Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China.
Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China.
BMC Med. 2024 Mar 7;22(1):104. doi: 10.1186/s12916-024-03317-y.
The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD.
The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites.
Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD.
We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.
与非酒精性脂肪性肝病（NAFLD）相关的特定微生物群和相关代谢物仍存在争议。因此，我们旨在了解核心肠道微生物群和代谢物如何影响 NAFLD。
发现队列的数据来自于 2014 年至 2018 年期间进行的广州营养与健康研究（GNHS）随访。我们从 1546 名个体中收集了 272 个元数据点。将这些元数据输入到四个可解释的机器学习模型中，以识别与 NAFLD 相关的重要肠道微生物群。这些模型随后应用于两个验证队列[内部验证队列（n=377）和前瞻性验证队列（n=749）]，以评估其泛化能力。我们基于鉴定出的肠道微生物群构建了个体微生物组风险评分（MRS），并使用来自不同 MRS 水平的个体的粪便样本进行了动物粪便微生物组移植实验，以确定 MRS 与 NAFLD 之间的关系。此外，我们对粪便样本进行了靶向代谢组学测序，以分析潜在的代谢物。
在所使用的四个机器学习模型中，lightGBM 算法的表现最佳。lightGBM 算法共选择了 12 个与微生物群相关的特征，进一步用于计算 MRS。MRS 每增加 1 个单位，NAFLD 的存在呈正相关，比值比（OR）为 1.86（1.72，2.02）。粪便微生物群（f__veillonellaceae）丰度的增加与 NAFLD 风险增加相关，而 f__rikenellaceae、f__barnesiellaceae 和 s__adolescentis 与 NAFLD 的存在减少相关。特定肠道微生物群衍生胆汁酸（牛磺胆酸）代谢物水平的升高可能与较高的 MRS 和 NAFLD 风险呈正相关。小鼠中的 FMT 进一步证实了较高的 MRS 与 NAFLD 发展之间存在因果关系。
我们证实，核心肠道微生物群组成的改变可能与 NAFLD 的发生具有生物学相关性。我们的工作证明了微生物群在 NAFLD 发生发展中的作用。
