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A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease.

Authors :
Wijeratne PA
Johnson EB
Gregory S
Georgiou-Karistianis N
Paulsen JS
Scahill RI
Tabrizi SJ
Alexander DC
Source :
Frontiers in big data [Front Big Data] 2021 Aug 05; Vol. 4, pp. 662200. Date of Electronic Publication: 2021 Aug 05 (Print Publication: 2021).
Publication Year :
2021

Abstract

Understanding the order and progression of change in biomarkers of neurodegeneration is essential to detect the effects of pharmacological interventions on these biomarkers. In Huntington's disease (HD), motor, cognitive and MRI biomarkers are currently used in clinical trials of drug efficacy. Here for the first time we use directly compare data from three large observational studies of HD (total N = 532) using a probabilistic event-based model (EBM) to characterise the order in which motor, cognitive and MRI biomarkers become abnormal. We also investigate the impact of the genetic cause of HD, cytosine-adenine-guanine (CAG) repeat length, on progression through these stages. We find that EBM uncovers a broadly consistent order of events across all three studies; that EBM stage reflects clinical stage; and that EBM stage is related to age and genetic burden. Our findings indicate that measures of subcortical and white matter volume become abnormal prior to clinical and cognitive biomarkers. Importantly, CAG repeat length has a large impact on the timing of onset of each stage and progression through the stages, with a longer repeat length resulting in earlier onset and faster progression. Our results can be used to help design clinical trials of treatments for Huntington's disease, influencing the choice of biomarkers and the recruitment of participants.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 Wijeratne, Johnson, Gregory, Georgiou-Karistianis, Paulsen, Scahill, Tabrizi and Alexander.)

Details

Language :
English
ISSN :
2624-909X
Volume :
4
Database :
MEDLINE
Journal :
Frontiers in big data
Publication Type :
Academic Journal
Accession number :
34423286
Full Text :
https://doi.org/10.3389/fdata.2021.662200