微生物特征识别出主要归因于NAFLD的慢性肝病患者的晚期纤维

StephenW

Sci Rep. 2020 Feb 17;10(1):2771. doi: 10.1038/s41598-020-59535-w.
A Microbial Signature Identifies Advanced Fibrosis in Patients with Chronic Liver Disease Mainly Due to NAFLD.
Dong TS1, Katzka W1,2, Lagishetty V1,2, Luu K1,2, Hauer M1,2, Pisegna J3, Jacobs JP4,5,6.
Author information

1
    The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
2
    UCLA Microbiome Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
3
    Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
4
    The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. [email protected].
5
    UCLA Microbiome Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. [email protected].
6
    Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA. [email protected].

Abstract

The presence of advanced fibrosis is an important measure of the severity of chronic liver disease. Prior works that have examined the gut microbiome as a novel biomarker for advanced fibrosis have only examined patients with nonalcoholic fatty liver disease. Therefore, our goal was to examine the gut microbiome across varying etiologies of liver disease to create a predictive model for liver fibrosis based upon a microbial signature. Stool samples were obtained from patients with chronic liver disease (n = 50) undergoing FibroScan (ultrasound elastography) at the VA Greater Los Angeles Healthcare System. Healthy control patients (n = 25) were also recruited as a reference population. Fecal samples underwent 16S ribosomal RNA sequencing. Using differentially abundant microbes, a random forest classifier model was created to distinguish advanced fibrosis from mild/moderate fibrosis. The findings were then validated in a separate cohort of chronic liver disease patients (n = 37). Etiologies for liver disease included non-alcoholic liver disease (58.0%), hepatitis C (26.0%), hepatitis B (10.0%), and alcohol (6.0%). Microbiome composition was distinct in liver patients with advanced fibrosis compared to those with minimal fibrosis and healthy controls (p = 0.003). In multivariate negative binomial modeling, 26 bacterial taxa were differentially abundant in patients with advanced fibrosis as compared to those with minimal/moderate fibrosis (q-value < 0.05). A random forests classifier based on these taxa had an AUROC of 0.90 to predict advanced fibrosis. Prevotella copri, which was enriched in patients with advanced fibrosis, was the most strongly predictive microbe in the classifier. The classifier had an AUROC of 0.82 for advanced fibrosis in the validation cohort and Prevotella copri remained the strongest predictive microbe for advanced fibrosis. There is a distinct microbial signature for patients with advanced fibrosis independent of liver disease etiology and other comorbidities. These results suggest that microbial profiles can be used as a non-invasive marker for advanced fibrosis and support the hypothesis that microbes and their metabolites contribute to hepatic fibrosis.

PMID:
    32066758
DOI:
    10.1038/s41598-020-59535-w

StephenW

科学代表2020年2月17日； 10（1）：2771。Doi：10.1038 / s41598-020-59535-w。
微生物特征识别出主要归因于NAFLD的慢性肝病患者的晚期纤维化。
Dong TS1，Katzka W1,2，Lagishetty V1,2，Luu K1,2，Hauer M1,2，Pisegna J3，Jacobs JP4、5、6。
作者信息

1个
美国加州大学洛杉矶分校戴维·格芬医学院医学院消化系消化系统疾病的Vatche和Tamar Manoukian部门。
2
加州大学洛杉矶分校戴维·格芬医学院的加州大学洛杉矶分校微生物中心，美国加利福尼亚。
3
弗吉尼亚州大洛杉矶医疗保健系统胃肠病学，肝病学和肠胃外营养科，美国加利福尼亚州洛杉矶。
4
美国加州大学洛杉矶分校戴维·格芬医学院医学院消化系消化系统疾病的Vatche和Tamar Manoukian部门。 [email protected]。
5
加州大学洛杉矶分校戴维·格芬医学院的加州大学洛杉矶分校微生物中心，美国加利福尼亚。 [email protected]。
6
弗吉尼亚州大洛杉矶医疗保健系统胃肠病学，肝病学和肠胃外营养科，美国加利福尼亚州洛杉矶。 [email protected]。

抽象

晚期纤维化的存在是衡量慢性肝病严重程度的重要指标。先前将肠道微生物组作为晚期纤维化的新型生物标志物进行检查的工作仅检查了非酒精性脂肪肝患者。因此，我们的目标是检查各种肝病病因中的肠道微生物组，以基于微生物特征创建肝纤维化的预测模型。从VA大洛杉矶医疗保健系统接受FibroScan（超声弹性成像）检查的慢性肝病患者（n = 50）获得粪便样本。还招募了健康对照患者（n = 25）作为参考人群。粪便样品进行16S核糖体RNA测序。使用差异丰富的微生物，创建了一个随机森林分类器模型以区分晚期纤维化与轻度/中度纤维化。然后在另一组慢性肝病患者（n = 37）中证实了这一发现。肝病的病因包括非酒精性肝病（58.0％），丙型肝炎（26.0％），乙型肝炎（10.0％）和酒精（6.0％）。与肝纤维化程度最低的患者和健康对照组相比，肝纤维化患者的微生物组组成明显不同（p = 0.003）。在多变量负二项式建模中，与轻度/中度纤维化患者相比，晚期纤维化患者中有26个细菌分类单元差异显着（q值<0.05）。基于这些分类单元的随机森林分类器的AUROC为0.90，可预测晚期纤维化。在晚期纤维化患者中富集的普氏杆菌是分类器中预测性最强的微生物。在验证队列中，对于晚期纤维化，分类器的AUROC为0.82，而Prevotella copri仍然是晚期纤维化的最强预测微生物。晚期肝纤维化患者有明显的微生物特征，与肝脏疾病，病因和其他合并症无关。这些结果表明，微生物谱可以用作晚期纤维化的非侵入性标记，并支持微生物及其代谢物促进肝纤维化的假说。

PMID：
32066758
DOI：
10.1038 / s41598-020-59535-w

StephenW

https://www.nature.com/articles/s41598-020-59535-w.pdf