The correlation of non-motor symptoms and sleep on balance in Parkinson’s disease patients with normal cognition and mild cognitive impairment

The following study protocol describes the rationale and methods of a cohort with a nested case-control study, which aims to identify risk factors and predictors of cognitive dysfunction in Parkinson's disease (PD). It is a study that will follow PD every 18 months with a comprehensive neuropsychological, clinical (motor and non-motor symptoms) and imaging (Magnetic Resonance Imaging) data collection. The criteria for diagnosing mild cognitive impairment (MCI) and dementia will respect the parameters previously published by the International Working Group on Mild Cognitive Impairment, and compared with those recommended by the Fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM-5) and the International Parkinson's and Movement Disorders Society (MDS) criteria. We will also evaluate the neural substrate and underpinnings of PD non-motor symptoms, using advanced neuroimaging techniques, such as diffusion tensor imaging (DTI) and gray matter and white matter volumetric measurements.

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Brasilia Parkinson Cohort: assessing clinical, neuropsychological and imaging predictors of cognitive decline in Parkinson's disease

10.7287/peerj.preprints.27639 ◽

2019 ◽

Author(s):

Pedro Renato de Paula Brandão ◽

Fernando Bisinoto Maluf ◽

Talyta Grippe ◽

Ingrid Faber ◽

Danilo Assis Pereira ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Diffusion Tensor ◽

Motor Symptoms ◽

Imaging Data ◽

Diagnostic And Statistical Manual ◽

Nested Case Control ◽

Non Motor Symptoms

The following study protocol describes the rationale and methods of a cohort with a nested case-control study, which aims to identify risk factors and predictors of cognitive dysfunction in Parkinson's disease (PD). It is a study that will follow PD every 18 months with a comprehensive neuropsychological, clinical (motor and non-motor symptoms) and imaging (Magnetic Resonance Imaging) data collection. The criteria for diagnosing mild cognitive impairment (MCI) and dementia will respect the parameters previously published by the International Working Group on Mild Cognitive Impairment, and compared with those recommended by the Fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM-5) and the International Parkinson's and Movement Disorders Society (MDS) criteria. We will also evaluate the neural substrate and underpinnings of PD non-motor symptoms, using advanced neuroimaging techniques, such as diffusion tensor imaging (DTI) and gray matter and white matter volumetric measurements.

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Development of a Predictive Model for Mild Cognitive Impairment in Parkinson’s Disease with Normal Cognition Using Kernel-Based C5.0 Machine Learning Blending: Preliminary Research

Engineering Proceedings ◽

10.3390/asec2021-11147 ◽

2021 ◽

Vol 11 (1) ◽

pp. 18

Author(s):

Haewon Byeon

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Single Machine ◽

Motor Symptoms ◽

Learning Models ◽

Normal Cognition ◽

Machine Learning Models

This preliminary study mainly compared the performance for predicting mild cognitive impairment in Parkinson’s disease (PDMCI) between single machine learning and hybrid machine learning. This study analyzed 185 patients with Parkinson’s disease (75 Parkinson’s disease) patients with normal cognition, and 110 patients with PDMCI. PDMCI, an outcome variable, was divided into “with PDMCI” and “with normal cognition” according to the diagnosis of the neurologist. This study used 48 variables (diagnostic data), including motor symptoms of Parkinson’s disease, non-motor symptoms of Parkinson’s disease, and sleep disorders, as explanatory variables. This study developed seven machine learning models using blending (three hybrid models (polydot + C5.0, vanilladot + C5.0, and RBFdot + C5.0) and four single machine learning models (polydot, vanilladot, RBFdot, and C5.0)). The results of this study showed that the RBFdot + C5.0 was the model with the best performance to predict PDMCI in Parkinson’s disease patients with normal cognition (AUC = 0.88) among the seven machine learning models. We will develop interpretable machine learning using C5.0 in a follow-up study based on the results of this study.

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Study of cognitive impairment and genetic polymorphism of SLC41A1 (rs11240569 allele) in Parkinson’s disease in Upper Egypt: case-control study

The Egyptian Journal of Neurology Psychiatry and Neurosurgery ◽

10.1186/s41983-021-00341-0 ◽

2021 ◽

Vol 57 (1) ◽

Author(s):

Hamdy N. El-Tallawy ◽

Tahia H. Saleem ◽

Wafaa M. Farghaly ◽

Heba Mohamed Saad Eldien ◽

Ashraf Khodaery ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Case Control Study ◽

Case Control ◽

Control Group ◽

Motor Symptoms ◽

And Control ◽

Non Motor Symptoms ◽

Control Study

Abstract Background Parkinson’s disease is one of the neurodegenerative disorders that is caused by genetic and environmental factors or interaction between them. Solute carrier family 41 member 1 within the PARK16 locus has been reported to be associated with Parkinson’s disease. Cognitive impairment is one of the non-motor symptoms that is considered a challenge in Parkinson’s disease patients. This study aimed to investigate the association of rs11240569 polymorphism; a synonymous coding variant in SLC41A1 in Parkinson’s disease patients in addition to the assessment of cognitive impairment in those patients. Results In a case -control study, rs11240569 single nucleotide polymorphisms in SLC41A1, genes were genotyped in 48 Parkinson’s disease patients and 48 controls. Motor and non-motor performance in Parkinson's disease patients were assessed by using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The genotype and allele frequencies were compared between the two groups and revealed no significant differences between case and control groups for rs11240569 in SLC41A1 gene with P value .523 and .54, respectively. Cognition was evaluated and showed the mean ± standard deviation (SD) of WAIS score of PD patients 80.4 ± 9.13 and the range was from 61 to 105, in addition to MMSE that showed mean ± SD 21.96 ± 3.8. Conclusion Genetic testing of the present study showed that rs11240569 polymorphism of SLC41A1 gene has no significant differences in distributions of alleles and genotypes between cases and control group, in addition to cognitive impairment that is present in a large proportion of PD patients and in addition to the strong correlation between cognitive impairment and motor and non-motor symptoms progression.

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Constipation is Associated with Development of Cognitive Impairment in de novo Parkinson’s Disease: A Longitudinal Analysis of Two International Cohorts

Journal of Parkinson s Disease ◽

10.3233/jpd-212570 ◽

2021 ◽

pp. 1-11

Author(s):

Valentina Leta ◽

Daniele Urso ◽

Lucia Batzu ◽

Daniel Weintraub ◽

Nataliya Titova ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

De Novo ◽

Motor Dysfunction ◽

Group Differences ◽

Motor Symptoms ◽

Kaplan Meier ◽

Patients At Risk ◽

Non Motor Symptoms

Background: Constipation is regarded as one of the prodromal features of Parkinson’s disease (PD) and there is emerging evidence linking gastrointestinal dysfunction and cognitive impairment (CI) in PD. Objective: We explored whether constipation is associated with development of CI in two independent cohorts of de novo PD patients (n = 196 from the Non-motor International Longitudinal Study [NILS] and n = 423 from the Parkinson’s Progression Markers Initiative [PPMI] study). Methods: Constipation was clinically defined using the Non-Motor Symptoms Scale (NMSS) item-21 [NILS] and Scales for Outcomes in PD-Autonomic (SCOPA-AUT) item-5 [PPMI]. We assessed baseline group differences (PD with or without constipation) in CI, global non-motor symptoms burden, motor dysfunction, and striatal dopaminergic denervation. Kaplan-Meier method estimated group differences in cumulative proportion of patients with incident CI over three years. In PPMI, we subsequently performed univariate and multivariate Cox survival analyses to evaluate whether constipation predicts incident mild cognitive impairment or dementia over a 6-year period, including constipation and other known predictors of CI as covariates. Results: Patients with constipation had greater motor and global non-motor burden in both cohorts at baseline (p < 0.05). Kaplan-Meier plots showed faster conversion to CI in patients with constipation in both cohorts (p < 0.05). In PPMI, 37 subjects developed dementia during a mean follow-up of 4.9 years, and constipation was an independent predictor of dementia onset (hazard ratio = 2.311; p = 0.02). Conclusion: Constipation in de novo PD patients is associated with development of cognitive decline and may serve as a clinical biomarker for identification of patients at risk for cognitive impairment.

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The association of cognitive impairment and other non-motor symptoms among Thai Parkinson's disease patients

Journal of the Neurological Sciences ◽

10.1016/j.jns.2021.119607 ◽

2021 ◽

Vol 429 ◽

pp. 119607

Author(s):

Chayasak Wantaneeyawong ◽

Kittithatch Booncharoen ◽

Kanokwan Wattana ◽

Orawan Ronran ◽

Siwahdol Chaimano ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Motor Symptoms ◽

Non Motor Symptoms

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P112: Reporting of non-motor symptoms and cognitive impairment in Parkinson's disease clinic attenders

European Geriatric Medicine ◽

10.1016/s1878-7649(14)70287-6 ◽

2014 ◽

Vol 5 ◽

pp. S118

Author(s):

K. Nagaratnam ◽

A. Monkhouse ◽

H. Jones ◽

S. Wheeler ◽

J. Beal ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Motor Symptoms ◽

Disease Clinic ◽

Non Motor Symptoms

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CSF sTREM2 correlates with CSF tau in advancing Parkinson’s disease

10.1101/687269 ◽

2019 ◽

Author(s):

Edward N. Wilson ◽

Michelle S. Swarovski ◽

Patricia Linortner ◽

Marian Shahid ◽

Abigail J. Zuckerman ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Microglial Activation ◽

Soluble Form ◽

Motor Symptoms ◽

Cognitively Normal ◽

Csf Concentration

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease (AD) and affects 1% of the population above 60 years old. Although PD commonly manifests with motor symptoms, a majority of patients with PD subsequently develop cognitive impairment which often progresses to dementia, a major cause of morbidity and disability. PD is characterized by α-synuclein accumulation that frequently associates with amyloid beta (Aβ) and tau fibrils, the hallmarks of AD neuropathologic changes; this co-occurrence suggests that onset of cognitive decline in PD may be associated with appearance of pathologic Aβ and/or tau. Recent studies have highlighted the appearance of the soluble form of the Triggering Receptor Expressed on Myeloid cells 2 (sTREM2) receptor in CSF during development of AD. Given the known association of microglial activation with advancing PD, we investigated whether CSF and/or plasma sTREM2 increased with progression to PD dementia. We examined 165 participants consisting of 17 cognitively normal elderly, 45 PD patients with no cognitive impairment, 86 with mild cognitive impairment, and 17 with dementia. Stratification of subjects by CSF Aβ and tau levels revealed that CSF sTREM2 concentrations were elevated in PD subgroups with abnormal tau, but not Aβ, CSF concentration. These findings indicate that CSF sTREM2 could serve as a surrogate immune biomarker of neuronal injury in PD that is associated with cognitive decline.One sentence summaryCSF sTREM2 correlates with CSF tau in PD

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Dynamic functional connectivity changes associated with dementia in Parkinson’s disease

Brain ◽

10.1093/brain/awz192 ◽

2019 ◽

Vol 142 (9) ◽

pp. 2860-2872 ◽

Cited By ~ 19

Author(s):

Eleonora Fiorenzato ◽

Antonio P Strafella ◽

Jinhee Kim ◽

Roberta Schifano ◽

Luca Weis ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Parkinson’S Disease Dementia ◽

Dynamic Functional Connectivity ◽

Control Subjects ◽

Healthy Control ◽

Normal Cognition

AbstractDynamic functional connectivity captures temporal variations of functional connectivity during MRI acquisition and it may be a suitable method to detect cognitive changes in Parkinson’s disease. In this study, we evaluated 118 patients with Parkinson’s disease matched for age, sex and education with 35 healthy control subjects. Patients with Parkinson’s disease were classified with normal cognition (n = 52), mild cognitive impairment (n = 46), and dementia (n = 20) based on an extensive neuropsychological evaluation. Resting state functional MRI and a sliding-window approach were used to study the dynamic functional connectivity. Dynamic analysis suggested two distinct connectivity ‘States’ across the entire group: a more frequent, segregated brain state characterized by the predominance of within-network connections, State I, and a less frequent, integrated state with strongly connected functional internetwork components, State II. In Parkinson’s disease, State I occurred 13.89% more often than in healthy control subjects, paralleled by a proportional reduction of State II. Parkinson’s disease subgroups analyses showed the segregated state occurred more frequently in Parkinson’s disease dementia than in mild cognitive impairment and normal cognition groups. Further, patients with Parkinson’s disease dementia dwelled significantly longer in the segregated State I, and showed a significant lower number of transitions to the strongly interconnected State II compared to the other subgroups. Our study indicates that dementia in Parkinson’s disease is characterized by altered temporal properties in dynamic connectivity. In addition, our results show that increased dwell time in the segregated state and reduced number of transitions between states are associated with presence of dementia in Parkinson’s disease. Further studies on dynamic functional connectivity changes could help to better understand the progressive dysfunction of networks between Parkinson’s disease cognitive states.

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Systematic review of genetic variants associated with cognitive impairment and depressive symptoms in Parkinson’s disease

Acta Neuropsychiatrica ◽

10.1017/neu.2019.28 ◽

2019 ◽

Vol 32 (1) ◽

pp. 10-22 ◽

Cited By ~ 2

Author(s):

Tyrra D’Souza ◽

Anto P. Rajkumar

Keyword(s):

Systematic Review ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Depressive Symptoms ◽

Genetic Variants ◽

Molecular Mechanisms ◽

Association Studies ◽

Motor Symptoms ◽

Non Motor Symptoms

AbstractObjective:Cognitive impairment and depression are among the most prevalent and most disabling non-motor symptoms in Parkinson’s disease (PD). The genetic factors that are associated with these symptoms remain uncertain. This systematic review aims to summarise the prevailing evidence from all genetic association studies investigating the genetic variants associated with cognitive impairment and depressive symptoms in people with PD.Method:A systematic review using five online databases: PubMed, PsycINFO, CINAHL, EMBASE and OpenGrey (PROSPERO protocol: CRD42017067431). We completed the quality assessment using the Q-Genie tool.Results:2353 articles were screened, and 43 articles were found to be eligible to be included. A meta-analysis of studies investigating LRRK2 rs34637584 confirmed that the minor allele carriers had significantly less cognitive impairment (p = 0.015). Further meta-analyses showed that GBA variants rs76763715 (p < 0.001) and rs421016 (p = 0.001) were significantly associated with more cognitive impairment in people with PD. Minor alleles of GBA variants rs76763715, rs421016, rs387906315 and rs80356773 were associated with more depressive symptoms in PD. Moreover, APOE ε4 allele has been associated with more cognitive impairment in PD. BDNF (rs6265) and CRY1 (rs2287161) variants have been associated with more depressive symptoms in people with PD.Conclusions:PD carriers of GBA variants are at high risk for cognitive decline and depression. Screening for these variants may facilitate early identification and effective management of these non-motor symptoms. The molecular mechanisms underlying favourable cognitive functioning in LRRK2 rs34637584 variant carriers warrant further investigation.

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