Comparison in detection of prodromal Parkinson's disease patients using original and updated MDS research criteria in two independent cohorts

Kulcsarová, Kristína; Ventosa, Joaquim Ribeiro; Feketeová, Eva; Maretta, M.; Leško, Norbert; Benca, Miroslav; Han, Vladimir; Gombošová, Laura; Baloghová, Janette; Slavkovská, Miriam; Brosmanova, Maria; Vančová, Zuzana; Lepej, J; Rabajdová, Miroslava; Ambro, Ĺuboš; Tóth, Štefan; Kudela, Filip; Kudela, Igor; Strigacova, Lujza; Roskovicova, Veronika; Gdovinová, Zuzana; Škorvánek, Matej

doi:10.1016/j.parkreldis.2021.04.028

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Study participants were recruited in a single Movement Disorder Centre in Kosice, Slovakia, between 2018 and 2021 after a nationwide media campaign, followed by a multistage screening described previously [17]. Patients were screened using the REM Sleep Behaviour Disorder Single-Question Screening [18], followed in the case of positive findings by the RBD-SQ [19].…”

Section: Methodsmentioning

confidence: 99%

“…The likelihood ratio (LR) of a given subject being in pPD at the time of examination was also calculated based on the updated Movement Disorder Society (MDS) Research Criteria for Prodromal Parkinson's Disease [24], using all risk factors and prodromal markers, excluding genetic testing, plasma urate levels in men, and physical inactivity, as described previously [17].…”

Section: Methodsmentioning

confidence: 99%

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Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

Ventosa¹,

Kulcsárová²,

Mertová³

et al. 2023

Neurol Neurochir Pol.

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Introduction.Nearly 80% of people diagnosed with idiopathic REM sleep behaviour disorder (iRBD) via video-polysomnography (v-PSG) are expected to be in the prodromal stage of an alpha-synucleinopathy. Signs of autonomic dysfunction can appear earlier than motor or cognitive alpha-synucleinopathy symptoms. Heart Rate Variability (HRV) can potentially be an objective measurement of autonomic dysfunction, and furthermore can be obtained directly from v-PSG.Objectives. The aim of this study was to evaluate dysautonomia in iRBD subjects using HRV obtained during different sleep stages and wakefulness from v-PSG. Material and methods.Subjects positively screened by an RBD screening questionnaire (RBD-SQ) underwent v-PSG to diagnose RBD. HRV obtained from v-PSG recordings was correlated to dysautonomia evaluated from a Non-Motor Symptoms Scale (NMSS) questionnaire. Optimal cut-off values of HRV parameters to predict dysautonomia were calculated using receiver operating characteristics (ROC) -area under the curve (AUC) analysis. The effect of confounder variables was predicted with binomial logistic regression and multiple regression analyses.Results. Out of 72 positively screened subjects, 29 subjects were diagnosed as iRBD (mean age 66 ± 7.7 years) by v-PSG. Eighty--three per cent of the iRBD subjects in our cohort were at the time of diagnosis classified as having possible or probable prodromal Parkinson's Disease (pPD) compared to zero subjects being positively screened in the control group. The iRBD-positive subjects showed significant inverse correlations of NMSS score, particularly to log low-frequency (LF) component of HRV during wakefulness: r = -0.59 (p = 0.001). Based on ROC analysis and correlation between NMSS score, log LF during wakefulness (AUC 0.74, cut-off 4.69, sensitivity 91.7%, specificity 64.7%, p = 0.028) was considered as the most accurate predictor of dysautonomia in the iRBD group. Apnoea-hypopnoea index (AHI) negatively predicted dysautonomia in the iRBD group. None of the HRV components was able to predict the presence of iRBD in the full cohort. Age, gender, and PSG variables were significant confounders of HRV prediction.Conclusions. The presented study did not confirm the possibility of using HRV from v-PSG records of patients with iRBD to predict dysautonomia expressed by questionnaire methods. This is probably due to several confounding factors capable of influencing HRV in such a cohort.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

Ventosa¹,

Kulcsárová²,

Mertová³

et al. 2023

Neurol Neurochir Pol.

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“…In our department, two enriched cohorts are prospectively followed up, namely the PARCAS cohort (recruited mostly via gastrointestinal symptoms) 6 and PDBIOM cohort (consisting of iRBD patients and healthy controls). 7 Within these cohorts, 42 subjects identified as pPD based on the updated MDS criteria have available follow-up (FU) data after 2-5 years and six of these subjects converted to manifest parkinsonism (previously unpublished, interim results). While five/six cases converted to PD (all of them previously correctly identified as probable pPD with probability ≥80%), the sixth patient from the PARCAS cohort converted unexpectedly to corticobasal syndrome (CBS).…”

Section: "Unexpected" Identification Of Prodromal Corticobasal Syndro...mentioning

confidence: 99%

Section: “Unexpected” Identification Of Prodromal Corticobasal Syndro...mentioning

confidence: 99%

Challenges and Future of the International Parkinson and Movement Disorder Society Prodromal Parkinson's Disease Criteria: Are We On the Right Track?

Kulcsarova,

Skorvanek

2024

Movement Disorders

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“…Asymptomatic LRRK2 carriers were recruited from first degree blood relatives of patients who are attending the movement disorders clinic at the National Hospital for Neurology and Neurosurgery, Queen Square, UK and known to be carriers for the LRRK2 G2019S mutation. In Kosice, participants were identified from a prospective data registry and a multistage screening for iRBD (PDBIOM study), which was diagnosed based on one or two nights of video-polysomnography (PSG) 53,54 . In addition to extensive demographic and clinical details (as outlined below), dopamine transporter scintigraphy (DaT scan), as well as calculated probability of prodromal Parkinson´s disease (pPD) based on the updated MDS criteria 55 as reported previously 53 were recorded and assessed.…”

Section: Study Cohortsmentioning

confidence: 99%

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

Hällqvist,

Schreglmann,

Kulcsarova

et al. 2023

Preprint

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Parkinson's disease is a progressive neurological disorder that affects movement and can cause a wide range of symptoms. Idiopathic REM-sleep behaviour disorder (iRBD) has been identified as the single most specific early symptom of Parkinson's disease to date. In order to facilitate the screening of individuals at high risk to develop Parkinson's disease, we developed a multiplexed panel of urine proteomics using machine learning and targeted mass spectrometry to detect iRBD. Random urine samples from clinically and genetically well characterized patients with iRBD, idiopathic and hereditary forms of Parkinson's disease, as well as matching controls, were collected in two academic centres in a standardized way and analysed using our assay. In the first instance, a biomarker discovery and exploratory comparison of samples from randomly selected idiopathic Parkinson's patients and age/sex-matched healthy controls were proteomic profiled and quantified (> 2500 proteins). The most differentially expressed biomarkers were combined into a high-throughput, multiplexed assay using targeted proteomics and specifically designed for use of tandem mass spectrometers for potential translation into clinical practise. This test was then validated on independent patient and control samples (n=184). After detecting a major influence of sex on the proteome, we focused subsequent analyses on the larger group of available male samples (n = 114) and report results based on iRBD (n = 14), idiopathic (n = 35) and young-onset Parkinson's disease (n = 15), carriers of LRRK2 (n = 10), PARKIN-gene mutations (n = 5), and healthy control subjects (n = 35). After establishing excellent compatibility between the two study sites, orthogonal partial least squares discriminant analysis (OPLS-DA) excluded a relevant effect of ageing, but detected significant differences between iRBD and healthy controls (ANOVA-CV P = 0.002), as well as the combination of iPD/iRBD and healthy controls (ANOVA-CV P = 0.01). Uni- and multivariate analyses detected a shared expression pattern for the protein biomarkers UBC, NCAM1, MIEN1, SPP2, REG1B, ITIH2, BCHE and C3 between iRBD and idiopathic Parkinson's disease. Utilizing split train/test-datasets in a multiple-regression classifier model resulted in a mean accuracy of 78% to differentiate iRBD from controls, matching the clinical conversion rate for iRBD to Parkinson's disease. Hierarchical clustering revealed greater similarities in urine proteomic changes between iRBD and idiopathic than monogenic Parkinson's disease. Several proteins identified correlated either with clinical severity (e.g. VCAM1, MSN, HPX), or risk for future conversion to Parkinson's disease (VCAM1, MSN, MYO10, HSPAIL). This assay demonstrates the power of machine learning and urine biomarkers to identify iRBD patients. As we develop new therapies and interventions, the ability to detect individuals at-risk of neurodegeneration in very early disease stage will be invaluable in treating Parkinson's disease.

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Comparison in detection of prodromal Parkinson's disease patients using original and updated MDS research criteria in two independent cohorts

Cited by 8 publications

References 26 publications

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

Heart Rate Variability in evaluation of autonomic dysfunction in idiopathic REM-sleep behaviour disorder

Challenges and Future of the International Parkinson and Movement Disorder Society Prodromal Parkinson's Disease Criteria: Are We On the Right Track?

Detection of prodromal Parkinson’s disease using a urine proteomics panel and machine learning

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