脑年龄作为亨廷顿病疾病分层的新指标

IF 7.6 1区 医学 Q1 CLINICAL NEUROLOGY Movement Disorders Pub Date : 2025-01-28 DOI:10.1002/mds.30109
Pubu M. Abeyasinghe PhD, James H. Cole PhD, Adeel Razi PhD, Govinda R. Poudel PhD, Jane S. Paulsen PhD, Sarah J. Tabrizi PhD, Jeffrey D. Long PhD, Nellie Georgiou-Karistianis PhD
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引用次数: 0

摘要

尽管在过去二十年中对亨廷顿舞蹈病(HD)的了解有所进展,但缺乏疾病修饰治疗仍然是一个挑战。准确表征进展状态对于制定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年龄的概念作为数据驱动的聚类方法的基础来描述各种进展状态,从而解决这一差距。受躯体扩张及其对脑容量影响的脑预测年龄,通过分层亚组和确定干预的最佳时机,为分层提供了一条有希望的途径。为了实现这一目标,我们仔细分析了来自不同队列(包括PREDICT‐HD、TRACK‐HD和IMAGE‐HD)的953名参与者的数据。使用复杂的算法计算大脑预测的年龄,并根据CAG和年龄产品评分将参与者分为四组。然后采用无监督k均值聚类与脑预测年龄差异(脑PAD)来识别不同的进展状态。结果分析显示,HD参与者和对照组在大脑预测年龄方面存在显著差异,随着疾病的进展,这些差异变得更加明显。Brain‐PAD显示出与疾病严重程度的相关性,有效地识别出具有显著纵向差异的五种不同进展状态。这些发现强调了脑- 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。Wiley期刊有限责任公司代表国际帕金森和运动障碍学会出版的《运动障碍》。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Brain Age as a New Measure of Disease Stratification in Huntington's Disease

Background

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.

Objective

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.

Methods

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.

Results

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.

Conclusions

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.

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来源期刊
Movement Disorders
Movement Disorders 医学-临床神经学
CiteScore
13.30
自引率
8.10%
发文量
371
审稿时长
12 months
期刊介绍: Movement Disorders publishes a variety of content types including Reviews, Viewpoints, Full Length Articles, Historical Reports, Brief Reports, and Letters. The journal considers original manuscripts on topics related to the diagnosis, therapeutics, pharmacology, biochemistry, physiology, etiology, genetics, and epidemiology of movement disorders. Appropriate topics include Parkinsonism, Chorea, Tremors, Dystonia, Myoclonus, Tics, Tardive Dyskinesia, Spasticity, and Ataxia.
期刊最新文献
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