Geng Mingyan, Li Min, Li Yun, Zhu Jiaying, Sun Chuqing, Wang Yan, Chen Wei-Hua
Institution of Medical Artificial Intelligence Binzhou Medical University Yantai China.
The Second School of Clinical Medicine Binzhou Medical University Yantai China.
Imeta. 2024 Jun 12;3(4):e212. doi: 10.1002/imt2.212. eCollection 2024 Aug.
We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.
我们分析了来自五个国家和三大洲的八个口腔微生物群鸟枪法宏基因组测序队列,识别出54种物种生物标志物和26种代谢生物标志物,这些标志物在三个或更多队列的健康和疾病状态中持续发生改变。此外,基于分类学概况的机器学习模型在区分牙周炎患者和对照组方面具有很高的准确性(受试者工作特征曲线的内部和外部曲线下面积分别为0.86和0.85)。这些结果支持基于宏基因组的牙周炎诊断,并为进一步研究和有效的治疗策略提供了基础。