Risk factors and predictive models in the progression from MCI to Alzheimer's disease.

IF 2.9 3区 医学 Q2 NEUROSCIENCES Neuroscience Pub Date : 2025-01-26 Epub Date: 2024-12-05 DOI:10.1016/j.neuroscience.2024.11.056
Chang Li, Shike Wang, Yuwei Xia, Feng Shi, Lin Tang, Qingning Yang, Junbang Feng, Chuanming Li
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Abstract

Background: The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy.

Methods: 162 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database. 68 patients progressed to AD. 94 patients did not convert to AD. We captured standard T1-weighted images, processed them for feature extraction, and selected relevant features using mRMR and LASSO to calculate cortical and nuclear scores. The computational causal structure discovery and regression analyses were adopted to analyze the intricate relationships among APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores. The individualized prediction nomogram was constructed.

Results: Our results indicated that APOE ε4 alleles was the promoter that caused MCI to transform into AD. Three independent pathways were identified, including P-tau, Aβ1-42, and cortical atrophy. P-tau was the cause of nuclear atrophy. The APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores all had good predictive value for the MCI conversion. The predictive accuracy of the combined model was the highest, with an AUC of 0.918 in the training cohort and 0.908 in the testing cohort. A multi-predictor nomogram was established.

Conclusion: Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.

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MCI发展为阿尔茨海默病的危险因素和预测模型
背景:轻度认知障碍(MCI)向阿尔茨海默病(AD)的转化与多种因素有关。这些因素之间的因果关系尚不清楚。本研究旨在运用因果分析的方法探讨其发展路径,并建立一个准确度较高的预测模型。方法:从阿尔茨海默病神经影像学倡议数据库中招募162名MCI患者。68例进展为AD。94例患者未转化为AD。我们捕获标准t1加权图像,对其进行特征提取,并使用mRMR和LASSO选择相关特征来计算皮质和核评分。采用计算因果结构发现和回归分析方法分析APOE ε4等位基因与P-tau、a - β1-42、皮质和核评分之间的复杂关系。构造了个性化预测图。结果:APOE ε4等位基因是MCI转化为AD的启动子。确定了三个独立的通路,包括P-tau, a - β1-42和皮质萎缩。P-tau是核萎缩的原因。APOE ε4等位基因、P-tau、a - β1-42、皮质和核评分对MCI转换均有较好的预测价值。联合模型的预测准确率最高,训练组的AUC为0.918,测试组的AUC为0.908。建立了多预测函数图。结论:我们的研究阐明了MCI向AD转化的启动因素和三个独立的途径。明确了各因子的预测值,并建立了准确度较高的多因子拟合图。
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来源期刊
Neuroscience
Neuroscience 医学-神经科学
CiteScore
6.20
自引率
0.00%
发文量
394
审稿时长
52 days
期刊介绍: Neuroscience publishes papers describing the results of original research on any aspect of the scientific study of the nervous system. Any paper, however short, will be considered for publication provided that it reports significant, new and carefully confirmed findings with full experimental details.
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