A BS TRACT: Background: No comprehensive metaanalysis has ever been performed to assess the value of neurofilament light chain (NfL) as a biomarker in genetic ataxia.Objective: We conducted a meta-analysis to summarize NfL concentration and evaluate its utility as a biomarker in genetic ataxia. Methods: Studies were included if they reported NfL concentration of genetic ataxia. We used log (mean AE SD) NfL to describe mean raw value of NfL. The effect size of NfL between genetic ataxia and healthy controls (HC) was expressed by mean difference. Correlation between NfL and disease severity was calculated. Results: We identified 11 studies of 624 HC and 1006 patients, here referred to as spinocerebellar ataxia (SCA1, 2, 3, 6, and 7), Friedreich ataxia (FRDA), and ataxia telangiectasia (A-T). The concentration of blood NfL (bNfL) elevated with proximity to expected onset, and progressively increased from asymptomatic to preclinical to clinical stage in SCA3. Compared with HC, bNfL levels were significantly higher in SCA1, 2, 3, and 7, FRDA, as well as A-T, and the difference increased with the advancing disease in SCA3. bNfL levels correlated with disease severity in SCA3. There was a significant correlation between bNfL and longitudinal progression in SCA3. Additionally, bNfL increased with age in HC, yet this is probably masked by higher diseaserelated effects on bNfL in genetic ataxia. Conclusions: bNfL can be used as a potential biomarker to predict disease onset, severity, and progression of genetic ataxia. Reference-value setting of bNfL should

Background In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease‐modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine‐learning algorithms and linear regression methods. Objective The objective of this study was to develop a machine‐learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) population from mainland China. Methods In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine‐learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ‐related genes, sex, and parental origin. Results Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine‐learning‐based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine‐learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2. Conclusion Machine‐learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society

Spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) is a progressive autosomal dominant neurodegenerative disease caused by abnormal CAG repeats in the exon 10 of ATXN3. The accumulation of the mutant ataxin-3 proteins carrying expanded polyglutamine (polyQ) leads to selective degeneration of neurons. Since the pathogenesis of SCA3 has not been fully elucidated, and no effective therapies have been identified, it is crucial to investigate the pathogenesis and seek new therapeutic strategies of SCA3. Induced pluripotent stem cells (iPSCs) can be used as the ideal cell model for the molecular pathogenesis of polyQ diseases. Abnormal CAG expansions mediated by CRISPR/Cas9 genome engineering technologies have shown promising potential for the treatment of polyQ diseases, including SCA3. In this study, SCA3-iPSCs can be corrected by the replacement of the abnormal CAG expansions (74 CAG) with normal repeats (17 CAG) using CRISPR/Cas9-mediated homologous recombination (HR) strategy. Besides, corrected SCA3-iPSCs retained pluripotent and normal karyotype, which can be differentiated into a neural stem cell (NSCs) and neuronal cells, and maintained electrophysiological characteristics. The expression of differentiation markers and electrophysiological characteristics were similar among the neuronal differentiation from normal control iPSCs (Ctrl-iPSCs), SCA3-iPSCs, and isogenic control SCA3-iPSCs. Furthermore, this study proved that the phenotypic abnormalities in SCA3 neurons, including aggregated IC2-polyQ protein, decreased mitochondrial membrane potential (MMP) and glutathione expressions, increased reactive oxygen species (ROS), intracellular Ca2+ concentrations, and lipid peroxidase malondialdehyde (MDA) levels, all were rescued in the corrected SCA3-NCs. For the first time, this study demonstrated the feasibility of CRISPR/Cas9-mediated HR strategy to precisely repair SCA3-iPSCs, and reverse the corresponding abnormal disease phenotypes. In addition, the importance of genetic control using CRISPR/Cas9-mediated iPSCs for disease modeling. Our work may contribute to providing a potential ideal model for molecular mechanism research and autologous stem cell therapy of SCA3 or other polyQ diseases, and offer a good gene therapy strategy for future treatment.

ObjectivesThe aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China.MethodsWe compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including −2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model.ResultsAmong these 6 parametric survival models, the logistic model had the lowest −2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age.ConclusionsWe first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.