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Phenotypic Variability and Diagnostic Characteristics in Inherited Peripheral Neuropathy in Latvia Cover

Phenotypic Variability and Diagnostic Characteristics in Inherited Peripheral Neuropathy in Latvia

Open Access
|Jun 2022

Abstract

Inherited peripheral neuropathies (IPN) are a clinically and genetically heterogeneous group of disorders. The most common IPN is Charcot-Marie-Tooth (CMT) disease. Here we describe IPN clinical variability and diagnostic characteristics in the Latvian population. A total of 101 patients were enrolled in the study. Genetic testing consisted of PMP22 copy number analysis and whole-exome sequencing (WES). Clinical assessment comprised CMT Neuropathy Score version 2 (CMTNSv2), CMT Examination Score, pain, anxiety and memory/cognitive ability testing. The diagnostic yields for PMP22 copy number detection and WES were 45.8% and 77.8%, respectively. Disease severity assessment indicated high clinical heterogeneity, with CMTNSv2 scores ranging between 0 and 33. More than one-third of patients reported pain, and it was found to be significantly more common in patients with at least a mild anxiety level. From the initial development of symptoms, on average, it took more than 13 years for a diagnosis of IPN to be confirmed. This study updates the IPN genetic and clinical profile of the Latvian population and demonstrates the presence of a high level of heterogeneity. The time to diagnosis for IPN patients needs to be improved by employing multiplex ligation-dependent probe amplification initially followed by WES.

DOI: https://doi.org/10.2478/prolas-2022-0035 | Journal eISSN: 2255-890X | Journal ISSN: 1407-009X
Language: English
Page range: 232 - 238
Submitted on: Aug 9, 2021
Accepted on: Oct 26, 2021
Published on: Jun 2, 2022
Published by: Latvian Academy of Sciences
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2022 Elīna Millere, Einārs Kupats, Ieva Mičule, Linda Gailīte, Viktorija Ķēniņa, published by Latvian Academy of Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.