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在使用机器学习的不同研究中,针对自闭症谱系障碍的强有力的微生物群签名
Lucia N Peralta-Marzal1, David Rojas-Velazquez1,2, Douwe Rigters1
1Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.
Scientific reports
|January 8, 2024
概括
肠道微生物组可能在自闭症谱系障碍 (ASD) 中发挥作用. 研究人员确定了可以预测儿童ASD状态的特定细菌种群,提供了潜在的治疗点.
科学领域:
- 微生物组研究的研究.
- 神经发育障碍 神经发育障碍
- 基因组学和生物信息学
背景情况:
- 自闭症谱系障碍 (ASD) 是一种复杂的神经发育状况,具有多种并发症.
- 患有自闭症的人经常出现胃肠道问题,并表现出明显的肠道微生物组成.
- 在ASD中现有的肠道微生物组研究缺乏有关特定细菌种群的共识.
研究的目的:
- 使用兄弟控制数据集识别与ASD分类相关的核心细菌种群.
- 为了在独立的队列中验证这些发现,考虑诸如生活方式等混因素.
- 探索机器学习对基于微生物的ASD预测的潜力.
主要方法:
- 应用递归组合特征选择 (REFS) 到16S rRNA基因测序数据.
- 分析了主要队列中117名受试者的数据 (60名自闭症病例,57名兄弟姐妹).
- 在两个独立的队列中使用十倍交叉验证验证的验证结果 (共223个样本).
主要成果:
- 确定了26种细菌种群,有效地将ASD病例与兄弟控制数据集中的对照区分开来.
- 在初级队列中,ASD预测的平均曲线下面积 (AUC) 为81.6%.
- 在两个独立验证队列中获得的平均AUC为74.8%和74%.
结论:
- 肠道微生物组与ASD有着强烈的关联,这表明它是潜在的治疗点.
- 已识别的细菌种群可以预测多个队列儿童的ASD状态.
- REFS方法可以用于识别其他16S rRNA基因测序数据集中的微生物群签名.
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