Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Curcumin protects mice with myasthenia gravis by regulating the gut microbiota, short-chain fatty acids, and the Th17/Treg balance

Curcumin is widely used as a traditional drug in Asia. Interestingly, curcumin and its metabolites have been demonstrated to influence the microbiota. However, the effect of curcumin on the gut microbiota in patients with myasthenia gravis (MG) remains unclear. This study aimed to investigate the effects of curcumin on the gut microbiota community, short-chain fatty acids (SCFAs) levels, intestinal permeability, and Th17/Treg balance in a Torpedo acetylcholine receptor (T-AChR)-induced MG mouse model. The results showed that curcumin significantly alleviated the clinical symptoms of MG mice induced by T-AChR. Curcumin modified the gut microbiota composition, increased microbial diversity, and, in particular, reduced endotoxin-producing Proteobacteria and Desulfovibrio levels in T-AChR-induced gut dysbiosis. Moreover, we found that curcumin significantly increased fecal butyrate levels in mice with T-AChR-induced gut dysbiosis. Butyrate levels increased in conjunction with the increase in butyrate-producing species such as Oscillospira, Akkermansia, and Allobaculum in the curcumin-treated group. In addition, curcumin repressed the increased levels of lipopolysaccharide (LPS), zonulin, and FD4 in plasma. It enhanced Occludin expression in the colons of MG mice induced with T-AChR, indicating dramatically alleviated gut permeability. Furthermore, curcumin treatment corrected T-AChR-induced imbalances in Th17/Treg cells. In summary, curcumin may protect mice against myasthenia gravis by modulating both the gut microbiota and SCFAs, improving gut permeability, and regulating the Th17/Treg balance. This study provides novel insights into curcumin's clinical value in MG therapy.

Mendelian randomization study revealed a gut microbiota-neuromuscular junction axis in myasthenia gravis

A growing number of studies have implicated that gut microbiota abundance is associated with myasthenia gravis (MG). However, the causal relationship underlying the associations is still unclear. Here, we aim to investigate the causal effect of gut microbiota on MG using Mendelian randomization (MR) method. Publicly available Genome-wide association study (GWAS) summary-level data for gut microbiota and for MG were extracted. Inverse variance weighted was used as the main method to analyze causality. The robustness of the results was validated with sensitivity analyses. Our results indicated that genetically predicted increased phylum Lentisphaerae (OR = 1.319, p = 0.026), class Lentisphaerae (OR = 1.306, p = 0.044), order Victivallales (OR = 1.306, p = 0.044), order Mollicutes (OR = 1.424, p = 0.041), and genus Faecalibacterium (OR = 1.763, p = 0.002) were potentially associated with a higher risk of MG; while phylum Actinobacteria (OR = 0.602, p = 0.0124), class Gammaproteobacteria (OR = 0.587, p = 0.036), family Defluviitaleaceae (OR = 0.695, p = 0.047), family Peptococcaceae (OR = 0.698, p = 0.029), and family Family XIII (OR = 0.614, p = 0.017) were related to a lower risk of MG. The present study provides genetic evidence for the causal associations between gut microbiota and MG, thus suggesting novel insights into the gut microbiota-neuromuscular junction axis in the pathogenesis of MG.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Altered gut microbiota and metabolites in untreated myasthenia gravis patients

Objective

The homeostasis of the immune system is influenced by the gut microbiota. Previous studies have reported dysbiosis in the gut microbiota of myasthenia gravis (MG) patients. To investigate potential alterations in gut microbiota and metabolites in newly diagnosed and untreated MG patients, we conducted a case-control study.

Methods

Fecal samples were collected from 11 newly diagnosed and untreated MG patients as well as 11 age-and sex-matched healthy controls. These samples underwent analysis for gut microbiota using 16S ribosomal RNA (rRNA) gene sequencing, while fecal metabolome was analyzed using liquid chromatography-electrospray tandem mass spectrometry system (LC-ESI-MS/MS).

Results

The microbial community richness (observed species) and diversity (Shannon and Simpson indices) were significantly lower in the MG group compared to the control group. Microbiota composition analysis revealed significant differences between the MG and control groups at phylum, family, and genus levels. Linear discriminant analysis effect size (LEfSe) analysis showed a substantial decrease in abundance of the genus Faecalibacterium within the MG group. Fecal metabolome analysis identified three up-regulated metabolites involved in amino acid metabolism (taurine, creatinine, L-carnitine), one up-regulated metabolite involved in lipid metabolism (oleic acid), with correlation analysis indicating a positive association between Faecalibacterium abundance and creatinine levels.

Conclusion

Our findings suggest that dysbiosis already exists in newly diagnosed and untreated MG patients, implying that dysbiosis within the gut microbiota may be an initiating factor contributing to MG pathogenesis. Furthermore, F. prausnitzii may hold promise as a probiotic for treating MG.

Gut microbiota in muscular atrophy development, progression, and treatment: New therapeutic targets and opportunities

Skeletal muscle atrophy is a debilitating condition that significantly affects quality of life and often lacks effective treatment options. Muscle atrophy can have various causes, including myogenic, neurogenic, and other factors. Recent investigation has underscored a compelling link between the gut microbiota and skeletal muscle. Discerning the potential differences in the gut microbiota associated with muscle atrophy-related diseases, understanding their influence on disease development, and recognizing their potential as intervention targets are of paramount importance. This review aims to provide a comprehensive overview of the role of the gut microbiota in muscle atrophy-related diseases. We summarize clinical and pre-clinical studies that investigate the potential for gut microbiota modulation to enhance muscle performance and promote disease recovery. Furthermore, we delve into the intricate interplay between the gut microbiota and muscle atrophy-related diseases, drawing from an array of studies. Emerging evidence suggests significant differences in gut microbiota composition in individuals with muscle atrophy-related diseases compared with healthy individuals. It is conceivable that these alterations in the microbiota contribute to the pathogenesis of these disorders through bacterium-related metabolites or inflammatory signals. Additionally, interventions targeting the gut microbiota have demonstrated promising results for mitigating disease progression in animal models, underscoring the therapeutic potential of modulating the gut microbiota in these conditions. By analyzing the available literature, this review sheds light on the involvement of the gut microbiota in muscle atrophy-related diseases. The findings contribute to our understanding of the underlying mechanisms and open avenues for development of novel therapeutic strategies targeting the gut-muscle axis.

Microbiota dysbiosis and myasthenia gravis: Do all roads lead to Rome?

Dysregulated immune system with a failure to recognize self from non-self-antigens is one of the common pathogeneses seen in autoimmune diseases. The complex interplay of genetic and environmental factors is important for the occurrence and development of the disease. Among the environmental factors, disturbed gut microbiota (gut dysbiosis) has recently attracted particular attention, especially with advancement in human microbiome research. Although the alterations in microbiota have been seen in various autoimmune diseases, including those of nervous system, there is paucity of information on neuromuscular system diseases. Myasthenia gravis (MG) is one such rare autoimmune disease of neuromuscular junction, and is caused by generation of pathogenic autoantibodies to components of the postsynaptic muscle endplate. In the recent years, accumulating evidences have endorsed the key role of host microbiota, particularly those of gut, in the pathogenesis of MG. Differential microbiota composition, characterized by increased abundance of Fusobacteria, Bacteroidetes, and Proteobacteria, and decreased abundance of Actinobacteria and Firmicutes, has been seen in MG patients in comparison to healthy subjects. Disturbance of microbiota composition, particularly reduced ratio of Firmicutes/Bacteroidetes, alter the gut permeability, subsequently triggering the immunological response. Resultant reduction in levels of short chain fatty acids (SCFAs) is another factor contributing to the immunological response in MG patients. Modulation of gut microbiota via intervention of probiotics, prebiotics, synbiotics, postbiotics (metabiotics), and fecal microbiota transplantation (FMT) is considered to be the futuristic approach for the management of MG. This review summarizes the role of gut microbiota and their metabolites (postbiotics) in the progression of MG. Also, various bacteriotherapeutic approaches involving gut microbiota are discussed for the prevention of MG progression.

The Role of Human Microbiota in Myasthenia Gravis: A Narrative Review

Myasthenia gravis (MG) is an autoimmune neuromuscular disease characterized by fluctuating weakness of the skeletal muscles. Although antibodies against the neuromuscular junction components are recognized, the MG pathogenesis remains unclear, even if with a well-known multifactorial character. However, the perturbations of human microbiota have been recently suggested to contribute to MG pathogenesis and clinical course. Accordingly, some products derived from commensal flora have been demonstrated to have anti-inflammatory effects, while other have been shown to possess pro-inflammatory properties. In addition, patients with MG when compared with age-matched controls showed a distinctive composition in the oral and gut microbiota, with a typical increase in Streptococcus and Bacteroides and a reduction in Clostridia as well as short-chain fatty acid reduction. Moreover, restoring the gut microbiota perturbation has been evidenced after the administration of probiotics followed by an improvement of symptoms in MG cases. To highlight the role of the oral and gut microbiota in MG pathogenesis and clinical course, here, the current evidence has been summarized and reviewed.

The role of the gut microbiota and fecal microbiota transplantation in neuroimmune diseases

The gut microbiota plays a key role in the function of the host immune system and neuroimmune diseases. Alterations in the composition of the gut microbiota can lead to pathology and altered formation of microbiota-derived components and metabolites. A series of neuroimmune diseases, such as myasthenia gravis (MG), multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSDs), Guillain-Barré syndrome (GBS), and autoimmune encephalitis (AIE), are associated with changes in the gut microbiota. Microecological therapy by improving the gut microbiota is expected to be an effective measure for treating and preventing some neuroimmune diseases. This article reviews the research progress related to the roles of gut microbiota and fecal microbiota transplantation (FMT) in neuroimmune diseases.

The diagnostic potential and barriers of microbiome based therapeutics

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis

Background: Thymoma-associated myasthenia gravis (TAMG) is a well-described subtype of Myasthenia gravis (MG). Nevertheless, the detailed proteins and bioprocess differentiating TAMG from TAMG (−) thymoma have remained unclear.

Methods: The proteomics and metabolomics were carried out on serum samples from thymoma group (n = 60, TNMG), TAMG (+) thymoma group (n = 70, TAMG (+)), and TAMG (−) thymomas group (n = 62, TAMG (−)), and controls (n = 159). groups. Proteomics and metabolomics analyses, including weighted gene co-expression network analysis (WGCNA), was conducted to detect the hub proteins and metabolomics processes that could differentiate TAMG (+) from TAMG (−) thymomas. MetaboAnalyst was used to examine the integration of proteomic and metabolomic analysis to differentiate TAMG (+) from TAMG (−) thymomas.

Results: The of module–trait correlation of WGCNA analysis identified KRT1, GSN, COL6A1, KRT10, FOLR2, KRT9, KRT2, TPI1, ARF3, LYZ, ADIPOQ, SEMA4B, IGKV1-27, MASP2, IGF2R was associated with TAMG (+) thymomas. In addition, organismal systems-immune system and metabolism-biosynthesis of other secondary metabolites were closely related to the mechanism of TAMG (+) pathogenesis.

Conclusion: Our integrated proteomics and metabolomics analysis supply a systems-level view of proteome changes in TAMG (+), TAMG (−) thymomas and exposes disease-associated protein network alterations involved in.

Oral Microbiota Profile in a Group of Anti-AChR Antibody–Positive Myasthenia Gravis Patients

Myasthenia gravis (MG) is an autoimmune disorder caused by autoantibodies directed against the postsynaptic membrane at the neuromuscular junction. Perturbation of gut microbiota is thought to contribute to the development of MG, as reflected by fecal metabolomic signatures in humans, but there have been few studies on the relationship between oral microbiota profile and MG. The current study evaluated the correlation between oral microbiota composition and diversity and anti-acetylcholinereceptor (AChR) antibody–positive MG by comparing oral microbiota communities of patients (n = 20) and healthy controls (HCs; n = 20) by 16S rRNA gene sequencing. Principal coordinate analysis and Adonis analysis revealed significant differences in oral microflora profile between the twogroups. Compared to HCs, the abundance of the phyla Firmicutes and Actinobacteria and genera Streptococcus, Rothia, and Lachnoanerobaculum was significantly increased whereas that of phyla Proteobacteria and Spirochaetotaand genera Neisseria, Haemophilus, and Treponema was significantly decreased in MG patients. The Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the biosynthesis of ansamycins and amino acid metabolism pathways were altered in MG. These results indicate that oral microbiota composition is perturbed in patients with anti-AChR antibody–positive MG, providing new potential avenues for targeted therapeutic interventions.

Multi-Omics Analysis of the Microbiome and Metabolome Reveals the Relationship Between the Gut Microbiota and Wooden Breast Myopathy in Broilers

Wooden breast (WB) is a widely prevalent myopathy in broiler chickens. However, the role of the gut microbiota in this myopathy remains largely unknown, in particular the regulatory effect of gut microbiota in the modulation of muscle metabolism. Totally, 300 1-day-old Arbor Acres broilers were raised until 49 days and euthanized, and the breast filets were classified as normal (NORM), mild (MILD), or severe wooden breast (SEV). Birds with WB comprised 27.02% of the individuals. Severe WB filets had a greater L* value, a* value, and dripping loss but a lower pH (P < 0.05). WB filets had abundant myofiber fragmentation, with a lower average myofiber caliber and more fibers with a diameter of <20 μm (P < 0.05). The diversity of the intestinal microflora was decreased in birds with severe WB, with decreases in Chao 1, and observed species indices. At the phylum level, birds with severe WB had a lower Firmicutes/Bacteroidetes ratio (P = 0.098) and a decreased abundance of Verrucomicrobia (P < 0.05). At the species level, gut microbiota were positively correlated with 131 digesta metabolites in pathways of glutamine and glutamate metabolism and arginine biosynthesis but were negatively correlated with 30 metabolites in the pathway of tyrosine metabolism. In plasma, WB induced five differentially expressed metabolites (DEMs), including anserine and choline, which were related to the severity of the WB lesion. The microbial-derived metabolites, including guanidoacetic acid, antiarol, and (2E)-decenoyl-ACP, which entered into plasma were related to meat quality traits and myofiber traits. In summary, WB filets differed in gut microbiota, digesta, and plasma metabolites. Gut microbiota respond to the wooden breast myopathy by driving dynamic changes in digesta metabolites that eventually enter the plasma.

Effect of Fufang Huangqi Decoction on the Gut Microbiota in Patients With Class I or II Myasthenia Gravis

Objective

To investigate the effect of Fufang Huangqi Decoction on the gut microbiota in patients with class I or II myasthenia gravis (MG) and to explore the correlation between gut microbiota and MG (registration number, ChiCTR2100048367; registration website, http://www.chictr.org.cn/listbycreater.aspx; NCBI: SRP338707).

Methods

In this study, microbial community composition and diversity analyses were carried out on fecal specimens from MG patients who did not take Fufang Huangqi Decoction (control group, n = 8) and those who took Fufang Huangqi Decoction and achieved remarkable alleviation of symptoms (medication group, n = 8). The abundance, diversity within and between habitats, taxonomic differences and corresponding discrimination markers of gut microbiota in the control group and medicated group were assessed.

Results

Compared with the control group, the medicated group showed a significantly decreased abundance of Bacteroidetes (P < 0.05) and significantly increased abundance of Actinobacteria at the phylum level, a significantly decreased abundance of Bacteroidaceae (P < 0.05) and significantly increased abundance of Bifidobacteriaceae at the family level and a significantly decreased abundance of Blautia and Bacteroides (P < 0.05) and significantly increased abundance of Bifidobacterium, Lactobacillus and Roseburia at the genus level. Compared to the control group, the medicated group had decreased abundance, diversity, and genetic diversity of the communities and increased coverage, but the differences were not significant (P > 0.05); the markers that differed significantly between communities at the genus level and influenced the differences between groups were Blautia, Bacteroides, Bifidobacterium and Lactobacillus.

Conclusions

MG patients have obvious gut microbiota-associated metabolic disorders. Fufang Huangqi Decoction regulates the gut microbiota in patients with class I or II MG by reducing the abundance of Blautia and Bacteroides and increasing the abundance of Bifidobacterium and Lactobacillus. The correlation between gut microbiota and MG may be related to cell-mediated immunity.

Gut Microbiota as Regulators of Th17/Treg Balance in Patients With Myasthenia Gravis

Myasthenia gravis (MG) is an acquired neurological autoimmune disorder characterized by dysfunctional transmission at the neuromuscular junction, with its etiology associated with genetic and environmental factors. Anti-inflammatory regulatory T cells (Tregs) and pro-inflammatory T helper 17 (Th17) cells functionally antagonize each other, and the immune imbalance between them contributes to the pathogenesis of MG. Among the numerous factors influencing the balance of Th17/Treg cells, the gut microbiota have received attention from scholars. Gut microbial dysbiosis and altered microbial metabolites have been seen in patients with MG. Therefore, correcting Th17/Treg imbalances may be a novel therapeutic approach to MG by modifying the gut microbiota. In this review, we initially review the association between Treg/Th17 and the occurrence of MG and subsequently focus on recent findings on alterations of gut microbiota and microbial metabolites in patients with MG. We also explore the effects of gut microbiota on Th17/Treg balance in patients with MG, which may provide a new direction for the prevention and treatment of this disease.

Calprotectin as potential novel biomarker in myasthenia gravis

Myasthenia gravis (MG) is the most common autoimmune disease affecting the neuromuscular junction by specific autoantibodies. The etiology of MG and its heterogeneity in clinical courses are poorly understood, although it was recently shown that gut microbial dysbiosis plays a critical role. Since levels of Calprotectin (CLP) seem to correlate with level of dysbiosis, we hypothesize that CLP may serve as potential disease activity biomarker in MG. Sera from 251 patients with MG and 90 controls were analyzed in an explorative, cross-sectional design. Prospectively, we tested CLP levels in MG patients up to 3 years. Association of CLP levels with socio-demographics, disease activity (quantitative myasthenia gravis (QMG) score, myasthenia gravis-specific Activities of Daily Living scale (MG-ADL)), antibody (Abs) status, history of myasthenic crisis, treatment regime, and history of thymectomy were investigated using univariate analysis. Mean baseline serum levels of CLP were significantly higher in MG patients compared to controls (4.3 μg/ml vs. 2.1 μg/ml; p < 0.0001). Higher levels of CLP were associated with a higher clinical disease severity measured by MGFA classification and QMG score. Nevertheless, the only weak correlation of CLP with clinical outcome parameters needs confirmation in future studies. Currently, there are no validated blood biomarkers for MG. The significantly elevated CLP and mild correlation with parameters of disease activity suggests that CLP holds promise as a biomarker for measurement of individual disease severity.

Metagenome-wide association study of gut microbiome revealed potential microbial marker set for diagnosis of pediatric myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is an acquired immune-mediated disorder of the neuromuscular junction that causes fluctuating skeletal muscle weakness and fatigue. Pediatric MG and adult MG have many different characteristics, and current MG diagnostic methods for children are not quite fit. Previous studies indicate that alterations in the gut microbiota may be associated with adult MG. However, it has not been determined whether the gut microbiota are altered in pediatric MG patients.

METHODS

Our study recruited 53 pediatric MG patients and 46 age- and gender-matched healthy controls (HC). We sequenced the fecal samples of recruited individuals using whole-genome shotgun sequencing and analyzed the data with in-house bioinformatics pipeline.

RESULTS

We built an MG disease classifier based on the abundance of five species, Fusobacterium mortiferum, Prevotella stercorea, Prevotella copri, Megamonas funiformis, and Megamonas hypermegale. The classifier obtained 94% area under the curve (AUC) in cross-validation and 84% AUC in the independent validation cohort. Gut microbiome analysis revealed the presence of human adenovirus F/D in 10 MG patients. Significantly different pathways and gene families between MG patients and HC belonged to P. copri, Clostridium bartlettii, and Bacteroides massiliensis. Based on functional annotation, we found that the gut microbiome affects the production of short-chain fatty acids (SCFAs), and we confirmed the decrease in SCFA levels in pediatric MG patients via serum tests.

CONCLUSIONS

The study indicated that altered fecal microbiota might play vital roles in pediatric MG's pathogenesis by reducing SCFAs. The microbial markers might serve as novel diagnostic methods for pediatric MG.

Efficacy and safety of Buzhong Yiqi decoction combined with western medicine in the treatment of myasthenia gravis: A protocol for systematic review and meta-analysis

BACKGROUND

Myasthenia gravis is a common autoimmune disease in clinic. Although there are various ways and drugs for the treatment of myasthenia gravis in Western medicine, there are still a variety of adverse reactions. Studies have shown that Buzhong Yiqi decoction combined with Western medicine has a certain efficacy in the treatment of myasthenia gravis, but there is a lack of evidence-based medicine. The research carried out in this scheme is to systematically evaluate the efficacy and safety of Buzhong Yiqi decoction combined with Western medicine in the treatment of myasthenia gravis, and to provide reliable evidence for guiding clinical practice.

METHODS

English databases (the Cochrane Library, PubMed, Web of Science, Embase) and Chinese databases (China Biomedical Database, China Science and Technology Journal Database, China National Knowledge Infrastructure, Wanfang) will be searched by computer. In addition, Baidu Academic and Chinese Clinical Trial Registration Center will be searched manually. A randomized controlled clinical trial of Buzhong Yiqi decoction combined with Western medicine in the treatment of myasthenia gravis will be conducted from the establishment of the database to December 2020. The 2 researchers independently carry out data extraction and literature quality evaluation on the quality of the included study, and meta-analysis of the included literature will be carried out by using RevMan 5.3 software.

RESULTS

This study will evaluate the efficacy and safety of Buzhong Yiqi decoction combined with Western medicine in the treatment of myasthenia gravis by Quantitive MGscore, the number of Tregs cells and the content of anti-acetylcholine receptor antibody (AchR-Ab).

CONCLUSION

This study will provide reliable evidence-based evidence for the clinical application of Buzhong Yiqi decoction combined with Western medicine in the treatment of myasthenia gravis.

ETHICS AND DISSEMINATION

Private information from individuals will not be published. This systematic review also does not involve endangering participant rights. Ethical approval was not required. The results may be published in a peer-reviewed journal or disseminated at relevant conferences.

OSF REGISTRATION NUMBER

DOI 10.17605/OSF.IO/MXUPK.