Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.

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.

Symptom Analysis of Early Parkinson's Disease and its Correlation with High Sensitivity C-reactive Protein.

Parkinson's disease is a debilitating neurodegenerative disease for which there is no cure. It is characterized by bradykinesia, resting tremor, rigidity and postural instability, due to impairment of function of the basal ganglia which is involved in the coordination of body movement. Neuro-inflammation is pathogenesis of development in early Parkinson's disease. High-sensitivity C-reactive protein level is a useful non-specific biochemical marker of inflammation. Objective of this study was to analyze the symptoms of Parkinson disease and it's correlation with high sensitive CRP. Seventy-six Parkinson's disease patients were enrolled in this Cross-sectional observational study that was attended in the Department of Neurology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh from September 2014 to March 2016. Analysis of the symptoms of Parkinson disease and it's correlation with high sensitive CRP were done among these patients. This study was performed on 76 Parkinson disease patients with presented early with symptoms. a positive and highly significant correlation were seen in between duration of tremor and High sensitivity CRP (r=0.430, p<0.001) and between duration of bradykinesia and High sensitivity CRP (r=0.426, p<0.001) which indicate increase duration causes increase level of high-sensitivity C-reactive protein value. The neuro-inflammation plays a significant role in the pathogenesis of symptoms development in early Parkinson's disease.

Glutamate Signaling in Patients With Parkinson Disease With REM Sleep Behavior Disorder.

BACKGROUND AND OBJECTIVES: Clinical heterogeneity of patients with Parkinson disease (PD) is well recognized. PD with REM sleep behavior disorder (RBD) is a more malignant phenotype with faster motor progression and higher nonmotor symptom burden. However, the neural mechanisms underlying this clinical divergence concerning imbalances in neurotransmitter systems remain elusive. METHODS: Combining magnetic resonance (MR) spectroscopy and [11C]ABP688 PET on a PET/MR hybrid system, we simultaneously investigated two different mechanisms of glutamate signaling in patients with PD. Patients were grouped according to their RBD status in overnight video-polysomnography and compared with age-matched and sex-matched healthy control (HC) participants. Total volumes of distribution (VT) of [11C]ABP688 were estimated with metabolite-corrected plasma concentrations during steady-state conditions between 45 and 60 minutes of the scan following a bolus-infusion protocol. Glutamate, glutamine, and glutathione levels were investigated with single-voxel stimulated echo acquisition mode MR spectroscopy of the left basal ganglia. RESULTS: We measured globally elevated VT of [11C]ABP688 in 16 patients with PD and RBD compared with 17 patients without RBD and 15 HC participants (F(2,45) = 5.579, p = 0.007). Conversely, glutamatergic metabolites did not differ between groups and did not correlate with the regional VT of [11C]ABP688. VT of [11C]ABP688 correlated with the amount of REM sleep without atonia (F(1,42) = 5.600, p = 0.023) and with dopaminergic treatment response in patients with PD (F(1,30) = 5.823, p = 0.022). DISCUSSION: Our results suggest that patients with PD and RBD exhibit altered glutamatergic signaling indicated by higher VT of [11C]ABP688 despite unaffected glutamate levels. The imbalance of glutamate receptors and MR spectroscopy glutamate metabolite levels indicates a novel mechanism contributing to the heterogeneity of PD and warrants further investigation of drugs targeting mGluR5.

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.

AIMS: Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. METHODS: The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. RESULTS: Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). CONCLUSION: This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

An α-helical peptide-based plasmonic biosensor for highly specific detection of α-synuclein toxic oligomers.

BACKGROUND: α-Synuclein (αS) aggregation is the main neurological hallmark of a group of neurodegenerative disorders, collectively referred to as synucleinopathies, of which Parkinson's disease (PD) is the most prevalent. αS oligomers are elevated in the cerebrospinal fluid (CSF) of PD patients, standing as a biomarker for disease diagnosis. However, methods for early PD detection are still lacking. We have recently identified the amphipathic 22-residue peptide PSMα3 as a high-affinity binder of αS toxic oligomers. PSMα3 displayed excellent selectivity and reproducibility, binding to αS toxic oligomers with affinities in the low nanomolar range and without detectable cross-reactivity with functional monomeric αS. RESULTS: In this work, we leveraged these PSMα3 unique properties to design a plasmonic-based biosensor for the direct detection of toxic oligomers under label-free conditions. SIGNIFICANCE AND NOVELTY: We describe the integration of the peptide in a lab-on-a-chip plasmonic platform suitable for point-of-care measurements of αS toxic oligomers in CSF samples in real-time and at an affordable cost, providing an innovative biosensor for PD early diagnosis in the clinic.

Transcranial alternating current stimulation improves quality of life in Parkinson's disease: study protocol for a randomized, double-blind, controlled trial.

BACKGROUND: The neural cells in the brains of patients with Parkinson's disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson's disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson's disease based on the central-peripheral-central theory. METHODS: The present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson's disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes. DISCUSSION: The objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.

To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson's disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS: Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.

Diagnostic value of vietnamese smell identification test in Parkinson's disease.

INTRODUCTION: The Vietnamese Smell Identification Test (VSIT) has been validated in determining olfactory dysfunction in the Vietnamese population; however, its value in diagnosing Parkinson's disease (PD) has not been established. METHODS: This case-control study was conducted at University Medical Center HCMC, Ho Chi Minh City, Vietnam. The study sample included non-demented PD patients and healthy controls (HC) who were gender- and age-matched. All participants were evaluated for odor identification ability using the VSIT and the Brief Smell Identification Test (BSIT). RESULTS: A total of 218 HCs and 218 PD patients participated in the study. The median VSIT and BSIT scores were significantly different between PD and HC groups (VSIT, 5 (3) vs. 9 (2), P < 0.0001; BSIT, 6 (3) vs 8 (2), P < 0.0001). Using the cut-off of <8 for correct answers out of 12 odorants, the VSIT had higher sensitivity (84.4%) and specificity (86.2%) than those of the BSIT (sensitivity of 81.7% and specificity of 69.3%) for the diagnosis of PD. The area under the curve (AUC) value was greater for the VSIT than for the BSIT (0.909 vs 0.818). The smell identification scores were not significantly correlated with disease duration, disease severity, or LEDD (all p > 0.05). CONCLUSION: The VSIT can be a valuable ancillary tool for supporting the diagnosis of PD in Vietnam. Olfactory dysfunction in PD was unrelated to the disease duration and severity. The VSIT can be applied to improve the accuracy of clinical PD diagnosis.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease.

BACKGROUNDS: Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context. OBJECTIVES: Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD. METHODS: This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter. FINDINGS: This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD. CONCLUSION: The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

U.S. Tax Credits to Promote Practical Proactive Preventative Care for Parkinson's Disease.

Persons with Parkinson's disease (PD) and society at large can profit from a strategic investment into a forward leaning, practical, preventative, and proactive multidisciplinary care policy. The American healthcare system is not easily bent to accommodate this type of care, and thus a tax benefit is an attractive option. An individual federal income tax benefit of $6200 each year for every person residing in the US with a diagnosis of PD, could among other offerings provide monthly access to a licensed clinical social worker and access to mental health services. The implementation of more coordinated care has the potential reduce the burden of depression, anxiety, and demoralization. Personal training would also be covered and directed by physical and occupational therapists. The combination of home-based and telemedicine services would have the added benefit of improving access. The tax benefit would also provide access to a dietician. This type of care strategy could be designed to proactively identify early signs of aspiration and urinary tract infections to 'head off' significant morbidity. A $6200/year individual tax benefit for those diagnosed with PD will thus translate into more fall prevention, more care in the home setting, less hospitalizations, less depression, less anxiety, less demoralization, better diets, and less persons placed in nursing facilities. Additionally, this tax benefit will provide the potential for billions of dollars in savings to the healthcare system. A tax benefit for PD is a practical preventative and proactive strategy which can serve to advantage both this generation and the next.

Application of sensory nerve quantitative tests to analyze the subtypes of motor disorders in Parkinson's disease.

This study investigated the sensory nerve function in people with different subtypes of Parkinson's disease (PD), which included the tremor-dominant (TD) group (n = 30), postural instability and gait disorder (PIGD) group (n = 33), and healthy-controls (HC) group (n = 33). Sural nerve's current perception threshold (CPT) and pain tolerance threshold (PTT) in both feet were measured at different frequencies. Results were evaluated using the mini-mental state examination (MMSE), Hoehn Yahr scale (H-Y) , and 3-meter timed-up-and-go-test (TUGT). The MMSE scores of the TD and HC groups were higher than those of the PIGD group (TD < HC). The 3-meter TUGT scores of the PIGD group were higher than theTD and HC groups (TD > HC). The PIGD patients experienced a significantly shorter disease duration and higher H-Y score than the TD patients ( P  < 0.05). The values of 2 KHz CPT of left-side (CPTL), 2KHz CPT of right-side (CPTR), and 5 Hz CPTR in the PIGD group were significantly higher compared to the TD and HC groups ( P  < 0.05, Bonferroni correction). Additionally, the values of 250 Hz CPTL, 5 Hz CPTL, 250 Hz CPTR, 2 kHz PTT of left-side (PTTL), 250 Hz PTTL, and 5 Hz PTTL in the PIGD group were significantly elevated relative to the TD group ( P  < 0.05, Bonferroni correction). Distinctive current threshold perception and PTT of the sural nerve can be observed in patients with varying PD subtypes, and sensory nerve conduction threshold electrical diagnostic testing can detect these discrepancies in sensory nerve function.

Efficacy of acupuncture (Jin's three-needle) on motor symptoms and anxiety in patients with Parkinson's disease: protocol for a multicentre, randomised, assessor-blinded clinical trial.

INTRODUCTION: Parkinson's disease (PD) has a significant impact on a substantial number of individuals in China. Notably, 31% of patients with PD also grapple with the additional burden of anxiety. This dual challenge of managing both PD and anxiety underscores the complexity of the condition and the diverse range of symptoms patients may experience. Considering the circumstances, the cost and potential drawbacks associated with traditional antiparkinsonian drugs become increasingly relevant. Acupuncture emerges as a significant non-pharmacological adjunct therapy. Offering a potentially safer and more cost-effective option, acupuncture addresses the pressing need for holistic and complementary treatments that may alleviate both the motor symptoms of PD and the accompanying anxiety. METHODS AND ANALYSIS: This is a multicentre, randomised controlled and assessor-blind trial. A total of 210 eligible patients with PD will be randomly assigned (1:1) to Jin's three-needle (JTN) acupuncture group or waitlist (WL) group. Patients in the JTN group will receive acupuncture therapy three times per week for 4 weeks. Patients in the WL group will maintain their original dosage of antiparkinsonian drugs and receive acupuncture therapy after the observation period. The primary outcome measure will be the Unified Parkinson's Disease Rating Scale score. The secondary outcome measures will be the scores of the Hoehn-Yahr Rating Scale, Unified Dyskinesia Rating Scale, Non-Motor Symptoms Scale, 39-item Parkinson's Disease Questionnaire, Parkinson Anxiety Scale, Hamilton Anxiety Scale, Hamilton Depression Scale, Zarit burden interview and the level of cortisol and adrenocorticotropic hormone. The evaluation will be executed at baseline, the end of the treatment and a follow-up period. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (K[2023]014). All patients have to provide written, informed consent. The study will be disseminated through presentations in peer-reviewed international journals and at national and international conferences. TRIAL REGISTRATION NUMBER: Chinese Clinical Trial Registry; ChiCTR2300074675.

Covid-19 in Parkinson's Disease treated by drugs or brain stimulation.

PURPOSE: Covid-19 has affected all people, especially those with chronic diseases, including Parkinson's Disease (PD). Covid-19 may affect both motor and neuropsychiatric symptoms of PD patients. We intend to evaluate different aspects of Covid-19 impact on PD patients. METHODS: 647 PD patients were evaluated in terms of PD-related and Covid-19-related clinical presentations in addition to past medical history during the pandemic through an online questioner. They were compared with an age-matched control group consist of 673 individuals and a sample of the normal population consist of 1215 individuals. RESULTS: The prevalence of Covid-19 in PD patients was 11.28%. The mortality was 1.23% among PD patients. The prevalence of Covid-19 in PD patients who undergone Deep Brain Stimulation (DBS) was 18.18%. No significant association was found between the duration of disease and the prevalence of Covid-19. A statistically significant higher prevalence of Covid-19 in PD patients who had direct contact with SARS-CoV-19 infected individuals was found. No statistically significant association has been found between the worsening of motor symptoms and Covid-19. PD patients and the normal population may differ in the prevalence of some psychological disorders, including anxiety and sleeping disorders, and Covid-19 may affect the psychological status. CONCLUSION: PD patients possibly follow tighter preventive protocols, which lead to lower prevalence and severity of Covid-19 and its consequences in these patients. Although it seems Covid-19 does not affect motor and psychological aspects of PD as much as it was expected, more accurate evaluations are suggested in order to clarify such effects.

Patient-specific game-based transfer method for Parkinson's disease severity prediction.

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. The mean values of mean absolute error, root mean square error, and volatility obtained by predicting motor-UPDRS and total-UPDRS for target patients are 1.59, 1.95, 1.56 and 1.98, 2.54, 1.94, respectively. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.

An examination of anxiety and its influence on health-related quality of life in Parkinson's disease using the geriatric anxiety scale: a cross-sectional study.

BACKGROUND: Anxiety is one of the most common but often overlooked mood-related nonmotor symptoms in people with Parkinson's disease (PD). To improve the well-being of people with PD, it is important to understand the impact of anxiety in PD, especially its association with depressive and motor symptoms and its impact on health-related quality of life (HRQoL). METHODS: 91 people with PD were assessed between June 2017 and June 2018. Anxiety was measured using the Geriatric Anxiety Scale (GAS) and its cognitive, somatic, and affective subscales. HRQoL was assessed using the Parkinson's Disease Questionnaire 39 (PDQ-39). Moreover, sociodemographic information, depressive symptoms, cognition, motor and nonmotor symptoms were assessed. Descriptive statistics, regression analyses, and path analyses were performed to understand predictors of anxiety and its influence on HRQoL. RESULTS: Of the 91 people with PD, 35 (38.5%) experienced anxiety. Anxiety symptoms in these individuals primarily manifest as somatic sensations. Anxiety, motor, and depressive symptoms are interlinked but contribute individually to HRQoL. Beyond motor symptoms, cognitive and affective aspects of anxiety impact HRQoL. While anxiety and depression overlap, the somatic and cognitive aspects of anxiety play a significant role in determining HRQoL in addition to depressive symptoms. CONCLUSION: Our study used the GAS and its three subscales to shed light on the connections between anxiety, depression, and motor impairment in people with PD. Although anxiety is linked to depression and motor symptoms, it independently affects the HRQoL of people with PD. Thus, it is crucial to adopt a comprehensive diagnostic approach that detects and considers the impact of anxiety on HRQoL in PD.