Qian Yuqin, Tang Xinlu, Shen Ruinan, Lu Yong, Ding Jianqing, Qian Xiaohua, Zhang Chencheng
Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Can J Psychiatry. 2024 Dec;69(12):869-879. doi: 10.1177/07067437241300947. Epub 2024 Nov 25.
Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, DNAm is underutilized as a diagnostic biomarker for these diseases. We aimed to evaluate the diagnostic performance of DNAm for MCI and AD, both individually and in combination with well-established AD biosignatures.
A total of 1,891 blood samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) studies were used to identify potential candidate DNAm biomarkers. Multimodal clinical data from 635 samples (normal control (NC), = 193; MCI, = 352; AD, = 90) in the TADPOLE dataset were utilized to construct eight different classification models using a graph convolutional network, a machine learning framework.
After feature selection, 17 DNAm sites were selected for subsequent analysis. Remarkable differences in DNAm levels were observed at the screened DNAm loci in all three cohorts. Adopting DNAm features into multimodal models significantly improved the classification performance for three dichotomous subtasks (NC vs. non-NC, MCI vs. non-MCI, and AD vs. non-AD), especially when combined with cerebrospinal fluid (CSF) features for NC (area under the curve (AUC): 0.8534) and MCI classification (AUC: 0.7675). A weak correlation between DNAm and both magnetic resonance imaging and CSF features in the NC and MCI cohorts suggests good complementarity between modalities (correlation coefficient ≤0.2).
Our study offers new insights into peripheral DNAm in MCI and AD and suggests promising diagnostic performance of models integrating epigenomics, imaging, or CSF biomarkers.
Using Machine Learning and Blood-Based Genetic Markers to Help Diagnose Mild Cognitive Impairment and Alzheimer's Disease.
血液DNA甲基化（DNAm）改变在轻度认知障碍（MCI）和阿尔茨海默病（AD）的发病及进展过程中已有广泛报道；然而，DNAm作为这些疾病的诊断生物标志物尚未得到充分利用。我们旨在评估DNAm对MCI和AD的诊断性能，包括单独评估以及与已确立的AD生物标志物联合评估。
来自阿尔茨海默病神经影像倡议（ADNI）研究的总共1891份血液样本用于识别潜在的候选DNAm生物标志物。利用TADPOLE数据集中635份样本（正常对照（NC），n = 193；MCI，n = 352；AD，n = 90）的多模态临床数据，使用图卷积网络（一种机器学习框架）构建八个不同的分类模型。
经过特征选择，选择了17个DNAm位点用于后续分析。在所有三个队列的筛选DNAm位点处观察到DNAm水平存在显著差异。将DNAm特征纳入多模态模型显著提高了三个二分法子任务（NC与非NC、MCI与非MCI、AD与非AD）的分类性能，特别是与脑脊液（CSF）特征联合用于NC分类（曲线下面积（AUC）：0.8534）和MCI分类（AUC：0.7675）时。NC和MCI队列中DNAm与磁共振成像及CSF特征之间的弱相关性表明各模态之间具有良好的互补性（相关系数≤0.2）。
我们的研究为MCI和AD中的外周DNAm提供了新见解，并表明整合表观基因组学、影像学或CSF生物标志物的模型具有良好的诊断性能。
使用机器学习和基于血液的遗传标记物辅助诊断轻度认知障碍和阿尔茨海默病。
