便微生物标记面板用于帮助诊断自闭症谱系障碍
Yating Wan1,2,3, Oscar W H Wong3,4, Hein M Tun1,5,6
1Microbiota I-Center (MagIC), Hong Kong SAR, China.
Gut microbes
|October 29, 2024
概括
肠道微生物组分析可以帮助检测自闭症谱系障碍 (ASD). 使用细菌种群和微生物基因组的综合方法在识别儿童的自闭症方面取得了很高的准确性,与症状严重程度相关.
科学领域:
- 微生物组研究的研究.
- 神经科学是一个神经科学.
- 计算生物学是一种计算生物学.
背景情况:
- 肠道微生物群的改变越来越多地与大脑功能和神经系统疾病有关.
- 便微生物群的特征显示出潜在的自闭症谱系障碍 (ASD) 诊断生物标志物.
- 之前的研究还没有充分探讨结合微生物分类学和基因组学标记物用于ASD检测的实用性.
研究的目的:
- 评估微生物标记物 (分类学和基因组) 在区分患有自闭症儿童与典型发育的同龄人的有效性.
- 评估机器学习模型的性能,将这些标记物整合到ASD检测中.
- 调查已识别的微生物组生物标志物,ASD风险得分和ASD核心症状之间的相关性.
主要方法:
- 分析了598名儿童的便微生物群组成和基因组级数据 (273名患有自闭症,325名典型发展).
- 开发和测试一种机器学习模型,使用细菌种类和元基因组组合基因组 (MAG) 的组合.
- 使用接收运营商曲线下的面积 (AUROC) 验证模型的性能,并与社会响应度-2 (SRS-2) 评分进行相关性.
主要成果:
- 综合标记方法 (taxa + MAGs) 在ASD检测方面表现优于单个标记类型.
- 机器学习模型在发现队列中实现了0.886的AUROC,在验证队列中达到0.734.
- 生物标志物和ASD风险得分与ASD症状严重程度 (SRS-2) 有显著的相关性,在年幼的儿童中表现更好.
结论:
- 便微生物组分析,特别是使用组合的分类学和基因组标记,显示出有助于ASD诊断的巨大潜力.
- 开发的机器学习模型在不同人群中证明了临床有效性,无论伴随疾病或胃肠道症状如何.
- 这项研究为了解肠道微生物群在ASD中的作用及其症状严重程度提供了基础.
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