Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD).Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD.Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis.Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage.Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.

Introduction: Mepolizumab is a monoclonal antibody against IL-5 for the treatment of severe eosinophilic asthma. The aim of the current study was to present a predesigned interim analysis of the data of patients who have completed 1 year of therapy with mepolizumab. Methods: This study is a prospective multicenter, noninterventional 2-year observational study and aims to describe the clinical benefit and safety profile of mepolizumab in patients with severe eosinophilic asthma. Results: Compared to the year preceding the initiation of treatment, the annual rate of exacerbations decreased significantly, from 4.3 ± 2.3 to 1.3 ± 1.8; p < 0.0001. Forty-two patients received maintenance dose of oral corticosteroids (OCS) at baseline. From these patients at the end of 1 year of therapy with mepolizumab, 17 patients (40%) had achieved OCS discontinuation. A reduction in the median dose of OCS was also achieved. After 1 year of treatment with mepolizumab, the asthma control test score significantly increased from 16.3 ± 3.7 to 21.2 ± 3.8 (p < 0.0001). This marked clinical improvement was paralleled by a significant reduction of blood eosinophil count. All patients showed a considerable improvement of airflow limitation. In respect to adverse events of treatment with mepolizumab, 19 patients (27%) were recorded to have at least one such occurrence during their 1-year treatment. Conclusions: We have shown that in patients with severe eosinophilic asthma, 1 year of treatment with mepolizumab was safe, resulted in significant reduction of the annual exacerbation rate, reduction (or even discontinuation) of the needed dose of OCS, and improvements of asthma control and lung function.

Background: Different factors influence severity, progression, and outcomes in Parkinson's disease (PD). Lack of standardized clinical assessment limits comparison of outcomes and availability of well-characterized cohorts for collaborative studies. Methods: Structured clinical documentation support (SCDS) was developed within the DNA Predictions to Improve Neurological Health (DodoNA) project to standardize clinical assessment and identify molecular predictors of disease progression. The Longitudinal Clinical and Genetic Study of Parkinson's Disease (LONG-PD) was launched within the Genetic Epidemiology of Parkinson's disease (GEoPD) consortium using a Research Electronic Data Capture (REDCap) format mirroring the DodoNA SCDS. Demographics, education, exposures, age at onset (AAO), Unified Parkinson's Disease Rating Scale (UPDRS) parts I-VI or Movement Disorders Society (MDS)–UPDRS, Montreal Cognitive Assessment (MoCA)/Short Test of Mental Status (STMS)/Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Epworth Sleepiness Scale (ESS), dopaminergic therapy, family history, nursing home placement, death and blood samples were collected. DodoNA participants (396) with 6 years of follow-up and 346 LONG-PD participants with up to 3 years of follow-up were analyzed using group-based trajectory modeling (GBTM) focused on: AAO, education, family history, MMSE/MoCA/STMS, UPDRS II-II, UPDRS-III tremor and bradykinesia sub-scores, Hoehn and Yahr staging (H&Y) stage, disease subtype, dopaminergic therapy, and presence of autonomic symptoms. The analysis was performed with either cohort as the training/test set. Results: Patients are classified into slowly and rapidly progressing courses by AAO, MMSE score, H &Y stage, UPDRS-III tremor and bradykinesia sub-scores relatively early in the disease course. Late AAO and male sex assigned patients to the rapidly progressing group, whereas tremor to the slower progressing group. Classification is independent of which cohort serves as the training set. Frequencies of disease-causing variants in LRRK2 and GBA were 1.89 and 2.96%, respectively. Conclusions: Standardized clinical assessment provides accurate phenotypic characterization in pragmatic clinical settings. Trajectory analysis identified two different trajectories of disease progression and determinants of classification. Accurate phenotypic characterization is essential in interpreting genomic information that is generated within consortia, such as the GEoPD, formed to understand the genetic epidemiology of PD. Furthermore, the LONGPD study protocol has served as the prototype for collecting standardized phenotypic information at GEoPD sites. With genomic analysis, this will elucidate disease etiology and lead to targeted therapies that can improve disease outcomes.

Olfactory dysfunction is associated with normal aging, multiple neurodegenerative disorders, including Parkinson’s disease, Lewy body disease and Alzheimer’s disease, and other diseases such as diabetes, sleep apnea and the autoimmune disease myasthenia gravis. The wide spectrum of neurodegenerative disorders associated with olfactory dysfunction suggests different, potentially overlapping, underlying pathophysiologies. Studying olfactory dysfunction in presymptomatic carriers of mutations known to cause familial parkinsonism provides unique opportunities to understand the role of genetic factors, delineate the salient characteristics of the onset of olfactory dysfunction, and understand when it starts relative to motor and cognitive symptoms. We evaluated olfactory dysfunction in 28 carriers of two MAPT mutations (p.N279K, p.P301L), which cause frontotemporal dementia with parkinsonism, using the University of Pennsylvania Smell Identification Test. Olfactory dysfunction in carriers does not appear to be allele specific, but is strongly age-dependent and precedes symptomatic onset. Severe olfactory dysfunction, however, is not a fully penetrant trait at the time of symptom onset. Principal component analysis revealed that olfactory dysfunction is not odor-class specific, even though individual odor responses cluster kindred members according to genetic and disease status. Strikingly, carriers with incipient olfactory dysfunction show poor inter-test consistency among the sets of odors identified incorrectly in successive replicate tests, even before severe olfactory dysfunction appears. Furthermore, when 78 individuals without neurodegenerative disease and 14 individuals with sporadic Parkinson’s disease were evaluated twice at a one-year interval using the Brief Smell Identification Test, the majority also showed inconsistency in the sets of odors they identified incorrectly, independent of age and cognitive status. While these findings may reflect the limitations of these tests used and the sample sizes, olfactory dysfunction appears to be associated with the inability to identify odors reliably and consistently, not with the loss of an ability to identify specific odors. Irreproducibility in odor identification appears to be a non-disease-specific, general feature of olfactory dysfunction that is accelerated or accentuated in neurodegenerative disease. It may reflect a fundamental organizational principle of the olfactory system, which is more “error-prone” than other sensory systems.

Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.

Background: The severity, progression, and outcomes of motor and non-motor symptoms in Parkinson’s disease (PD) are quite variable. Following PD cohorts holds promise for identifying predictors of disease severity and progression.Methods: PD patients (N = 871) were enrolled at five sites. Enrollment occurred within 5 years of initial motor symptom onset. Disease progression was assessed annually for 2-to-10 years after onset. Group-based trajectory modeling was used to identify groups differing in disease progression. Models were developed for UPDRS-III scores, UPDRS-III tremor and bradykinesia-rigidity subscores, Hoehn & Yahr (H&Y) stage, Mini-Mental Status Exam (MMSE) scores, and UPDRS-III, H&Y and MMSE scores considered together. Predictors of trajectory-group membership were modeled simultaneously with the trajectories. Kaplan–Meier survival analysis evaluated survival free of PD outcomes.Results: The best fitting models identified three groups. One showed a relatively benign, slowly progressing trajectory (Group 1), a second showed a moderate, intermediately progressing trajectory (Group 2), and a third showed a more severe, rapidly progressing trajectory (Group 3). Stable trajectory-group membership occurred relatively early in the disease course, 5 years after initial motor symptom. Predictors of intermediate and more severe trajectory-group membership varied across the single variable models and the multivariable model jointly considering UPDRS-III, H&Y and MMSE scores. In the multivariable model, membership in Group 2 (28.4% of patients), relative to Group 1 (50.5%), was associated with male sex, younger age-at-onset, fewer education-years, pesticide exposure, absence of reported head injury, and akinetic/rigid subtype at initial presentation. Membership in Group 3 (21.3%), relative to Group 1, was associated with older age-at-onset, fewer education-years, pesticide exposure, and the absence of a tremor-predominant subtype at initial presentation. Persistent freezing, persistent falls, and cognitive impairment occurred earliest and more frequently in Group 3, later and less frequently in Group 2, and latest and least frequently in Group 1. Furthermore, autonomic complications, dysphagia, and psychosis occurred more frequently in Groups 2 and 3 than in Group 1.Conclusion: Modeling disease course using multiple objective assessments over an extended follow-up duration identified groups that more accurately reflect differences in PD course, prognosis, and outcomes than assessing single parameters over shorter intervals.