Binderupdamm8740
A new model of Parkinson's disease (PD) pathogenesis is proposed, the α-Synuclein Origin site and Connectome (SOC) model, incorporating two aspects of α-synuclein pathobiology that impact the disease course for each patient the anatomical location of the initial α-synuclein inclusion, and α-synuclein propagation dependent on the ipsilateral connections that dominate connectivity of the human brain. In some patients, initial α-synuclein pathology occurs within the CNS, leading to a brain-first subtype of PD. In others, pathology begins in the peripheral autonomic nervous system, leading to a body-first subtype. In brain-first cases, it is proposed that the first pathology appears unilaterally, often in the amygdala. If α-synuclein propagation depends on connection strength, a unilateral focus of pathology will disseminate more to the ipsilateral hemisphere. Thus, α-synuclein spreads mainly to ipsilateral structures including the substantia nigra. The asymmetric distribution of pathology leads to asymmetric dopaminergic degeneration and motor asymmetry. In body-first cases, the α-synuclein pathology ascends via the vagus to both the left and right dorsal motor nuclei of the vagus owing to the overlapping parasympathetic innervation of the gut. Consequently, the initial α-synuclein pathology inside the CNS is more symmetric, which promotes more symmetric propagation in the brainstem, leading to more symmetric dopaminergic degeneration and less motor asymmetry. At diagnosis, body-first patients already have a larger, more symmetric burden of α-synuclein pathology, which in turn promotes faster disease progression and accelerated cognitive decline. The SOC model is supported by a considerable body of existing evidence and may have improved explanatory power.
In Parkinson's disease (PD), there is heterogeneity in the clinical presentation and underlying biology. Research on PD subtypes aims to understand this heterogeneity with potential contribution for the knowledge of disease pathophysiology, natural history and therapeutic development. There have been many studies of PD subtypes but their impact remains unclear with limited application in research or clinical practice.
To critically evaluate PD subtyping systems.
We conducted a systematic review of PD subtypes, assessing the characteristics of the studies reporting a subtyping system for the first time. We completed a critical appraisal of their methodologic quality and clinical applicability using standardized checklists.
We included 38 studies. The majority were cross-sectional (n = 26, 68.4%), used a data-driven approach (n = 25, 65.8%), and non-clinical biomarkers were rarely used (n = 5, 13.1%). Motor characteristics were the domain most commonly reported to differentiate PD subtypes. Most of the al, rendering PD resistant to meaningful cluster solutions. New approaches that acknowledge the individual-level heterogeneity and that are more aligned with personalized medicine are needed.
Rheumatoid arthritis (RA) and the genetic risk landscape of autoimmune disorders and Parkinson's disease (PD) overlap. Additionally, anti-inflammatory medications used to treat RA might influence PD risk.
To use a population-based approach to determine if there is an association between pre-occurring rheumatoid arthritis (RA) and later-life risk of PD.
The study population was 3.6 million residents of Sweden, who were alive during part or all of the follow-up period; 1997-2016. We obtained diagnoses from the national patient registry and identified 30,032 PD patients, 8,256 of whom each was matched to ten controls based on birth year, sex, birth location, and time of follow-up. We determined the risk reduction for PD in individuals previously diagnosed with RA. We also determined if the time (in relation to the index year) of the RA diagnosis influenced PD risk and repeated the analysis in a sex-stratified setting.
Individuals with a previous diagnosis of RA had a decreased risk of later developing PD by 30-50% compared to individuals without an RA diagnosis. This relationship was strongest in our conservative analysis, where the first PD diagnosis occurred close to the earliest PD symptoms (odds ratio 0.47 (CI 95% 0.28-0.75, p = 0.0006); with the greatest risk reduction in females (odds ratio 0.40 (CI 95% 0,19-0.76, p = 0.002).
Our findings provide evidence that individuals diagnosed with RA have a significantly lower risk of developing PD than the general population. Our data should be considered when developing or repurposing therapies aimed at modifying the course of PD.
Our findings provide evidence that individuals diagnosed with RA have a significantly lower risk of developing PD than the general population. Our data should be considered when developing or repurposing therapies aimed at modifying the course of PD.Sleep disturbances are prevalent in neurodegenerative diseases in general, and in Parkinson's disease (PD) in particular. click here Recent evidence points to the clinical value of sleep in disease progression and improving quality of life. Therefore, monitoring sleep quality in an ongoing manner at the convenience of one's home has the potential to improve clinical research and to contribute to significantly better personalized treatment. Further, precise mapping of sleep patterns of each patient can contribute to a better understanding of the disease, its progression and the appropriate medical treatment. Here we review selective, state-of-the-art, home-based devices for assessing sleep and sleep related disorders. We highlight the large potential as well as the main challenges. In particular, we discuss medical validity, standardization and regulatory concerns that currently impede widespread clinical adoption of existing devices. Finally, we propose a roadmap with the technological and scientific steps that are required to impact PD research and treatment.Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson's disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis show that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.
