99 ± 4.53 vs. 3.62 ± 2.23, P < 0.001), respectively. The non-depressed patients were better adherent to treatment (1.71 ± 1.5 vs. 0.56 ± 0.91). The quality of life of patients was significantly low for depressed patients (21.90 ± 6.91 vs. 13.16 ± 6.93, P < 0.001). Depression in Parkinson's patients was positively correlated with the duration of the disease (P-value <0.001); disease staging (P-value <0.001). Quality of life (QOL) had a strong correlation with depression (P-value <0.001) and Hoehn and Yahr (HY) staging (P-value <0.05). Depression was found in 39.22% of PD patients and was more significantly associated with disease duration, non-adherence to treatment and decreased quality of life.

Home-based exercise training by using a smartphone app in patients with Parkinson's disease: a feasibility study.

Parkinson's disease (PD) patients experience deterioration in mobility with consequent inactivity and worsened health and social status. Physical activity and physiotherapy can improve motor impairments, but several barriers dishearten PD patients to exercise regularly. Home-based approaches (e.g., via mobile apps) and remote monitoring, could help in facing this issue. This study aimed at testing the feasibility, usability and training effects of a home-based exercise program using a customized version of Parkinson Rehab® application. Twenty PD subjects participated in a two-month minimally supervised home-based training. Daily session consisted in performing PD-specific exercises plus a walking training. We measured: (i) feasibility (training adherence), usability and satisfaction (via an online survey); (ii) safety; (iii) training effects on PD severity, mobility, cognition, and mood. Evaluations were performed at: baseline, after 1-month of training, at the end of training (T2), and at 1-month follow-up (T3). Eighteen out of twenty participants completed the study without important adverse events. Participants' adherence was 91% ± 11.8 for exercise and 105.9% ± 30.6 for walking training. Usability and satisfaction survey scored 70.9 ± 7.7 out of 80. Improvements in PD severity, mobility and cognition were found at T2 and maintained at follow-up. The home-based training was feasible, safe and seems to positively act on PD-related symptoms, mobility, and cognition in patients with mild to moderate stage of PD disease. Additionally, the results suggest that the use of a mobile app might increase the amount of daily physical activity in our study population. Remote monitoring and tailored exercise programs appear to be key elements for promoting exercise. Future studies in a large cohort of PD participants at different stages of disease are needed to confirm these findings.

Irritable bowel syndrome based on Rome IV diagnostic criteria associates with non-motor symptoms of Parkinson's disease

BackgroundRecent concepts suggest that the neuropathological hallmark of Parkinson's disease (PD) may in part originate from the enteric nervous system. We evaluated the frequency of functional gastrointestinal disorders in PD patients using Rome IV criteria and correlated the clinical severity of PD. MethodsPD patients and matched controls were recruited between January 2020 and December 2021. Rome IV criteria were used to diagnose constipation and irritable bowel syndrome (IBS). Severity of PD motor symptoms was evaluated using UPDRS part III scores and non-motor symptoms using Non-motor Symptoms Scale (NMSS). ResultsA total of 99 PD patients and 64 controls were enrolled. The prevalence of constipation (65.7% vs. 34.3%, P < 0.001) and IBS (18.1% vs 5%, P = 0.02) were significantly higher in PD patients than controls. The prevalence of IBS was higher in early-stage PD than advanced-stage PD (14.43% vs. 8.25%, P = 0.02), whereas constipation was more common in advanced stages (71.43% vs. 18.56%, P < 0.001). PD patients with IBS had higher NMSS total scores (P < 0.01) than those without IBS. The severity of IBS correlated with NMSS scores (r = 0.71, P < 0.001), especially subscores in domain 3 assessing mood disorders (r = 0.83, P < 0.001), but not UPDRS part III scores (r = 0.06, P = 0.45). The severity of constipation correlated with the UPDRS part III scores (r = 0.59, P < 0.001) but not the domain 3 mood subscores (r = 0.15, P = 0.07). ConclusionThe prevalence of IBS and constipation was higher in PD patients than controls and phenotypic correlation supported the occurrence of IBS with higher non-motor symptom burden, especially mood symptoms, in PD patients.

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.

Unveiling How Hydroxytyrosol Destabilizes α-Syn Oligomers Using Molecular Simulations.

The etiology of Parkinson's disease (PD) is mainly linked to the α-synuclein (α-Syn) fibrillogenesis. Hydroxytyrosol (HT), also known as 3,4-dihydroxyphenylethanol, is a naturally occurring polyphenol, found in extra virgin olive oil, and has been shown to have cardioprotective, anticancer, antiobesity, and antidiabetic properties. HT has neuroprotective benefits in neurodegenerative diseases and lessens the severity of PD by reducing the aggregation of α-Syn and destabilizing the preformed toxic α-Syn oligomers. However, the molecular mechanism by which HT destabilizes α-Syn oligomers and alleviates the accompanying cytotoxicity remains unexplored. The impact of HT on the α-Syn oligomer structure and its potential binding mechanism was examined in this work by employing molecular dynamics (MD) simulations. The secondary structure analysis depicted that HT significantly reduces the β-sheet and concomitantly increases the coil content of α-Syn trimer. Visualization of representative conformations from the clustering analysis depicted the hydrogen bond interactions of the hydroxyl groups in HT with the N-terminal and nonamyloid-β component (NAC) region residues of α-Syn trimer, which, in turn, leads to the weakening of interchain interactions in α-Syn trimer and resulted in the disruption of the α-Syn oligomer. The binding free energy calculations depict that HT binds favorably to α-Syn trimer (ΔGbinding = -23.25 ± 7.86 kcal/mol) and a notable reduction in the interchain binding affinity of α-Syn trimer on the incorporation of HT, which, in turn, highlights its potential to disrupt α-Syn oligomers. The current research provided mechanistic insights into the destabilization of α-Syn trimer by HT, which, in turn, will provide new clues for developing therapeutics against PD.

An Observational Study of Respiratory Dysfunction in Parkinson’s Disease at a Tertiary Care Hospital in Delhi

Abstract Objectives: The purpose of this study is to evaluate the impact of Parkinson’s disease (PD) on respiratory function using spirometry. In addition, the study aims to investigate the relationship between the severity of PD and any impairments in a respiratory capacity as measured by forced vital capacity (FVC%). Methods: This case–control study (40 cases of PD vs. 40 healthy controls) was conducted from July 1, 2021, to October 31, 2022, in a tertiary care hospital of New Delhi. Clinical symptoms and severity (Movement Disorder Society Unified Parkinson’s Disease Rating Scale [MDS-UPDRS] part-III score and Modified H and Y Scale) of the patients of PD were noted. A pulmonary function test (by spirometry) was performed and parameters such as forced expiratory volume in 1 s (FEV1), FVC, FEV1/FVC, and peak expiratory flow rate (PEFR) were noted. Mann–Whitney U-test (for two groups) and Chi-square test were used for the comparison of variables. Spearman rank correlation coefficient was used for the correlation of FVC (% predicted) with MDS-UPDRS part-III score and Modified H and Y stage. Results: Compared to controls, cases had significantly lower FVC% (70.5 vs. 88, P &lt; 0.01), significantly lower FEV1% (68 vs. 83.5, P &lt; 0.01), comparable FEV1/FVC (% predicted) (97 vs. 96, P = 0.805) and significantly lower PEFR (% predicted) (80.5 vs. 92, P &lt; 0.01). The pattern of lung involvement was restrictive, normal, and obstructive in 72.50%, 20%, and 7.50% of cases, respectively. FVC (% predicted) showed a significant negative correlation with MDS-UPDRS part-III score (r = −0.895, P &lt; 0.01) and with Modified H and Y Scale (r = −0.792, P &lt; 0.01). Conclusion: Patients with PD had significantly more deranged respiratory dysfunction and there was significantly higher respiratory dysfunction with increasing severity of PD. A proper screening of lung functions may allow an early detection of respiratory dysfunction, thereby helping to initiate an early pulmonary rehabilitation to prevent respiratory complications.

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.

Translated article] Total knee arthroplasty is effective in patients with Parkinson's disease. Functional assessment and analysis of complications

IntroductionThanks to improvements in the medical treatment of Parkinson's disease (PD), the life expectancy of these patients has increased, but the overall outcome after total knee arthroplasty (TKA) is controversial. We aim to analyse a series of patients with PD, evaluating their clinical status, functional results, complications and survival rates after TKA. MethodsWe retrospectively studied 31 patients with PD operated between 2014 and 2020. The mean age was 71 years (SD±5.8). There were 16 female patients. The mean follow-up was 68.2 months (SD±36). We used the knee scoring system (KSS) and the visual analogue scale (VAS) for the functional evaluation. The modified Hoehn and Yahr scale was used to assess the severity of PD. All complications were recorded, and survival curves were performed. ResultsThe mean postoperative KSS evaluation had an increase of 40 points [35 (SD±15) vs 75 (SD±15) (p<.001)]. The mean postoperative VAS decreased 5 points [8 (SD±2) vs 3 (SD±2) (p<.001)]. Thirteen patients reported being very satisfied, 13 were satisfied, and only 5 were poorly satisfied. Seven patients suffered surgical complications, and 4 patients had recurrent patellar instability. At a mean 68.2 months follow-up, the overall survival rate was 93.5%. When considering the secondary patellar resurfacing as the endpoint, the survival rate was 80.6%. ConclusionsIn this study, TKA was associated with excellent functional outcomes in patients with PD. At a mean 68.2 months follow-up, TKA had excellent survivorship in the short term, with recurrent patellar instability as the most common complication. Even though these findings confirm the effectiveness of TKA in this population, a thorough clinical evaluation and multidisciplinary approach are needed to decrease the likelihood of complications.