Authors
Kaining Chen, Xin Wan, Wenli Guo, Hongyan Yu, Yuantao Tong, Huiwen Ke, Junjie Fang, Xilian Luo, Di Che, Chao Hou, Xu Mei, Bingxue Hu, Yuehong Liu, Xiaoxiao Yu, Daoman Xiang, Wensi Chen, Yingjia Zeng, Yufen Xu, Jun Shen, Qiuxia Chen, Lei Pi, Lanyan Fu, Huazhong Zhou, Bingshao Liang, Meng Zhang, Yifan Dong, Yanqiu Chen, Wenhao Zhou, Xiaoqiong Gu
Published in
iMetaOmics. Volume 3. Issue 2. Pages e70101. Epub May 10, 2026.
Abstract
This study characterizes the upper respiratory microbiome in 276 children (101 Adenoid hypertrophy (AH) 119 Adenotonsillar hypertrophy (ATH), 11 Tonsil hypertrophy (TH), and 45 healthy controls by analyzing 1149 samples across five distinct niches: nasopharyngeal swabs (NS), adenoid swab (AS), and tonsil swabs (TS), plus adenoid tissues (AT) and tonsil tissues (TT). Utilizing a workflow of 16S rRNA sequencing, qPCR, and metagenomics, we identified significant spatial heterogeneity and niche-specific dominance, such as Staphylococcus in the nasopharynx and Streptococcus on mucosal surfaces. AT exhibited a transitional microbiome with intermediate composition and unique Ralstonia enrichment. Machine learning models demonstrate that AT microbial profiles (area under curve, AUC = 0.752) offer superior predictive power for AH severity compared to surface swabs. SHAP analysis identified key contributing genera, such as Roseburia and Flavobacterium, as primary drivers of these predictive outcomes. These findings highlight how tissue-infiltrating microbial dynamics drive chronic inflammation and pediatric hypertrophy.
PMID:
42891316
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.
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