OBJECTIVE: To characterize Alexander disease (AxD) phenotypes and determine correlations with age at onset (AAO) and genetic mutation. AxD is an astrogliopathy usually characterized on MRI by leukodystrophy and caused by glial fibrillary acidic protein (GFAP) mutations. METHODS: We present 30 new cases of AxD and reviewed 185 previously reported cases. We conducted Wilcoxon rank sum tests to identify variables scaling with AAO, survival analysis to identify predictors of mortality, and χ(2) tests to assess the effects of common GFAP mutations. Finally, we performed latent class analysis (LCA) to statistically define AxD subtypes. RESULTS: LCA identified 2 classes of AxD. Type I is characterized by early onset, seizures, macrocephaly, motor delay, encephalopathy, failure to thrive, paroxysmal deterioration, and typical MRI features. Type II is characterized by later onset, autonomic dysfunction, ocular movement abnormalities, bulbar symptoms, and atypical MRI features. Survival analysis predicted a nearly 2-fold increase in mortality among patients with type I AxD relative to those with type II. R79 and R239 GFAP mutations were most common (16.6% and 20.3% of all cases, respectively). These common mutations predicted distinct clinical outcomes, with R239 predicting the most aggressive course. CONCLUSIONS: AAO and the GFAP mutation site are important clinical predictors in AxD, with clear correlations to defined patterns of phenotypic expression. We propose revised AxD subtypes, type I and type II, based on analysis of statistically defined patient groups.
OBJECTIVE: To characterize Alexander disease (AxD) phenotypes and determine correlations with age at onset (AAO) and genetic mutation. AxD is an astrogliopathy usually characterized on MRI by leukodystrophy and caused by glial fibrillary acidic protein (GFAP) mutations. METHODS: We present 30 new cases of AxD and reviewed 185 previously reported cases. We conducted Wilcoxon rank sum tests to identify variables scaling with AAO, survival analysis to identify predictors of mortality, and χ(2) tests to assess the effects of common GFAP mutations. Finally, we performed latent class analysis (LCA) to statistically define AxD subtypes. RESULTS: LCA identified 2 classes of AxD. Type I is characterized by early onset, seizures, macrocephaly, motor delay, encephalopathy, failure to thrive, paroxysmal deterioration, and typical MRI features. Type II is characterized by later onset, autonomic dysfunction, ocular movement abnormalities, bulbar symptoms, and atypical MRI features. Survival analysis predicted a nearly 2-fold increase in mortality among patients with type I AxD relative to those with type II. R79 and R239 GFAP mutations were most common (16.6% and 20.3% of all cases, respectively). These common mutations predicted distinct clinical outcomes, with R239 predicting the most aggressive course. CONCLUSIONS: AAO and the GFAP mutation site are important clinical predictors in AxD, with clear correlations to defined patterns of phenotypic expression. We propose revised AxD subtypes, type I and type II, based on analysis of statistically defined patient groups.
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