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Background: Multiple system atrophy (MSA) is a disease with diverse symptoms and the commonly used classifications, MSA-P and MSA-C, do not cover all the different symptoms seen in MSA patients. Additionally, these classifications do not provide information about how the disease progresses over time or the expected outcome for patients., Objective: To explore clinical subtypes of MSA with a natural disease course through a data-driven approach to assist in the diagnosis and treatment of MSA., Methods: We followed 122 cases of MSA collected from 3 hospitals for 3 years. Demographic characteristics, age of onset, clinical signs, scale assessment scores, and auxiliary examination were collected. Age at onset; time from onset to assisted ambulation; and UMSARS I, II, and IV, COMPASS-31, ICARS, and UPDRS III scores were selected as clustering elements. K-means, partitioning around medoids, and self-organizing maps were used to analyze the clusters., Results: The results of all three clustering methods supported the classification of three MSA subtypes: The aggressive progression subtype (MSA-AP), characterized by mid-to-late onset, rapid progression and severe clinical symptoms; the typical subtype (MSA-T), characterized by mid-to-late onset, moderate progression and moderate severity of clinical symptoms; and the early-onset slow progression subtype (MSA-ESP), characterized by early-to-mid onset, slow progression and mild clinical symptoms., Conclusions: We divided MSA into three subtypes and summarized the characteristics of each subtype. According to the clustering results, MSA patients were divided into three completely different types according to the severity of symptoms, the speed of disease progression, and the age of onset.

Background: While multiple system atrophy (MSA) presents with high heterogeneous motor and nonmotor symptoms, the associations between clinical phenotypes and prognosis are unclear., Objective: We aimed to evaluate clinical phenotypes of MSA using data-driven approach and measure the impact of phenotypes on survival and bedbound status., Methods: 193 MSA patients were recruited from Xuanwu Hospital Capital Medical University, whose history, motor and non-motor symptoms were examined using cluster analysis. Ninety-five participants were followed-up via telephone after a mean of 31.87 months. We employed Kaplan- Meier analysis to examine survival and performed Cox and logistic regression analyses to identify factors associated with survival and bedbound status., Results: We identified four clinical profiles of MSA: cerebellar symptom-dominant, sleep and mood disorder-dominant, rigid akinetic-dominant, and malignant diffuse. The overall median survival was 7.75 years (95% CI 7.19-8.31). After adjusting for years from symptom onset to diagnosis, age and sex, patients in the malignant diffuse and rigid akinetic-dominant clusters had greater risk of death than sleep and mood disorder-dominant cluster. Furthermore, patients in the malignant diffuse and rigid akinetic-dominant clusters had higher risk of being bedbound than cerebellar symptom-dominant cluster., Conclusions: The malignant diffuse and sleep and mood disorder-dominant were identified besides the two classical subtypes, parkinsonism, and cerebellar symptom-variant. Patients with rigid-akinetic motor profiles have a worse prognosis than cerebellar symptom-dominant profiles in general. Diffuse symptoms, especially postural instability, and cognitive alterations at diagnosis, indicate rapid functional loss and disease progression. The different profiles and prognoses might indicate varied underlying pathological mechanisms.