Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation.
Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation.
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DOI：
10.1177/07067437231210787
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发表时间：
2023-11
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影响因子：
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通讯作者：
Tianyu Wang;Chenjie Gao;Jiaxing Li;Lei Li;Y. Yue;Xiaoyun Liu;Suzhen Chen;Z. Hou;Y. Yin;Wenhao Jiang;Zhi Xu;Youyong Kong;Yonggui Yuan
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作者：
Tianyu Wang;Chenjie Gao;Jiaxing Li;Lei Li;Y. Yue;Xiaoyun Liu;Suzhen Chen;Z. Hou;Y. Yin;Wenhao Jiang;Zhi Xu;Youyong Kong;Yonggui Yuan
目的建立基于静息态脑功能活动和P11基因DNA甲基化多维数据的机器学习模型，预测抑郁症患者抗抑郁治疗的早期疗效。方法对98例汉族抑郁症患者的临床资料进行分析。根据治疗2周后汉密尔顿抑郁量表（HAMD-17）减分率是否≥50%将患者分为应答者51例和非应答者47例。在基线时，使用Illumina HiSeq Platform检测外周血样本中P11基因的74个CpG位点的甲基化。静息态功能磁共振成像（rs-fMRI）检测116个脑区的低频波动幅度（ALFF）、区域均匀性（ReHo）和功能连接性（FC）。采用最小绝对收缩选择算子分析方法进行特征约简和特征选择。采用4种典型的机器学习方法，分别基于筛查后脑功能活动、P11基因DNA甲基化和临床/人口统计学特征的不同组合，建立支持向量机（SVM）、随机森林（RF）、朴素贝叶斯（NB）和逻辑回归（LR）预测模型。结果基于ALFF、ReHo、FC、P11甲基化和临床/人口统计学特征的SVM模型表现出最好的性能，预测准确率为95.92%，受试者工作特征曲线下面积为0.9967，优于RF、NB和LR模型。结论结合rs-fMRI、DNA甲基化和临床/人口统计学特征的多维数据特征可以预测MDD的早期抗抑郁疗效。
OBJECTIVE This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). METHODS A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. RESULTS The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. CONCLUSION The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.