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一种口腔微生物组模型用于预测动脉样硬化心血管疾病
Qiuli Sui1, Jie Yu1, Shuping Cui2
1Department of Stomatology, People's Hospital of Zhengzhou University & Henan Provincial People's Hospital, Zhengzhou, China.
Frontiers in cellular and infection microbiology
|February 11, 2026
概括
将口腔微生物组数据与临床因素相结合,显著改善了动脉样硬化心血管疾病 (ASCVD) 风险预测. 这种新的方法为预防心血管疾病提供了一个新的生物标志物策略.
科学领域:
- 心血管研究研究心血管研究
- 微生物组分析
- 机器学习在医学中的应用
背景情况:
- 动脉样硬化心血管疾病 (ASCVD) 构成了全球重大健康负担.
- 早期检测和风险分层对于有效的初级预防至关重要.
- 传统的风险因素可能无法完全捕捉一个人的ASCVD风险概况.
研究的目的:
- 为早期ASCVD发病开发一个预测模型.
- 将口腔微生物组数据与已确定的临床风险因素整合起来.
- 提高ASCVD风险预测模型的准确性.
主要方法:
- 对331名年龄在50至70岁之间的参与者进行了回顾性研究,没有先前的ASCVD.
- 使用16S rRNA/元基因组测序进行口腔微生物组的表征.
- 开发和验证机器学习模型 (随机森林,梯度提升,K-最近邻居),整合临床和微生物组数据.
主要成果:
- 多变量分析确定了与ASCVD风险相关的特定口腔细菌 (例如,Fusobacterium nucleatum,Prevotella).
- 随机森林模型显示了最高的预测性能 (AUC培训:0.888,验证:0.845).
- 与单独的临床数据相比,综合模型显著改善了风险重新分类 (NRI=0.315,IDI=0.227).
结论:
- 将口腔微生物组数据与临床因素相结合,可以提高ASCVD风险预测的准确性.
- 口腔微生物群签名代表了对初级心血管预防的有前途的新生物标志物.
- 这种综合方法为识别ASCVD高风险个体提供了潜在的新策略.
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