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利用混合临床和血浆生物标志物测量及机器学习对唐氏综合征中阿尔茨海默病进展进行分期

Staging of Alzheimer's disease progression in Down syndrome using mixed clinical and plasma biomarker measures with machine learning.

作者信息

Idris Mina, Saini Fedal, Ivain Phoebe, Baksh R Asaad, Bianchi Leda A, Pape Sarah E, Zetterberg Henrik, Wijeratne Peter A, Strydom André

机构信息

Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.

Natbrainlab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.

出版信息

Alzheimers Dement. 2025 Jul;21(7):e70446. doi: 10.1002/alz.70446.

Abstract

INTRODUCTION

Adults with Down syndrome (DS) have a high risk for Alzheimer's disease (AD). Although the sequence of plasma biomarker and cognitive changes in AD in DS is well studied, their related trajectories are not fully characterized. Data-driven methods can estimate disease progression from cross-sectional data.

METHODS

In 57 adults with DS and no AD, we used the event-based model to sequence plasma biomarker and cognitive changes in preclinical AD. Generalized additive models assessed the relationship between age and plasma biomarkers.

RESULTS

The earliest changes occurred in the amyloid beta 42/40 ratio, followed by memory changes. Later alterations in neurofilament light and tau concentrations preceded executive and visuomotor function changes, with glial fibrillary acidic protein levels changing last. The highest rate of plasma biomarker changes occurred between ages 39 and 52.

CONCLUSION

Changes in DS follow a pattern similar to that of sporadic and familial AD. Event-based modeling offers individual-level staging, potentially optimizing diagnosis and clinical trial patient selection.

HIGHLIGHTS

The pre-clinical stages of Alzheimer's disease (AD) development in Down syndrome (DS) are not well defined, despite the extremely high prevalence of AD. Better understanding of early AD progression would aid in diagnostics and treatment. Data-driven methods, such as the event-based model, can aid in clarifying the sequence of cognitive and plasma biomarker changes in the early stages of AD while accounting for baseline variability. We find that plasma amyloid beta 42/40 ratio and memory changes precede changes in plasma biomarker levels of neurodegeneration, with changes in executive and visuomotor functions occurring later, before neuroinflammatory marker changes. Combining plasma biomarkers could be a useful measure of preclinical AD for trials, particularly in individuals between 39 and52 years of age.

摘要

引言

成年唐氏综合征(DS)患者患阿尔茨海默病(AD)的风险很高。尽管血浆生物标志物序列以及DS中AD的认知变化已得到充分研究,但其相关轨迹尚未完全明确。数据驱动方法可从横断面数据估计疾病进展。

方法

在57名无AD的成年DS患者中,我们使用基于事件的模型对临床前AD的血浆生物标志物和认知变化进行排序。广义相加模型评估年龄与血浆生物标志物之间的关系。

结果

最早的变化发生在淀粉样β蛋白42/40比值上,随后是记忆变化。神经丝轻链和tau蛋白浓度的后期变化先于执行功能和视觉运动功能变化,而胶质纤维酸性蛋白水平的变化最后出现。血浆生物标志物变化率最高的年龄段在39岁至52岁之间。

结论

DS的变化模式与散发性和家族性AD相似。基于事件的建模提供了个体水平的分期,有可能优化诊断和临床试验患者的选择。

要点

尽管AD在唐氏综合征(DS)中患病率极高,但其阿尔茨海默病(AD)发展的临床前阶段仍未明确界定。更好地了解AD早期进展将有助于诊断和治疗。数据驱动方法,如基于事件的模型,有助于在考虑基线变异性的同时阐明AD早期认知和血浆生物标志物变化的顺序。我们发现血浆淀粉样β蛋白42/40比值和记忆变化先于神经退行性变血浆生物标志物水平的变化,执行功能和视觉运动功能变化随后出现,在神经炎症标志物变化之前。联合检测血浆生物标志物可能是临床试验中临床前AD的有用指标,特别是在39至52岁的个体中。

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