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Consensus guidelines on the construct validity of rodent models of restless legs syndrome
Our understanding of the causes and natural course of restless legs syndrome (RLS) is incomplete. The lack of objective diagnostic biomarkers remains a challenge for clinical research and for the development of valid animal models. As a task force of preclinical and clinical scientists, we have prev...
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Published in: | Disease models & mechanisms 2022-08, Vol.15 (8) |
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Main Authors: | , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Our understanding of the causes and natural course of restless legs syndrome (RLS) is incomplete. The lack of objective diagnostic biomarkers remains a challenge for clinical research and for the development of valid animal models. As a task force of preclinical and clinical scientists, we have previously defined face validity parameters for rodent models of RLS. In this article, we establish new guidelines for the construct validity of RLS rodent models. To do so, we first determined and agreed on the risk, and triggering factors and pathophysiological mechanisms that influence RLS expressivity. We then selected 20 items considered to have sufficient support in the literature, which we grouped by sex and genetic factors, iron-related mechanisms, electrophysiological mechanisms, dopaminergic mechanisms, exposure to medications active in the central nervous system, and others. These factors and biological mechanisms were then translated into rodent bioequivalents deemed to be most appropriate for a rodent model of RLS. We also identified parameters by which to assess and quantify these bioequivalents. Investigating these factors, both individually and in combination, will help to identify their specific roles in the expression of rodent RLS-like phenotypes, which should provide significant translational implications for the diagnosis and treatment of RLS. |
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ISSN: | 1754-8403 1754-8411 |
DOI: | 10.1242/dmm.049615 |