The evolution of comprehensive genetic analysis in neurology: Implications for precision medicine.

Technological advancements have facilitated the availability of reliable and thorough genetic analysis in many medical fields, including neurology. In this review, we focus on the importance of selecting the appropriate genetic test to aid in the accurate identification of disease utilizing currently employed technologies for analyzing monogenic neurological disorders. Moreover, the applicability of comprehensive analysis via NGS for various genetically heterogeneous neurological disorders is reviewed, revealing its efficiency in clarifying a frequently cloudy diagnostic picture and delivering a conclusive and solid diagnosis that is essential for the proper management of the patient. The feasibility and effectiveness of medical genetics in neurology require interdisciplinary cooperation among several medical specialties and geneticists, to select and perform the most relevant test according to each patient's medical history, using the most appropriate technological tools. The prerequisites for a comprehensive genetic analysis are discussed, highlighting the utility of appropriate gene selection, variant annotation, and classification. Moreover, genetic counseling and interdisciplinary collaboration could improve diagnostic yield further. Additionally, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, with a focus on neurology-related genes, to clarify the value of suitable variant categorization. Finally, we review the current applications of genetic analysis in the diagnosis and personalized management of neurological patients and the advances in the research and scientific knowledge of hereditary neurological disorders that are evolving the utility of genetic analysis towards the individualization of the treatment strategy.

The Parkinson's Disease-Cognitive Rating Scale: Greek Normative Data, Clinical Utility and Cultural Considerations.

BACKGROUND: The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) is a comprehensive screening procedure for the evaluation of cognitive impairment in patients with Parkinson's disease (PD). OBJECTIVES: In the present study we adjusted the PD-CRS for the Greek population, developed normative data and examined its clinical utility for the assessment of cognitive functioning in Greek PD patients. In addition, the correlation of clinical characteristics with cognitive performance in PD patients was examined. METHODS: Three hundred four community-dwelling healthy adults and 59 patients with PD, completed the adapted PD-CRS. RESULTS: Healthy adults outperformed the PD patients on the total, the cortical and subcortical scores of the PD-CRS. Normative data indicated effects of both education and age on the PD-CRS. The optimal total PD-CRS cutoff score for the identification of cognitive impairment in a heterogeneous sample of PD patients, with regard to the severity of cognitive difficulties, was 79, yielding a modest sensitivity and specificity. Clinical characteristics of the patients (i.e., disease duration and functional disease burden) were related to poor performance on the PD-CRS. CONCLUSIONS: The Greek version of the PD-CRS is a useful instrument for the assessment of cognition in PD. Future prospective studies should examine its clinical utility to identify PD-cognitive subtypes (i.e., PD patients with mild cognitive impairment), to monitor cognitive changes, as well as its predictive accuracy for subsequent dementia.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease.

Freezing of Gait (FoG) is a common symptom in Parkinson's Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial.

BACKGROUND: Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. OBJECTIVE: We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. METHODS: PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson's Disease Rating Scale estimations by clinicians were explored. RESULTS: Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers' burden. Motor Aspects of Experiences of Daily Living and patients' self-rated health status also influence the system's use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. CONCLUSIONS: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. TRIAL REGISTRATION: ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-018-2767-4.

Postpartum intracranial hypotension complicated by posterior reversible encephalopathy syndrome: a case report.

Purpose: To present an unusual case of posterior encephalopathy syndrome (PRES) preceded by intracranial hypotension.Materials and Methods: We present a case of a 27-year-old parturient with an uneventful pregnancy that shortly after labor developed a persistent headache with characteristics compatible with intracranial hypotension. The patient had undergone epidural anesthesia for caesarian section. Results: The symptomatology of intracranial hypotension was attributed to inadvertent dural puncture during epidural anesthesia. The MRI revealed multiple white matter lesions located in frontal, temporal and parietal regions of both hemispheres. The type of lesions was suggestive of PRES. Pachymeningeal enhancement was also observed. The patient was managed conservatively. The symptoms improved gradually and the imaging findings resolved completely. Conclusions: This case demonstrates the need for clinical alertness for PRES in patients with prolonged and possibly atypical symptoms of intracranial hypotension. As probable causal relationship between these disorders we propose a sympathetic over-activation as a result of cerebrospinal fluid leakage leading to vasospasm and manifestation of PRES.

Multifocal alterations of white matter accompany the transition from normal cognition to dementia in Parkinson's disease patients.

The purpose of the present study was to investigate the pattern of white matter (WM) changes associated with Parkinson's disease (PD)-related cognitive impairment by using fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) measures. Diffusion Tensor Imaging (DTI) was performed in 21 PD-patients with dementia (PDD) and in an age-matched control group including 40 PD-patients without dementia (PD-CTRL). The Parkinson's disease-Cognitive Rating Scale (PD-CRS) was used for patients' neuropsychological assessment. Local microstructural WM differences associated with the presence of cognitive impairment were tested using tract-based spatial statistics analysis. Multiple regression models investigated the association of DTI indices with total PD-CRS score, PD-CRS raw items and other clinical measures across the whole study sample. Significant FA decreases were found in PDD compared to PD-CTRL patients mainly in the body of corpus callosum, corona radiata and cingulum. Lower PD-CRS score was significantly associated with decreased FA, MD and AD values in multiple WM tracts primarily located in prefrontal and limbic areas as well as across the corpus callosum. Lower performance in specific PD-CRS raw items was also associated with FA decreases in major WM tracts. The results suggest that multifocal microstructural changes of WM accompany the transition from normal to demented cognitive state in PD-patients. The corpus callosum, the corona radiata and the cingulum are among the regions mostly affected during this course. A progressive axonal degeneration is proposed as a key underlying mechanism.

Skull Base Metastasis Revealed by Bone Scintigraphy in a Patient With Hypoglossal Nerve Palsy.

Even though different imaging modalities are available in sole or in combination for the optimal detection of bone metastases, whole-body bone scintigraphy (BS) in a single session seems to be advantageous. We present an 80-year-old male with unilateral left hypoglossal nerve palsy (HNP) and no other focal deficits on neurological examination. Initial brain computed tomography (CT) scan revealed no pathological findings, while the subsequent cranial CT and magnetic resonance imaging (MRI) scans uncovered only mild nonspecific sclerotic lesions in left occipital condyle. All laboratory examinations were within normal limits, except for an elevated alkaline phosphatase (170 U/L) and a markedly increased prostate-specific antigen (609 ng/mL). The patient underwent whole-body BS with technetium-99m that revealed increased radiotracer deposition compatible with metastases in multiple foci, including the left occipital condyle. Prostate biopsy confirmed the diagnosis of prostate adenocarcinoma. Our case suggests that a complete and thorough workup for hidden malignancies should be performed in all patients with HNP, even in the absence of a finding in brain neuroimaging. Bone scintigraphy is an essential investigation that should be considered in uncertain cases of HNP, and especially in those with negative CT and MRI scans.

Levodopa-induced dyskinesia in Parkinson's disease: still no proof? A meta-analysis.

We investigated whether there is a linear relationship between levodopa (LD) dose and treatment duration, and the development of levodopa-induced dyskinesia (LID) among patients with early untreated Parkinson's disease (PD). We performed a meta-analysis of randomized-controlled trials (RCTs) comparing LD monotherapy to any other antiparkinsonian treatment in early PD patients. Meta-regressions were conducted including as covariates the effects of LD dose, treatment duration, and age. We further proceeded in subgroup analyses based on the type of medications in the non-LD monotherapy (control) group and on whether patients in the control group received additional levodopa or not. Thirteen eligible RCTs were included, which revealed a significantly higher risk for dyskinesia in patients initially treated with LD monotherapy compared to any other treatment (OR = 2.82). None of the subsequent meta-regressions revealed any significant relationship with dose, treatment duration or age. Patients treated on LD monotherapy or MAOΙ plus LD were at a greater risk to develop LID than patients who received DA only or DA plus supplemental LD. The increased heterogeneity compromised the robustness of the results. The alleged correlation between LID and LD dose and treatment duration cannot be verified based on the data available so far. Well-designed, large-scale, long-term, RCTs on drug-naïve PD patients could allow the better comprehension of the pattern of the association between LID and LD treatment parameters.

Exposure to pesticides and diabetes: A systematic review and meta-analysis.

BACKGROUND: Diabetes mellitus has a multifactorial pathogenesis with a strong genetic component as well as many environmental and lifestyle influences. Emerging evidence suggests that environmental contaminants, including pesticides, might play an important role in the pathogenesis of diabetes. OBJECTIVES: We performed a systematic review and meta-analysis of observational studies that assessed the association between exposure to pesticides and diabetes and we examined the presence of heterogeneity and biases across available studies. METHODS: A comprehensive literature search of peer-reviewed original research pertaining to pesticide exposure and diabetes, published until 30st May 2015, with no language restriction, was conducted. Eligible studies were those that investigated potential associations between pesticides and diabetes without restrictions on diabetes type. We included cohort studies, case-control studies and cross-sectional studies. We extracted information on study characteristics, type of pesticide assessed, exposure assessment, outcome definition, effect estimate and sample size. RESULTS: We identified 22 studies assessing the association between pesticides and diabetes. The summary OR for the association of top vs. bottom tertile of exposure to any type of pesticide and diabetes was 1.58 (95% CI: 1.32-1.90, p=1.21×10(-6)), with large heterogeneity (I(2)=66.8%). Studies evaluating Type 2 diabetes in particular (n=13 studies), showed a similar summary effect comparing top vs. bottom tertiles of exposure: 1.61 (95% CI 1.37-1.88, p=3.51×10(-9)) with no heterogeneity (I(2)=0%). Analysis by type of pesticide yielded an increased risk of diabetes for DDE, heptachlor, HCB, DDT, and trans-nonachlor or chlordane. CONCLUSIONS: The epidemiological evidence, supported by mechanistic studies, suggests an association between exposure to organochlorine pesticides and Type 2 diabetes.

Multimodal imaging evaluation of excessive daytime sleepiness in Parkinson's disease.

PURPOSE OF THE STUDY: The multimodal imaging investigation of excessive daytime sleepiness (EDS) in Parkinson's disease (PD). The role of dopaminergic treatment and other clinical parameters was also evaluated. MATERIALS AND METHODS: Seventeen non-demented PD patients with EDS (PD-EDS) and 17 PD patients without EDS were enrolled. Clinical, treatment and MRI data were acquired. Gray matter (GM) volume was examined with voxel-based morphometry, while white matter (WM) integrity was assessed with diffusion tensor imaging by means of fractional anisotropy, mean diffusivity, axial diffusivity (AD) and radial diffusivity measures. RESULTS: Increased regional GM volume was found in the PD-EDS group bilaterally in the hippocampus and parahippocampal gyri. Increased AD values were also shown in the PD-EDS group, in the left anterior thalamic radiation and the corticospinal tract and bilaterally in the superior corona radiata and the superior longitudinal fasciculus. Levodopa equivalent dose differed significantly between the groups and was the only predictor of EDS, while the only predictor of the Epworth sleepiness scale score in the PD-EDS group was the dopamine-agonist dose. Increased frequency of gamblers was also observed in the PD-EDS group. CONCLUSIONS: Regional GM increases and increased AD values in certain WM tracts were found in the PD-EDS group. The changes could result from disinhibited signaling pathways or represent compensatory changes in response to anatomical or functional deficits elsewhere. The study findings support also the contribution of the total dopaminergic load in the development of EDS, while the dose of dopamine agonists was found to predict the severity of the disorder.

Monitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform.

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

Tremor UPDRS estimation in home environment.

In this paper, a method for the assessment of the Unified Parkinson Disease Rating scale (UPDRS) related to tremor is presented. The method described consists of hand resting and posture state detection, tremor detection and tremor quantification based on accelerometer and gyroscope readings from a wrist worn sensor. The initial results on PD patient recordings on home environment indicate the feasibility of the proposed method in monitoring UPDRS tremor in patient home environment.

ALZCARE: an information system for screening, management and tracking of demented patients.

The ALZCARE project aims at assisting people at risk or already suffering of dementia, their family and health professionals in the dementia care pathway by providing an integrated ICT-enabled information System. The system consists of a mobile platform for screening people at risk, a Clinical Information System and a satellite-based patient tracking system. The system is currently on the evaluation phase focusing on addressing the needs of citizens of the cross-border areas of Greece and Albania.

PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

Clinical characteristics of Parkinson's disease patients in Greece: a multicenter, nation-wide, cross-sectional study.

Parkinson's disease is a neurodegenerative disease, with a constantly increasing prevalence and a high global financial impact arising from direct and indirect costs. Large-scale, observational studies provide data that support the better comprehension of disease aspects, constitute a baseline reference for future studies and assist comparisons among different patient populations, allowing the recognition of distinctive characteristics and special needs. The present study is the first to depict the clinical characteristics and their interplay in a large sample of Parkinson's disease (PD) patients in Greece. Nine hundred eighty six consecutive PD outpatients were recruited from 17 centers around Greece in the time period from 8/2007 to 7/2009 and were examined and interviewed by movement disorders experts. Multiple clinical characteristics were recorded including age at diagnosis, disease severity, patients' self classification of PD symptoms and their relevance to physician's global clinical impression, smoking, alcohol consumption, presence of family history for PD, dementia, depression, hypertension, cancer and other comorbidities. Associations of high clinical significance were found between certain clinical characteristics.

Automatic detection of freezing of gait events in patients with Parkinson's disease.

The aim of this study is to detect freezing of gait (FoG) events in patients suffering from Parkinson's disease (PD) using signals received from wearable sensors (six accelerometers and two gyroscopes) placed on the patients' body. For this purpose, an automated methodology has been developed which consists of four stages. In the first stage, missing values due to signal loss or degradation are replaced and then (second stage) low frequency components of the raw signal are removed. In the third stage, the entropy of the raw signal is calculated. Finally (fourth stage), four classification algorithms have been tested (Naïve Bayes, Random Forests, Decision Trees and Random Tree) in order to detect the FoG events. The methodology has been evaluated using several different configurations of sensors in order to conclude to the set of sensors which can produce optimal FoG episode detection. Signals recorded from five healthy subjects, five patients with PD who presented the symptom of FoG and six patients who suffered from PD but they do not present FoG events. The signals included 93 FoG events with 405.6s total duration. The results indicate that the proposed methodology is able to detect FoG events with 81.94% sensitivity, 98.74% specificity, 96.11% accuracy and 98.6% area under curve (AUC) using the signals from all sensors and the Random Forests classification algorithm.