Abstract
Charcot-Marie-Tooth disease (CMT) is a genetically heterogeneous group of disorders sharing the same clinical phenotype, characterized by distal limb muscle wasting and weakness, usually with skeletal deformities, distal sensory loss, and abnormalities of deep tendon reflexes. Mutations of genes involved in different functions eventually lead to a length-dependent axonal degeneration, which is the likely basis of the distal predominance of the CMT phenotype. Nerve conduction studies are important for classification, diagnosis, and understanding of pathophysiology. The subdivision into demyelinating CMT1 and axonal CMT2 types was a milestone and is still valid for the majority of patients. However, exceptions to this partition are increasing. Intermediate conduction velocities are often found in males with X-linked CMT (CMTX), and different intermediate CMT types have been identified. Moreover, for some genes, different mutations may result either in demyelinating CMT with slow conduction, or in axonal CMT. Nerve conduction slowing is uniform and diffuse in the most common CMT1A associated with the 17p12 duplication, whereas it is often asymmetric and nonhomogeneous in CMTX, sometimes rendering difficult the differential diagnosis with acquired inflammatory neuropathies. The demyelinating recessive forms, termed CMT4, usually have early onset and run a more severe course than the dominant types. Pure motor CMT types are now classified as distal hereditary motor neuronopathy. The diagnostic approach to the identification of the CMT subtype is complex and cannot be based on the clinical phenotype alone, as different forms are often clinically indistinguishable. However, there are features that may be of help in addressing molecular investigation in a single patient. Late onset, prominent or peculiar sensory manifestations, autonomic nervous system dysfunction, cranial nerve involvement, upper limb predominance, subclinical central nervous system abnormalities, severe scoliosis, early-onset glaucoma, neutropenia are findings helpful for diagnosis.
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