ABSTRACT
Background@#and Purpose Premanifest mutation carriers with spinocerebellar ataxia (SCA) can exhibit subtle abnormalities before developing ataxia. We summarized the preataxic manifestations of SCA1, -2, -3, and -6, and their associations with ataxia onset. @*Methods@#We included studies of the premanifest carriers of SCA published between January 1998 and December 2019 identified in Scopus and PubMed by searching for terms including ‘spinocerebellar ataxia’ and several synonyms of ‘preataxic manifestation’. We systematically reviewed the results obtained in studies categorized based on clinical, imaging, and laboratory markers. @*Results@#We finally performed a qualitative analysis of 48 papers. Common preataxic manifestations appearing in multiple SCA subtypes were muscle cramps, abnormal muscle reflexes, instability in gait and posture, lower Composite Cerebellar Functional Severity scores, abnormalities in video-oculography and transcranial magnetic stimulation, and gray-matter loss and volume reduction in the brainstem and cerebellar structures. Also, decreased sensory amplitudes in nerve conduction studies were observed in SCA2. Eotaxin and neurofilament lightchain levels were revealed as sensitive blood biomarkers in SCA3. Concerning potential predictive markers, hyporeflexia and abnormalities of somatosensory evoked potentials showed correlations with the time to ataxia onset in SCA2 carriers. However, no longitudinal data were found for the other SCA gene carriers. @*Conclusions@#Our results suggest that preataxic manifestations vary among SCA1, -2, -3, and -6, with some subtypes sharing specific features. Combining various markers into a standardized index for premanifest carriers may be useful for early screening and assessing the risk of disease progression in SCA carriers.
ABSTRACT
Background@#and Purpose Premanifest mutation carriers with spinocerebellar ataxia (SCA) can exhibit subtle abnormalities before developing ataxia. We summarized the preataxic manifestations of SCA1, -2, -3, and -6, and their associations with ataxia onset. @*Methods@#We included studies of the premanifest carriers of SCA published between January 1998 and December 2019 identified in Scopus and PubMed by searching for terms including ‘spinocerebellar ataxia’ and several synonyms of ‘preataxic manifestation’. We systematically reviewed the results obtained in studies categorized based on clinical, imaging, and laboratory markers. @*Results@#We finally performed a qualitative analysis of 48 papers. Common preataxic manifestations appearing in multiple SCA subtypes were muscle cramps, abnormal muscle reflexes, instability in gait and posture, lower Composite Cerebellar Functional Severity scores, abnormalities in video-oculography and transcranial magnetic stimulation, and gray-matter loss and volume reduction in the brainstem and cerebellar structures. Also, decreased sensory amplitudes in nerve conduction studies were observed in SCA2. Eotaxin and neurofilament lightchain levels were revealed as sensitive blood biomarkers in SCA3. Concerning potential predictive markers, hyporeflexia and abnormalities of somatosensory evoked potentials showed correlations with the time to ataxia onset in SCA2 carriers. However, no longitudinal data were found for the other SCA gene carriers. @*Conclusions@#Our results suggest that preataxic manifestations vary among SCA1, -2, -3, and -6, with some subtypes sharing specific features. Combining various markers into a standardized index for premanifest carriers may be useful for early screening and assessing the risk of disease progression in SCA carriers.
ABSTRACT
Objective@#Suicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individualswith SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machinelearning methods. @*Methods@#The 2010–2013 dataset of the Korea National Health and Nutritional Examination Survey was used as the training dataset(n=16,437), and the subset collected in 2015 was used as the testing dataset (n=3,788). Various machine learning algorithms were appliedand compared to the conventional logistic regression (LR)-based model. @*Results@#Common risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressivedisorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of themachine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were betterthan that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventionalLR model. @*Conclusion@#A machine learning-based approach could provide better SI prediction performance compared to a conventional LRbasedmodel. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.Psychiatry Investig 2020;17(4):331-340