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      Study on the Mechanism of Jiaotai Pill Intervention on Insomnia Animal Model Based on Gut Microbiome and Metabolomics


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          With the continuous advancement of clinical application and experimental research of JTP, the application prospect of JTP in nervous system diseases and metabolic diseases is becoming increasingly clear. Jiaotai Pill (JTP) is a traditional Chinese medicine formula for insomnia, consisting of Coptidis rhizoma and Cinnamomi cortex, which dates back to Han Shi Yi Tong in the Ming Dynasty of China.


          Based on the brain-gut axis theory, this paper aims to explore the potential mechanism of JTP in the intervention of insomnia by using intestinal microbiome and metabolomics technology, taking the animal model of insomnia as the research object, so as to provide experimental basis for its further application and research.


          The insomnia mouse model was induced by intraperitoneal injection of para-chlorophenylalanine (PCPA). The clinical equivalent dose of JTP was administered by gavage for one week. The efficacy of JTP was evaluated by behavioral tests, serum biochemical detection, and brain histomorphological observation. The contents of cecum were analyzed by microbiomics and metabolomics.


          The results show that insomnia caused by PCPA led to daytime dysfunction, higher HPA axis hormone levels, and morphologically impaired hippocampus. JTP reversed these anomalies. Omics research indicates that JTP significantly reduced gut α diversity; at the phylum level, JTP reduced the relative abundance of Firmicutes, Deferribacterota, Cyanobacteria, and Actinobacteriota and increased the relative abundance of Verrucomicrobiota, Proteobacteria, and Desulfobacterota. At the genus level, JTP reduced the relative abundance of Muribaculaceae, Lachnospiraceae_NK4A136_group, Alistipes, Colidextribacter, Muribaculum, and Mucispirillum and increased the relative abundance of Bacteroides and Akkermansia. JTP also reversed the activation of the linoleic acid metabolism pathway induced by insomnia. The combined analysis of omics suggests that JTP may play a role by regulating the inflammatory state of the body. Further gene expression analysis of brain tissue confirmed this.


          We hypothesize that JTP may achieve insomnia relief by eliminating inflammation-causing bacteria in the gut and reducing inflammation levels through the brain-gut axis, pointing to potential targets and pathways for future research on JTP.

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          Most cited references53

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          IL-6 in inflammation, immunity, and disease.

          Interleukin 6 (IL-6), promptly and transiently produced in response to infections and tissue injuries, contributes to host defense through the stimulation of acute phase responses, hematopoiesis, and immune reactions. Although its expression is strictly controlled by transcriptional and posttranscriptional mechanisms, dysregulated continual synthesis of IL-6 plays a pathological effect on chronic inflammation and autoimmunity. For this reason, tocilizumab, a humanized anti-IL-6 receptor antibody was developed. Various clinical trials have since shown the exceptional efficacy of tocilizumab, which resulted in its approval for the treatment of rheumatoid arthritis and juvenile idiopathic arthritis. Moreover, tocilizumab is expected to be effective for other intractable immune-mediated diseases. In this context, the mechanism for the continual synthesis of IL-6 needs to be elucidated to facilitate the development of more specific therapeutic approaches and analysis of the pathogenesis of specific diseases.
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            Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults

            Background Recent evidence suggests that there is a link between metabolic diseases and bacterial populations in the gut. The aim of this study was to assess the differences between the composition of the intestinal microbiota in humans with type 2 diabetes and non-diabetic persons as control. Methods and Findings The study included 36 male adults with a broad range of age and body-mass indices (BMIs), among which 18 subjects were diagnosed with diabetes type 2. The fecal bacterial composition was investigated by real-time quantitative PCR (qPCR) and in a subgroup of subjects (N = 20) by tag-encoded amplicon pyrosequencing of the V4 region of the 16S rRNA gene. The proportions of phylum Firmicutes and class Clostridia were significantly reduced in the diabetic group compared to the control group (P = 0.03). Furthermore, the ratios of Bacteroidetes to Firmicutes as well as the ratios of Bacteroides-Prevotella group to C. coccoides-E. rectale group correlated positively and significantly with plasma glucose concentration (P = 0.04) but not with BMIs. Similarly, class Betaproteobacteria was highly enriched in diabetic compared to non-diabetic persons (P = 0.02) and positively correlated with plasma glucose (P = 0.04). Conclusions The results of this study indicate that type 2 diabetes in humans is associated with compositional changes in intestinal microbiota. The level of glucose tolerance should be considered when linking microbiota with metabolic diseases such as obesity and developing strategies to control metabolic diseases by modifying the gut microbiota.
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              Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies.

              In many patients with depression, symptoms of insomnia herald the onset of the disorder and may persist into remission or recovery, even after adequate treatment. Several studies have raised the question whether insomniac symptoms may constitute an independent clinical predictor of depression. This meta-analysis is aimed at evaluating quantitatively if insomnia constitutes a predictor of depression. PubMed, Medline, PsycInfo, and PsycArticles databases were searched from 1980 until 2010 to identify longitudinal epidemiological studies simultaneously investigating insomniac complaints and depressed psychopathology. Effects were summarized using the logarithms of the odds ratios for insomnia at baseline to predict depression at follow-up. Studies were pooled with both fixed- and random-effects meta-analytic models in order to evaluate the concordance. Heterogeneity test and sensitivity analysis were computed. Twenty-one studies met inclusion criteria. Considering all studies together, heterogeneity was found. The random-effects model showed an overall odds ratio for insomnia to predict depression of 2.60 (confidence interval [CI]: 1.98-3.42). When the analysis was adjusted for outliers, the studies were not longer heterogeneous. The fixed-effects model showed an overall odds ratio of 2.10 (CI: 1.86-2.38). The main limit is that included studies did not always consider the role of other intervening variables. Non-depressed people with insomnia have a twofold risk to develop depression, compared to people with no sleep difficulties. Thus, early treatment programs for insomnia might reduce the risk for developing depression in the general population and be considered a helpful general preventive strategy in the area of mental health care. Copyright © 2011 Elsevier B.V. All rights reserved.

                Author and article information

                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                Evidence-based Complementary and Alternative Medicine : eCAM
                23 May 2023
                23 May 2023
                : 2023
                : 2442505
                School of Life Sciences, Beijing University of Chinese Medicine, No. 11 East Road, North 3rd Ring Road, Beijing 100029, China
                Author notes

                Academic Editor: Rajeev K. Singla

                Author information
                Copyright © 2023 Yang Yang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 13 August 2022
                : 2 April 2023
                : 13 April 2023
                Funded by: Beijing University of Chinese Medicine
                Award ID: 2170071720009
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine


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