Unravelling the complex MRI pattern in glutaric aciduria type I using statistical models—a cohort study in 180 patients. Issue 5 (9th May 2014)

• Record Type: Journal Article
• Authors: Garbade, Sven F.; Greenberg, Cheryl R.; Demirkol, Mübeccel; Gökçay, Gülden; Ribes, Antonia; Campistol, Jaume; Burlina, Alberto B.; Burgard, Peter; Kölker, Stefan
• Is Part Of: Journal of inherited metabolic disease. Volume 37:Issue 5(2014)
• Page Start: 763
• Page End: 773
• Publication Date: 2014-05-09
• Subjects: Metabolism, Inborn errors of -- Periodicals; Metabolism -- Disorders -- Periodicals; 616.39042
• DOI: 10.1007/s10545-014-9676-9
• Languages: English
• ISSNs: 0141-8955

Abstract

Abstract: Background: Glutaric aciduria type I (GA‐I) is a cerebral organic aciduria caused by inherited deficiency of glutaryl‐CoA dehydrogenase and is characterized biochemically by an accumulation of putatively neurotoxic dicarboxylic metabolites. The majority of untreated patients develops a complex movement disorder with predominant dystonia during age 3–36 months. Magnetic resonance imaging (MRI) studies have demonstrated striatal and extrastriatal abnormalities. Aims/methods: The major aim of this study was to elucidate the complex neuroradiological pattern of patients with GA‐I and to associate the MRI findings with the severity of predominant neurological symptoms. In 180 patients, detailed information about the neurological presentation and brain region‐specific MRI abnormalities were obtained via a standardized questionnaire. Results: Patients with a movement disorder had more often MRI abnormalities in putamen, caudate, cortex, ventricles and external CSF spaces than patients without or with minor neurological symptoms. Putaminal MRI changes and strongly dilated ventricles were identified as the most reliable predictors of a movement disorder. In contrast, abnormalities in globus pallidus were not clearly associated with a movement disorder. Caudate and putamen as well as cortex, ventricles and external CSF spaces clearly collocalized on a two‐dimensional map demonstrating statistical similarity and suggesting the same underlying pathomechanism. Conclusions: This study demonstrates that complex statistical methods are useful to decipher the age‐dependent and region‐specific MRI patterns of rare neurometabolic diseases and that these methods are helpful to elucidate the clinical relevance of specific MRI findings.