Variations in fecal microbiota of first episode schizophrenia associated with clinical assessment and serum metabolomics.
- Author:
Xue Ping WANG
1
;
Yu Ya Nan ZHANG
1
;
Tian Lan LU
1
;
Zhe LU
1
;
Zhe Wei KANG
1
;
Yao Yao SUN
1
;
Wei Hua YUE
1
Author Information
1. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China.
- Publication Type:Journal Article
- Keywords:
Drug naïve;
Gut microbiota;
Schizophrenia;
Serum metabolomics
- MeSH:
Humans;
Antipsychotic Agents;
Homovanillic Acid;
Metabolomics/methods*;
Methionine;
Microbiota;
Proline;
RNA, Ribosomal, 16S/genetics*;
Schizophrenia;
Vitamin B 6;
Feces
- From:
Journal of Peking University(Health Sciences)
2022;54(5):863-873
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the role of the microbiota in drug naïve first-onset schizophrenia patients and to seek evidence from multidimensional longitudinal analyses of the intestinal microbiome and clinical phenotype with antipsychotic drugs (APDs) therapy.
METHODS:In this study, 28 drug naïve first onset schizophrenia patients and age-, gender- and education-matched 29 healthy controls were included, and the patients were treated with APDs. We collected fecal and serum samples at baseline and after 6 weeks of treatment to identify the different microbiota strains and analyse their correlation with clinical symptoms and serum metabolites. The 16S rRNA genes of the gut microbiota were sequenced, and the diversity and relative abundance at the phylum and genus levels were analyzsed in detail. The PANSS score, BMI changed value, and serum metabolome were included in the data analyses.
RESULTS:A multiomics study found a potential connection among the clinical phenotype, microbiota and metabolome. The species diversity analyses revealed that the alpha diversity index (chao1, ACE, and goods_coverage) in the schizophrenia APDs group was significantly lower than that in the control group, and the schizophrenia group had clear demarcation from the control group. The microbiota composition analysis results showed that the relative abundance of the genera of Bacteroides, Streptococcus, Romboutsia, and Eubacterium ruminantium group significantly changed after APDs treatment in the schizophrenia patients. These strains could reflect the APDs treatment effect. More genera had differences between the patient and control groups. The LEfSe analysis showed that Prevotella_9 and Bacteroides were enriched in schizophrenia, while Blautia, Dialister, and Roseburia were enriched in the control group. The correlation analysis between microbiota and clinical symptoms showed that Bifidobacterium in schizophrenia was positively correlated with the PANSS reduction rate of the general psychopathology scale. The BMI changed value was positively correlated with the alteration of Clostridium_sensu_stricto_1 during treatment and the baseline abundance of Bacteroides. Moreover, metabolomic data analysis revealed a significant correlation between specific genera and metabolites, such as L-methionine, L-proline, homovanillic acid, N-acetylserotonin, and vitamin B6.
CONCLUSION:Our study found some microbiota features in schizophrenia patients and healthy controls, and several strains were correlated with APDs effects. Furthermore, the multiomics analysis implies the intermediate role of microbiota between antipsychotic effects and serum metabolites and provides new evidence to interpret the difference from multiple levels in the pathogenesis and pharmacological mechanism of schizophrenia.
