scholarly journals Advanced brain ageing in Parkinson’s disease is related to disease duration and individual impairment

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Claudia R Eickhoff ◽  
Felix Hoffstaedter ◽  
Julian Caspers ◽  
Kathrin Reetz ◽  
Christian Mathys ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Duration ◽  
Reference Sample ◽  
Biological Age ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Brain Ageing ◽  
Brain Age

Abstract Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson’s disease. Here, we evaluate brain age in two cohorts of Parkinson’s patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson’s disease, newly diagnosed cases from the Parkinson’s Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson’s patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson’s disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.

scholarly journals Alterations of Erythrocytic Phosphorylated Alpha-Synuclein in Different Subtypes and Stages of Parkinson's Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Xu-Ying Li ◽  
Wei Li ◽  
Xin Li ◽  
Xu-Ran Li ◽  
Linjuan Sun ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Duration ◽  
Late Onset ◽  
Area Under The Curve ◽  
Alpha Synuclein ◽  
Enzyme Linked Immunosorbent Assay ◽  
Healthy Controls ◽  
Olfactory Loss ◽  
Potential Biomarker

Serine 129-phosphorylated alpha-synuclein (pS-α-syn) is a major form of α-syn relevant to the pathogenesis of Parkinson's disease (PD), which has been recently detected in red blood cells (RBCs). However, alterations of RBC-derived pS-α-syn (pS-α-syn-RBC) in different subtypes and stages of PD remains to be investigated. In the present study, by using enzyme-linked immunosorbent assay (ELISA) to measure pS-α-syn-RBC, we demonstrated significantly higher levels of pS-α-syn-RBC in PD patients than in healthy controls. pS-α-syn-RBC separated the patients well from the controls, with a sensitivity of 93.39% (95% CI: 90.17–95.81%), a specificity of 93.11% (95% CI: 89.85–95.58%), and an area under the curve (AUC) of 0.96. Considering motor subtypes, the levels of pS-α-syn-RBC were significantly higher in late-onset than young-onset PD (p = 0.013) and in those with postural instability and gait difficulty than with tremor-dominant (TD) phenotype (p = 0.029). In addition, the levels of pS-α-syn-RBC were also different in non-motor subtypes, which were significantly lower in patients with cognitive impairment (p = 0.012) and olfactory loss (p = 0.004) than in those without such symptoms. Moreover, the levels of pS-α-syn-RBC in PD patients were positively correlated with disease duration and Hoehn & Yahr stages (H&Y) (p for trend =0.02 and <0.001) as well as UPDRS III (R2 = 0.031, p = 0.0042) and MoCA scores (R2 = 0.048, p = 0.0004). The results obtained suggest that pS-α-syn-RBC can be used as a potential biomarker for not only separating PD patients from healthy controls but also predicting the subtypes and stages of PD.

scholarly journals Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuya Uehara ◽  
Shin-Ichi Ueno ◽  
Haruka Amano-Takeshige ◽  
Shuji Suzuki ◽  
Yoko Imamichi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Alpha Synuclein ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Non Invasive

AbstractParkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.

scholarly journals SNPs rs11240569, rs708727, and rs823156 in SLC41A1 Do Not Discriminate Between Slovak Patients with Idiopathic Parkinson’s Disease and Healthy Controls: Statistics and Machine-Learning Evidence

2019 ◽  
Vol 20 (19) ◽  
pp. 4688
Author(s):  
Michal Cibulka ◽  
Maria Brodnanova ◽  
Marian Grendar ◽  
Milan Grofik ◽  
Egon Kurca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Susceptibility Locus ◽  
Healthy Controls ◽  
Diverse Populations ◽  
Slovak Population ◽  
Asian Populations ◽  
Clinical Diagnostic

Gene SLC41A1 (A1) is localized within Parkinson’s disease-(PD)-susceptibility locus PARK16 and encodes for the Na+/Mg2+-exchanger. The association of several A1 SNPs with PD has been studied. Two, rs11240569 and rs823156, have been associated with reduced PD-susceptibility primarily in Asian populations. Here, we examined the association of rs11240569, rs708727, and rs823156 with PD in the Slovak population and their power to discriminate between PD patients and healthy controls. The study included 150 PD patients and 120 controls. Genotyping was performed with the TaqMan® approach. Data were analyzed by conventional statistics and Random Forest machine-learning (ML) algorithm. Individually, none of the three SNPs is associated with an altered risk for PD-onset in Slovaks. However, a combination of genotypes of SNP-triplet GG(rs11240569)/AG(rs708727)/AA(rs823156) is significantly (p < 0.05) more frequent in the PD (13.3%) than in the control (5%) cohort. ML identified the power of the tested SNPs in isolation or of their singlets (joined), duplets and triplets to discriminate between PD-patients and healthy controls as zero. Our data further substantiate differences between diverse populations regarding the association of A1 polymorphisms with PD-susceptibility. Lack of power of the tested SNPs to discriminate between PD and healthy cases render their clinical/diagnostic relevance in the Slovak population negligible.

scholarly journals Neurocognitive Correlates of Apathy and Anxiety in Parkinson's Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Yelena Bogdanova ◽  
Alice Cronin-Golomb
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neuropsychological Tests ◽  
Rating Scales ◽  
Disease Duration ◽  
Differential Association ◽  
Motor Symptoms ◽  
Healthy Controls ◽  
Nonmotor Symptoms ◽  
Neurocognitive Profiles

Parkinson's disease (PD) is associated with various nonmotor symptoms including neuropsychiatric and cognitive dysfunction. We examined the relation between apathy, anxiety, side of onset of motor symptoms, and cognition in PD. We hypothesized that PD patients would show different neuropsychiatric and neurocognitive profiles depending on the side of onset. 22 nondemented PD patients (11 right-side onset (RPD) with predominant left-hemisphere pathology, and 11 LPD) and 22 matched healthy controls (NC) were administered rating scales assessing apathy and anxiety, and a series of neuropsychological tests. PD patients showed a higher anxiety level than NC. There was a significant association between apathy, anxiety, and disease duration. In LPD, apathy but not anxiety was associated with performance on nonverbally mediated executive function and visuospatial measures, whereas, in RPD, anxiety but not apathy correlated with performance on verbally mediated tasks. Our findings demonstrated a differential association of apathy and anxiety to cognition in PD.

scholarly journals Quality of Life in Newly Diagnosed Patients With Parkin-Related Parkinson's Disease

2020 ◽  
Vol 11 ◽  
Author(s):  
Xin-Yue Zhou ◽  
Feng-Tao Liu ◽  
Chen Chen ◽  
Su-Shan Luo ◽  
Jue Zhao ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Disease Duration ◽  
Motor Symptoms ◽  
Newly Diagnosed ◽  
Non Motor Symptoms

Introduction: Mutations in the Parkin gene are the most common cause of autosomal recessive early-onset Parkinson's disease (PD). However, little is known about the quality of life (QoL) in Parkin-related PD. Here, we investigated the patterns of QoL in newly diagnosed Parkin-related PD patients.Methods: Newly diagnosed PD patients (diagnosis made within 12 months) who had an age of onset (AOO) below 40 and underwent a PD-related genetic testing, were recruited (n = 148). Among them, 24 patients carried bi-allelic variants in Parkin (PD-Parkin) and 24 patients did not have any known causative PD mutations, or risk variants (GU-EOPD). The clinical materials, relevant factors and determinants of QoL were analyzed.Results: PD-Parkin patients had a younger AOO (p = 0.003) and longer disease duration (p = 0.005). After adjustment for AOO and disease duration, more dystonia (p = 0.034), and worse scores of non-motor symptoms including Beck depression inventory (BDI, p = 0.035), Epworth sleepiness scale (ESS, p = 0.044), and subdomains of depression/anxiety (p = 0.015) and sleep disorders (p = 0.005) in Non-motor symptoms questionnaire, were found in PD-Parkin comparing with GU-EOPD. PD-Parkin patients had poorer QoL (adjusted p = 0.045), especially in the mobility (adjusted p = 0.025), emotional well-being (adjusted p = 0.015) and bodily discomfort dimensions (adjusted p = 0.016). BDI scores (p = 0.005) and ESS scores (p = 0.047) were significant determinants of QoL in PD-Parkin.Conclusion: Newly diagnosed PD-Parkin patients showed worse QoL. More depression and excessive daytime sleepiness predicted worse QoL. For clinicians, management of depression and excessive daytime sleepiness is suggested to better improve QoL in patients with Parkin mutations.

Cognitive Change in Newly-Diagnosed Patients with Parkinson's Disease: A 5-Year Follow-up Study

2013 ◽  
Vol 19 (6) ◽  
pp. 695-708 ◽  
Author(s):  
Mark Broeders ◽  
Daan C. Velseboer ◽  
Rob de Bie ◽  
Johannes D. Speelman ◽  
Dino Muslimovic ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Decline ◽  
Age At Onset ◽  
Cognitive Change ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Cognitive Changes ◽  
Cognitive Domains ◽  
Healthy Elderly

AbstractCognitive change is frequently observed in patients with Parkinson's disease (PD). However, the exact profile and extent of cognitive impairments remain unclear due to the clinical heterogeneity of PD and methodological issues in many previous studies. In this study, we aimed to examine the severity, frequency, and profile of cognitive changes in newly diagnosed PD patients over 5 years. At baseline and after 3 and 5 years, a hospital-based sample of PD patients (n = 59) and healthy controls (n = 40) were given neuropsychological tests covering six cognitive domains. Patients showed greater decline over time than healthy controls on all cognitive domains, except for attention. The profile of decline showed that psychomotor speed and memory were most affected. At the individual level 53% of the patients showed more cognitive decline than controls. Age at onset and memory impairment at baseline predicted cognitive decline. Cognitive functions in PD patients show greater decline in most domains than in healthy elderly over the course of 5 years. Due to selection bias as a result of attrition, the actual degree of decline may be greater than reported here. (JINS, 2013, 19, 1–14)

scholarly journals Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

2021 ◽  
Author(s):  
long qian ◽  
chaoyong xiao ◽  
Sidong Liu ◽  
zaixu cui ◽  
xiao hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Rating Scale ◽  
Mean Diffusivity ◽  
Learning Approach ◽  
Healthy Controls ◽  
White Matter Tracts ◽  
Machine Learning Approach

Abstract The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

Metabolite and Peptide Levels in Plasma and CSF Differentiating Healthy Controls from Patients with Newly Diagnosed Parkinson's Disease

2014 ◽  
Vol 4 (3) ◽  
pp. 549-560 ◽  
Author(s):  
Miles Trupp ◽  
Pär Jonsson ◽  
Annika Öhrfelt ◽  
Henrik Zetterberg ◽  
Ogonna Obudulu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Controls ◽  
Newly Diagnosed

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification
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