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使用 AD 病理学、脑血管病和神经退行性变的基线指标预测老年人认知能力下降。

Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration.

机构信息

From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.

出版信息

Neurology. 2023 Feb 21;100(8):e834-e845. doi: 10.1212/WNL.0000000000201572. Epub 2022 Nov 10.

Abstract

BACKGROUND AND OBJECTIVES

Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD).

METHODS

Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups.

RESULTS

For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, = 0.002; fully adjusted model: HR 1.98, = 0.003) and lower hippocampal volume (individual: HR 0.54, = 0.001; fully adjusted: HR 0.40, < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, < 0.001; fully adjusted model: HR 0.55, < 0.001) and whole-brain volume (individual: HR 0.31, < 0.001; fully adjusted: HR 0.48, = 0.02), increased CSF ptau (individual: HR 1.88, < 0.001; fully adjusted: HR 1.61, < 0.001), and lower CSF amyloid (individual: HR 0.37, < 0.001; fully adjusted: HR 0.62, = 0.008) were most strongly associated with conversion to AD individually and independently.

DISCUSSION

Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.

摘要

背景与目的

痴呆是一个日益严重的社会经济学挑战,需要早期干预。识别能够可靠预测疾病早期临床进展的生物标志物,将有助于更好地选择未来参与试验的个体。在这里,我们比较了基线、单次时间点生物标志物(CSF 淀粉样蛋白 1-42、CSF ptau-181、脑白质高信号(WMH)、脑微出血、全脑体积和海马体积)在预测后来转化为轻度认知障碍(MCI)的认知正常个体(CNtoMCI)和后来转化为阿尔茨海默病(AD)诊断的 MCI 患者(MCItoAD)方面的能力。

方法

使用 AD 神经影像学倡议 2(ADNI2)/GO 的标准化基线生物标志物数据和纵向诊断数据(包括 ADNI3)。Cox 回归模型使用所有可用的随访时间点评估生物标志物与临床诊断变化之间的关系。模型分别在单变量和多变量模型中对生物标志物进行拟合。风险比(HRs)用于比较评估生物标志物。在 CNtoMCI 和 MCItoAD 组中分别进行分析。

结果

对于 CNtoMCI(n = 189),有强有力的证据表明,较高的 WMH 体积(个体模型:HR 1.79, = 0.002;完全调整模型:HR 1.98, = 0.003)和较低的海马体体积(个体:HR 0.54, = 0.001;完全调整:HR 0.40, < 0.001)单独和独立地与向 MCI 的转化相关。对于 MCItoAD(n = 345),较低的海马体(个体模型:HR 0.45, < 0.001;完全调整模型:HR 0.55, < 0.001)和全脑体积(个体模型:HR 0.31, < 0.001;完全调整模型:HR 0.48, = 0.02)、增加的 CSF ptau(个体模型:HR 1.88, < 0.001;完全调整模型:HR 1.61, < 0.001)和较低的 CSF 淀粉样蛋白(个体模型:HR 0.37, < 0.001;完全调整模型:HR 0.62, = 0.008)与单独和独立向 AD 的转化最密切相关。

讨论

较低的海马体体积是向 MCI 和 AD 临床转化的一致预测因子。CSF 和脑体积生物标志物可预测 MCI 向 AD 的转化,而 WMH 可预测认知正常向 MCI 的转化。WMH 在 CNtoMCI 组中的预测能力可解释为一些患者处于不同的病理途径,例如血管性认知障碍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1409/9984210/fbff3167c838/WNL-2022-201411f1.jpg

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