Work

Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease

Year: 2021

Type: article

Abstract: Huntington's disease (HD) is characterised by a triad of cognitive, behavioural, and motor symptoms which lead to functional decline and loss of independence. With potential disease-modifying therapies in development, there is interest in accurately measuring HD progression and characterising prognostic variables to improve efficiency of clinical trials. Using the large, prospective Enroll-HD cohort, we investigated the relative contribution and ranking of potential prognostic variables in patients with manifest HD. A random forest regression model was trained to predict change of clinical outcomes based on the variables, which were ranked based on their contribution to the prediction. The highest-ranked variables included novel predictors of progression-being accompanied at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics use-in addition to established predictors, cytosine adenine guanine (CAG) repeat length and CAG-age product. The novel prognostic variables improved the ability of the model to predict clinical outcomes and may be candidates for statistical control in HD clinical studies.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2021.678484

Authors: Naghmeh Ghazaleh, Richard Houghton, Giuseppe Palermo, Scott A. Schobel, Peter A. Wijeratne, Jeffrey D. Long

Institutions: F. Hoffmann-La Roche AG, UCL Queen Square Institute of Neurology, University College London, University of Iowa

Language: English

Topic: Genetic Neurodegenerative Diseases

Subfield: Cellular and Molecular Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,950