Lin Yu-Cheng, Wu Chi-Chien, Li Yun-Er, Chen Chun-Liang, Lin Chia-Ray, Ni Yen-Hsuan
Department of Pediatrics, Taipei Veterans General Hospital, Taipei City, Taiwan.
Department of Healthcare Administration, Asia Eastern University of Science and Technology, New Taipei City, Taiwan.
BMC Microbiol. 2025 Mar 17;25(1):146. doi: 10.1186/s12866-025-03849-0.
The gut microbiota plays a crucial role in metabolic dysfunction-associated steatotic liver disease (MASLD). Next-generation sequencing technologies are essential for exploring the gut microbiome. While recent advancements in full-length 16S (FL16S) rRNA sequencing offer better taxonomic resolution, whether they establish stronger associations with the risk of MASLD remains to be determined.
This study utilized long-read FL16S and short-read V3-V4 16S rRNA sequencing to profile gut microbiome compositions in age-, sex-, and BMI-matched case-control pairs of obese children with and without MASLD. A random forest predictive model was employed, using gut-microbiota features selected based on the top 35 most abundant taxa or a linear discriminant analysis score greater than 3. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve (AUC) through a tenfold cross-validation method.
Subjects with MASLD exhibited significantly elevated serum alanine aminotransferase, triglycerides, and homeostasis model assessment of insulin resistance levels compared to controls. At the genus level, the gut microbiome compositions detected by both FL16S and V3-V4 sequencing were similar, predominantly comprising Phocaeicola and Bacteroides, followed by Prevotella, Bifidobacterium, Parabacteroides, and Blautia. The AUC for the model based on FL16S sequencing data (86.98%) was significantly higher than that based on V3-V4 sequencing data (70.27%), as determined by DeLong's test (p = 0.008).
FL16S rRNA sequencing data demonstrates stronger associations with the risk of MASLD in obese children, highlighting its potential for real-world clinical applications.
肠道微生物群在代谢功能障碍相关脂肪性肝病（MASLD）中起关键作用。下一代测序技术对于探索肠道微生物组至关重要。虽然全长16S（FL16S）rRNA测序的最新进展提供了更好的分类分辨率，但它们是否与MASLD风险建立更强的关联仍有待确定。
本研究利用长读长FL16S和短读长V3-V4 16S rRNA测序对年龄、性别和体重指数匹配的患有和未患有MASLD的肥胖儿童病例对照对的肠道微生物组组成进行分析。采用随机森林预测模型，使用基于35个最丰富的分类群或线性判别分析得分大于3选择的肠道微生物群特征。通过十倍交叉验证方法比较受试者工作特征曲线下面积（AUC）来评估模型的性能。
与对照组相比，患有MASLD的受试者血清丙氨酸转氨酶、甘油三酯和胰岛素抵抗稳态模型评估水平显著升高。在属水平上，FL16S和V3-V4测序检测到的肠道微生物组组成相似，主要包括嗜海豹杆菌属和拟杆菌属，其次是普雷沃菌属、双歧杆菌属、副拟杆菌属和布劳特氏菌属。根据德龙检验，基于FL16S测序数据的模型AUC（86.98%）显著高于基于V3-V4测序数据的模型AUC（70.27%）（p = 0.008）。
FL16S rRNA测序数据表明其与肥胖儿童MASLD风险的关联更强，突出了其在实际临床应用中的潜力。
