Author:
Miyajima Yuna,Karashima Shigehiro,Mizoguchi Ren,Kawakami Masaki,Ogura Kohei,Ogai Kazuhiro,Koshida Aoi,Ikagawa Yasuo,Ami Yuta,Zhu Qiunan,Tsujiguchi Hiromasa,Hara Akinori,Kurihara Shin,Arakawa Hiroshi,Nakamura Hiroyuki,Tamai Ikumi,Nambo Hidetaka,Okamoto Shigefumi
Abstract
AbstractHyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40 years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.
Funder
Japan Society for the Promotion of Science
Yakult Bio-Science Foundation
Publisher
Springer Science and Business Media LLC