精神分裂症谱系障碍的年轻成年人肠道微生物群的差异:使用机器学习来区分病例与对照
Miranda Stiernborg1, Stefanie Prast-Nielsen2, Philippe A Melas3
1Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden.
Brain, behavior, and immunity
|January 27, 2024
概括
年轻精神分裂症谱系障碍 (SSD) 患者的肠道微生物组与对照组相比显示出改变的多样性和组成. 机器学习模型可以使用这些肠道微生物组配置文件来区分SSD患者.
科学领域:
- 微生物组研究的研究.
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
背景情况:
- 建议肠道微生物组与精神分裂症谱系障碍 (SSD) 之间存在关联,但不确定.
- 以前的研究表明,SSD患者的肠道微生物组可能发生变化.
研究的目的:
- 分析年轻SSD患者和健康对照的肠道微生物群中的细菌和基因多样性.
- 通过机器学习研究肠道微生物组概况的潜力,以区分SSD患者.
主要方法:
- 在52名年轻SSD患者和52名对照人群中进行了便中枪元基因组测序.
- 分析包括α-多样性,β-多样性和机器学习分类.
- 差异丰度分析确定了关键的微生物和功能特征.
主要成果:
- 年轻的SSD患者在分类学和功能性肠道微生物群数据中表现出明显较低的α-多样性和明显的β-多样性.
- 机器学习模型根据微生物组数据准确分类SSD患者 (准确率≥70%,AUROC≥0.75).
- 在SSD患者中注意到口腔细菌和氨基酸生物合成模块的增加,而丁酸盐和酸盐降解模块较低.
结论:
- 这些发现支持SSD中的肠道微生物组变化,并突出了它们作为生物标志物的潜力.
- 机器学习方法显示出基于肠道微生物群签名识别SSD患者的前景.
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