Identification of a prospective early motor progression cluster of Parkinson's disease: Data from the PPMI study

Hypoperfusion in Supramarginal and Orbital Gyrus, Position Discrimination Test, and Microsaccades as a Predictor of Pisa Syndrome in Parkinson's Disease

Patients with Parkinson's disease (PD) experience significantly reduced quality of life when PD is complicated with Pisa syndrome (PS). PS is a postural abnormality associated with a lateral bending of the trunk, causing the patient to lean to one side. Microsaccades during fixation are transmitted to the visual cortex, and this gaze movement may be impaired in PD. We aimed to detect presymptomatic signs of PS. We enrolled 50 patients with PD without dementia and investigated the visual systems in patients with concurrent PD and PS based on a Romberg ratio of<1.0. Gaze analysis, pupil diameter, stabilization tests, neuropsychological tests, and cerebral perfusion scintigraphy were reviewed and statistically analyzed. Two years later, we divided the patients into three groups as follows: PISA++ (patients who had PS at enrollment), PISA-+ (patients without PS that developed PS during the 2-year period), and PISA-- (patients without PS that did not develop PS during the 2-year period). The PISA-+ group exhibited a significantly higher daily levodopa dose and longer fixations, as well as lower position discrimination, Wechsler Adult Intelligence Scale-Third Edition blocking, and blood flow in the left supramarginal and orbital gyri than that in the PISA-- group. The PISA++ group showed a significantly longer fixation time and lower Mini-Mental State Examination score, Romberg ratio of area, amplitude, velocity of microsaccades, and blood flow in the left precuneus and cuneus than that in the PISA-+ group. Before the onset of PS, hypoperfusion occurred in the correlative visual cortex and the position discrimination test. Patients with PS have reduced saccades and slow microsaccades.

3-Year test-retest reliability in Parkinson's disease and healthy older adults: The Parkinson's progression markers initiative study

Repeated neuropsychological assessments are often conducted in clinical and research settings to track cognitive changes over single or multiple intervals in patients with Parkinson's disease (PD). Yet few studies have documented test-retest reliability in PD. To address this gap, we used data from the Parkinson's Progression Markers Initiative (PPMI) to investigate the reliability of five well-known neuropsychological tests over a 3-year follow-up assessment in early-stage PD with either normal (PD-NC; N = 158) or abnormal (PD-AC; N = 39) cognitive screening, categorized based on recommended cutoffs for the Montreal Cognitive Assessment (MoCA), and healthy older adults (HOA; N = 102). All participants analyzed maintained the same cognitive status category across the assessment points. Intraclass correlation coefficients (ICCs) estimated reliability. The overall ICCs calculated across time points were as follows: Judgment of Line Orientation (PD-NC = .47, PD-AC = .50, HOA = .59); Letter-Number Sequencing (PD-NC = .64, PD-AC = .64, HOA = .65); Semantic Fluency (PD-NC = .69, PD-AC = .89, HOA = .77); Symbol Digit Modalities Test (PD-NC = .67, PD-AC = .83, HOA = .71). For the two primary components of the Hopkins Verbal Learning Test-Revised, we found the following ICCs: immediate recall (PD-NC = .46, PD-AC = .57, HOA = .58); delayed recall (PD-NC = .42, PD-AC = .57, HOA = .54). Findings from this study provide useful information for clinicians and researchers toward selecting suitable neuropsychological tests to monitor cognition at two or more time points among newly diagnosed individuals with PD and HOA.

Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests

We have recently published the notion of the "vitals" of Parkinson's, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This "dashboard," termed the Chaudhuri's vitals of Parkinson's, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson's. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson's syndrome to describe Parkinson's disease, as the term "disease" is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson's, which is now considered by many as a syndrome.

Dysautonomia and REM sleep behavior disorder contributions to progression of Parkinson's disease phenotypes

Non-motor symptoms of Parkinson's disease (PD) such as dysautonomia and REM sleep behavior disorder (RBD) are recognized to be important prodromal symptoms that may also indicate clinical subtypes of PD with different pathogenesis. Unbiased clustering analyses showed that subjects with dysautonomia and RBD symptoms, as well as early cognitive dysfunction, have faster progression of the disease. Through analysis of the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort, we tested the hypothesis that symptoms of dysautonomia and RBD, which are readily assessed by standard questionnaires in an ambulatory care setting, may help to independently prognosticate disease progression. Although these two symptoms associate closely, dysautonomia symptoms predict severe progression of motor and non-motor symptoms better than RBD symptoms across the 3-year follow-up period. Autonomic system involvement has not received as much attention and may be important to consider for stratification of subjects for clinical trials and for counseling patients.

COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States

The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University’s Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration <30 days were included in this study) were used sequentially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August’s 66,165 included responders, was subsequently validated in data from March−December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.

Synuclein in neurodegeneration

Neurodegenerative diseases are a heterogeneous group of disorders characterized by gradual progressive neuronal loss in the central nervous system. Unfortunately, the pathogenesis of many of these diseases remains unknown. Synucleins are a family of small, highly charged proteins expressed predominantly in neurons. Following their discovery, much has been learned about their structure, function, interaction with other proteins and role in neurodegenerative disease over the last two decades. One of these proteins, α-Synuclein (α-Syn), appears to be involved in many neurodegenerative disorders. These include Parkinson's disease (PD), dementia with Lewy bodies (DLB), Rapid Eye Movement Sleep Behavior Disorder (RBD) and Pure Autonomic Failure (PAF), i.e., collectively termed α-synucleinopathies. This review focuses on α-Syn dysfunction in neurodegeneration and assesses its role in synucleinopathies from a biochemical, genetic and neuroimaging perspective.

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.

New markers in Parkinson's disease

Parkinson's disease (PD) is a chronic, debilitating neurodegenerative disorder characterized clinically by a variety of progressive motor and nonmotor symptoms. Currently, there is a dearth of diagnostic tools available to predict, diagnose or mitigate disease risk or progression, leading to a challenging dilemma within the healthcare management system. The search for a reliable biomarker for PD that reflects underlying pathology is a high priority in PD research. Currently, there is no reliable single biomarker predictive of risk for motor and cognitive decline, and there have been few longitudinal studies of temporal progression. A combination of multiple biomarkers might facilitate earlier diagnosis and more accurate prognosis in PD. In this review, we focus on the recent developments of serial biomarkers for PD from a variety of clinical, biochemical, genetic and neuroimaging perspectives.

Subtyping of Parkinson's Disease - Where Are We Up To?

Heterogenous clinical presentations of Parkinson's disease have aroused several attempts in its subtyping for the purpose of strategic implementation of treatment in order to maximise therapeutic effects. Apart from a priori classifications based purely on motor features, cluster analysis studies have achieved little success in receiving widespread adoption. A priori classifications demonstrate that their chosen factors, whether it be age or certain motor symptoms, do have an influence on subtypes. However, the cluster analysis approach is able to integrate these factors and other clinical features to produce subtypes. Differences in inclusion criteria from datasets, in variable selection and in methodology between cluster analysis studies have made it difficult to compare the subtypes. This has impeded such subtypes from clinical applications. This review analysed existing subtypes of Parkinson's disease, and suggested that future research should aim to discover subtypes that are robustly replicable across multiple datasets rather than focussing on one dataset at a time. Hopefully, through clinical applicable subtyping of Parkinson's disease would lead to translation of these subtypes into research and clinical use.