ABSTRACT
Background: Associations between phenotypic traits, environmental exposures, and Parkinson's disease have largely been evaluated one-by-one, piecemeal, and pre-selections. A comprehensive picture of comorbidities, phenotypes, exposures, and polypharmacy characterizing the complexity and heterogeneity of real-world patients presenting to academic movement disorders clinics in the US is missing. Objectives: To portrait the complexity of features associated with patients with Parkinson's disease in a study of 933 cases and 291 controls enrolled in the Harvard Biomarkers Study. Methods: The primary analysis evaluated 64 health features for associations with Parkinson's using logistic regression adjusting for age and sex. We adjusted for multiple testing using the false discovery rate (FDR) with £ 0.05 indicating statistical significance. Exploratory analyses examined feature correlation clusters and feature combinations. Results: Depression (OR = 3.11, 95% CI 2.1 to 4.71), anxiety (OR = 3.31, 95% CI 2.01-5.75), sleep apnea (OR 2.58, 95% CI 1.47-4.92), and restless leg syndrome (RLS; OR 4.12, 95% CI 1.81-12.1) were significantly more common in patients with Parkinson's than in controls adjusting for age and sex with FDR £ 0.05. The prevalence of depression, anxiety, sleep apnea, and RLS were correlated, and these diseases formed part of a larger cluster of mood traits and sleep traits linked to PD. Exposures to pesticides (OR 1.87, 95% CI 1.37-2.6), head trauma (OR 2.33, 95% CI 1.51-3.73), and smoking (OR 0.57, 95% CI 0.43-0.75) were significantly associated with the disease consistent with previous studies. Vitamin supplementation with cholecalciferol (OR 2.18, 95% CI 1.4-3.45) and coenzyme Q10 (OR 2.98, 95% CI 1.89-4.92) was more commonly used by patients than controls. Cumulatively, 43% (398 of 933) of Parkinson's patients had at least one psychiatric or sleep disorder, compared to 21% (60 of 291) of healthy controls. Conclusions: 43% of Parkinson's patients seen at Harvard-affiliated teaching hospitals have depression, anxiety, and disordered sleep. This syndromic cluster of mood and sleep traits may be pathophysiologically linked and clinically important.
ABSTRACT
Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.
ABSTRACT
BACKGROUND AND OBJECTIVES: Integrating advanced practice providers (APPs) into neurologic practice can improve access, promote patient education, and reduce health care costs. APPs receive limited formal education in neurology, so on-the-job training is essential. We set out to identify common challenges and best practices for onboarding, training, and integrating APPs into neurologic practice. METHODS: We conducted a survey and focus group with 8 APPs currently practicing within an academic neurology department as part of a clinical quality improvement initiative. We explored their roles in multidisciplinary teams, challenges faced during onboarding and training, and strategies for success. Qualitative thematic analysis was performed. RESULTS: Neurology APPs serve diverse roles including caring for hospitalized and ambulatory patients, performing procedures, assisting trainees, and performing research. Participants reported limited formal neurologic education before their job and a need for educational sessions and resources tailored to APPs. Neuroanatomy, neuroimaging, and generating a neurologic differential diagnosis were key knowledge gaps identified. We identified 7 informal strategies for on-the-job training, 7 challenges to on-the-job training, and factors promoting or threatening job satisfaction. Graded responsibility and clinical mentorship were essential for successful onboarding. APPs desired peer-to-peer mentorship and structured educational opportunities. DISCUSSION: Common challenges and success strategies identified can inform the design of a formal curriculum for onboarding neurology APPs. Our findings suggest that an optimal APP training process involves graded responsibility and support for self-directed learning, employs peer mentors, and targets education of the multidisciplinary team including physicians and patients. Our results may inform other institutions recruiting, hiring, and training APPs.
