Explore the vast range of titles available.

Parkinson disease (PD) is the second most common neurodegenerative disorder, affecting >1% of the population ≥65 years of age and with a prevalence set to double by 2030. In addition to the defining motor symptoms of PD, multiple non-motor symptoms occur; among them, cognitive impairment is common and can potentially occur at any disease stage. Cognitive decline is usually slow and insidious, but rapid in some cases. Recently, the focus has been on the early cognitive changes, where executive and visuospatial impairments are typical and can be accompanied by memory impairment, increasing the risk for early progression to dementia. Other risk factors for early progression to dementia include visual hallucinations, older age and biomarker changes such as cortical atrophy, as well as Alzheimer-type changes on functional imaging and in cerebrospinal fluid, and slowing and frequency variation on EEG. However, the mechanisms underlying cognitive decline in PD remain largely unclear. Cortical involvement of Lewy body and Alzheimer-type pathologies are key features, but multiple mechanisms are likely involved. Cholinesterase inhibition is the only high-level evidence-based treatment available, but other pharmacological and non-pharmacological strategies are being tested. Challenges include the identification of disease-modifying therapies as well as finding biomarkers to better predict cognitive decline and identify patients at high risk for early and rapid cognitive impairment. Cognitive impairment is common in patients with Parkinson disease and ranges in severity. This Primer reviews the epidemiology, pathophysiology, diagnosis and treatment of cognitive impairment in Parkinson disease and describes the effects on patient quality of life and the future outlook for the field.

Gastrointestinal symptoms and gut dysbiosis may occur before the onset of motor symptoms in Parkinson's disease (PD). Prediagnostic and prodromal features, such as constipation and α-synuclein pathology, can be detected several years before the clinical diagnosis of PD and have the potential to develop as early PD biomarkers. Environmental toxins and gut dysbiosis may trigger oxidative stress and mucosal inflammation, and initiate α-synuclein accumulation in the enteric nervous system, early in PD. Chronic gut inflammation can lead to a leaky gut and systemic inflammation, neuro inflammation, and neuro degeneration via gut–vagus–brain signaling or through blood–brain barrier permeability. Concepts regarding the gut–brain signaling in PD pathogenesis are changing rapidly and more investigation is required. The gut microbiota interacts with the human body by modulating the enteric and central nervous systems, and immune activity. Understanding the immune responses between gut microbiota and human body might help in elucidating the PD pathogenesis. As changes in gut microbiota composition might be associated with different clinical phenotypes of PD, gut microbiota-modulating interventions, such as probiotics and fecal microbiota transplantation (FMT), have the potential to restore the gut dysbiosis, reduce inflammation, and possibly modulate the clinical PD phenotype.

Many advances in understanding the pathophysiology of Parkinson disease (PD) have been based on research addressing its motor symptoms and phenotypes. Various data-driven clinical phenotyping studies supported by neuropathological and in vivo neuroimaging data suggest the existence of distinct non-motor endophenotypes of PD even at diagnosis, a concept further strengthened by the predominantly non-motor spectrum of symptoms in prodromal PD. Preclinical and clinical studies support early dysfunction of noradrenergic transmission in both the CNS and peripheral nervous system circuits in patients with PD that results in a specific cluster of non-motor symptoms, including rapid eye movement sleep behaviour disorder, pain, anxiety and dysautonomia (particularly orthostatic hypotension and urinary dysfunction). Cluster analyses of large independent cohorts of patients with PD and phenotype-focused studies have confirmed the existence of a noradrenergic subtype of PD, which had been previously postulated but not fully characterized. This Review discusses the translational work that unravelled the clinical and neuropathological processes underpinning the noradrenergic PD subtype. Although some overlap with other PD subtypes is inevitable as the disease progresses, recognition of noradrenergic PD as a distinct early disease subtype represents an important advance towards the delivery of personalized medicine for patients with PD.Some patients with Parkinson disease (PD) present with mostly non-motor symptoms. Here, Chaudhuri et al. discuss the evidence for CNS abnormalities in noradrenergic function in these individuals. Recognition of this noradrenergic subtype of PD might ultimately lead to subtype-specific treatments and personalized medicine.

Fluctuation-related pain (FRP) affects more than one third of people with Parkinson’s disease (PwP, PD) and has a harmful effect on health-related quality of life (HRQoL), but often remains under-reported by patients and neglected by clinicians. The National Institute for Health and Care Excellence (NICE) recommends The Parkinson KinetiGraph TM (the PKG TM ) for remote monitoring of motor symptoms. We investigated potential links between the PKG TM -obtained parameters and clinical rating scores for FRP in PwP in an exploratory, cross-sectional analysis of two prospective studies: “ The Non-motor International Longitudinal , Real-Life Study in PD—NILS ” and “ An observational-based registry of baseline PKG™ in PD—PKGReg ”. 63 PwP (41.3% female; age: 64.24±9.88 years; disease duration, DD: 6.83±5.63 years; Hoehn and Yahr Stage, H&Y: 2 (1–4); Levodopa Equivalent Daily Dose 535 (0–3230) mg) were included. PwP with FRP (n = 23) had longer DD (8.88 (1.29–19.05) vs. 3.16 (0.34–28.92), p = 0.001), higher severity of motor symptoms (H&Y 3 (1–4) vs. 2 (1–4), p = 0.015; SCOPA Motor total score 21.35±10.19 vs. 13.65±8.99, p = 0.003), more dyskinesia (SCOPA Motor Item 18 ≥1 60.9% vs. 7.5%, p <0.001), and worse HRQoL (PDQ-8 Total Score 10.74±5.98 vs. 6.78±5.13, p = 0.007) then PwP without FRP (n = 40). In the multivariate logistic regression, after the adjustment for DD, H&Y and SCOPA-Motor total score, the presence of FRP was significantly associated with the PKG TM -derived Fluctuation-dyskinesia score (Exp (B) = 1.305, 95% CI for Exp (B) 1.012–1.683, p = 0.040) and the Bradykinesia score (Exp (B) = 0.917, 95% CI for Exp (B) 0.842–0.999, p = 0.048). The PKG TM system may potentially advance the way we screen for, assess, and treat FRP in clinical practice.

Several studies, including work from the Parkinson's disease (PD) non-motor group and others, have established that the non-motor symptoms of PD are common, occur across all stages of PD, are under-reported, and are a key determinant of quality of life. Research suggests that the non-motor symptoms of the disease are frequently unrecognised by clinicians and remain untreated. Even when identified, there is a common perception that many of these symptoms are untreatable. The role of dopaminergic drugs in treating the various non-motor problems of PD, although clinically recognised, has received little attention. In this Review, we investigate the dopaminergic basis of the range of non-motor symptoms that occur in PD such as depression, apathy, sleep disorders (including rapid-eye movement sleep behaviour disorder), and erectile dysfunction. We discuss the evidence that these symptoms are treatable, at least in part, with various dopaminergic strategies and, where relevant, we also refer to the use of deep-brain stimulation of appropriate targets in the brain. This Review provides a comprehensive overview of the management of this challenging aspect of PD.

The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson’s disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82–0.90, I 2  = 79.49%) and 0.83 (CI 0.79–0.87, I 2  = 83.45%) for PD, 0.83 (95% CI 0.65–1.00, I 2  = 79.10%) and 0.87 (95% CI 0.80–0.93, I 2  = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74–0.96, I 2  = 50.39%) and 0.82 (95% CI 0.70–0.94, I 2  = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a “co-creation” approach that stems from mechanistic explanations of patients’ characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.

Early descriptions of subtypes of Parkinson's disease (PD) are dominated by the approach of predetermined groups. Experts defined, from clinical observation, groups based on clinical or demographic features that appeared to divide PD into clinically distinct subsets. Common bases on which to define subtypes have been motor phenotype (tremor dominant vs akinetic-rigid or postural instability gait disorder types), age, nonmotor dominant symptoms, and genetic forms. Recently, data-driven approaches have been used to define PD subtypes, taking an unbiased statistical approach to the identification of PD subgroups. The vast majority of data-driven subtyping has been done based on clinical features. Biomarker-based subtyping is an emerging but still quite undeveloped field. Not all of the subtyping methods have established therapeutic implications. This may not be surprising given that they were born largely from clinical observations of phenotype and not in observations regarding treatment response or biological hypotheses. The next frontier for subtypes research as it applies to personalized medicine in PD is the development of genotype-specific therapies. Therapies for GBA-PD and LRRK2-PD are already under development. This review discusses each of the major subtyping systems/methods in terms of its applicability to therapy in PD, and the opportunities and challenges designing clinical trials to develop the evidence base for personalized medicine based on subtypes.

Key Points Parkinson disease (PD) psychosis refers to a spectrum of illusions, hallucinations and delusions that occur throughout the disease course Evolving literature highlights the importance of recognizing and treating PD psychosis, and understanding its role as a clinical biomarker of disease stage, distribution and future progression Current evidence points to PD psychosis as a set of symptoms with distinct pathophysiological mechanisms, as opposed to a single pathophysiological symptom with a spectrum of severity The relationship between neuropathology in PD psychosis and in vivo measures of reduced metabolism, functional MRI alterations and cortical volume loss remains unclear Further studies are needed to explore the role of PD medication in unmasking psychosis symptoms, why psychosis symptoms predict worse cognitive outcome, comparisons of psychosis symptoms and mechanisms in different clinical conditions, and development of novel treatments The publication of a consensus definition of Parkinson disease (PD) psychosis in 2007 led to a rapid expansion of literature focusing on clinical aspects, mechanisms and treatment. The authors review this literature and discuss the evolving view of PD psychosis, from distinct classes of symptoms to a continuum progressing over the course of PD. In 2007, the clinical and research profile of illusions, hallucinations, delusions and related symptoms in Parkinson disease (PD) was raised with the publication of a consensus definition of PD psychosis. Symptoms that were previously deemed benign and clinically insignificant were incorporated into a continuum of severity, leading to the rapid expansion of literature focusing on clinical aspects, mechanisms and treatment. Here, we review this literature and the evolving view of PD psychosis. Key topics include the prospective risk of dementia in individuals with PD psychosis, and the causal and modifying effects of PD medication. We discuss recent developments, including recognition of an increase in the prevalence of psychosis with disease duration, addition of new visual symptoms to the psychosis continuum, and identification of frontal executive, visual perceptual and memory dysfunction at different disease stages. In addition, we highlight novel risk factors — for example, autonomic dysfunction — that have emerged from prospective studies, structural MRI evidence of frontal, parietal, occipital and hippocampal involvement, and approval of pimavanserin for the treatment of PD psychosis. The accumulating evidence raises novel questions and directions for future research to explore the clinical management and biomarker potential of PD psychosis.

BackgroundSleep disorders can occur in early Parkinson’s disease (PD). However, the relationship between different sleep disturbances and their longitudinal evolution has not been fully explored.ObjectiveTo describe the frequency, coexistence, and longitudinal change in excessive daytime sleepiness (EDS), insomnia, and probable REM sleep behavior disorder (pRBD) in early PD.MethodsData were obtained from the Parkinson’s Progression Markers Initiative (PPMI). EDS, insomnia, and pRBD were defined using the Epworth Sleepiness Scale, MDS-UPDRS Part I sub-item 1.7, and RBD screening questionnaire.Results218 PD subjects and 102 controls completed 5 years of follow-up. At baseline, 69 (31.7%) PD subjects reported one type of sleep disturbance, 25 (11.5%) reported two types of sleep disturbances, and three (1.4%) reported all three types of sleep disturbances. At 5 years, the number of PD subjects reporting one, two, and three types of sleep disturbances was 85 (39.0%), 51 (23.4%), and 16 (7.3%), respectively. Only 41(18.8%) patients were taking sleep medications. The largest increase in frequency was seen in insomnia (44.5%), followed by EDS (32.1%) and pRBD (31.2%). Insomnia was the most common sleep problem at any time over the 5-year follow-up. The frequency of sleep disturbances in HCs remained stable.ConclusionsThere is a progressive increase in the frequency of sleep disturbances in PD, with the number of subjects reporting multiple sleep disturbances increasing over time. Relatively a few patients reported multiple sleep disturbances, suggesting that they can have different pathogenesis. A large number of patients were not treated for their sleep disturbances.

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.