小柯机器人

近日，澳大利亚墨尔本大学Michael Inouye、Yang Liu等研究人员合作利用传统的风险因素和肠道微生物增强梯度提升法实现肝脏疾病发生的早期预测。2022年3月29日，《细胞—代谢》杂志在线发表了这项成果。

研究人员利用浅层猎枪元基因组测序对一个大型人群队列（N>7,000）进行了随访，并结合机器学习来研究了肠道微生物预测因子的单独预测能力，以及与常规肝病风险因素的结合。分开来看，常规因素和微生物因素显示出相当的预测能力。然而，使用机器学习对常规风险因素进行微生物组的增强，显著提高了性能。同样，无病生存分析显示，使用微生物组增强的模型可以明显改善分层。对预测性微生物特征的调查发现了以前未知的肝病分类群，以及以前与肝功能和疾病有关的分类群。这项研究支持肠道元基因组测序的潜在临床有效性，可用于补充预测肝病的传统风险因素。

据介绍，肠道微生物组已经显示出作为各种疾病的预测性生物标志物的前景。然而，肠道微生物群对肝脏疾病的前瞻性风险预测的潜力还没有得到评估。

附：英文原文

Title: Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting

Author: Yang Liu, Guillaume Méric, Aki S. Havulinna, Shu Mei Teo, Fredrik berg, Matti Ruuskanen, Jon Sanders, Qiyun Zhu, Anupriya Tripathi, Karin Verspoor, Susan Cheng, Mohit Jain, Pekka Jousilahti, Yoshiki Vázquez-Baeza, Rohit Loomba, Leo Lahti, Teemu Niiranen, Veikko Salomaa, Rob Knight, Michael Inouye

Issue&Volume: 2022-03-29

Abstract: The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with ～15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease-free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.

DOI: 10.1016/j.cmet.2022.03.002

Source: https://www.cell.com/cell-metabolism/fulltext/S1550-4131(22)00090-0

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