Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

IF 5.4 3区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Ying Zhu, Shi-Yu Geng, Yao Chen, Qing-Jing Ru, Yi Zheng, Na Jiang, Fei-Ye Zhu, Yong-Sheng Zhang
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引用次数: 0

Abstract

Background: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.

Aim: To explore the complex relationship between chronic hepatitis B (CHB)-related HF and gut microbiota to identify microbiota signatures significantly associated with HF progression in CHB patients using advanced machine learning algorithms.

Methods: This study included patients diagnosed with CHB and classified them into HF and non-HF groups based on liver stiffness measurements. The HF group was further subdivided into four subgroups: F1, F2, F3, and F4. Data on clinical indicators were collected. Stool samples were collected for 16S rRNA sequencing to assess the gut microbiome. Microbiota diversity, relative abundance, and linear discriminant analysis effect size (LEfSe) were analyzed in different groups. Correlation analysis between clinical indicators and the relative abundance of gut microbiota was performed. The random forest and eXtreme gradient boosting algorithms were used to identify key differential gut microbiota. The Shapley additive explanations were used to evaluate microbiota importance.

Results: Integrating the results from univariate analysis, LEfSe, and machine learning, we identified that the presence of Dorea in gut microbiota may be a key feature associated with CHB-related HF. Dorea possibly serves as a core differential feature of the gut microbiota that distinguishes HF from non-HF patients, and the presence of Dorea shows significant variations across different stages of HF (P < 0.05). The relative abundance of Dorea significantly decreases with increasing HF severity (P = 0.041). Moreover, the gut microbiota composition in patients with different stages of HF was found to correlate with several liver function indicators, such as γ-glutamyl transferase, alkaline phosphatase, total bilirubin, and the aspartate aminotransferase/alanine transaminase ratio (P < 0.05). The associated pathways were predominantly enriched in biosynthesis, degradation/utilization/assimilation, generation of precursors, metabolites, and energy, among other categories.

Conclusion: HF affects the composition of the gut microbiota, indicating that the gut microbiota plays a crucial role in its pathophysiological processes. The abundance of Dorea varies significantly across various stages of HF, making it a potential microbial marker for identifying HF onset and progression.

机器学习算法揭示与慢性乙型肝炎相关肝纤维化相关的肠道微生物群特征。
背景:肝纤维化(HF)是肝硬化进展和潜在逆转的关键阶段,强调了早期识别和治疗干预对调节疾病轨迹的重要性。目的:利用先进的机器学习算法探索慢性乙型肝炎(CHB)相关HF与肠道微生物群之间的复杂关系,以识别与CHB患者HF进展显著相关的微生物群特征。方法:本研究纳入诊断为CHB的患者,并根据肝脏硬度测量将其分为HF组和非HF组。HF组进一步细分为F1、F2、F3、F4四个亚组。收集临床指标数据。收集粪便样本进行16S rRNA测序以评估肠道微生物组。分析各组微生物群多样性、相对丰度和线性判别分析效应量(LEfSe)。对临床指标与肠道菌群相对丰度进行相关性分析。随机森林和极端梯度增强算法用于识别关键的差异肠道微生物群。沙普利加性解释用于评价微生物群的重要性。结果:综合单变量分析、LEfSe和机器学习的结果,我们确定肠道微生物群中Dorea的存在可能是与chb相关的HF相关的关键特征。Dorea可能是区分HF与非HF患者肠道微生物群的核心差异特征,并且Dorea的存在在HF的不同阶段表现出显著差异(P < 0.05)。随着HF严重程度的增加,Dorea的相对丰度显著降低(P = 0.041)。不同阶段HF患者肠道菌群组成与γ-谷氨酰转移酶、碱性磷酸酶、总胆红素、天冬氨酸转氨酶/丙氨酸转氨酶比值等肝功能指标相关(P < 0.05)。相关途径主要富集于生物合成、降解/利用/同化、前体生成、代谢物和能量等方面。结论:HF影响肠道菌群组成,表明肠道菌群在其病理生理过程中起着至关重要的作用。在HF的不同阶段,Dorea的丰度差异显著,使其成为鉴定HF发病和进展的潜在微生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
World Journal of Gastroenterology
World Journal of Gastroenterology 医学-胃肠肝病学
CiteScore
7.80
自引率
4.70%
发文量
464
审稿时长
2.4 months
期刊介绍: The primary aims of the WJG are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in gastroenterology and hepatology.
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