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| Home > In process > Brain atrophy staging in spinocerebellar ataxia type 3 for clinical prognosis and trial enrichment. |
| Home > In process > Brain atrophy staging in spinocerebellar ataxia type 3 for clinical prognosis and trial enrichment. |
| Journal Article | DZNE-2026-00072 |
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2026
Elsevier
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.ebiom.2025.106090
Abstract: Spinocerebellar ataxia type 3 (SCA3) is characterised by progressive brain atrophy, with regional volume loss detectable via MRI prior to clinical manifestation. We aimed to identify the previously unknown sequence of brain atrophy in SCA3 and evaluate whether this sequence can be translated into an atrophy staging framework to enable accurate clinical prognosis and trial enrichment.We included data from 322 SCA3 mutation carriers, enrolled in observational studies conducted across Europe, the Americas, and Asia. Participants underwent follow-up assessments up to five years after baseline. The Subtype and Stage Inference machine learning algorithm was applied to estimate the most likely atrophy sequence(s) from baseline anatomical MRI. The Scale for the Assessment and Rating of Ataxia (SARA) was used to capture ataxia severity. Atrophy stages were analysed in relation to SARA and time from disease onset. Interventional trials were simulated to estimate required sample sizes under different atrophy stage eligibility criteria.We identified a uniform sequence of brain atrophy in SCA3, characterised by earliest volumetric decline in the caudal brainstem and substantial involvement of the white matter. Atrophy stage was associated with both SARA and time from disease onset. Atrophy staging outperformed single-region volumetrics in predicting SARA over time. Applying atrophy stage cut-offs substantially reduced the sample sizes needed to adequately power hypothetical clinical trials.These findings yield mechanistic insights into the progression of neurodegeneration in SCA3 and possess immediate translational relevance, facilitating patient stratification and sample enrichment for interventional trials.National Ataxia Foundation (NAF).
Keyword(s): Humans (MeSH) ; Atrophy (MeSH) ; Machado-Joseph Disease: pathology (MeSH) ; Machado-Joseph Disease: diagnosis (MeSH) ; Machado-Joseph Disease: genetics (MeSH) ; Machado-Joseph Disease: diagnostic imaging (MeSH) ; Prognosis (MeSH) ; Male (MeSH) ; Magnetic Resonance Imaging (MeSH) ; Brain: pathology (MeSH) ; Brain: diagnostic imaging (MeSH) ; Female (MeSH) ; Middle Aged (MeSH) ; Adult (MeSH) ; Disease Progression (MeSH) ; Severity of Illness Index (MeSH) ; Aged (MeSH) ; Ataxia ; Disease progression modelling ; Imaging biomarker ; Machine learning ; Movement disorders
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