基于淋巴细胞子组和休息状态功能性MRI的重大抑郁障碍与并发性失眠多变量诊断模型的开发和验证
Guangyuan Xia1, Yongxue Hu1, Jingyu Shi2
1Department of Psychiatry, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou province, China.
Depression and anxiety
|December 9, 2025
概括
重度抑郁症 (MDD) 和失眠症 (ID) 患者具有并发性疾病显示出不同的大脑活动模式和改变的免疫细胞概况. 这些发现可能有助于针对MDD和ID同时发生的个体制定个性化治疗策略.
科学领域:
- 神经科学是一个神经科学.
- 免疫学 免疫学 免疫学
- 精神病学是一个精神病学.
背景情况:
- 伴随性重度抑郁症 (MDD) 和失眠症 (ID) 是一个重大的临床挑战.
- 了解底层的神经生物学和免疫机制对于有效治疗至关重要.
研究的目的:
- 为了研究淋巴细胞子集和休息状态功能磁共振成像 (rs-fMRI) 模式之间的关系,在患有并发性MDD和ID的患者中.
- 确定潜在的生物标志物来诊断和管理这些疾病.
主要方法:
- 招募了114名MDD,108名ID,126名并发性MDD和ID (iMDD),以及168名健康对照 (HC).
- 通过使用标准化尺度 (HAMD-17,SDS,HAMA,SAS,PSQI,ISI) 评估情绪和睡眠质量.
- 分析了rs-fMRI数据 (fALFF) 和淋巴细胞子集 (CD3 +,CD4 +,NK细胞);使用LASSO和逻辑回归构建了多变量预测模型.
主要成果:
- 与其他组相比,iMDD组在右舌头和状回旋中显示出减少的fALFF.
- 观察到CD3+和CD4+T细胞的增加,NK细胞的百分比降低,在iMDD组中最明显.
- fALFF值与淋巴细胞百分比相关;fALFF,CD4+ T,NK细胞百分比,SDS和ISI得分是关键风险预测指标.
结论:
- 已识别的风险预测因素可能有助于IMDD患者个性化临床决策.
- 多变量预测模型显示了MDD,ID和iMDD的高诊断准确性.
- 建议进行外部验证,以确认发现的概括性.
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