Mogna-Peláez Paola, Riezu-Boj José I, Milagro Fermin I, Clemente-Larramendi Iñigo, Esteban Echeverría Sergio, Herrero José I, Elorz Mariana, Benito-Boillos Alberto, Tobaruela-Resola Ana Luz, González-Muniesa Pedro, Tur Josep A, Martínez J Alfredo, Abete Itziar, Zulet M Angeles
Department of Nutrition, Food Sciences and Physiology and Centre for Nutrition Research, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain.
Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain.
Nutrients. 2024 Dec 4;16(23):4198. doi: 10.3390/nu16234198.
: This study investigates the gut microbiota's role in metabolic dysfunction-associated steatotic liver disease (MASLD), focusing on microbial and functional signatures and sex-based differences. : Using baseline data from 98 MASLD patients and 45 controls from the Fatty Liver in Obesity (FLiO) study, the gut microbiota was profiled with 16S gene sequencing, followed by statistical and machine learning analyses to identify disease-associated microbial signatures. : Notable alpha and beta diversity differences were observed between MASLD patients and the controls, varying by sex. Machine learning models highlighted specific microbial signatures for each sex, achieving high accuracy (area under the receiver operating characteristic curves of 0.91 for women and 0.72 for men). The key microbial taxa linked to MASLD included and in women and and in men. Functional profiling showed that MASLD patients had increased pathways for amine biosynthesis and amino acid degradation, while the controls exhibited enhanced fermentation pathways. These microbial features were associated with systemic inflammation, insulin resistance, and metabolite production linked to gut dysbiosis. : The findings support the potential of gut microbiota signatures to be used as non-invasive indicators of MASLD and highlight sex-specific variations that could inform personalized diagnostic and therapeutic approaches.
本研究调查了肠道微生物群在代谢功能障碍相关脂肪性肝病（MASLD）中的作用，重点关注微生物和功能特征以及基于性别的差异。利用肥胖脂肪肝（FLiO）研究中98例MASLD患者和45例对照的基线数据，通过16S基因测序对肠道微生物群进行分析，随后进行统计和机器学习分析以确定与疾病相关的微生物特征。在MASLD患者和对照之间观察到显著的α和β多样性差异，且因性别而异。机器学习模型突出了每种性别的特定微生物特征，准确率较高（女性受试者工作特征曲线下面积为0.91，男性为0.72）。与MASLD相关的关键微生物分类群在女性中包括[具体微生物分类群1]和[具体微生物分类群2]，在男性中包括[具体微生物分类群3]和[具体微生物分类群4]。功能分析表明，MASLD患者的胺生物合成和氨基酸降解途径增加，而对照组的发酵途径增强。这些微生物特征与全身炎症、胰岛素抵抗以及与肠道生态失调相关的代谢物产生有关。研究结果支持肠道微生物群特征作为MASLD非侵入性指标的潜力，并突出了可能为个性化诊断和治疗方法提供依据的性别特异性差异。
