Background-The Parkinson's Progression Markers Initiative (PPMI) is an ongoing observational, longitudinal cohort study of participants with Parkinson's disease, healthy controls, and carriers of the most common Parkinson's disease-related genetic mutations, which aims to define biomarkers of Parkinson's disease diagnosis and progression. All participants are assessed annually with a battery of motor and non-motor scales, 123-I Ioflupane dopamine transporter (DAT) imaging, and biological variables. We aimed to examine whether non-manifesting carriers of LRRK2 and GBA mutations have prodromal features of Parkinson's disease that correlate with reduced DAT binding.
There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (R = 0.94, P = 3.6 × 10–25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.
Telephone number: +31-0243615202 Word count for abstract: 200. Word count for text: 7143 words. Number of references: 139. Number of figures: 2. Number of tables: 2.
AbstractIntroduction: Parkinson's disease (PD) is a chronic multisystem disorder that causes a wide variety of motor and non-motor symptoms. Over time, the progressive nature of the disease increases the risk of complications such as falls and loss of independence, having a profound impact on quality of life. The complexity and heterogeneity of symptoms therefore warrant a holistic, multidisciplinary approach. Specific healthcare professionals, e.g. the movement disorders neurologist and the PD nurse specialist, are considered essential members of this multidisciplinary team. However, with our increasing knowledge about different aspects of the disease, other disciplines are also being recognized as important contributors to the healthcare team. Areas covered: We describe a selection of these relatively newly-recognized disciplines, including the specialist in vascular medicine, gastroenterologist, pulmonologist, neuroophthalmologist, urologist, geriatrician/elderly care physician, palliative care specialist and the dentist. Furthermore, we share the view of a person with PD on how patients and caregivers should be involved in the multidisciplinary team. Finally, we have included a perspective on the new role of the movement disorder neurologist, with care delivery via "tele-neurology". Expert commentary: Increased awareness about the potential role of these 'new' professionals will further improve disease management and quality of life of PD patients.
Objective: Reduction in glucocerebrosidase (GCase; encoded by GBA) enzymatic activity has been linked to Parkinson's disease (PD). Here, we correlated GCase activity and PD phenotype in the Parkinson's Progression Markers Initiative (PPMI) cohort. Methods: We measured GCase activity in dried blood spots from 1559 samples of participants in the inception PPMI cohort, collected in four annual visits (from baseline visit to Year-3). Participants (PD, n = 392; controls, n = 175) were fully sequenced for GBA variants by means of genomewide genotyping arrays, whole-exome sequencing, whole-genome sequencing, Sanger sequencing, and RNA-sequencing. Results: Fifty-two PD participants (13.4%) and 13 (7.4%) controls carried a GBA variant. GBA status was strongly associated with GCase activity. Among noncarriers, GCase activity was similar between PD and controls. Among GBA p.E326K carriers (PD, n = 20; controls, n = 5), activity was significantly lower in PD carriers than control carriers (9.53 µmol/L/h vs. 11.68 µmol/L/h, P = 0.035). Glucocerebrosidase activity was moderately (r = 0.45) associated with white blood cell (WBC) count. Next, we divided the noncarriers with PD to tertiles based on WBC count-corrected enzymatic activity. Members of the lower tertile had higher MDS-Unified Parkinson's Disease Rating Scale motor score in the "off" medication examination at year-III exam. Longitudinal analyses demonstrated slight reduction of activity in samples collected earlier on in the study, likely because of longer storage time. Interpretation: GCase activity is associated with GBA genotype, WBC count, and among p.E326K variant carriers, with PD status. Reduced
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