血浆淀粉样蛋白-β寡聚化倾向是轻度认知障碍转化为阿尔茨海默氏痴呆症的潜在预测因素:来自GMCII队列的研究结果

IF 4.4 Q1 CLINICAL NEUROLOGY Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2025-02-24 eCollection Date: 2025-01-01 DOI:10.1002/dad2.70064
Yuhan Xie, Xue Meng, Tao Li, Haifeng Zhang, Yaonan Zheng, SangYun Kim, Chen Zhang, Xin Yu, Huali Wang
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引用次数: 0

摘要

本研究旨在探讨血浆淀粉样蛋白-β寡聚化倾向(OAβ)与阿尔茨海默病(AD)患者认知表现的关系,并确定其对轻度认知障碍(MCI)预后的预测价值。方法:采用多时间点检测系统(MDS)测定血浆OAβ,对727例患者(AD 286例,MCI 260例,对照181例)的血浆进行分析。结果:血浆OAβ升高与MCI和AD患者的多领域认知表现密切相关。基线血浆OAβ较高的MCI患者进展为痴呆的风险较高(风险比= 1.083,95%可信区间[CI] 1.032-1.137)。基线血浆OAβ可有效预测mci -痴呆转化(曲线下面积[AUC] = 0.824, 95% CI 0.752-0.897)。讨论:现实世界的研究结果强调了血浆OAβ作为轻度认知障碍(MCI)向痴呆转化的潜在预测因子的临床相关性。重点:我们招募了阿尔茨海默氏痴呆(AD)、轻度认知障碍(MCI)和认知正常对照的研究参与者。我们使用多时间检测系统(MDS)来测量血浆淀粉样蛋白-β寡聚化倾向(OAβ)。我们观察到血浆OAβ升高与MCI和AD患者的多领域认知表现密切相关。具有高基线血浆OAβ的MCI个体表现出更高的发展为痴呆的风险。现实世界的研究结果强调了血浆Oaβ作为MCI向痴呆转化的潜在预测因子的临床相关性。
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Plasma amyloid-β oligomerization tendency as a potential predictor for conversion from mild cognitive impairment to Alzheimer's dementia: Findings from the GMCII cohort.

Introduction: This study aimed to explore the association between plasma amyloid-β oligomerization tendency (OAβ) and cognitive performance in Alzheimer's disease (AD) and determine its predictive value for outcomes of mild cognitive impairment (MCI).

Methods: Plasma from 727 subjects (286 AD, 260 MCI, and 181 controls) in a case registry was analyzed using the multimer detection system (MDS) to measure plasma OAβ.

Results: Elevated plasma OAβ was strongly correlated with multidomain cognitive performance in patients with MCI and AD. Patients with MCI with high baseline plasma OAβ demonstrated a higher risk of progressing to dementia (hazard ratio = 1.083, 95% confidence interval [CI] 1.032-1.137). Baseline plasma OAβ effectively predicted MCI-dementia conversion (area under the curve [AUC] = 0.824, 95% CI 0.752-0.897).

Discussion: The real-world findings underscore the clinical relevance of plasma OAβ as a potential predictor for the conversion from mild cognitive impairment (MCI) to dementia.

Highlights: We recruit study participants of Alzheimer's dementia (AD), mild cognitive impairment (MCI), and cognitively normal controls in a case registry.We use the multimer detection system (MDS) to measure plasma amyloid-β oligomerization tendency (OAβ).We observe that elevated plasma OAβ strongly correlates with multidomain cognitive performance in patients with MCI and AD.MCI individuals with high baseline plasma OAβ demonstrate a higher risk of progressing to dementia.The real-world findings underscore the clinical relevance of plasma Oaβ as a potential predictor for the conversion from MCI to dementia.

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来源期刊
CiteScore
7.80
自引率
7.50%
发文量
101
审稿时长
8 weeks
期刊介绍: Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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