Diagnosis, prognosis, and treatment of leukodystrophies
Abstract
Leukodystrophies comprise a large group of rare genetic disorders primarily affecting CNS white matter. Historically, the diagnostic process was slow and patient prognosis regarded as poor because curative treatment was only available for very few leukodystrophies in early stages of the disease. Whole-exome sequencing has both greatly increased the number of known leukodystrophies and improved diagnosis. Whether MRI keeps its central place in diagnosis and what the role is of whole-exome sequencing are relevant questions for neurologists. Improved diagnosis has revealed the phenotypic variability of leukodystrophies, requiring adaptation of prognostication. Technological advance in molecular techniques and improved insight into the pathophysiology of individual leukodystrophies have led to therapeutic developments, including drug design and gene therapy. Despite this progress, therapies are only beneficial early in the disease course, emphasising the need for a speedy diagnosis and for research on regenerative approaches to repair the damage already present.
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Genetic screening using next-generation sequencing is the most successful approach with the highest diagnostic yield in cases of underlying genetic disorders, such as CALD, and provides the fastest results.
“Genetic screening using next-generation sequencing (NGS) is the most successful approach with the highest diagnostic yield in cases of underlying genetic disorders, such as CALD, and provides the fastest results [7,8].”
From Identification of a Novel Non-Canonical Splice-Site Variant in ABCD1 (2023) · cites this paper as [8]
Trio-WES has substantially increased the number of known leukodystrophies and improved diagnoses.
“Trio-WES has substantially increased the number of known leukodystrophies and improved diagnoses [7,8,39].”
From Identification of a Novel Non-Canonical Splice-Site Variant in ABCD1 (2023) · cites this paper as [8]
Cites
Hematopoietic stem-cell gene therapy for cerebral adrenoleukodystrophy
Eichler, F., Duncan, C., Musolino, P. L., Orchard, P. J., De Oliveira, S., Thrasher, A. J., Armant, M., Dansereau, C., Lund, T. C., Miller, W. P., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1700554 · Cited by 604 · see citing works
Sessa, M., Lorioli, L., Fumagalli, F., Acquati, S., Redaelli, D., Baldoli, C., Canale, S., Lopez, I. D., Morena, F., Calabria, A., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30374-9 · Cited by 502 · see citing works
van der Knaap, M. S., & Bugiani, M. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1739-1 · Cited by 403 · see citing works
Whole exome sequencing in patients with white matter abnormalities
Vanderver, A., Simons, C., Helman, G., Crawford, J., Wolf, N. I., Bernard, G., Pizzino, A., Schmidt, J. L., Takanohashi, A., Miller, D., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24650 · Cited by 145 · see citing works
Self-citationUpdate on leukodystrophies: A historical perspective and adapted definition
Kevelam, S., Steenweg, M., Srivastava, S., Helman, G., Naidu, S., Schiffmann, R., Blaser, S., Vanderver, A., Wolf, N., & van der Knaap, M. (2016) · Neuropediatrics · doi:10.1055/s-0036-1588020 · Cited by 112 · see citing works
Self-citationLynch, D. S., Zhang, W. J., Lakshmanan, R., Kinsella, J. A., Uzun, G. A., Karbay, M., Tüfekçioglu, Z., Hanagasi, H., Burke, G., Foulds, N., et al. (2016) · JAMA Neurology · doi:10.1001/jamaneurol.2016.2229 · Cited by 90 · see citing works
De Santis, S., Bastiani, M., Droby, A., Kolber, P., Zipp, F., Pracht, E., Stoecker, T., Groppa, S., & Roebroeck, A. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2018.03.048 · Cited by 56 · see citing works
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Gene therapy for the leukodystrophies: From preclinical animal studies to clinical trials.
Metovic, J., Li, Y., Gong, Y., & Eichler, F. (2024) · Neurotherapeutics · doi:10.1016/j.neurot.2024.e00443 · Cited by 16 · see citing works
Identification of a Novel Non-Canonical Splice-Site Variant in ABCD1
Zheng, F., Lin, Z., Hu, Y., Shi, X., Zhao, Q., & Lin, Z. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020473 · Cited by 1 · see citing works
- Authors
- van der Knaap, M. S., Schiffmann, R., Mochel, F., & Wolf, N. I.
- Affiliations
- Baylor Scott & White Research Institute (US)
- Centre National de la Recherche Scientifique (FR)
- Emma Kinderziekenhuis (NL)
- Venue
- The Lancet Neurology
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 18
- Issue
- 10
- Pages
- 962-972
- ISSN
- 1474-4422 (ISSN-L)
- OpenAlex
- W2961527627
- Cited by
- 175
- References
- 7
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 45 · 2023: 30 · 2022: 20 · 2021: 23 · 2020: 17 · 2019: 4
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Neurology (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Hospital for Sick Children
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Hereditary Neurological Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- leukodystrophy, white matter disease, genetic disorders, exome sequencing, MRI diagnosis, prognosis, phenotypic variability, gene therapy, drug development, regenerative therapy
- MeSH terms
- Exome Sequencing, Humans, Magnetic Resonance Imaging, Phenotype, Prognosis, Drug Design, Genetic Therapy, Early Diagnosis, Leukoencephalopathies
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