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IntroductionHuntington's disease (HD) is a rare neurodegenerative disease characterized by cognitive, behavioral and motor symptoms that progressively worsen with time. Cognitive and behavioral signs of HD are generally present in the years prior to a diagnosis; however, manifest HD is typically assessed by genetic confirmation and/or the presence of unequivocal motor symptoms. Nevertheless, there is a large variation in symptom severity and rate of progression among individuals with HD.MethodsIn this retrospective study, longitudinal natural history of disease progression was modeled in individuals with manifest HD from the global, observational Enroll-HD study (NCT01574053). Unsupervised machine learning (k-means; km3d) was used to jointly model clinical and functional disease measures simultaneously over time, based on one-dimensional clustering concordance such that individuals with manifest HD (N = 4,961) were grouped into three clusters: rapid (Cluster A; 25.3%), moderate (Cluster B; 45.5%) and slow (Cluster C; 29.2%) progressors. Features that were considered predictive of disease trajectory were then identified using a supervised machine learning method (XGBoost).ResultsThe cytosine adenine guanine-age product score (a product of age and polyglutamine repeat length) at enrollment was the top predicting feature for cluster assignment, followed by years since symptom onset, medical history of apathy, body mass index at enrollment and age at enrollment.ConclusionsThese results are useful for understanding factors that affect the global rate of decline in HD. Further work is needed to develop prognostic models of HD progression as these could help clinicians with individualized clinical care planning and disease management.

Background: Personality impacts life expectancy and comprehensive treatment efficacy for patients with Parkinson's disease (PD). However, current research fails to involve the family caregiver's personality despite significant external support provided by family caregivers. This study explored neuroticism and conscientiousness personality factors of the patient and family caregiver associated with quality of life (QoL) of PD patients.Methods: 134 couples of patients presenting with PD and their family caregivers that met inclusion criteria, were recruited for this cross-sectional study at Shanghai Tenth People's Hospital from October 2015 to 2017. The Parkinson's Disease Questionnaire-39 Items (PDQ-39) for QoL, including the physical component summary (PCS) and mental component summary (MCS), the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) for neuroticism and conscientiousness personalities, and the Unified Parkinson's Disease Rating Scale (UPDRS) for PD severity questionnaires were employed. Multivariate stepwise linear regression determined the contributions of demographic, clinical and personality variables in PDQ-39, PCS, and MCS.Results: PD patients and neuroticism were significantly associated with total PDQ-39, PCS, and MCS. Additionally, conscientiousness was significantly associated with PDQ-39 and PCS. After adding neuroticism and caregiver conscientiousness personality, the importance of neuroticism for the QoL model dramatically decreased, and caregiver conscientiousness personality was associated with lower scores in total PDQ-39 and PCS.Conclusion: We revealed a significant association between neuroticism and physical or mental status of PD patients; however, this association decreased when caregiver conscientiousness was added to the model. Moreover, conscientiousness of patients and caregivers were associated with improved QoL.

Introduction: Huntington's disease (HD) is a rare neurodegenerative disease characterized by cognitive, behavioral and motor symptoms that progressively worsen with time. Cognitive and behavioral signs of HD are generally present in the years prior to a diagnosis; however, manifest HD is typically assessed by genetic confirmation and/or the presence of unequivocal motor symptoms. Nevertheless, there is a large variation in symptom severity and rate of progression among individuals with HD. Methods: In this retrospective study, longitudinal natural history of disease progression was modeled in individuals with manifest HD from the global, observational Enroll-HD study (NCT01574053). Unsupervised machine learning (k-means; km3d) was used to jointly model clinical and functional disease measures simultaneously over time, based on one-dimensional clustering concordance such that individuals with manifest HD (N = 4,961) were grouped into three clusters: rapid (Cluster A; 25.3%), moderate (Cluster B; 45.5%) and slow (Cluster C; 29.2%) progressors. Features that were considered predictive of disease trajectory were then identified using a supervised machine learning method (XGBoost). Results: The cytosine adenine guanine-age product score (a product of age and polyglutamine repeat length) at enrollment was the top predicting feature for cluster assignment, followed by years since symptom onset, medical history of apathy, body mass index at enrollment and age at enrollment. Conclusions: These results are useful for understanding factors that affect the global rate of decline in HD. Further work is needed to develop prognostic models of HD progression as these could help clinicians with individualized clinical care planning and disease management. [ABSTRACT FROM AUTHOR]