The Characteristics Of Human Gut Microbiota And Its Correlation With Schizophrenia

Schizophrenia(SZ)is a common severe mental disorder,which accounts for about 50% of the total number of hospitalized psychiatric patients in China,and the lifetime prevalence rate is about 0.3%-2% globally.Because SZ has many similarities with other mental disorders in terms of susceptibility genes,precursors core symptoms,brain structure and function abnormalities,cognitive impairment,etc.,the clinical misdiagnosis rate of SZ is relatively high,and also in the treatment of SZ There is some overlap with other mental disorders,so finding new physiological indicators that can accurately diagnose SZ has certain clinical significance.At present,the pathogenesis of SZ has not been fully elucidated.With the introduction of the concept of "microbiota-gut-brain" axis,increasing number of studies began to focus on the correlation between gut microbiome and brain function,but so far there are few studies on the role of gut microbiome in SZ.Meanwhile,with the progress of high-throughput sequencing technology,the study on gut microbiome of SZ can provide abundant clues for the diagnosis and prevention of SZ,which is helpful to discover the role of gut microbiome in the pathogenesis of mental diseases.In addition,the study of blood biochemical indexes in SZ can also provide clues for the diagnosis and treatment of SZ.This study focuses on studying the specificity of blood biochemical indicators and gut microbiota of SZ,and analyzing the correlation between these indicators and the severity of symptoms of patients.On this basis,objective and effective auxiliary diagnostic techniques and methods are explored for clinical identification of SZ.A total of 82 SZ patients and 80 NC subjects were recruited in this study.Firstly,the differences of blood biochemical indexes between SZ patients and NC subjects were analyzed.Among the 24 biochemical indexes,14 blood biochemical indexes with significant differences between groups were found,and two blood biochemical indexes related to SZ symptoms,MONO and ALP,were identified through correlation analysis.Secondly,we analyzed the difference of gut microbiota of 2 groups,found that the diversity of gut microbiota is significant difference in 2 groups.And then compared with NC subjects,the relative abundance of two microbial phylum and 11 microbial genus are changed in SZ patients.obvious changes have taken place in the relative abundance of microorganisms of the genus,and found the differences between groups of microbiotas ofthe genus Succinivibrio and Corynebacterium are associated with symptom severity.Lastly,the genomic functions of the two groups of gut microbiome were predicted,and it was found that the gut microbiome of the two groups were different in important pathways such as metabolism and synthesis of SCFAs and amino acids.After determining the change in blood biochemical indices and gut microbiota in SZ patients,the difference of these physiological indexes as prediction variables,at the same time the PANSS as response variables fitting multiple linear regression model,found that Succinivibrio and ALP can effectively predict the patient's total PANSS score and general symptom scores,illustrating the two physiological indexes can be used to objectively assess the patient's symptoms.In order to explore the feasibility of using blood biochemical markers and gut microbiota as biomarkers for the diagnosis of SZ,the machine learning method was used to predict SZ and NC with two kinds of physiological markers with inter-group differences.The results showed that after feature selection by ANOVA algorithm,five classification algorithms of SVM,RF,LDA,LR and KNN could accurately classify the two groups of subjects.In particular,the classification performance of LDA classifier was the best(AUC = 0.95).And we found the top 10 weight of features are NEUT,WBC,MONO,Cr,TG,LDL,INS,LYMPH,Collinsella and undefined Ruminococcus.These results suggest that specific gut microbiota and blood biochemical markers can be used to effectively differentiate the two groups of participants,thus can be used as biomarkers for the diagnosis of SZ.