Background The gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible biological basis of psychiatric disorders. With the advance of next-generation sequencing technology, characterization of the gut microbiota in schizophrenia (SZ) patients can provide rich clues for the diagnosis and prevention of SZ. Methods In this study, we compared the differences in the fecal microbiota between 82 SZ patients and 80 demographically matched normal controls (NCs) by 16S rRNA sequencing and analyzed the correlations between altered gut microbiota and symptom severity. Results The alpha diversity showed no significant differences between the NC and SZ groups, but the beta diversity revealed significant community-level separation in microbiome composition between the two groups (pseudo-F =3.337, p < 0.001, uncorrected). At the phylum level, relatively more Actinobacteria and less Firmicutes (p < 0.05, FDR corrected) were found in the SZ group. At the genus level, the relative abundances of Collinsella, Lactobacillus, Succinivibrio, Mogibacterium, Corynebacterium, undefined Ruminococcus and undefined Eubacterium were significantly increased, whereas the abundances of Adlercreutzia, Anaerostipes, Ruminococcus and Faecalibacterium were decreased in the SZ group compared to the NC group (p < 0.05, FDR corrected). We performed PICRUSt analysis and found that several metabolic pathways differed significantly between the two groups, including the Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism, Phenylpropanoid biosynthesis, Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways (p < 0.05, FDR corrected). Among the SZ group, the abundance of Succinivibrio was positively correlated with the total Positive and Negative Syndrome Scale (PANSS) scores (r = 0.24, p < 0.05, uncorrected) as well as the general PANSS scores (r = 0.22, p < 0.05, uncorrected); Corynebacterium was negatively related to the negative scores of PANSS (r = 0.22, p < 0.05, uncorrected). Conclusions Our findings provided evidence of altered gut microbial composition in SZ group. In addition, we found that Succinvibrio and Corynebacterium were associated with the severity of symptoms for the first time, which may provide some new biomarkers for the diagnosis of SZ.

Obesity is common comorbidity in patients with schizophrenia. Previous studies have reported that homocysteine (Hcy) is increased in schizophrenia. However, no study has reported the association between BMI and Hcy levels in schizophrenia. This cross-sectional naturalistic study aimed to evaluate the relationship between BMI, Hcy and clinical symptoms in Chinese Han patients with chronic schizophrenia. Clinical and anthropometric data as well as plasma Hcy level and glycolipid parameters were collected. Psychopathology was measured with the Positive and Negative Syndrome Scale (PANSS). Our results showed that compared with the low BMI group, the high BMI group had a higher PANSS general psychopathology subscore, higher levels of blood glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol (all p < 0.05). Hcy levels were negatively associated with BMI in patients (p < 0.001). Hcy level, the PANSS general psychopathology subscale, total cholesterol and education (all p < 0.05) were the influencing factors of high BMI. Our study suggest that Hcy level may be associated with BMI in patients with schizophrenia. Moreover, patients with high BMI show more severe clinical symptoms and higher glucose and lipid levels.

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Objective To present the clinical characteristics and dynamic changes in laboratory parameters of the COVID-19 in Guangzhou, and explore the probable early warning indicators of disease progressing. Method We enrolled all the patients diagnosed as COVID-19 in the Guangzhou No. 8 People’s Hospital. The patients’ demographic, and epidemiologic data were collected, including chief complaints, lab results and imaging examination. Results The characteristics of the patients in Guangzhou are different from that in Wuhan. They were younger in age, female dominated, not commonly combined with other disease. 75% of patients suffered fever on admission, followed by cough occurring in 62% patients. By comparing the mild/normal and severe/critical patients, male, aged, combined with hypertension, abnormal in blood routine result, raised creatine kinase, glutamic oxaloacetic transaminase, lactate dehydrogenase, CRP, procalcitonin, D-dimer, fibrinogen, APTT, and positive in proteinuria can be candidate of early warning indicators to severe disease. Conclusion The patients in outside epidemic areas showed different characteristics from that in Wuhan. The abnormal laboratory parameters were markedly changed in 4 weeks after admission, and also shown different between the mild and severe patients. The highest specificity and sensitivity potential early warning indicators of severe disease need more evidence to confirm.