Diagnostic model for predicting hyperuricemia based on alterations of the gut microbiome in individuals with different serum uric acid levels.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Triglyceride Glucose Index Is More Closely Related to Hyperuricemia Than Obesity Indices in the Medical Checkup Population in Xinjiang, China.

Background: Hyperuricemia (HUA) is a metabolic anomaly with an increased incidence rate, causing a global medical burden. Several studies have confirmed that obesity and insulin resistance (IR) are the risk factors for HUA. Reports on the predictive power of different obesity indices for HUA are limited. This study aimed to compare the association between different general, abdominal, and visceral obesity indices and markers of the IR-triglyceride glucose (TyG) index with serum uric acid (SUA) and to assess the ability of these indices to predict HUA. Methods: A total of 2243 participants were recruited from Barkol County Hospital and surrounding township hospitals in Xinjiang. Obesity indices, including the atherogenic index of plasma, cardiometabolic index, visceral adiposity index, lipid accumulation product index, a body shape index, body roundness index, waist circumference, waist-to-height ratio, body mass index, and TyG index, were divided into four quartiles. Moreover, partial correlations and logistic regression were used to analyze the association between these indices and SUA. The area under the curve (AUC) and receiver operating characteristic curves were used to analyze the predictive value of these indices for HUA. Results: After controlling for confounding variables, the association between the TyG index and HUA was stronger than that between the obesity indices in both males and females. The odds ratios (ORs) for HUA in the highest quartile of the TyG index were 2.098 (95% confidence interval, 1.555-2.831) in males and 7.788 (95% CI, 3.581-16.937) in females. For males, the AIP, CMI, VAI, LAP index, and TyG index were able to discriminate HUA, and the TyG index showed the highest AUC value of 0.586 (95% CI, 0.557-0.614; P < 0.001). For females, all indices, except BMI, can discriminate HUA. Moreover, the visceral obesity index CMI showed the highest AUC value of 0.737 (95% CI, 0.691-0.782; P < 0.001). Meanwhile, the TyG index had a relatively high AUC value of 0.728 (95% CI, 0.682-0.773; P < 0.001). Conclusion: The TyG index was significantly related to HUA and was superior to obesity indices in identifying HUA in the medical checkup population in Xinjiang, China.

Chryseobacterium endalhagicum sp. nov., isolated from seed of leguminous plant.

A Gram-stain-negative, yellow-pigmented bacterium, designated as L7T, was isolated from seeds of Alhagi sparsifolia Shap., a leguminous plant that grows in northwest PR China. Strain L7T was found to be non-flagellated, non-spore forming rods which can grow at 10-37 °C, pH 6.0-8.5 and in 0-3 % (v/w) NaCl concentration. The 16S rRNA gene sequence analysis showed that strain L7T belongs to the genus Chryseobacterium with sequence similarities to Chryseobacterium vietnamense GIMN1.005T (98.1%), C. bernardetii NCCTC13530T (98.0%), C. vrystaatense LMG 22846T (97.9%), C. nakagawai NCTC13529T (97.7%), C. shigense DSM 17126T (97.6%) and C. rhizosphaerae RSB3-1T (97.5%). The average nucleotide identity of strain L7T to 31 reference strains were 78.6-85.6 %, lower than the species delineation threshold of 95 %. MK-6 was the only respiratory quinone of L7T and major fatty acids were iso-C15 : 0, iso-C17 : 0 3-OH, C16 : 1 ω7c and/or C16 : 1 ω6c, isoC17 : 1 ω9c and/or C16 : 0 10-methyl. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol, three unidentified aminophospholipids, two unidentified aminolipids, three unidentified glycolipids and two unidentified lipids. The G+C content of the genome was 38.58 mol%. On the basis of polyphasic taxonomy analyses in this study, strain L7T is considered to represent a novel species in the genus Chryseobacterium, for which the name Chryseobacterium endalhagicum sp. nov. is proposed. The type strain is L7T (=MCCC 1K05687T=JCM 34506T).