Parkinson's Disease Severity Research Articles

Peripheral immune cell abundance differences link blood mitochondrial DNA copy number and Parkinson's disease.

Mitochondrial dysfunction plays an important role in Parkinson's disease (PD), with mitochondrial DNA copy number (mtDNA-CN) emerging as a potential marker for mitochondrial health. We investigated the links between blood mtDNA-CN and PD severity and risk using the Accelerating Medicines Partnership program for Parkinson's Disease dataset, replicating our results in the UK Biobank. Our findings reveal that reduced blood mtDNA-CN levels are associated with heightened PD risk and increased severity of motor symptoms and olfactory dysfunction. We estimated blood cell composition using complete blood cell profile when available or RNA-sequencing data as a surrogate. After adjusting for blood cell composition, the associations between mtDNA-CN and PD risk and clinical symptoms became non-significant. Bidirectional Mendelian randomization analysis also found no evidence of a direct causal relationship between blood mtDNA-CN and PD susceptibility. Hence peripheral inflammatory immune responses rather than mitochondrial dysfunction underpin these previously identified associations in PD.

Respiratory Dysfunction and Abnormal Hypoxic Ventilatory Response in Mild to Moderate Parkinson's Disease.

Respiratory dysfunction is an important contributor to morbidity and mortality in advanced Parkinson's disease (PD), but it is unclear what parameters are sensitive to diagnose and monitor respiratory dysfunction across disease phases. We aimed to characterize respiratory dysfunction in mild to moderate PD. In 20 individuals without cardiopulmonary comorbidity, pulmonary and inspiratory muscle function testing were performed ON-medication. Subsequently, the acute ventilatory response to hypoxia (HVR) was assessed by gradually decreasing FIO2 from 0.209 (room air) to 0.127, which was compared to eight age- and sex-matched healthy controls under arterial blood gas monitoring. Lastly, on different days, the same 20 individuals with PD underwent six blinded exposures to 45-min normobaric hypoxia at FiO2 0.163 and 0.127 or placebo OFF-medication to assess breathing responses. At rest, individuals with greatest PD severity had a lower tidal volume (pairwise comparisons: 0.59 vs. 0.74, P = 0.038-0.050) and tended to have a higher breathing frequency (17.7 vs. 14.4, P = 0.076), despite normal pulmonary function. A 45-min exposure to hypoxia induced a significantly lower acute HVR in individuals with PD compared to controls (-0.0489 vs. 0.133 L.min/%, P = 0.0038). Acute HVR was reduced regardless of disease severity. Subacute HVR in individuals with milder disease tended to be higher compared to those with more advanced disease (P = 0.079). Respiratory dysfunction is present in individuals with PD, including those with relatively mild disease severity, and is characterized by altered breathing patterns at rest, as well as a lower HVR, despite normal pulmonary and inspiratory muscle function testing.

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techniques to identify cognitive progression in individuals with and without PD. Using data from the Parkinson's Progression Marker Initiative, shallow Markov, deep recurrent (long short-term memory [LSTM]), and nonrecurrent (temporal fusion transformer [TFT]) models were compared to predict cognitive status over time. Cognitive status was categorized into normal cognition (NC), mild cognitive impairment (MCI), and dementia. Predictions were made annually for up to 3 years using clinical data, including demographics, cognitive assessments, PD severity, and medical history. Each approach was evaluated using inverse probability weighted (IPW-) F1 scores. An ensemble method combined outputs from the Markov, LSTM, and TFT models. The dataset included 917 individuals (53% PD; 30% at risk for PD; 17% Healthy Controls). The TFT model outperformed others across all annual periods (IPW-F1 = 0.468) compared to the Markov (IPW-F1 = 0.349) and LSTM (IPW-F1 = 0.414) models, with improved performance using an ensemble approach (IPW-F1 = 0.502). For MCI and dementia predictions, which were rarer occurrences compared to NC status (ratios: 50:8:1), the TFT model consistently outperformed competing models, achieving IPW-F1 scores of 0.496 and 0.533 for MCI and dementia, respectively. In conclusion, sequential deep learning models like TFT, which mitigate long-term memory loss and can interpret complex, high-dimensional data, perform best overall in predicting clinically important cognitive transitions. These methods should be further explored for predicting degenerative conditions.

High correlation of quantitative susceptibility mapping and echo intensity measurements of nigral iron overload in Parkinson's disease.

Quantitative susceptibility mapping (QSM) and transcranial sonography (TCS) offer proximal evaluations of iron load in the substantia nigra. Our prospective study aimed to investigate the relationship between QSM and TCS measurements of nigral iron content in patients with Parkinson's disease (PD). In secondary analyses, we wanted to explore the correlation of substantia nigra imaging data with clinical and laboratory findings. Eighteen magnetic resonance imaging and TCS examinations were performed in 15 PD patients at various disease stages. Susceptibility measures of substantia nigra were calculated from referenced QSM maps. Echogenicity of substantia nigra on TCS was measured planimetrically (echogenic area) and by digitized analysis (echo-intensity). Iron-related blood serum parameters were measured. Clinical assessments included the Unified PD Rating Scale and non-motor symptom scales. Substantia nigra susceptibility correlated with echogenic area (Pearson correlation, r = 0.53, p = 0.001) and echo-intensity (r = 0.78, p < 0.001). Individual asymmetry indices correlated between susceptibility and echogenic area measurements (r = 0.50, p = 0.042) and, more clearly, between susceptibility and echo-intensity measurements (r = 0.85, p < 0.001). Substantia nigra susceptibility (individual mean of bilateral measurements) correlated with serum transferrin saturation (Spearman test, r = 0.78, p < 0.001) and, by trend, with serum iron (r = 0.69, p = 0.004). Nigral echogenicity was not clearly related to serum values associated with iron metabolism. Susceptibility and echogenicity measurements were unrelated to PD duration, motor subtype, and severity of motor and non-motor symptoms. The present results support the assumption that iron accumulation is involved in the increase of nigral echogenicity in PD. Nigral echo-intensity probably reflects ferritin-bound iron, e.g. stored in microglia.

Proximity Elongation Assay and ELISA for the Identification of Serum Diagnostic Biomarkers in Parkinson's Disease and Progressive Supranuclear Palsy.

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and late onset of PSP specific symptoms, highlighting the need for easily assessable biomarkers. We used proximity elongation assay (PEA) to analyze 460 proteins in serum samples from 46 PD, 30 PSP patients, and 24 healthy controls. ANCOVA was used to identify the most promising proteins and machine learning (ML) XGBoost and random forest algorithms to assess their classification performance. Promising proteins were also quantified by ELISA. Moreover, correlations between serum biomarkers and biological and clinical features were investigated. We identified five proteins (TFF3, CPB1, OPG, CNTN1, TIMP4) showing different levels between PSP and PD, which achieved good performance (AUC: 0.892) when combined by ML. On the other hand, when the three most significant biomarkers (TFF3, CPB1 and OPG) were analyzed by ELISA, there was no difference between groups. Serum levels of TFF3 positively correlated with age in all subjects' groups, while for OPG and CPB1 such a correlation occurred in PSP patients only. Moreover, CPB1 positively correlated with disease severity in PD, while no correlations were observed in the PSP group. Overall, we identified CPB1 correlating with PD severity, which may support clinical staging of PD. In addition, our results showing discrepancy between PEA and ELISA technology suggest that caution should be used when translating proteomic findings into clinical practice.

Deep learning-based detection of affected body parts in Parkinson’s disease and freezing of gait using time-series imaging

We proposed a deep learning method using a convolutional neural network on time-series (TS) images to detect and differentiate affected body parts in people with Parkinson’s disease (PD) and freezing of gait (FOG) during 360° turning tasks. The 360° turning task was performed by 90 participants (60 people with PD [30 freezers and 30 nonfreezers] and 30 age-matched older adults (controls) at their preferred speed. The position and acceleration underwent preprocessing. The analysis was expanded from temporal to visual data using TS imaging methods. According to the PD vs. controls classification, the right lower third of the lateral shank (RTIB) on the least affected side (LAS) and the right calcaneus (RHEE) on the LAS were the most relevant body segments in the position and acceleration TS images. The RHEE marker exhibited the highest accuracy in the acceleration TS images. The identified markers for the classification of freezers vs. nonfreezers vs. controls were the left lateral humeral epicondyle (LELB) on the more affected side and the left posterior superior iliac spine (LPSI). The LPSI marker in the acceleration TS images displayed the highest accuracy. This approach could be a useful supplementary tool for determining PD severity and FOG.

Single-cell RNA sequencing reveals peripheral immunological features in Parkinson’s Disease

Although many researchers of Parkinson’s disease (PD) have shifted their focus from the central nervous system (CNS) to the peripheral blood, a significant knowledge gap remains between PD severity and the peripheral immune response. In the current study, we aimed to map the peripheral immunity atlas in peripheral blood mononuclear cells (PBMCs) from PD patients and healthy controls using single-cell RNA sequencing (scRNA-seq). Our study employed scRNA-seq analysis to map the peripheral immunity atlas in PD by profiling PBMCs from PD-Early, PD-Late patients and matched controls. By enlarging the blood sample size, we validated the roles of NK cells in numerous immune-related biological processes. We also detected the infiltration of NK cells into the cerebral motor cortex as the disease progressed, using human brain sections, and elucidated the communication between the periphery and CNS and its implications for PD. As a result, cell subpopulation atlases in PBMCs from PD patients and healthy controls along with differentially expressed genes in NK cells were identified by scRNA-seq analysis, representing 6 major immune cell subsets among which NK cells declined in the progression of PD. We further validated NK cell reduction in increasing samples and found that they participated in numerous immune-related biological processes and infiltration into the cerebral motor cortex as the disease proceeded, evidencingd the close communication between the peripheral immune response and CNS. Strikingly, XCL2 positively correlated with PD severity, with good predictive performance of PD and specific expression in subclusters C2 and C5 of NK cells. All these findings delineated the critical role of peripheral immune response mediated by NK cells in the pathogenesis of PD. NK cell-specific XCL2 could be used as a diagnostic marker for treating PD. The indispensable function of NK cells and NK cell-specific molecular biomarkers highlighted the implication of the peripheral immune response in PD progression. Trial registration: ChiCTR, ChiCTR1900023975. Registered 20 June 2019 - Retrospectively registered, https://www.chictr.org.cn/showproj.html?proj=31035.

Motor and non-motor predictors of freezing of gait in Parkinson's disease: A retrospective cohort study

IntroductionFreezing of gait (FOG) is a debilitating episodic gait disorder that significantly reduces the quality of life (QoL) in patients with Parkinson's disease (PD). Diagnosing and treating FOG remains a major medical challenge. This study aimed to assess the correlation between FOG and both motor and non-motor clinical characteristics in patients with PD. MethodsIn this retrospective cohort study, 112 patients with PD were divided into two groups using the New Freezing of Gait Questionnaire (NFOG-Q): one group with FOG (PD-FOG, 53 patients) and one group without FOG (PD-nFOG, 59 patients). The severity of PD and FOG was assessed using the Unified Parkinson's Disease Rating Scale (UPDRS), the Hoehn-Yahr (H–Y) staging system, and the NFOG-Q. The study also analyzed non-motor symptoms, including sleep disturbances, cognitive impairments, depression, anxiety, apathy, fatigue, and QoL. ResultThe prevalence of FOG was 47.3%. The PD-FOG group exhibited a longer duration of PD (P = 0.002), a higher H–Y stage indicating PD progression (P = 0.003), and elevated anxiety levels (P = 0.003) compared to the PD-nFOG group. According to binary logistic regression analysis, the higher H–Y stage (P = 0.022), anxiety level (P = 0.005), UPDRS part II (P = 0.001), and part III (P = 0.008) were significant predictors for the occurrence of FOG. ConclusionPatients with Parkinson's disease who have a higher Hoehn-Yahr (H–Y) stage, higher UPDRS score, and elevated levels of anxiety are more likely to experience FOG.

Association of plasma neurofilament light chain and Lipoprotein-related phospholipase A2 with motor subtypes of Parkinson’s disease

Neurofilament light chain (NfL) levels were reliable biomarkers of neurodegeneration in Parkinson’s disease (PD). Lipoprotein-related Phospholipase A2(Lp-PLA2) levels have also been increasingly studied in PD. We aimed to explore the association of plasma NfL and Lp-PLA2 with the diagnosis, motor subtypes and disease severity of PD. Plasma NfL and Lp-PLA2 were assayed separately in 106 participants (74 PD and 32 healthy controls, HC). The motor subtypes of PD were classified according to the MDS-UPDRS components, and motor and non-motor manifestations of patients were also evaluated. Subsequently, correlation analyses were performed. The plasma NfL levels were higher in the PD than HC, and were positively correlated with age, UPDRS II, UPDRS III and the modified Hoehn and Yahr staging scale (H&Y stage) in the PD. Moreover, plasma Lp-PLA2 levels were lower in the PD than HC, and were positively correlated with Parkinson’s Disease Quality of Life Questionnaire (PDQ-39) in the PD. For further distinguishing tremor-dominant (TD) from postural instability and gait difficulty-dominant (PIGD), plasma Lp-PLA2 levels were higher in the TD than PIGD, but there was no significant difference in NfL. plasma Lp-PLA2 levels were positively correlated with UPDRS I, Hamilton Anxiety Rating Scale (HAMA) and PDQ-39 in the TD. These resultssuggest that NfL and Lp-PLA2 may be potential biomarkers for the diagnosis of PD. We first demonstrated the potential utility of plasma Lp-PLA2 in differentiating motor subtypes. These findings deserve further evidence in larger PD cohorts.

Deep Learning for Parkinson's Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein-Peptide Datasets.

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.

A Comprehensive Multi-Functional Approach for Measuring Parkinson's Disease Severity.

This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multi-functional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach. We recruited thirty-seven individuals diagnosed with PD, each of whom completed a series of tablet-based neurocognitive tests assessing motor, memory, speech, executive functions, and tasks ranging in complexity from single to multi-functional. Then, the collected data was used to develop a hybrid feature scoring system to calculate a weighted vector for each function. We evaluated current PD staging schemes and developed a new approach based on the features selected and extracted using Random Forest and Principal Component Analysis. Our findings indicate a substantial bias in current PD staging systems toward fine-motor skills, i.e., other neurological functions (memory, speech, executive function, etc.) do not map into current PD stages as well as fine-motor skills do. The results demonstrate that a more accurate and personalized assessment of PD severity could be achieved by including a more exhaustive range of neurocognitive functions in the staging systems either by involving multiple functions in a unified staging score or by designing a function-specific staging system. The proposed hybrid feature score approach provides a comprehensive understanding of PD by highlighting the need for a staging system that covers various neurocognitive functions. This approach could potentially lead to more effective, objective, and personalized treatment strategies. Further, this proposed methodology could be adapted to other neurodegenerative conditions such as Alzheimer's disease or ALS.

Cardiovascular outcomes in Parkinson’s disease patients from a retrospective cohort study

Parkinson’s disease (PD) reports high rates of morbidity and mortality, but the risk of adverse cardiovascular outcomes in patients with PD has not been fully elucidated. This bi-center retrospective cohort study using the electronic health records (EHR) database of two tertiary hospitals screened a total of 327,292 subjects who visited the outpatient clinic, and 1194 patients with PD were propensity score-matched with a control population. The primary outcome was the occurrence of major adverse cardiovascular events (MACE). Key secondary outcomes included all-cause death, cardiovascular (CV) death, stroke, myocardial infarction (MI), heart failure hospitalization and 30-day CV death. After PS matching, MACE occurrence was not significantly different between PD and non-PD groups (18.2% vs. 17.5%, log-rank p = 0.98). Key secondary outcomes were also similar between the two groups. In patients with PD, MACE rate, and also CV risk score, were higher in patients with more severe PD (according to Hoehn and Yahr scale and unified Parkinson’s disease rating scale), and after multivariable analysis, PD severity was not an independent predictor of MACE. Patients with PD are at an increased risk of adverse cardiovascular outcomes, but the contribution from other common CV risk factors cannot be ignored. The management of prevalent CV risk factors is therefore important in mitigating adverse outcomes among patients with PD.

Association between plasma Netrin-1 levels and motor and nonmotor symptoms in Parkinson's disease.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron degeneration and diverse motor and nonmotor symptoms. Early diagnosis and intervention are crucial but challenging due to reliance on clinical presentation. Recent research suggests potential biomarkers for early detection, including plasma netrin-1 (NTN-1), a protein implicated in neuronal survival. This cross-sectional study recruited 105 PD patients and 65 healthy controls, assessing plasma NTN-1 levels and correlating them with clinical characteristics. Statistical analyses explored associations between NTN-1 levels and PD symptoms, considering demographic factors. PD patients exhibited significantly lower plasma NTN-1 levels compared to controls. NTN-1 demonstrated moderate potential as a PD biomarker. Positive correlations were found between NTN-1 levels and motor, depression, and cognitive symptoms. Multiple regression analysis revealed disease duration and NTN-1 levels as key factors influencing symptom severity. Gender also impacted symptom scores. Reduced plasma NTN-1 levels correlate with PD severity, suggesting its potential as a biomarker. However, further research is needed to elucidate the roles of NTN-1 in PD pathophysiology and validate its diagnostic and therapeutic implications. Understanding the involvement of NTN-1 may lead to personalized management strategies for PD.

Secondary analysis reveals gut microbiota differences in patients with Parkinson’s disease and/or cognitive impairment

Background: Parkinson’s disease (PD) is a neurodegenerative disorder, and the main clinical characteristics are bradykinesia and muscle stiffness. Cognitive impairment (CI) is a prevalent non-motor manifestation observed in individuals with PD. According to disease severity, it can be divided into PD with mild cognitive impairment (MCI) and PD dementia. CI in PD patients may precede motor symptoms, and the gut microbiota plays an important role in PD pathogenesis. Therefore, gut microbiota may be one of the diagnostic targets for PD-CI. Methods: This study compared the gut microbiota of 43 PD-CI patients [Montreal Cognitive Assessment (MoCA) score &lt; 26] and 38 PD patients without CI (MoCA ≥ 26). Patients’ neuropsychological conditions, depression scale, and brain structure scanned by magnetic resonance imaging (MRI) were also recorded. The fecal metagenomic datasets of patients with PD, PD-CI, and CI only were retrieved from public databases for reanalysis to explore the relationship between PD, CI, and gut microbiota. Results: We found that the cortical thickness and the volume of the hippocampus, gray matter, and thalamus were significantly reduced among patients with PD-CI compared to PD without CI (P &lt; 0.05). Moreover, the gut microbiome in patients with PD-CI had fewer short-chain fatty acid (SCFA) producing bacteria and more pathogenic bacteria. There were also alterations in patterns of metabolic pathway-encoding genes. Additionally, PD affected gut microbiota more than CI. Conclusion: CI may aggravate the severity of PD, but it did not drastically alter subjects’ gut microbiota. This study reveals the relationship between gut microbiota, PD, and CI.

Blunted Melatonin Circadian Rhythm in Parkinson's Disease: Express Bewilderment.

Melatonin (MTN) is a neuro-hormone released from the pineal gland. MTN secretion is regulated by different neuronal circuits, including the retinohypothalamic tract and suprachiasmatic nucleus (SCN), which are affected by light. MTN is neuroprotective in various neurodegenerative diseases, including Parkinson's disease (PD). MTN circulating level is highly blunted in PD. However, the underlying causes were not fully clarified. Thus, the present review aims to discuss the potential causes of blunted MTN levels in PD. Distortion of MTN circadian rhythmicity in PD patients causies extreme daytime sleepiness. The underlying mechanism for blunted MTN response may be due to reduction for light exposure, impairment of retinal light transmission, degeneration of circadian pacemaker and dysautonomia. In conclusion, degeneration of SCN and associated neurodegeneration together with neuroinflammation and activation of NF-κB and NLRP3 inflammasome, induce dysregulation of MTN secretion. Therefore, low serum MTN level reflects PD severity and could be potential biomarkers. Preclinical and clinical studies are suggested to clarify the underlying causes of low MTN in PD.

Serum cholesterol levels and Parkinson’s disease: a detailed investigation in Turkish population

ABSTRACT Objectives Parkinson’s disease (PD) involves the progressive loss of dopaminergic neurons and the accumulation of α-synuclein. Elevated cholesterol levels may exacerbate α-synuclein aggregation, potentially contributing to PD. This study investigates the link between lipid profiles and PD severity, as well as cognitive functions in patients, aiming to inform pathogenesis and management strategies. Methods Data from 250 PD patients and 100 healthy controls were analyzed. Serum cholesterol levels were compared with disease severity using Unified Parkinson’s Disease Rating Scale (UPDRS) and modified Hoehn & Yahr Rating Scale (mH&Y). Mini-Mental State Examination (MMSE) assessed cognitive functions. Results Of the participants, 45.4% were female, 54.6% male, with a mean age of 69.09 ± 11.13 years. Mean UPDRS score was 52.34 ± 26.32, mH&Y was 2.28 ± 0.91. Patients had significantly higher HDL levels (47.92 ± 11.63) than controls (45.40 ± 13.89) (p = 0.024). HDL levels were significantly higher in patients with cognitive impairment than in patients with cognitive normal (p = 0.004). On the contrary, triglyceride levels were significantly lower in those with cognitive impairment compared to those with cognitively normal (p = 0.005). Multivariate logistic regression showed being male associated with 3.796 times higher risk of illness, and HDL is associated with 1.030 times increased illness risk. Conclusion High HDL levels and male gender particularly increase the risk of Parkinson’s disease. Additionally, HDL and triglyceride levels affect the cognition of PD patients. Further studies on the impact of cholesterol metabolism on the pathogenesis of PD could contribute to identifying effective treatment targets.

2332 Feasibility and utility of introducing handgrip strength measurement for outpatients living with Parkinson's disease

Abstract Background Patients living with Parkinson’s disease (PD) who are sarcopenic are at significantly higher risk of falling (Cai et al., Frontiers in Neurology,2021,12,598,035). Handgrip strength is a useful tool to assess for sarcopenia but is not commonly measured in clinical practice, despite the consequences that sarcopenia poses. This study aims to incorporate handgrip strength into the assessment of outpatients living with PD. Secondary objectives are to increase the understanding of whether exercise is associated with increased handgrip strength and to implement interventions for patients who are identified as sarcopenic; to improve their health outcomes. Methods Questionnaires were designed to gather quantitative data about patients’ demographics, how frequently they fall, disease severity and their weekly exercise. These were given to patients attending the movement disorders clinic at Crawley hospital, between February and October 2023. Patients without a diagnosis of PD were excluded. Their grip strength was measured using a standardised technique with a calibrated manometer. Data was input to Microsoft Excel and analysed using Spearman’s rank and Kruskal-Wallis test. Results Handgrip strength was obtained for 125 of 271 patients (46%) attending clinic over this period. Initially healthcare workers took 9.2 minutes to complete the questionnaire, but this improved to 4.3 minutes after updating the form. Sixteen patients were excluded, leaving 51 females and 58 males; both with a mean age of 80. Grip strength reduced with PD severity when adjusted for gender; this was significant in males (H = 51.9, p = 0.00) but not females (H = 4.8,p = 0.31). Grip strength was weakly correlated with exercise, although not significant (r2 = 0.15,p = 0.15) but did not appear to be related to frequency of falls (r2 = 0.01,p = 0.92). Conclusions Handgrip measurement can be successfully implemented into outpatient assessment. Handgrip strength could be used to monitor the effect of lifestyle change in individuals. Limitations include self-reporting bias; which activities each individual classifies as exercise.

Changes in the correlation between substantia nigra hyperechogenicity area and Parkinson's disease severity at different Hoehn and Yahr stages.

It is debatable whether the area of substantia nigra hyperechogenicity (SN+) in transcranial sonography (TCS) is related to Parkinson's disease (PD) severity. Iron deposition, which is associated with the formation of SN+, may have different effects on dopamine nerve function as PD progresses. However, little research has explored the association between the SN + area and disease severity of PD in stages. 612 PD patients with sufficient bone window were retrospectively included from a PD database, and disease severity was assessed by the Unified Parkinson's Disease Rating Scale (UPDRS) scores. Based on the Hoehn and Yahr (H-Y) scale, we classified the patients into seven groups (H-Y stage 1, 1.5, 2, 2.5, 3, 4, and 5) and then analyzed the correlations between the SN + area and the UPDRS scores separately. Our results indicated a U-shaped relationship between the initial-SN + area and disease severity in PD: In the H-Y stage 1 group, the initial-SN + area was negatively correlated with the UPDRS total score (r = - 0.456, p < 0.001) and UPDRS-III score (r = - 0.497, p < 0.001). No correlation was observed in the groups of H-Y stages 1.5, 2, and 2.5. In the groups of H-Y stage ≥ 3, the initial-SN + area was positively correlated with the UPDRS total score and UPDRS-III score, with strongest correlation in the H-Y stage 5 group (all p values < 0.05). Moreover, the larger SN + area and average SN + area showed a similar evolutionary trend of correlation with UPDRS total score and UPDRS-III score. Our study indicated a U-shaped correlation between the SN + area with the UPDRS total score and UPDRS-III score as H-Y stage progressed. The evolution of the correlation may reflect the evolution of underlying pathological mechanisms related to iron deposition in the substantia nigra.

A retrospective study of Parkinson's disease in Southwest China 2021–2024: An age-based approach

BackgroundGlobally, Parkinson's disease (PD) is one of the common neurodegenerative diseases in the elderly with increasing morbidity and disability, and its clinical pathogenesis is not clear. ObjectiveTo compare the differences in disease severity and blood biomarkers levels and their correlation between patients with early-onset Parkinson's disease (EOPD) and late-onset Parkinson's disease (LOPD). MethodsA total of 342 patients diagnosed with PD were retrospectively collected. PD patients were categorized into EOPD (24 patients) and LOPD (318 patients) according to the age of onset of the disease. The Hoehn-Yahr (HY) staging was used to assess the severity of the disease in PD patients. Subjective rating scales such as the Mini-mental State Examination (MMSE) were used to assess the motor and non-motor functions of the patients. The differences of objective blood biomarkers such as triglyceride (TG) between the two groups were investigated. The correlation between them and PD was explored by logistic analysis. ResultsPercentage of EOPD group with HY staged as intermediate to late and Scales for Outcomes in Parkinson's Disease-Autonomic (SCOPA-AUT), Movement Disorder Society-Unified Parkinson's disease Rating Scale-III (MDS-UPDRS-III), Montreal Cognitive Assessment (MoCA) score and TG, non-high-density lipoprotein-cholesterol (N-HDL-C), homocysteine (HCY), apolipoprotein B (Apo-B), free triiodothyronine (FT3), free thyroxine (FT4), high-sensitivity C-reactive protein (hs-CRP) levels were lower than those in the LOPD group (P < 0.05); and the proportion of HY staged as early stage, Hamilton Anxiety Scale (HAMA) and Fatigue severity scale (FSS) scores and the levels of vitamin B12 were higher than those in the LOPD group (P < 0.05). The results of Multifactorial Logistic regression analysis showed that N-HDL-C [OR = 1.409, 95 % CI (1.063, 1.868)], Apo-B [OR = 0.797, 95 % CI (0.638, 0.997)], Vitamin B12 [OR = 0.992, 95 % CI (0.987, 0.998)] and hs-CRP [OR = 1.124, 95 % CI (1.070, 1.182)] were independent factors affecting the severity of PD, with significant differences between groups (P < 0.05). ConclusionN-HDL-C, Apo-B, Vitamin B12, and hs-CRP levels play an important role in the progression of PD.

Diffusion Tensor Imaging-Along the Perivascular-Space Index Is Associated with Disease Progression in Parkinson's Disease.

The glymphatic clearance pathway is a waste clearance system that allows for removal of soluble proteins such as amyloid β (Aβ) from the brain. Higher Aβ levels are associated with cognitive dysfunction in Parkinson's disease (PD). Diffusion tensor imaging-along the perivascular space (DTI-ALPS) is an imaging measure proposed to indirectly measure glymphatic function. Evaluate differences in DTI-ALPS-index between PD and healthy controls (HC) and characterize relationships between this proposed measure of glymphatic clearance, cognition, and disease severity in PD. PD (n = 32) and HC (n = 23) participants underwent brain imaging to assess DTI-ALPS. PD participants were classified as PD-normal cognition (PD-NC; n = 20) or PD-mild cognitive impairment (PD-MCI; n = 12) based on a Level II comprehensive cognitive assessment. A subgroup of PD participants (n = 21) returned for annual assessments for up to 4 years after baseline. Longitudinal outcomes included changes in performance on the comprehensive cognitive assessment and changes in the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PD participants had lower DTI-ALPS-index compared to HC. PD participants classified as PD-MCI had significantly lower DTI-ALPS-index compared to PD-NC. Lower DTI-ALPS-index at baseline was associated with longitudinal cognitive decline and worse longitudinal disease severity. Glymphatic clearance, as measured with DTI-ALPS, has potential to serve as a marker of longitudinal disease progression. Interventions targeting glymphatic function should be explored for potential to slow cognitive decline in PD. © 2024 International Parkinson and Movement Disorder Society.