Loomba Rohit, Seguritan Victor, Li Weizhong, Long Tao, Klitgord Niels, Bhatt Archana, Dulai Parambir Singh, Caussy Cyrielle, Bettencourt Richele, Highlander Sarah K, Jones Marcus B, Sirlin Claude B, Schnabl Bernd, Brinkac Lauren, Schork Nicholas, Chen Chi-Hua, Brenner David A, Biggs William, Yooseph Shibu, Venter J Craig, Nelson Karen E
NAFLD Research Center, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Division of Epidemiology, Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Division of Gastroenterology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Human Longevity, San Diego, CA 92121, USA.
Cell Metab. 2017 May 2;25(5):1054-1062.e5. doi: 10.1016/j.cmet.2017.04.001.
The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota-derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, of which 72 had mild/moderate (stage 0-2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of 40 features (p < 0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a fecal-microbiome-derived metagenomic signature to detect advanced fibrosis in NAFLD.
非酒精性脂肪性肝病（NAFLD）中晚期纤维化的存在是肝脏死亡率的最重要预测指标。关于肠道微生物群衍生特征预测晚期纤维化存在的诊断准确性的数据有限。在这项前瞻性研究中，我们使用从粪便样本中提取的DNA进行全基因组鸟枪法测序来表征肠道微生物组组成。本研究纳入了86例经活检证实为NAFLD且特征明确的患者，其中72例患有轻度/中度（0-2期纤维化）NAFLD，14例患有晚期纤维化（3期或4期纤维化）。我们鉴定出一组40个特征（p < 0.006），其中包括37种细菌，用于构建随机森林分类器模型，以区分轻度/中度NAFLD和晚期纤维化。该模型在检测晚期纤维化方面具有强大的诊断准确性（AUC 0.936）。本研究为粪便微生物组衍生的宏基因组特征检测NAFLD中的晚期纤维化提供了初步证据。
