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Updated: Jul 18, 2026

Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts
Published on: February 23, 2024
Clinical features and natural history of neuroferritinopathy caused by the FTL1 460InsA mutation
Patrick F Chinnery1, Douglas E Crompton, Daniel Birchall
1Institute of Human Genetics, University of Newcastle upon Tyne, UK. p.f.chinnery@ncl.ac.uk
Abstract:
Neuroferritinopathy is a progressive potentially treatable adult-onset movement disorder caused by mutations in the ferritin light chain gene (FTL1). Features overlap with common extrapyramidal disorders: idiopathic torsion dystonia, idiopathic Parkinson's disease and Huntington's disease, but the phenotype and natural history have not been defined. We studied a genetically homogeneous group of 41 subjects with the 460InsA mutation in FTL1, documenting the presentation, clinical course, biochemistry and neuroimaging. The mean age of onset was 39.4 years (SD = 13.3, range 13-63), beginning with chorea in 50%, focal lower limb dystonia in 42.5% and parkinsonism in 7.5%. The majority reported a family history of a movement disorder often misdiagnosed as Huntington's disease. The disease progressed relentlessly, becoming generalized over a 5-10 year period, eventually leading to aphonia, dysphagia and severe motor disability with subcortical/frontal cognitive dysfunction as a late feature. A characteristic action-specific facial dystonia was common (65%), and in 63% there was asymmetry throughout the disease course. Serum ferritin levels were low in the majority of males and post-menopausal females, but within normal limits for pre-menopausal females. MR brain imaging was abnormal on all affected individuals and one presymptomatic carrier. In conclusion, isolated parkinsonism is unusual in neuroferritinopathy, and unlike Huntington's disease, cognitive changes are absent or subtle in the early stages. Depressed serum ferritin is common and provides a useful screening test in routine practice, and gradient echo brain MRI will identify all symptomatic cases.
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