Abeyasinghe Pubu M, Cole James H, Razi Adeel, Poudel Govinda R, Paulsen Jane S, Tabrizi Sarah J, Long Jeffrey D, Georgiou-Karistianis Nellie
School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Mov Disord. 2025 Apr;40(4):627-641. doi: 10.1002/mds.30109. Epub 2025 Jan 28.
Despite advancements in understanding Huntington's disease (HD) over the past two decades, absence of disease-modifying treatments remains a challenge. Accurately characterizing progression states is crucial for developing effective therapeutic interventions. Various factors contribute to this challenge, including the need for precise methods that can account for the complex nature of HD progression.
This study aims to address this gap by leveraging the concept of the brain's biological age as a foundation for a data-driven clustering method to delineate various states of progression. Brain-predicted age, influenced by somatic expansion and its impact on brain volumes, offers a promising avenue for stratification by stratifying subgroups and determining the optimal timing for interventions.
To achieve this, data from 953 participants across diverse cohorts, including PREDICT-HD, TRACK-HD, and IMAGE-HD, were meticulously analyzed. Brain-predicted age was computed using sophisticated algorithms, and participants were categorized into four groups based on CAG and age product score. Unsupervised k-means clustering with brain-predicted age difference (brain-PAD) was then employed to identify distinct progression states.
The analysis revealed significant disparities in brain-predicted age between HD participants and controls, with these differences becoming more pronounced as the disease progressed. Brain-PAD demonstrated a correlation with disease severity, effectively identifying five distinct progression states characterized by significant longitudinal disparities.
These findings highlight the potential of brain-PAD in capturing HD progression states, thereby enhancing prognostic methodologies and providing valuable insights for future clinical trial designs and interventions. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
尽管在过去二十年中对亨廷顿舞蹈症（HD）的认识有所进步，但缺乏疾病修正疗法仍然是一项挑战。准确描述疾病进展状态对于开发有效的治疗干预措施至关重要。各种因素导致了这一挑战，包括需要精确的方法来解释HD进展的复杂性。
本研究旨在通过利用大脑生物学年龄的概念作为数据驱动聚类方法的基础来划分不同的进展状态，以填补这一空白。受体细胞扩增及其对脑容量影响的大脑预测年龄，通过对亚组进行分层并确定干预的最佳时机，为分层提供了一条有前景的途径。
为实现这一目标，对来自不同队列（包括PREDICT-HD、TRACK-HD和IMAGE-HD）的953名参与者的数据进行了细致分析。使用复杂算法计算大脑预测年龄，并根据CAG和年龄乘积分数将参与者分为四组。然后采用基于大脑预测年龄差异（brain-PAD）的无监督k均值聚类来识别不同的进展状态。
分析显示HD参与者与对照组之间大脑预测年龄存在显著差异，且随着疾病进展这些差异变得更加明显。Brain-PAD与疾病严重程度相关，有效识别出五个不同的进展状态，其特征为显著的纵向差异。
这些发现凸显了brain-PAD在捕捉HD进展状态方面的潜力，从而增强了预后方法，并为未来的临床试验设计和干预提供了有价值的见解。© 2025作者。《运动障碍》由Wiley Periodicals LLC代表国际帕金森和运动障碍协会出版。
