Search Results (44)

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder that impairs mobility. Treadmill training (TT) is a common rehabilitation strategy for improving gait parameters in individuals with PD. This systematic review evaluated the effectiveness of TT in improving motor function, walking ability, and overall functional mobility in PD patients. Methods: We compared TT to other forms of gait and motor rehabilitation, including conventional and robotic gait training. Trials that compared a treadmill training group with a non-intervention group were excluded from this review. We searched multiple databases for RCTs involving Parkinson’s patients until January 2025. The primary outcomes were motor function (UPDRS-III) and walking ability (6 MWT and TUG test). Results: We identified 285 articles; 199 were excluded after screening. We assessed the full text of 86 articles for eligibility, and 13 RCTs met the inclusion criteria. Some of them were included in the meta-analysis. The TT group showed a significant improvement in UPDRS-III scores [mean difference (MD): −1.36 (95% CI: −2.60 to −0.11)] and greater improvement in TUG performance [MD, −1.75 (95% CI: −2.69 to −0.81)]. No significant difference in walking capacity as assessed through the 6 MWT was observed [MD: 26.03 (95% CI: −6.72 to 58.77). Conclusions: The current study suggests that TT is effective in improving the motor symptoms and functional mobility associated with PD. Further studies are needed to develop protocols that consider the patients’ clinical characteristics, disease stage, exercise tolerance, and respiratory function. Full article

Background/Objectives: The study aims to identify key cognitive and non-cognitive variables (e.g., clinical, neuroimaging, and genetic data) predicting cognitive decline in Parkinson’s disease (PD) patients using machine learning applied to a sample (N = 618) from the Parkinson’s Progression Markers Initiative database. Traditional research has mainly employed explanatory approaches to explore variable relationships, rather than maximizing predictive accuracy for future cognitive decline. In the present study, we implemented a predictive framework that integrates a broad range of baseline cognitive, clinical, genetic, and imaging data to accurately forecast changes in cognitive functioning in PD patients. Methods: An artificial neural network was trained on baseline data to predict general cognitive status three years later. Model performance was evaluated using 5-fold stratified cross-validation. We investigated model interpretability using explainable artificial intelligence techniques, including Shapley Additive Explanations (SHAP) values, Group-Wise Feature Masking, and Brute-Force Combinatorial Masking, to identify the most influential predictors of cognitive decline. Results: The model achieved a recall of 0.91 for identifying patients who developed cognitive decline, with an overall classification accuracy of 0.79. All applied explainability techniques consistently highlighted baseline MoCA scores, memory performance, the motor examination score (MDS-UPDRS Part III), and anxiety as the most predictive features. Conclusions: From a clinical perspective, the findings can support the early detection of PD patients who are more prone to developing cognitive decline, thereby helping to prevent cognitive impairments by designing specific treatments. This can improve the quality of life for patients and caregivers, supporting patient autonomy. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder commonly marked by upper limb tremors that interfere with daily activities. Wearable devices, such as smartwatches, represent a promising solution for continuous and objective monitoring in PD. This study aimed to develop and validate a tremor-detection algorithm using smartwatch sensors. Data were collected from 21 individuals with PD and 27 healthy controls using both a commercial inertial measurement unit (G-Sensor, BTS Bioengineering, Italy) and a smartwatch (Apple Watch Series 3). Participants performed standardized arm movements while sensor signals were synchronized and processed to extract relevant features. Statistical analyses assessed discriminant and concurrent validity, reliability, and accuracy. The algorithm demonstrated moderate to strong correlations between smartwatch and commercial IMU data, effectively distinguishing individuals with PD from healthy controls showing associations with clinical measures, such as the MDS-UPDRS III. Reliability analysis demonstrated agreement between repeated measurements, although a proportional bias was noted. Power spectral density (PSD) analysis of accelerometer and gyroscope data along the x-axis successfully detected the presence of tremors. These findings support the use of smartwatches as a tool for detecting tremors in PD. However, further studies involving larger and more clinically impaired samples are needed to confirm the robustness and generalizability of these results. Full article

Background: Metabolic syndrome and Parkinson’s disease have common pathophysiological denominators. This study aimed to investigate how metabolic syndrome contributes to Parkinson’s disease progression, as well as the genetic traits shared by PD and MetS. Methods: Four hundred and twenty-three newly diagnosed drug-naïve PD patients were analyzed from the Parkinson’s Progression Markers Initiative (PPMI) database. We compared longitudinal changes in the total and subscale scores of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) between PD patients with and without metabolic syndrome over a five-year follow-up. We assessed the frequency of PD-associated genetic variants in both groups. Results: At baseline, Parkinson’s patients with MetS were typically men (p < 0.01) and older (p = 0.04), with a higher Hoehn and Yahr score (p = 0.01) compared with their counterparts without MetS. They showed higher Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) total scores at baseline and in follow-up years 2, 3, 4, and 5 (all p-values < 0.05) as analyzed by the Generalized Estimating Equation model. These differences were primarily driven by elevated motor scores (MDS-UPDRS Part III) (p < 0.01). MetS was associated with a higher frequency of the ZNF646.KAT8.BCKDK_rs14235 variant and a lower frequency of the NUCKS1_rs823118 and CTSB_rs1293298 variants. Conclusions: PD patients with MetS had worse motor symptomatology. Both conditions appear to share genetic susceptibility, involving genes related to lipid metabolism (BCKDK), autophagy and inflammation (CTSB), and chromatin regulation (NUCKS1). Full article

Objective: We aimed to analyze the real-world use of COMT inhibitors associated with levodopa in patients with Parkinson’s disease (PD) who present early fluctuations and to explore whether early COMT inhibition optimizes treatment outcomes. Methods: REONPARK is an ongoing 2-year prospective observational study. We included patients diagnosed with PD who presented signs of end-of-dose motor fluctuations for <2 years and started COMT inhibitors according to clinical practice. Outcomes included the clinician and patient global impression of change (CGI-C, PGI-C), the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the Parkinson’s Disease Questionnaire-8 (PDQ-8), Non-Motor Symptoms Scale (NMSS), 19-Symptom Wearing-off Questionnaire (WOQ-19), and safety. We present a pre-planned interim analysis (cut-off date 3 July 2023) of patients who completed the first 3 months of follow-up. Results: Seventy patients were analyzed (mean levodopa dose at inclusion 484.8 mg; duration of motor fluctuations 0.6 years). In all cases, COMT inhibition was initiated with opicapone, and 81% maintained a stable levodopa dose at 3 months. After 3 months of treatment with opicapone, 73.5% and 62.8% of patients improved on CGI-C and PGI-C, respectively. MDS-UPDRS scores improved significantly with a mean change from baseline of −3.3 ± 7.7 (p < 0.001) for Part III and −1.3 ± 1.7 (p < 0.001) for Part IV. The mean OFF time decreased from 3.7 ± 2.6 h at baseline to 2.2 ± 2.3 h, and 20.6% of patients no longer experienced OFF periods. Patients experiencing no impact of fluctuations increased from 10% to 45.6%. Conclusions: In PD patients with early fluctuations, three months of opicapone reduced the OFF time and improved functional outcomes, suggesting potential benefits in the early stages. Full article

Background/Objectives: Parkinson’s disease (PD) and hypertension are often coexistent conditions that interact in entwined ways at various levels. Cardiovascular autonomic dysfunction (CAD), a non-motor feature of PD occurring across all stages, alters blood pressure (BP) regulation. Methods: We conducted a cross-sectional study enrolling patients with PD and primary hypertension, without diabetes mellitus or other causes of secondary CAD, aiming to characterize BP profiles/patterns by ambulatory BP monitoring. We also sought associations between different CAD phenotypes and PD characteristics, disability, and cardiovascular comorbidities. Results: We included 47 patients with a median age of 71 years, PD duration of 9 years, and Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III score of 40. Diurnal and nocturnal BP values were within the reference range, but BP load was excessive. Almost one-third had neurogenic orthostatic hypotension (OH) and 80% were non-dippers. The overall burden of non-motor symptoms was significant in these phenotypes. Patients with neurogenic OH were more prone to constipation, anxiety, and urinary problems, whereas gustatory dysfunction, loss of libido, and erectile dysfunction were more frequently reported by non-dippers. No significant differences with regard to cognitive decline were identified in subjects with and without neurogenic OH. Neurogenic OH was symptomatic in 78% of the cases, whereas 56% of those with orthostatic symptoms did not have OH at repeated measurements. Conclusions: Neurogenic OH is an independent predictor of disability in patients with PD and hypertension, after adjusting for PD duration, Hoehn and Yahr stage, levodopa equivalent daily dose (LEDD), and Montreal Cognitive Assessment (MoCA) score. Full article

Background and Purpose: This pilot randomized controlled trial evaluated the effects of 12 sessions of patient-specific adaptive dynamic cycling (PSADC) versus non-adaptive cycling (NA) on motor function and mobility in individuals with Parkinson’s disease (PD), using inertial measurement unit (IMU) sensors for objective assessment. Methods: Twenty-three participants with PD (13 in the PSADC group and 10 in the NA group) completed the study over a 4-week period. Motor function was measured using the Kinesia™ sensors and the MDS-UPDRS Motor III, while mobility was assessed with the TUG test using OPAL IMU sensors. Results: The PSADC group showed significant improvements in MDS-UPDRS Motor III scores (t = 5.165, p < 0.001) and dopamine-sensitive symptoms (t = 4.629, p = 0.001), whereas the NA group did not improve. Both groups showed non-significant improvements in TUG time. IMU sensors provided continuous, quantitative, and unbiased measurements of motor function and mobility, offering a more precise and objective tracking of improvements over time. Conclusions: PSADC demonstrated enhanced treatment effects on PD motor function compared to NA while also reducing variability in individual responses. The integration of IMU sensors was essential for precise monitoring, supporting the potential of a data-driven, individualized exercise approach to optimize treatment outcomes for individuals with PD. Full article

Abstract: Background: An important neurological disorder of Parkinson’s Disease (PD) is characterized by motor and non-motor activity of the patients. Empirical condition of the patient: PD assessment uses the Movement Disorder Society Unified Parkinson’s Rating Scale part III (MDS-UPDRS-III) measures for identifying the prediction of PD. Due to the unstable value of the measurement, the PD prediction and tracking lead to a lower prediction rate. Methods: To overcome this limitation, this paper proposed the Graph Wavelet Transform (GWT) based weighted feature extraction along with the Graph Neutral Network (GNN) classification. The main contribution of this research is (i) The weighted correlation between the data is calculated by GWT for effective prediction of PD. (ii) Machine learning algorithms were trained to predict Parkinson’s disease based on these patterns. In this research, we developed a new model called Graph Neural Network (GNN) to predict PD tremors’ MDS-UPDRS-III score using input data. To strengthen PD research and enable the construction of individualized treatment plans, these linked networks work together to methodically examine the data and find significant discoveries. Results: The proposed approach for predicting PD severity (motor- and MDS_UPDRS) has a mean squared error of 0.1796 and a root mean squared error of 0.2845, according to the experimental data. The prediction accuracy is increased by 27.66%, 54.11%, and 0.71%, correspondingly, when compared with the most effective State-of-the-Art methods of DNN, ANFIS + SVR, and Mixed MLP models. Conclusion: In conclusion, this proves that the proposed strategy is more effective at making predictions. Full article

The acute levodopa challenge is widely used to distinguish Parkinson’s disease (PD) from atypical parkinsonian syndromes (APSs) such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). In APSs, very few patients present a clinically relevant response to levodopa. The aim of this study was to determine whether patients with atypical parkinsonism benefit from levodopa in any aspect of their multiple motor deficits despite the generally poor response. This retrospective study analyzed individual motor responses to the acute levodopa challenge using the MDS-UPDRS III in 47 PSP, 26 MSA, and 71 PD patients at Hannover Medical School. Despite the generally poor levodopa response in both PSP and MSA patients, bradykinesia and rigidity were the symptoms most notably affected by levodopa in PSP patients, while MSA patients experienced significant improvements in bradykinesia and action tremor. These findings underscore the variability in levodopa response among PSP and MSA patients and highlight the need for personalized treatment approaches in atypical parkinsonism. Full article

Background: high-intensity exercise is a feasible and effective modality in the early stages of Parkinson’s disease (PD). However, there are only a few studies that have carried out this type of intervention in customizable immersive virtual reality (IVR) environments. We explore the feasibility and effects of IVR-based high-intensity training through rowing and cycling exercises on the functional capacity, quality of life, and progression of PD. Methods: a total of 12 participants (61.50 ± 10.49 years old; 41.7% female, 58.3% male; stages I–III) were part of the study, which consisted of interventions of rowing and cycling in an IVR commercial exergame (25 min; twice per week for 14 weeks). The main variables measured were functional capacity, handgrip strength, functional mobility (TUG), functional lower-limb strength (FTSST), aerobic capacity (2-min step test), quality of life (PDQ-39), and Parkinson’s disease progression (MDS-UPDRS). Results: the results showed a general improvement in handgrip strength in both hands (p = 0.008; d = 0.28), FTSST (p = 0.029; d = 0.96), and TUG times (p = 0.152; d = 0.22). Aerobic capacity, assessed by a 2-min step test, showed enhanced scores (p = 0.031; d = 0.78). Related to the PDQ-39, all dimensions of the scale were enhanced, highlighting activities of daily living (p = 0.047; d = 0.29) and bodily discomfort (p = 0.041; d = 0.37). Finally, the main symptoms of the disease were reduced, with an improvement in the parameters that show a better incidence of disease progression, such as Part IA and IB (p = 0.013; d = 0.29 and p = 0.021; d = 0.25, respectively), Part II (p = 0.021; d = 0.23), Part III (p = 0.040; d = 0.39), and Part IV (p = 0.013; d = 0.39). Conclusions: the therapeutic exercise (rowing and cycling), when carried out at a high intensity and in a personalized IVR scenario, appear to be a feasible and safe modality for patients with stages I–III of PD, improving their functional capacity, quality of life, and disease progression. Full article

Neurosteroids are pleiotropic molecules involved in various neurodegenerative diseases with neuroinflammation. We assessed neurosteroids’ serum levels in a cohort of Parkinson’s Disease (PD) patients with heterozygous glucocerebrosidase (GBA) mutations (GBA-PD) compared with matched cohorts of consecutive non-mutated PD (NM-PD) patients and healthy subjects with (GBA-HC) and without (NM-HC) GBA mutations. A consecutive cohort of GBA-PD was paired for age, sex, disease duration, Hoehn and Yahr stage, and comorbidities with a cohort of consecutive NM-PD. Two cohorts of GBA-HC and HC were also considered. Clinical assessment included the Movement Disorder Society revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and the Montreal Cognitive Assessment (MoCA). Serum samples were processed and analyzed by liquid chromatography coupled with the triple quadrupole mass spectrometry. Twenty-two GBA-PD (males: 11, age: 63.68), 22 NM-PD (males: 11, age: 63.05), 14 GBA-HC (males: 8; age: 49.36), and 15 HC (males: 4; age: 60.60) were studied. Compared to NM-PD, GBA-PD showed more hallucinations and psychosis (p < 0.05, Fisher’s exact test) and higher MDS-UPDRS part-II (p < 0.05). Most of the serum neurosteroids were reduced in both GBA-PD and NM-PD compared to the respective control cohorts, except for 5α-dihydroprogesterone. Allopregnanolone was the only neurosteroid significantly lower (p < 0.01, Dunn’s test) in NM-PD compared to GBA-PD patients. Only in GBA-PD, allopregnanolone, and pregnanolone levels correlated (Spearman) with a more severe MDS-UPDRS part-III. Allopregnanolone levels also negatively correlated with MoCA scores, and pregnanolone levels correlated with more pronounced bradykinesia. This pilot study provides the first observation of changes in neurosteroid peripheral levels in GBA-PD. The involvement of the observed changes in the development of neuropsychological and motor symptoms of GBA-PD deserves further attention. Full article

Background: The Chilean population has experienced increased longevity in recent decades, leading to an increased incidence of and mortality from neurodegenerative diseases such as Parkinson’s disease (PD). PD is a chronic degenerative condition that affects the central nervous system. The main objective of this research is to evaluate the effect of 12-week programs of tonic, isometric, and isometric/vibratory muscular strength training while controlling the manipulation of the intensity variable on motor and non-motor symptomatology in PD patients. The secondary objective is to assess the levels of muscular strength in PD patients and their relationship with motor and non-motor symptomatology. Methods: A parallel-group, randomized trial will randomly assign (n = 34) people of both sexes with Parkinson’s disease between stages I–III Hoehn and Yahr (H&Y), aged between 50 and 70 years to one of the experimental groups, in which they will undergo a total of 24 strength training sessions during 12 weeks. During the intervention period, the participants will be advised not to undertake additional exercise programs, to avoid substances that may disrupt metabolism and circadian cycles, and to maintain their medication regimen. The primary or motor evaluation of rest tremor will be performed with an accelerometer (Actigraphy), balance with the Mini-BESTest balance test, gait speed with the Ten Meters Walk Test, and non-motor symptomatology through anxiety, depression (MDS-UPDRS), and quality of life (PDQ-39) questionnaires. The Secondary evaluation of muscle strength will be performed with a functional electromechanical dynamometer. Discussion: Established as a hypothesis is that manipulating intensity variables in 12-week tonic, isometric, and isometric/vibratory muscle strength training programs has an effect on motor and non-motor symptomatology in people with Parkinson’s disease. The research will establish the extent to which controlled muscular strength training has an effect on relevant factors related to motor and non-motor symptomatology. Full article

Patients with movement disorders such as Parkinson’s disease (PD) living in remote and underserved areas often have limited access to specialized healthcare, while the feasibility and reliability of the video-based examination remains unclear. The aim of this narrative review is to examine which parts of remote neurological assessment are feasible and reliable in movement disorders. Clinical studies have demonstrated that most parts of the video-based neurological examination are feasible, even in the absence of a third party, including stance and gait—if an assistive device is not required—bradykinesia, tremor, dystonia, some ocular mobility parts, coordination, and gross muscle power and sensation assessment. Technical issues (video quality, internet connection, camera placement) might affect bradykinesia and tremor evaluation, especially in mild cases, possibly due to their rhythmic nature. Rigidity, postural instability and deep tendon reflexes cannot be remotely performed unless a trained healthcare professional is present. A modified version of incomplete Unified Parkinson’s Disease Rating Scale (UPDRS)-III and a related equation lacking rigidity and pull testing items can reliably predict total UPDRS-III. UPDRS-II, -IV, Timed “Up and Go”, and non-motor and quality of life scales can be administered remotely, while the remote Movement Disorder Society (MDS)-UPDRS-III requires further investigation. In conclusion, most parts of neurological examination can be performed virtually in PD, except for rigidity and postural instability, while technical issues might affect the assessment of mild bradykinesia and tremor. The combined use of wearable devices may at least partially compensate for these challenges in the future. Full article

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson’s disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments. Full article

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