The severity progression of non-motor symptoms in Parkinson's disease: a 6-year longitudinal study in Taiwanese patients.
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ABSTRACT:
SUBMITTER: Chen YC
PROVIDER: S-EPMC8292315 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Parkinson's disease (PD) is a chronic progressive neurodegenerative disease that is clinically manifested by a triad of cardinal motor symptoms - rigidity, bradykinesia and tremor - due to loss of dopaminergic neurons. The motor symptoms of PD become progressively worse as the disease advances. PD is also a heterogeneous disease since rigidity and bradykinesia are the major complaints in some patients whereas tremor is predominant in others. In recent years, many studies have investigated the progression of the hallmark symptoms over time, and the cardinal motor symptoms have different rates of progression, with the disease usually progressing faster in patients with rigidity and bradykinesia than in those with predominant tremor. The current treatment regime of dopamine-replacement therapy improves motor symptoms and alleviates disability. Increasing the dosage of dopaminergic medication is commonly used to combat the worsening symptoms. However, the drug-induced involuntary body movements and motor complications can significantly contribute to overall disability. Further, none of the currently-available therapies can slow or halt the disease progression. Significant research efforts have been directed towards developing neuroprotective or disease-modifying agents that are intended to slow the progression. In this article, the most recent clinical studies investigating disease progression and current progress on the development of disease-modifying drug trials are reviewed.
| S-EPMC5560285 | biostudies-other
Project description:The progression of global non-motor symptoms (NMS) in Chinese patients with Parkinson's disease (PD) has not been explored. We aimed to examine the longitudinal evolution of overall NMS in a 3-year prospective Chinese cohort with early-stage PD. We included 224 patients with early PD who underwent annual evaluation of motor and non-motor symptoms. NMS was assessed using the non-motor symptoms scale (NMSS). We observed an increased number of NMS in the majority of the NMSS domains except mood/apathy and sexual dysfunctions. Significant deterioration was observed in the sleep/fatigue, perceptual problems/hallucinations, attention/memory, gastrointestinal, urinary, and miscellaneous domains during the follow-up (P < 0.05). Notably, the number and the score of sexual dysfunctions decreased with the progression of the disease. All NMSS domains showed a small effect size from baseline to 1-, 2-, and 3-year follow-ups (effect size < 0.5). The generalized estimating equations model indicated that the total number of NMS was significantly associated with age and the Unified Parkinson's Disease Rating Scale (UPDRS) III score (P < 0.05). Multiple logistic regression indicated that a high number of NMS at baseline was associated with a 3-point, a 6-point, and a 9-point increase in the UPDRS III score from baseline to 1-year (odds ratio [OR] 1.074, P = 0.017), 2-year (OR 1.113, P = 0.001), and 3-year (OR 1.117, P < 0.001), respectively. Our study indicated that overall NMS evolution in early PD is mild and multidimensional; a high NMS burden in early PD predicts the faster motor progression of PD. Our study is helpful for understanding the longitudinal evolution of NMS in PD.
| S-EPMC8282608 | biostudies-literature
Project description:BackgroundIdentifying individuals with high-risk Parkinson's disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.MethodsWe retrieved the data from the Parkinson's Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for "optimism" or overfitting with the bootstrap resampling approach.ResultsFor constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91-0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.ConclusionThe findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.
| S-EPMC9459236 | biostudies-literature
Project description:Objective:The aim of this study is to present a predictive model of Parkinson's disease (PD) global severity, measured with the Clinical Impression of Severity Index for Parkinson's Disease (CISI-PD). Methods:This is an observational, longitudinal study with annual follow-up assessments over 3 years (four time points). A multilevel analysis and multiple imputation techniques were performed to generate a predictive model that estimates changes in the CISI-PD at 1, 2, and 3 years. Results:The clinical state of patients (CISI-PD) significantly worsened in the 3-year follow-up. However, this change was of small magnitude (effect size: 0.44). The following baseline variables were significant predictors of the global severity change: baseline global severity of disease, levodopa equivalent dose, depression and anxiety symptoms, autonomic dysfunction, and cognitive state. The goodness-of-fit of the model was adequate, and the sensitive analysis showed that the data imputation method applied was suitable. Conclusion:Disease progression depends more on the individual's baseline characteristics than on the 3-year time period. Results may contribute to a better understanding of the evolution of PD including the non-motor manifestations of the disease.
| S-EPMC5674937 | biostudies-literature
Project description:The nature of the inflammatory response in Parkinson's disease (PD) remains to be better understood. Here, we used highly sensitive Single Molecule Array (SIMOA) technology to measure the levels of the inflammatory mediators Interleukin 6 (IL-6), Interleukin 17A (IL-17A), Tumour Necrosis Factor ? (TNF?) and Transforming Growth Factor ? (TGF?) in plasma from PD patients and age- and gender-matched healthy controls. We report that IL-17A correlates with non-motor symptoms (NMS) scores, while IL-6 positively correlates with motor scores. We found no correlations between cytokines and disease duration suggesting that IL-6 and IL-17A are associated with disease severity rather than disease duration in this cohort, furthermore IL-17A may be involved in the underlying pathophysiology of NMS in PD.
| S-EPMC6839458 | biostudies-literature
Project description:Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson's disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson's disease have been identified, and the serum lipid signature of Parkinson's disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson's disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assessed. Machine learning models performed best when baseline serum lipids were used to predict the 2-year future Unified Parkinson's disease rating scale part three (UPDRS III) and Geriatric Depression Scale scores (both normalised root mean square error = 0.7). Feature analysis of machine learning models indicated that species of lysophosphatidylethanolamine, phosphatidylcholine, platelet-activating factor, sphingomyelin, diacylglycerol and triacylglycerol were top predictors of both motor and non-motor scores. Serum lipids were overall more important predictors of clinical outcomes than subject sex, age and mutation status of the Parkinson's disease risk gene LRRK2. Furthermore, lipids were found to better predict clinical scales than a panel of 27 serum cytokines previously measured in this cohort (The Michael J. Fox Foundation LRRK2 Clinical Cohort Consortium). These results suggest that lipid changes may be associated with clinical phenotypes in Parkinson's disease.
| S-EPMC11199659 | biostudies-literature
Project description:ObjectiveTo examine the baseline prevalence and longitudinal evolution in non-motor symptoms (NMS) in a prospective cohort of, at baseline, patients with de novo Parkinson's disease (PD) compared with healthy controls (HC).MethodsParkinson's Progression Markers Initiative (PPMI) is a longitudinal, ongoing, controlled study of de novo PD participants and HC. NMS were rated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I score and other validated NMS scales at baseline and after 2 years. Biological variables included cerebrospinal fluid (CSF) markers and dopamine transporter imaging.Results423 PD subjects and 196 HC were enrolled and followed for 2 years. MDS-UPDRS Part I total mean (SD) scores increased from baseline 5.6 (4.1) to 7.7 (5.0) at year 2 in PD subjects (p<0.001) versus from 2.9 (3.0) to 3.2 (3.0) in HC (p=0.38), with a significant difference between the groups (p<0.001). In the multivariate analysis, higher baseline NMS score was associated with female sex (p=0.008), higher baseline MDS-UPDRS Part II scores (p<0.001) and more severe motor phenotype (p=0.007). Longitudinal increase in NMS severity was associated with the older age (0.008) and lower CSF Aβ1-42 (0.005) at baseline. There was no association with the dose or class of dopaminergic therapy.ConclusionsThis study of NMS in early PD identified clinical and biological variables associated with both baseline burden and predictors of progression. The association of a greater longitudinal increase in NMS with lower baseline Aβ1-42 level is an important finding that will have to be replicated in other cohorts.Trial registrationClinicalTrials.gov identifier: NCT01141023.
| S-EPMC5732865 | biostudies-literature
Project description:IntroductionParkinson's disease (PD) is a multisystem neurodegenerative disorder characterized by motor and non-motor symptoms. In particular, non-motor symptoms have become increasingly relevant to disease progression. This study aimed to reveal which non-motor symptoms have the highest impact on the complex interacting system of various non-motor symptoms and to determine the progression of these interactions over time.MethodsWe performed exploratory network analyses of 499 patients with PD from the Cohort of Patients with Parkinson's Disease in Spain study, who had Non-Motor Symptoms Scale in Parkinson's Disease ratings obtained at baseline and a 2-year follow-up. Patients were aged between 30 and 75 years and had no dementia. The strength centrality measures were determined using the extended Bayesian information criterion and the least absolute shrinkage and selection operator. A network comparison test was conducted for the longitudinal analyses.ResultsOur study revealed that the depressive symptoms anhedonia and feeling sad had the strongest impact on the overall pattern of non-motor symptoms in PD. Although several non-motor symptoms increase in intensity over time, their complex interacting networks remain stable.ConclusionOur results suggest that anhedonia and feeling sad are influential non-motor symptoms in the network and, thus, are promising targets for interventions as they are closely linked to other non-motor symptoms.
| S-EPMC9971229 | biostudies-literature
Project description:OBJECTIVE:The objective of this study was to assess the efficacy and safety of nabilone, a synthetic tetrahydrocannabinol analogue, as a treatment for non-motor symptoms (NMS) in Parkinson's disease (PD). METHODS:This was a phase II placebo-controlled, double-blind, parallel-group, enriched enrollment randomized withdrawal trial conducted at the Medical University Innsbruck. A random sample of 47 patients with PD with stable motor disease and disturbing NMS defined by a score of ?4 points on the Movement Disorder Society - Unified PD Rating Scale-I (MDS-UPDRS-I) underwent open-label nabilone titration (0.25 mg once daily to 1 mg twice daily, phase I). Responders were randomized 1:1 to continue with nabilone or switch to placebo for 4?weeks (phase II). The primary efficacy criterion was the change of the MDS-UPDRS-I between randomization and week 4. Safety was analyzed in all patients who received at least one nabilone dose. RESULTS:Between October 2017 and July 2019, 19 patients received either nabilone (median dose = 0.75 mg) or placebo. At week 4, mean change of the MDS-UPDRS-I was 2.63 (95% confidence interval [CI] 1.53 to 3.74, p =?0.002, effect size = 1.15) in the placebo versus 1.00 (95% CI -0.16 to 2.16, p =?0.280, effect size = 0.42) in the nabilone-group (difference: 1.63, 95% CI 0.09 to 3.18, p =?0.030, effect size = 0.66). Seventy-seven percent of patients had adverse events (AEs) during open-label titration, most of them were transient. In the double-blind phase, similar proportions of patients in each group had AEs (42% in the placebo group and 32% in the nabilone group). There were no serious AEs. INTERPRETATION:Our results highlight the potential efficacy of nabilone for patients with PD with disturbing NMS, which appears to be driven by positive effects on anxious mood and night-time sleep problems. TRIAL REGISTRY:ClinicalTrials.gov (NCT03769896) and EudraCT (2017-000192-86). ANN NEUROL 2020;88:712-722.
| S-EPMC7540547 | biostudies-literature
Project description:Parkinson's disease is now considered a complex, multi-peptide, central, and peripheral nervous system disorder with considerable clinical heterogeneity. Non-motor symptoms play a key role in the trajectory of Parkinson's disease, from prodromal premotor to end stages. To understand the clinical heterogeneity of Parkinson's disease, this study used cluster analysis to search for subtypes from a large, multi-center, international, and well-characterized cohort of Parkinson's disease patients across all motor stages, using a combination of cardinal motor features (bradykinesia, rigidity, tremor, axial signs) and, for the first time, specific validated rater-based non-motor symptom scales. Two independent international cohort studies were used: (a) the validation study of the Non-Motor Symptoms Scale (n = 411) and (b) baseline data from the global Non-Motor International Longitudinal Study (n = 540). k-means cluster analyses were performed on the non-motor and motor domains (domains clustering) and the 30 individual non-motor symptoms alone (symptoms clustering), and hierarchical agglomerative clustering was performed to group symptoms together. Four clusters are identified from the domains clustering supporting previous studies: mild, non-motor dominant, motor-dominant, and severe. In addition, six new smaller clusters are identified from the symptoms clustering, each characterized by clinically-relevant non-motor symptoms. The clusters identified in this study present statistical confirmation of the increasingly important role of non-motor symptoms (NMS) in Parkinson's disease heterogeneity and take steps toward subtype-specific treatment packages.
| S-EPMC5611404 | biostudies-literature