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.

Background: Anxiety has been a common mental state during the epidemic of Coronavirus Disease 2019 (COVID-19) and is usually closely related to somatization. However, no study on somatization in anxiety and its relationship with insomnia has been conducted. Therefore, this study aimed to identify the prevalence of anxiety, somatization and insomnia and explore the relationships between different psychological states in the general population during the COVID-19 outbreak. Methods: A total of 1,172 respondents were recruited from 125 cities in mainland China by an online questionnaire survey. All subjects were evaluated with the 7-item Generalized Anxiety Disorder (GAD-7) scale, the somatization subscale of the Symptom Checklist 90-Revised (SCL-90-R), and the 7-item Insomnia Severity Index (ISI). Results: The percentages of anxiety, somatization, and insomnia were 33.02%, 7.59%, and 24.66%, respectively. The prevalence of somatization was 19.38% in participants with anxiety. Compared to the anxiety without somatization group, the anxiety with somatization group had a significantly higher percentage of patients with a history of physical disease and insomnia, as well as higher GAD-7 scores and SCL-90 somatization subscores (all p < 0.001). The SCL-90 somatization subscores were positively correlated with age, history of physical disease, GAD-7 scores, and ISI scores (all p < 0.001). Furthermore, multivariate logistic regression showed that GAD-7 score, ISI score, and age were risk factors for somatization in the anxious population.

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Parkinson's disease (PD) is associated with gastrointestinal motility abnormalities that could favor the occurrence of small intestinal bacterial overgrowth (SIBO). The aim of the study was to assess the prevalence of SIBO in Chinese patients with PD and the potential impact of SIBO on gastrointestinal symptoms and motor function. 182 consecutive Chinese patients with PD patients and 200 sex, age, and BMI-matched subjects without PD were included. All participants underwent the glucose breath test to assess SIBO. We examined the associations between factors and SIBO with logistic regression using SPSS. Fifty-five of the 182 PD patients were SIBO positive (30.2 %; 95 % CI 23.5-36.9 %) compared with 19 of 200 in the control group (9.5 %; 95 % CI 5.4-13.6 %); the difference was statistically significant (P < 0.0001; OR 4.13; 95 % CI 2.34-7.29). Motor fluctuations present was higher in the PD patients with SIBO than in the patients without SIBO (70.9 vs. 45.7 %; P = 0.002). Multivariate analysis showed that disease duration, Hoehn and Yahr stage, Unified PD Rating-III score, Unified PD Rating-IV score, and Non-Motor Symptoms Scale score were the factors associated with the SIBO-positive status in PD patients. SIBO was highly prevalent in PD, and nearly one-third was detected. SIBO was associated with worse gastrointestinal symptoms and worse motor function. Further studies are needed to specify the reasons underlying SIBO and worse motor function in PD.

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The effect of the gut microbiome on the central nervous system and its possible role in mental disorders have received increasing attention. However, knowledge about the relationship between the gut microbiome and brain structure and function is still very limited. Here, we used 16S rRNA sequencing with structural magnetic resonance imaging (sMRI) and resting-state functional (rs-fMRI) to investigate differences in fecal microbiota between 38 patients with schizophrenia (SZ) and 38 demographically matched normal controls (NCs) and explored whether such differences were associated with brain structure and function. At the genus level, we found that the relative abundance of Ruminococcus and Roseburia was significantly lower, whereas the abundance of Veillonella was significantly higher in SZ patients than in NCs. Additionally, the analysis of MRI data revealed that several brain regions showed significantly lower gray matter volume (GMV) and regional homogeneity (ReHo) but significantly higher amplitude of low-frequency fluctuation in SZ patients than in NCs. Moreover, the alpha diversity of the gut microbiota showed a strong linear relationship with the values of both GMV and ReHo. In SZ patients, the ReHo indexes in the right STC (r = − 0.35, p = 0.031, FDR corrected p = 0.039), the left cuneus (r = − 0.33, p = 0.044, FDR corrected p = 0.053) and the right MTC (r = − 0.34, p = 0.03, FDR corrected p = 0.052) were negatively correlated with the abundance of the genus Roseburia. Our results suggest that the potential role of the gut microbiome in SZ is related to alterations in brain structure and function. This study provides insights into the underlying neuropathology 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.

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.