Parkinson's Disease Severity Research Articles

Discovery of plasma biomarkers for Parkinson's disease diagnoses based on metabolomics and lipidomics

Parkinson's disease (PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates. The current gold standard for diagnosing PD is clinical evaluation, which is often challenging and inaccurate. Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics. Here, we used comprehensive untargeted metabolomics and lipidomics methodologies based on liquid chromatography-mass spectrometry to evaluate metabolic abnormalities linked with PD. Two well-characterized cohorts of 288 plasma samples (143 PD patients and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers. Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures, based on which the discriminant models for PD were established by integrating multiple machine learning algorithms. A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort, and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6 % both in the discovery cohort and validation cohort. The results indicated that PC (40:7), eicosatrienoic acid were negatively correlated with severity of PD, and pentalenic acid, PC (40:6p) and aspartic acid were positively correlated with severity of PD. In summary, we developed a multi-metabolite predictive model which can diagnose PD with over 81.6 % accuracy based on this unique metabolic signature. Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.

A Comprehensive Review of Deep Learning Techniques for Identifying Parkinson's Disease from Gait Analysis

A group of neurological disorders that manifests in a wide range of motor and cognitive symptoms, Parkinson's Disease (PD) ranks second among those affecting older adults. Many people mistakenly believe that PD symptoms are caused by other conditions, such as essential tremor or natural aging. There may not be a cure for PD, but there are numerous medications that can help with symptoms. Since gait impairment is a prominent and early sign of PD in a clinical context, doctors typically use visual observations to analyse gait abnormalities as one of many manifestations to determine the severity of PD. Nevertheless, therapists' reliance on their own knowledge and judgment makes gait examination a difficult and subjective process. Extracting useful gait features from various input signals for gait analysis has recently been used Deep Learning (DL) models. Intermediate Medical Unit (IMU) patients with movement problems may benefit from a quick and clinically relevant evaluation of gait abnormalities performed automatically utilizing DL algorithms. Using gait analysis, this study provides a comprehensive overview of various DL frameworks that have been created for the purpose of PD prediction and categorization. At the outset, In the first part of the study, various PD prediction and classification systems that have been developed by various researchers and that rely on DL algorithms via gait analysis are reviewed briefly. To find a better way to identify and categorize the PD, we next undertake a comparative analysis to learn about the shortcomings of those algorithms.

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.

Distinctive CD56dim NK subset profiles and increased NKG2D expression in blood NK cells of Parkinson’s disease patients

Mounting data suggest an important role for the immune system in Parkinson’s disease (PD). Previous evidence of increased natural killer (NK) cell populations in PD suggests a potential role of NK cells in the pathogenesis of the disease. Previous studies have analyzed NK cell populations using aggregation by variable expression of CD56 and CD16. It remains unknown what differences may exist between NK cell subpopulations when stratified using more nuanced classification. Here, we profile NK cell subpopulations and elucidate the expressions of activating, NKG2D, inhibitory, NKG2A, and homing, CX3CR1, receptors on NK cell subpopulations in PD and healthy controls (HC). We analyzed cryopreserved PMBC samples using a 10-color flow cytometry panel to evaluate NK cell subpopulations in 31 individuals with sporadic PD and 27 HC participants. Here we identified significant differences in the CD56dim NK subset that changes with disease severity in PD. Furthermore, the expressions of NKG2D in all three NK cell subsets were significantly elevated in PD patients compared to HC. Notably, NKG2A expression in the CD56bright NK subset increased in PD patients with longer disease duration but there were no changes in CX3CR1. In summary, our data suggests that changes in NK cells may be influenced by the clinical severity and duration of PD.

Quantitative evaluation of swallowing function in Parkinson's disease using tongue pressure measurement: a mini-review.

Dysphagia is a common symptom of Parkinson's disease (PD) associated with aspiration pneumonia, choking, malnutrition, and a decreased quality of life, and is a leading cause of death among patients with PD. Tongue dysfunction in patients with PD affects the oral phase of swallowing, including the formation and propulsion of a bolus into the pharynx. Assessing tongue pressure, generated between the tongue and palate, is a method that quantitatively measures tongue function and is related to dysphagia in PD. Two assessment methods are used to measure tongue pressure: tongue strength and tongue pressure during swallowing. Previous studies measuring tongue pressure in PD have reported decreased tongue strength and pressure during swallowing, as well as a prolonged tongue pressure rise time, which are symptoms associated with PD severity and dysphagia. In this mini-review, we present a method for measuring tongue pressure and discuss its relationship with dysphagia in PD. We also describe limitations and future perspectives in tongue pressure measurement research.

Large-scale functional network connectivity mediates the association between nigral neuromelanin hypopigmentation and motor impairment in Parkinson's disease.

Neuromelanin hypopigmentation within substantia nigra pars compacta (SNc) reflects the loss of pigmented neurons, which in turn contributes to the dysfunction of the nigrostriatal and striato-cortical pathways in Parkinson's disease (PD). Our study aims to investigate the relationships between SN degeneration manifested by neuromelanin reduction, functional connectivity (FC) among large-scale brain networks, and motor impairment in PD. This study included 68 idiopathic PD patients and 32 age-, sex- and education level-matched healthy controls who underwent neuromelanin-sensitive magnetic resonance imaging (MRI), functional MRI, and motor assessments. SN integrity was measured using the subregional contrast-to-noise ratio calculated from neuromelanin-sensitive MRI. Resting-state FC maps were obtained based on the independent component analysis. Subsequently, we performed partial correlation and mediation analyses in SN degeneration, network disruption, and motor impairment for PD patients. We found significantly decreased neuromelanin within SN and widely altered inter-network FCs, mainly involved in the basal ganglia (BG), sensorimotor and frontoparietal networks in PD. In addition, decreased neuromelanin content was negatively correlated with the dorsal sensorimotor network (dSMN)-medial visual network connection (P = 0.012) and dSMN-BG connection (P = 0.004). Importantly, the effect of SN neuromelanin hypopigmentation on motor symptom severity in PD is partially mediated by the increased connectivity strength between BG and dSMN (indirect effect = - 1.358, 95% CI: - 2.997, - 0.147). Our results advanced our understanding of the interactions between neuromelanin hypopigmentation in SN and altered FCs of functional networks in PD and suggested the potential of multimodal metrics for early diagnosis and monitoring the response to therapies.

Wearable-Sensor-Based Weakly Supervised Parkinson's Disease Assessment with Data Augmentation.

Parkinson's disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient's in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance.

Microbiota-microglia crosstalk between Blautia producta and neuroinflammation of Parkinson's disease: A bench-to-bedside translational approach

Parkinson's disease (PD) is intricately linked to abnormal gut microbiota, yet the specific microbiota influencing clinical outcomes remain poorly understood. Our study identified a deficiency in the microbiota genus Blautia and a reduction in fecal short-chain fatty acid (SCFA) butyrate level in PD patients compared to healthy controls. The abundance of Blautia correlated with the clinical severity of PD. Supplementation with butyrate-producing bacterium B. producta demonstrated neuroprotective effects, attenuating neuroinflammation and dopaminergic neuronal death in mice, consequently ameliorating motor dysfunction. A pivotal inflammatory signaling pathway, the RAS-related pathway, modulated by butyrate, emerged as a key mechanism inhibiting microglial activation in PD. The change of RAS-NF-κB pathway in PD patients was observed. Furthermore, B. producta-derived butyrate demonstrated the inhibition of microglial activation in PD through regulation of the RAS-NF-κB pathway. These findings elucidate the causal relationship between specific gut microbiota and PD, presenting a novel microbiota-based treatment perspective for PD.

Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease.

Dementia is common in Parkinson's disease (PD), but there is wide variation in its timing. A critical gap in PD research is the lack of quantifiable markers of progression, and methods to identify early stages of dementia. Atrophy-based magnetic resonance imaging (MRI) has limited sensitivity in detecting or tracking changes relating to PD dementia, but quantitative susceptibility mapping (QSM), sensitive to brain tissue iron, shows potential for these purposes. The objective of the paper is to study, for the first time, the longitudinal relationship between cognition and QSM in PD in detail. We present a longitudinal study of clinical severity in PD using QSM, including 59 PD patients (without dementia at study onset), and 22 controls over 3 years. In PD, increased baseline susceptibility in the right temporal cortex, nucleus basalis of Meynert, and putamen was associated with greater cognitive severity after 3 years; and increased baseline susceptibility in basal ganglia, substantia nigra, red nucleus, insular cortex, and dentate nucleus was associated with greater motor severity after 3 years. Increased follow-up susceptibility in these regions was associated with increased follow-up cognitive and motor severity, with further involvement of hippocampus relating to cognitive severity. However, there were no consistent increases in susceptibility over 3 years. Our study suggests that QSM may predict changes in cognitive severity many months prior to overt cognitive involvement in PD. However, we did not find robust longitudinal changes in QSM over the course of the study. Additional tissue metrics may be required together with QSM for it to monitor progression in clinical practice and therapeutic trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network

The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment of PD. However, existing methods either have limitations based on prior knowledge or are invasive methods. To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network. First, we introduce the 3D attention-based convolution layer to extract video features. Second, features will be fed into LSTM and residual backbone networks, which can be used to capture the contextual information of the video. Finally, we design a feature compression module to condense the learned contextual features. We develop some interpretable experiments to better explain this black-box model so that it can be better generalized. Experiments show that our model can achieve state-of-the-art diagnosis performance. The proposed lightweight but effective model is expected to serve as a suitable end-to-end deep learning baseline in future research on PD video-based severity evaluation and has the potential for large-scale application in PD telemedicine. The source code is available at https://github.com/JackAILab/MARNet.

Criteria of syndrome frailty in Parkinson´s disease

To evaluate syndrome frailty by the Fried phenotype in patients with Parkinson's disease (PD). Seventy-three patients over 65 years of age with Hoehn and Yahr stage 2-4 PD were tested for the presence of subjective criteria of the Fried phenotype of syndrome frailty: fatigue, difficulty in performing habitual activities, weight loss and objective criteria: grip strength and walking speed. The relationships of the objective criteria of Fried with indicators of age, sex, sports history, prescription of PD, the number of medications, blood pressure and MDS UPDRS part III scores, the severity of depression on the Beck scale and cognitive disorders on the MOCA were evaluated. All patients complained of fatigue, difficulties in performing habitual activities. Four people noted a decrease in body weight of more than 5 kg per year. Objective criteria of Fried were absent in 38 (51%) patients, 23 (32%) people had one objective criterion: reduced walking speed (less than 0.8 m/s) or hand strength (less than 16 kg for women and 26 kg for men), in 12 (17%) people both objective criteria were reduced. The values of objective criteria of weakness were correlated with age, sex and MDS UPDRS part III total scores. Frailty syndrome is difficult to diagnose in patients with PD due to the coincidence of complaints of the underlying disease and the syndrome. Objective criteria of the Fried phenotype suggest the presence of syndrome frailty in patients. The increase in the age of the patient, the female sex and the severity of PD are interrelated with the presence of objective criteria for the frailty of an elderly person.

Progression of cognitive impairment in Parkinson's disease correlates with uric acid concentration.

This study assessed the relationship between the progression of Parkinson's disease (PD) with cognitive impairment and changes in serum uric acid (UA) and homocysteine (Hcy) concentrations and explored the factors influencing PD with cognitive impairment. The study randomly selected 74 patients with PD and evaluated their cognitive function using the Montreal Cognitive Assessment Scale (MoCA). Patients with PD were divided into two subgroups: those with and without cognitive impairment. PD severity was evaluated and graded using the Hoehn and Yahr (H-Y) scale. Another 60 middle-aged and older individuals without PD during the same period were selected as a control group. Blood UA and Hcy concentrations in each group were measured to assess the relationship between PD, cognitive impairment, and changes in UA and Hcy concentrations. The PD group with cognitive impairment had a lower UA concentration and higher Hcy concentration. The UA concentration was significantly higher in the early PD stages than in the middle and late stages (P<0.05). A significant negative relationship between MoCA scores and serum UA levels was found in patients with PD, whereas a positive relationship existed between MoCA scores and serum Hcy concentrations. Regression analysis showed that a higher UA concentration was an independent protective factor for PD with cognitive impairment, while a higher Hcy concentration was a risk factor (P<0.05). A serum UA concentration of 212.9 mmol/L and Hcy concentration of 13.35 mmol/L could distinguish between patients with PD with or without cognitive impairment with a sensitivity of 93.2% and specificity of 43.3%. PD and cognitive impairment were associated with a decrease in UA concentration; the later the H-Y stage was, the lower the UA concentration was. The increase in Hcy concentration was related to PD and its cognitive impairment, whereas it is not significantly correlated with PD progression.

Patients with Parkinson's disease demonstrate deficits in visual-spatial memory in the Chinese Visual Retention Test.

We aimed to explore the existence of visual-spatial memory deficit in patients with Parkinson's disease (PD) without dementia in the Chinese Visual Retention Test, as well as to assess whether their performance is related to age, duration, severity, stage, and dopamine (DA) dose. Forty-two patients with PD and 30 healthy controls were included in our study. The Chinese Visual Retention Test was used to evaluate the visual-spatial memory of the subjects. Parameters of the Chinese Visual Retention Test were compared between the two groups. Correlation analysis and multiple linear regression analysis were used to explore the associations of the Chinese Visual Retention Test with age, duration, severity, stage of PD, and DA dose. Three correct scores in the Chinese Visual Retention Test were all significantly lower in the PD group than in the control group. The total error scores, error scores of omissions, deformation, and persistence in the PD group were significantly higher than those in the control group. Correlation analysis showed the total error scores in the Chinese Visual Retention Test was positively correlated with UPDRS III score and H-Y classification. Multiple linear regression analysis showed that the total error scores in the Chinese Visual Retention Test were associated with the UPDRS III score and H-Y classification. Patients with PD without dementia had visual-spatial memory deficits in the Chinese Visual Retention Test which may be affected by the severity and clinical stage of PD.

Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson's disease.

The body-first Parkinson's disease (PD) hypothesis suggests initial gut Lewy body pathology initially propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels, a disease course presumed to likely occur in PD with rapid eye movement sleep behavior disorder (RBD). We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers. In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative analysis of NREM sleep and wake electroencephalography (EEG), confirmed it with automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined probable Arizona PD stage classifications based on sleep and wake EEG features. The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to probable Arizona PD stage classifications. Colonic PASH is strongly associated with widespread brain sleep and wake dysfunction, suggesting an extensive diffusion of the pathologic process in PD. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD. Name: SYNAPark, URL: https://clinicaltrials.gov/study/NCT01748409, registration: NCT01748409.

Validation of the DIGEST-FEES as a Global Outcome Measure for Pharyngeal Dysphagia in Parkinson’s Disease

Flexible Endoscopic Evaluation of Swallowing (FEES) is one of two diagnostic gold standards for pharyngeal dysphagia in Parkinson's disease (PD), however, validated global outcome measures at the patient level are widely lacking. The Dynamic Imaging Grade of Swallowing Toxicity for Flexible Endoscopic Evaluation of Swallowing (DIGEST-FEES) represents such an outcome measure but has been validated primarily for head and neck cancer collectives. The objective of this study was, therefore, to investigate the validity of the DIGEST-FEES in patients with PD. Content validity was evaluated with a modified Delphi expert survey. Subsequently, 66 FEES videos in PD patients were scored with the DIGEST-FEES. Criterion validity was determined using Spearman's correlation coefficient between the DIGEST-FEES and the Penetration-Aspiration Scale (PAS), the Yale-Residue-Rating-Scale, the Functional-Oral-Intake-Scale (FOIS), and the swallowing-related Unified-Parkinson-Disease-Rating-Scale (UPDRS) items. Inter-rater reliability was determined using 10 randomly selected FEES-videos examined by a second rater. As a result, the overall DIGEST-FEES-rating exhibited significant correlations with the Yale-Valleculae-Residue-Scale (r = 0.84; p < 0.001), the Yale-Pyriform-Sinus-Residue-Scale (r = 0.70; p < 0.001), the FOIS (r = − 0.55, p < 0.001), and the UPDRS-Swallowing-Item-Score (r = 0.42, p < 0.001). Further, the DIGEST-FEES-safety subscore correlated with the PAS (r = 0.63, p < 0.001). Inter-rater reliability was high for the overall DIGEST-FEES rating (quadratic weighted kappa of 0.82). Therefore, DIGEST-FEES is a valid and reliable score to evaluate overall pharyngeal dysphagia severity in PD. Nevertheless, the modified Delphi survey identified domains where DIGEST-FEES may need to be specifically adapted to PD or neurological collectives in the future.

Gait initiation in Parkinson's disease: comparison of timing and displacement during anticipatory postural adjustments as a function of motor severity and apathy in a large cohort

IntroductionGait initiation is preceded by three anticipatory postural adjustment (APA) phases. In Parkinson's disease (PD) generated force, displacement and timing during APA differ from healthy controls. APA might be influenced by disease status, weight or emotion. It is unknown how motor severity, disease duration or presence of apathy influences APA timing and displacement. MethodsWe included 99 people with PD and 50 healthy controls (HC) to perform five gait initiation trials following an auditory cue. Force plates measured timing and center of pressure (CoP) displacement during APA phases. ResultsTime to gait initiation (tGI) was higher in the PD group (p < 0.001, t = 2.74, 95%CI (0.008, 0.066)). The first two APA phases (APA1 and APA2a) lasted longer in PD (respectively p < 0.001, t = 3.87, 95%CI (0.091, 0.28) and p < 0.001, t = 4.1, 95%CI (0.031, 0.091)). Mean CoP displacement, variability in timing and displacement did not differ. A multiple regression model was used to determine if clinical variables were related to gait initiation parameters. tGI was predicted by age (p < 0.001) and weight (p = 0.005). The duration of APA1 was predicted by weight (p = 0.006) and APA2a by age (p < 0.001). Variability in duration of the locomotor phase (LOC) was predicted by age (p < 0.001). ConclusiontGI and initial APA phases are longer in PD than in HC. There are no significant differences in variability of timing or displacement between the two groups. Gait initiation parameters are independent of disease duration, motor severity, medication usage or apathy in PD. Our findings suggest that cueing does not speed up gait initiation but reduces variability.

Emerging Role of Long Noncoding RNAs in Regulating Inflammasome-Mediated Neurodegeneration in Parkinson's Disease.

Parkinson's disease (PD) is one of the complex neurodegenerative disorders, primarily characterized by motor deficits, including bradykinesia, tremor, rigidity, and postural instability. The underlying pathophysiology involves the progressive loss of dopaminergic neurons within the substantia nigra pars compacta, leading to dopamine depletion in the basal ganglia circuitry. While motor symptoms are hallmark features of PD, emerging research highlights a wide range of non-motor symptoms, including cognitive impairments, mood disturbances, and autonomic dysfunctions. Inflammasome activation is pivotal in inducing neuroinflammation and promoting disease onset, progression, and severity of PD. Several studies have shown that long noncoding RNAs (lncRNAs) modulate inflammasomes in the pathogenesis of neurodegenerative diseases. Dysregulation of lncRNAs is linked to aberrant gene expression and cellular processes in neurodegeneration, causing the activation of inflammasomes that contribute to neuroinflammation and neurodegeneration. Inflammasomes are cytosolic proteins that form complexes upon activation, inducing inflammation and neuronal cell death. This review explores the significance of lncRNAs in regulating inflammasomes in PD, primarily focusing on specific lncRNAs such as nuclear paraspeckle assembly transcript 1 (NEATNEAT1), X-inactive specific transcript (XIST), growth arrest-specific 5 (GAS5), and HOX transcript antisense RNA (HOTAIR), which have been shown to activate or inhibit the NLRP3 inflammasome and induce the release of proinflammatory cytokines. Moreover, some lncRNAs mediate inflammasome activation through miRNA interactions. Understanding the roles of lncRNAs in inflammasome regulation provides new therapeutic targets for controlling neuroinflammation and reducing the progression of neurodegeneration. Identifying lncRNA-mediated regulatory pathways paves the way for novel therapies in the battle against these devastating neurodegenerative disorders.

Mendelian randomization reveals association between retinal thickness and non-motor symptoms of Parkinson’s disease

Retinal thickness is related to Parkinson’s disease (PD), but its association with the severity of PD is still unclear. We conducted a Mendelian randomized (MR) study to explore the association between retinal thickness and PD. For the two-sample MR analysis, the summary statistics obtained from genome-wide association studies on the thickness of Retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) were employed as exposure, while the summary statistics associated with PD were used as the outcome. The primary approach utilized was inverse variance weighted. To correct for multiple testing, the false discovery rate (FDR) was employed. For sensitivity analysis, an array of robust MR methods was utilized. We found genetically predicted significant association between reduced RNFL thickness and a reduced risk of constipation in PD (odds ratio [OR] = 0.854, 95% confidence interval [CI] (0.782, 0.933), P < 0.001, FDR-corrected P = 0.018). Genetically predicted reduced RNFL thickness was associated with a reduced Unified Parkinson’s Disease Rating Scale total score (β = −0.042, 95% CI (−0.079, 0.005), P = 0.025), and reduced GCIPL thickness was associated with a lower risk of constipation (OR = 0.901, 95% CI (0.821, 0.988), P = 0.027) but a higher risk of depression (OR = 1.103, 95% CI (1.016, 1.198), P = 0.020), insomnia (OR = 1.090, 95% CI (1.013, 1.172), P = 0.021), and rapid eye movement sleep behaviour disorder (RBD) (OR = 1.198, 95% CI (1.061, 1.352), P = 0.003). In conclusion, we identify an association between retinal thickness and non-motor symptoms (constipation, depression, insomnia and RBD) in PD, highlighting the potential of retinal thickness as a biomarker for PD nonmotor symptoms.

Association Study Between Kynurenine 3-Monooxygenase (KMO) Gene and Parkinson's Disease Patients.

The influence of various risk factors such as aging, intricate cellular molecular processes, and lifestyle factors like smoking, alcohol consumption, caffeine intake, and occupational factors has received increased focus in relation to the risk and development of Parkinson's disease (PD). Limited research has been conducted on the assessment of lifestyle impact on kynurenine 3-monooxygenase (KMO) gene in PD. A total of 164 subjects, including 82 PD cases and 82 healthy individuals, were recruited based on specific inclusion and exclusion criteria. The severity of PD and clinical assessment were evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn and Yahr (HY) scaling. Sanger sequencing was performed to analyse the KMO gene in the recruited subjects, and case-control studies were conducted. The UPDRS assessment revealed significant impairments in smell, tremors, walking, and posture instability in the late-onset PD cohorts. The HY scaling indicated a higher proportion of late-onset cohorts in stage 2. Moreover, both alcoholic and non-alcoholic groups showed significantly increased levels of 3-HK in late-onset PD. Gene analysis identified missense variants at position g.241593373T > A (rs752312199) and intronic variants at positions g.241592623A > G (rs640718), g.241592800C > A (rs990388262), g.241592802A > C (rs1350160268), g.241592808T > C (rs1478255936), and g.241592812G > T (rs948928931). The alterations in the KMO gene were found to influence the levels of kynurenic acid (KYNA) and 3-hydroxykynurenine (3-HK). Genomic analysis revealed a high prevalence of missense mutations in the late-onset PD groups, leading to a decline in 3-HK levels in patients. This leads to the reduction of the progression of disease in late-onset groups which shows that this mutation may lead to the protective effect on the PD subjects. This study suggests the use of KYNA and 3-HK as potential biomarkers in analysing the progression of disease. This study is limited by its small sample size. To overcome this limitation, a larger study involving in greater number of participants is needed to thoroughly investigate the KMO gene and KP metabolites, to enhance our understanding of Parkinson's disease progression, and to enhance diagnostic capabilities.

The impact of gender‐specific survival on the incidence of dementia in Parkinson’s disease ‐ A cohort analysis of Parkinson’s disease patients in German health claims data for the years 2006 to 2017

AbstractBackgroundThe aim of our study is to analyze gender‐specific patterns of Parkinson’s disease dementia (PDD) incidence. In addition, we are investigating the extent to which gender differences in survival after initial Parkinson’s disease (PD) diagnosis influence differences in PDD risk among PD patients.MethodWe used a random sample of German longitudinal health claims data of persons at ages 65 years or older (2006‐2017). We identified incident PD cases who were followed‐up for a PDD diagnosis or death. We computed age‐ and gender‐specific PDD incidence rates, death rates, and cumulative PDD incidence functions with death as competing risk. In addition, we performed Cox regression models and Fine and Gray competing‐risk regression models, considering death as a competing event, to calculate PDD hazard ratios (HR), adjusted for important risk factors such as age at PD onset, PD severity in terms of modified Hoehn&amp;Yahr (HY) scale, comorbidities, and medications.ResultOf 2,195 incident PD cases, 602 people died and 750 people received a dementia diagnosis by the end of 2017. 44% of PD men and 45% of PD women had received a dementia diagnosis, 45% of PD men and 32% of PD women had died, and only 11% of PD men but 23% of PD women had survived without a PDD diagnosis. PDD risk among PD patients differs by gender when controlled for age at onset, severity of PD, comorbidities and medications, with men having a higher PDD risk than women (HR = 1.18, p = 0.029). When accounting for death, men and women do not differ regarding their PDD risk (HR = 1.02, p = 0.770). Gender‐specific analyses showed significant age and severity effects in women (Age: HR = 1.05, p&lt;0.001, HY 0‐2.5: reference, HY 3‐5: HR = 1.46, p = 0.011), but not in men.ConclusionWe found that older age at first PD diagnosis and higher disease severity increase PDD risk, but this association is attenuated for PD men when we consider death as a competing risk. This implies that the most frail PD men die rapidly before receiving a dementia diagnosis, whereas women with PD survive at higher rates than men, regardless of their age at onset and disease severity.