Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels.
Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels.
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DOI：
10.3389/fendo.2022.925119
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发表时间：
2022
影响因子：
5.2
通讯作者：
中科院分区：
文献类型：
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我们的目的是评估不同尿酸水平的参与者（高尿酸血症[HUA]患者，低血清尿酸[LSU]患者和正常水平的对照）之间肠道微生物组的差异，并开发一种基于微生物生物标志物预测HUA的模型。我们对来自HUA患者（n=50）、LSU患者（n=61）和对照组（n=57）的168份粪便样本的16 S rDNA基因的V3-V4可变区进行了测序。然后，我们分析了这些组之间肠道微生物组的差异。为了鉴定肠道微生物生物标志物，将107名HUA患者和对照随机分为（2：1）开发组和验证组，并进行随机森林模型的10倍交叉验证。然后，我们建立了三个诊断模型：临床模型，微生物生物标志物模型和组合模型。与对照组相比，LSU和HUA患者的肠道微生物α多样性（Shannon和Simpson指数）降低，但仅HUA组的降低具有显著性（分别为P=0.0029和P=0.013）。与对照组相比，HUA患者的变形菌门（P<0.001）和拟杆菌属（P=0.02）显著增加，而瘤胃球菌科（Ruminococcaceae）和瘤胃球菌属（Ruminococcus）减少（P=0.02）。鉴定了12种微生物生物标志物。发育组中这些生物标志物的曲线下面积（AUC）为84.9%（P<0.001）。值得注意的是，通过组合微生物生物标志物和临床因素实现了89.1%（P<0.001）的AUC。该组合模型是预测HUA的可靠工具，可用于辅助患者的临床评估和预防HUA。
We aimed to assess the differences in the gut microbiome among participants with different uric acid levels (hyperuricemia [HUA] patients, low serum uric acid [LSU] patients, and controls with normal levels) and to develop a model to predict HUA based on microbial biomarkers. We sequenced the V3-V4 variable region of the 16S rDNA gene in 168 fecal samples from HUA patients (n=50), LSU patients (n=61), and controls (n=57). We then analyzed the differences in the gut microbiome between these groups. To identify gut microbial biomarkers, the 107 HUA patients and controls were randomly divided (2:1) into development and validation groups and 10-fold cross-validation of a random forest model was performed. We then established three diagnostic models: a clinical model, microbial biomarker model, and combined model. The gut microbial α diversity, in terms of the Shannon and Simpson indices, was decreased in LSU and HUA patients compared to controls, but only the decreases in the HUA group were significant (P=0.0029 and P=0.013, respectively). The phylum Proteobacteria (P<0.001) and genus Bacteroides (P=0.02) were significantly increased in HUA patients compared to controls, while the genus Ruminococcaceae_Ruminococcus was decreased (P=0.02). Twelve microbial biomarkers were identified. The area under the curve (AUC) for these biomarkers in the development group was 84.9% (P<0.001). Notably, an AUC of 89.1% (P<0.001) was achieved by combining the microbial biomarkers and clinical factors. The combined model is a reliable tool for predicting HUA and could be used to assist in the clinical evaluation of patients and prevention of HUA.
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影响因子：
4.6
作者：
Guo Z;Zhang J;Wang Z;Ang KY;Huang S;Hou Q;Su X;Qiao J;Zheng Y;Wang L;Koh E;Danliang H;Xu J;Lee YK;Zhang H
通讯作者：
Zhang H
影响因子：
10.7
作者：
Sharaf El Din UAA;Salem MM;Abdulazim DO
通讯作者：
Abdulazim DO
影响因子：
32.4
作者：
Singh N;Gurav A;Sivaprakasam S;Brady E;Padia R;Shi H;Thangaraju M;Prasad PD;Manicassamy S;Munn DH;Lee JR;Offermanns S;Ganapathy V
通讯作者：
Ganapathy V
影响因子：
4.4
作者：
Emoto, Takuo;Yamashita, Tomoya;Hirata, Ken-ichi
通讯作者：
Hirata, Ken-ichi
影响因子：
4.7
作者：
Ellmerich, S;Schöller, M;Raul, F
通讯作者：
Raul, F