Department of General Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
The First Minzheng Mental Health Center, Shanghai, China.
Front Endocrinol (Lausanne). 2023 Jul 27;14:1190954. doi: 10.3389/fendo.2023.1190954. eCollection 2023.
AIMS/HYPOTHESIS: It is widely thought that the intestinal microbiota plays a significant role in the pathogenesis of metabolic disorders. However, the gut microbiota composition and characteristics of schizophrenia patients with metabolic syndrome (MetS) have been largely understudied. Herein, we investigated the association between the metabolic status of mainland Chinese schizophrenia patients with MetS and the intestinal microbiome.
Fecal microbiota communities from 115 male schizophrenia patients (57 with MetS and 58 without MetS) were assessed by 16S ribosomal RNA gene sequencing. We assessed the variations of gut microbiome between both groups and explored potential associations between intestinal microbiota and parameters of MetS. In addition, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on the KEGG database was used to predict the function of intestinal microbiota. We also conducted Decision Tree Analysis to develop a diagnostic model for the MetS in patients with schizophrenia based on the composition of intestinal microbiota.
The fecal microbial diversity significantly differed between groups with or without MetS (α-diversity (Shannon index and Simpson index): p=0.0155, p=0.0089; β-diversity: p=0.001). Moreover, the microbial composition was significantly different between the two groups, involving five phyla and 38 genera (p<0.05). In addition, a significant correlation was observed between the metabolic-related parameters and abundance of altered microbiota including HDL-c (r2 = 0.203, p=0.0005), GLU (r2 = 0.286, p=0.0005) and WC (r2 = 0.061, p=0.037). Furthermore, KEGG pathway analysis showed that 16 signaling pathways were significantly enriched between the two groups (p<0.05). Importantly, our diagnostic model based on five microorganisms established by decision tree analysis could effectively distinguish between patients with and without MetS (AUC = 0.94).
CONCLUSIONS/INTERPRETATION: Our study established the compositional and functional characteristics of intestinal microbiota in schizophrenia patients with MetS. These new findings provide novel insights into a better understanding of this disease and provide the theoretical basis for implementing new interventional therapies in clinical practice.
目的/假设:人们普遍认为肠道微生物群在代谢紊乱的发病机制中起着重要作用。然而,代谢综合征(MetS)精神分裂症患者的肠道微生物群组成和特征在很大程度上仍未得到充分研究。在此,我们研究了中国大陆精神分裂症患者代谢状态与肠道微生物组之间的关系。
通过 16S 核糖体 RNA 基因测序评估了 115 名男性精神分裂症患者(57 名患有 MetS,58 名无 MetS)的粪便微生物群落。我们评估了两组之间肠道微生物组的变化,并探索了肠道微生物群与 MetS 参数之间的潜在关联。此外,基于 KEGG 数据库的未观察状态重建的群落系统发育分析(PICRUSt)用于预测肠道微生物群的功能。我们还进行了决策树分析,根据肠道微生物群的组成,为精神分裂症患者的 MetS 开发诊断模型。
有或没有 MetS 的两组之间粪便微生物多样性差异显著(α多样性(Shannon 指数和 Simpson 指数):p=0.0155,p=0.0089;β多样性:p=0.001)。此外,两组之间的微生物组成也存在显著差异,涉及五个门和 38 个属(p<0.05)。此外,代谢相关参数与包括 HDL-c(r2=0.203,p=0.0005)、GLU(r2=0.286,p=0.0005)和 WC(r2=0.061,p=0.037)在内的改变菌群的丰度之间存在显著相关性。此外,KEGG 途径分析表明,两组之间有 16 个信号通路显著富集(p<0.05)。重要的是,我们通过决策树分析建立的基于五种微生物的诊断模型可以有效区分有无 MetS 的患者(AUC=0.94)。
结论/解释:我们的研究确定了代谢综合征精神分裂症患者肠道微生物群的组成和功能特征。这些新发现为更好地理解这种疾病提供了新的见解,并为临床实践中实施新的干预治疗提供了理论基础。