Clinical Research Directory

Natural History and Genotype-Phenotype Correlations in Mitochondrial Leukoencephalopathies

Mitochondrial disorders (MDs) are among the most common inherited metabolic diseases, caused by genetic defects in mitochondrial or nuclear DNA that impair cellular energy production. The central nervous system (CNS) is frequently affected, due to its high energy demands. Within the broad clinical spectrum of MDs, mitochondrial leukoencephalopathies (MLs) have emerged as a distinct subgroup characterized by CNS white matter involvement. MLs are clinically and genetically heterogeneous, with onset ranging from infancy to adulthood. Patients may experience episodes of neurological regression, often triggered by stressors such as febrile illnesses, alongside variable motor, cognitive, and occasionally extraneurological involvement. Neuroimaging typically reveals diffuse or focal white matter abnormalities, including cavitation and rarefaction, with additional involvement of deep gray matter structures. Certain ML subtypes exhibit characteristic MRI patterns that may aid in diagnosis. Despite recent advancements in the genetic and neuroradiological characterization of MLs, significant knowledge gaps persist. Although the number of disease-causing genes has increased over the last decades, a comprehensive understanding of genotype-phenotype correlations is still lacking. Additionally, knowledge on the natural history of these disorders is limited. Existing data often focus on imaging findings with insufficient integration of clinical, biochemical, and molecular features, limiting the understanding of disease mechanisms, progression, and therapeutic responses. In light of these gaps, this multicenter, observational, retrospective study aims to systematically collect and analyze clinical, biochemical, neuroradiological, and genetic data from individuals with MLs, assessed at international reference centers for MDs. The study seeks to better elucidate the phenotypic variability and genotype-phenotype correlations of MLs. This work is expected to enhance diagnostic accuracy, inform management strategies, support prognostic counseling, and ultimately contribute to the better overall management of affected individuals