Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Nontoxigenic Bacteroides fragilis: A double-edged sword

The contribution of commensal microbes to human health and disease is unknown. Bacteroides fragilis (B. fragilis) is an opportunistic pathogen and a common colonizer of the human gut. Nontoxigenic B. fragilis (NTBF) and enterotoxigenic B. fragilis (ETBF) are two kinds of B. fragilis. NTBF has been shown to affect the host immune system and interact with gut microbes and pathogenic microbes. Previous studies indicated that certain strains of B. fragilis have the potential to serve as probiotics, based on their observed relationship with the immune system. However, several recent studies have shown detrimental effects on the host when beneficial gut bacteria are found in the digestive system or elsewhere. In some pathological conditions, NTBF may have adverse reactions. This paper presents a comprehensive analysis of NTBF ecology from the host-microbe perspective, encompassing molecular disease mechanisms analysis, bacteria-bacteria interaction, bacteria-host interaction, and the intricate ecological context of the gut. Our review provides much-needed insights into the precise application of NTBF.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

Introduction

With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses.

Method

In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model.

Results and discussion

Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

A comparative study of the gut microbiome in Indian children with type 1 diabetes and healthy controls

INTRODUCTION

Type 1 diabetes mellitus (T1DM) occurs in genetically susceptible individuals due to certain environmental triggers causing destruction of insulin secreting beta cells. One of the environmental triggers studied recently in the pathogenesis and progression of T1DM is the role of gut microbiome.

OBJECTIVES

(1) To compare the gut microbiome profile of T1DM children with healthy age, gender, and body mass index (BMI) matched controls. (2) To assess the relationship of abundance of genera with glycemic control in children with T1DM.

METHODS

Cross-sectional, case-control study. Sixty-eight children with T1DM and 61 age-, gender-, and BMI-matched healthy controls were enrolled. QIAamp Fast DNA Stool Mini kit protocol and reagents were used for DNA isolation and Miseq sequencing platform for targeted gene sequencing.

RESULTS

Alpha and beta diversity analysis showed no significant differences in the abundance of microbes between the groups. At phylum level, Firmicutes was the dominant phylum followed by Actinobacteria and Bacteroidota in both groups. Analysis of microbiome at the genera level showed that percentage abundance for Parasutterella was higher in children with T1DM as compared to the healthy group (p < .05). A linear regression analysis showed that increase in abundance of Haemophilus (adjusted R2 = -1.481 p < .007) was associated with a significant decrease in glycated hemoglobin (HbA1c) concentrations (p < .05).

CONCLUSION

Our comparative study of gut microbiome profile showed significant differences in the taxonomial composition between Indian children with T1DM and healthy controls. Short chain fatty acid producers may play an important role in glycemic control.

Gut microbiota in relationship to diabetes mellitus and its late complications with a focus on diabetic foot syndrome: A review

Diabetes mellitus is a chronic disease affecting glucose metabolism. The pathophysiological reactions underpinning the disease can lead to the development of late diabetes complications. The gut microbiota plays important roles in weight regulation and the maintenance of a healthy digestive system. Obesity, diabetes mellitus, diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are all associated with a microbial imbalance in the gut. Modern technical equipment and advanced diagnostic procedures, including xmolecular methods, are commonly used to detect both quantitative and qualitative changes in the gut microbiota. This review summarises collective knowledge on the role of the gut microbiota in both types of diabetes mellitus and their late complications, with a particular focus on diabetic foot syndrome.

Research progress on the mechanism of TCM regulating intestinal microbiota in the treatment of DM mellitus

In recent years, with the improvement of people's living standards, the incidence of DM has increased year by year in China. DM is a common metabolic syndrome characterized by hyperglycemia caused by genetic, environmental and other factors. At the same time, long-term suffering from DM will also have an impact on the heart, blood vessels, eyes, kidneys and nerves, and associated serious diseases. The human body has a large and complex gut microbiota, which has a significant impact on the body's metabolism. Research shows that the occurrence and development of DM and its complications are closely related to intestinal microbiota. At present, western medicine generally treats DM with drugs. The hypoglycemic effect is fast and strong, but it can have a series of side effects on the human body. Compared with western medicine, Chinese medicine has its unique views and methods in treating DM. TCM can improve symptoms and treat complications by improving the imbalance of microbiota in patients with DM. Its characteristics of health, safety, and reliability are widely accepted by the general public. This article reviews the relationship between intestinal microbiota and DM, as well as the mechanism of TCM intervention in DM by regulating intestinal microbiota.

Xiasangju alleviate metabolic syndrome by enhancing noradrenaline biosynthesis and activating brown adipose tissue

Metabolic syndrome (MetS) is a clinical condition associated with multiple metabolic risk factors leading to type 2 diabetes mellitus and other metabolic diseases. Recent evidence suggests that modulating adipose tissue to adaptive thermogenesis may offer therapeutic potential for MetS. Xiasangju (XSJ) is a marketed drug and dietary supplement used for the treatment of metabolic disease with anti-inflammatory activity. This study investigated the therapeutic effects of XSJ and the underlying mechanisms affecting the activation of brown adipose tissue (BAT) in MetS. The results revealed that XSJ ameliorated MetS by enhancing glucose and lipid metabolism, leading to reduced body weight and abdominal circumference, decreased adipose tissue and liver index, and improved blood glucose tolerance. XSJ administration stimulated catecholamine biosynthesis, increasing noradrenaline (NA) levels and activating NA-mediated proteins in BAT. Thus, BAT enhanced thermogenesis and oxidative phosphorylation (OXPHOS). Moreover, XSJ induced changes in gut microbiota composition, with an increase in Oscillibacter abundance and a decrease in Bilophila, Candidatus Stoquefichus, Holdemania, Parasutterella and Rothia. XSJ upregulated the proteins associated with intestinal tight junctions corresponding with lower serum lipopolysaccharide (LPS), tumor necrosis factor α (TNF-α) monocyte chemoattractant protein-1 (MCP-1) and interleukin-6 (IL-6) levels to maintain NA signaling transport. In summary, XSJ may alleviate MetS by promoting thermogenesis in BAT to ultimately boost energy metabolism through increasing NA biosynthesis, strengthening intestinal barrier integrity and reducing low-grade inflammation. These findings suggest XSJ has potential as a natural therapeutic agent for the treatment of MetS.

Alteration in gut microbiota is associated with immune imbalance in Graves' disease

Background

Graves' disease (GD), characterized by immune aberration, is associated with gut dysbiosis. Despite the growing interest, substantial evidence detailing the precise impact of gut microbiota on GD's autoimmune processes remains exceedingly rare.

Objective

This study was designed to investigate the influence of gut microbiota on immune dysregulation in GD.

Methods

It encompassed 52 GD patients and 45 healthy controls (HCs), employing flow cytometry and enzyme-linked immunosorbent assay to examine lymphocyte and cytokine profiles, alongside lipopolysaccharide (LPS) levels. Gut microbiota profiles and metabolic features were assessed using 16S rRNA gene sequencing and targeted metabolomics.

Results

Our observations revealed a disturbed B-cell distribution and elevated LPS and pro-inflammatory cytokines in GD patients compared to HCs. Significant differences in gut microbiota composition and a marked deficit in short-chain fatty acid (SCFA)-producing bacteria, including ASV263(Bacteroides), ASV1451(Dialister), and ASV503(Coprococcus), were observed in GD patients. These specific bacteria and SCFAs showed correlations with thyroid autoantibodies, B-cell subsets, and cytokine levels. In vitro studies further showed that LPS notably caused B-cell subsets imbalance, reducing conventional memory B cells while increasing naïve B cells. Additionally, acetate combined with propionate and butyrate showcased immunoregulatory functions, diminishing cytokine production in LPS-stimulated cells.

Conclusion

Overall, our results highlight the role of gut dysbiosis in contributing to immune dysregulation in GD by affecting lymphocyte status and cytokine production.

Research progress in the treatment of an immune system disease-type 1 diabetes-by regulating the intestinal flora with Chinese medicine and food homologous drugs

Type 1 diabetes (T1D) is a specific autoimmune disease related to genetic and autoimmune factors. Recent studies have found that the intestinal flora is one of the important environmental factors in the development of T1D. The gut microbiota is the largest microbiota in the human body and has a significant impact on material and energy metabolism. Related studies have found that the intestinal floras of T1D patients are unbalanced. Compared with normal patients, the abundance of beneficial bacteria is reduced, and various pathogenic bacteria are significantly increased, affecting the occurrence and development of diabetes. Medicinal and food homologous traditional Chinese medicine (TCM) has a multicomponent, multitarget, and biphasic regulatory effect. Its chemical composition can increase the abundance of beneficial bacteria, improve the diversity of the intestinal flora, reduce blood sugar, and achieve the purpose of preventing and treating T1D by regulating the intestinal flora and its metabolites. Therefore, based on a review of T1D, intestinal flora, and TCM derived from medicine and food, this review describes the relationship between T1D and the intestinal flora, as well as the research progress of TCM interventions for T1D through regulation of the intestinal flora. Medicine and food homologous TCM has certain advantages in treating diabetes and regulating the intestinal flora. It can be seen that there is still great research space and broad development prospects for the treatment of diabetes by regulating the intestinal flora with drug and food homologous TCM.

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

BACKGROUND

Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine.

OBJECTIVE

We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies.

METHODS

We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility.

RESULTS

After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant.

CONCLUSIONS

The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

A unicentric cross-sectional observational study on chronic intestinal inflammation in total colonic aganglionosis: beware of an underestimated condition

BACKGROUND

Inflammatory Bowel Diseases (IBD) are known to occur in association with Hirschsprung disease (HSCR). Most of cases are represented by Crohn Disease (CD) occurring in patients with Total Colonic Aganglionosis (TCSA) with an estimated prevalence of around 2%. Based on these considerations and on a number of provisional data belonging to our Center for Digestive Diseases, we developed a unicentric cross-sectional observational study aimed at describing phenotype, genotype, pathology and metagenomics of all patients with TCSA and Crohn-like lesions.

RESULTS

Out of a series of 62 eligible TCSA patients, 48 fulfilled inclusion criteria and were enrolled in the study. Ten patients did not complete the study due to non-compliance or withdrawal of consent and were subsequently dropped out. A total of 38 patients completed the study. All patients were tested for chronic intestinal inflammation by a combination of fecal calprotectine (FC) or occult fecal blood (OFB) and underwent fecal metagenomics. Nineteen (50%) tested positive for FC, OFB, or both and subsequently underwent retrograde ileoscopy. Fourteen patients (36.8%) presented Crohn-like lesions, occurring after a median of 11.5 years after surgery (range 8 months - 21.5 years). No statistically significant differences regarding demographic, phenotype and genotype were observed comparing patients with and without lesions, except for need for blood transfusion that was more frequent in those with lesions. Faecal microbiome of patients with lesions (not that of caregivers) was less biodiverse and characterized by a reduction of Bacteroidetes, and an overabundance of Proteobacteria. FC tested negative in 3/14 patients with lesions (21%).

CONCLUSIONS

Our study demonstrated an impressive 10-folds higher incidence of chronic inflammation in TCSA. Up to 50% of patients may develop IBD-like lesions postoperatively. Nonetheless, we failed in identifying specific risk factors to be used to implement prevention strategies. Based on the results of our study, we suggest screening all TCSA patients with retrograde ileoscopy regardless of FC/OFB values. The frequency of endoscopic assessments and the role of FC/OFB screening in prompting endoscopy is yet to be determined.

Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar's test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar's test results found statistically significant differences between different Machine Learning approaches.

Gut-microbiota in children and adolescents with obesity: inferred functional analysis and machine-learning algorithms to classify microorganisms

The fecal microbiome of 55 obese children and adolescents (BMI-SDS 3.2 ± 0.7) and of 25 normal-weight subjects, matched both for age and sex (BMI-SDS - 0.3 ± 1.1) was analysed. Streptococcus, Acidaminococcus, Sutterella, Prevotella, Sutterella wadsworthensis, Streptococcus thermophilus, and Prevotella copri positively correlated with obesity. The inferred pathways strongly associated with obesity concern the biosynthesis pathways of tyrosine, phenylalanine, tryptophan and methionine pathways. Furthermore, polyamine biosynthesis virulence factors and pro-inflammatory lipopolysaccharide biosynthesis pathway showed higher abundances in obese samples, while the butanediol biosynthesis showed low abundance in obese subjects. Different taxa strongly linked with obesity have been related to an increased risk of multiple diseases involving metabolic pathways related to inflammation (polyamine and lipopolysaccharide biosynthesis). Cholesterol, LDL, and CRP positively correlated with specific clusters of microbial in obese patients. The Firmicutes/Bacteroidetes-ratio was lower in obese samples than in controls and differently from the literature we state that this ratio could not be a biomarker for obesity.

HLA-Haplotypes Influence Microbiota Structure in Northwestern Mexican Schoolchildren Predisposed for Celiac Disease or Type 1 Diabetes

To contribute to and elucidate the participation of microbiota in celiac disease (CD) and type 1 diabetes (T1D) development, we evaluated the influence of HLA haplotypes, familial risk, and diet on the microbiota of schoolchildren. We conducted a cross-sectional study on 821 apparently healthy schoolchildren, genotyping HLA DQ2/DQ8, and registering familial risk. We analyzed the fecal microbiota using 16S rRNA gene sequencing, and autoantibodies for CD or T1D by ELISA. After analyses, we created three groups: at-high-risk children (Group 1), at-high-risk children plus autoantibodies (Group 2), and nonrisk children (Group 3). HLA influenced the microbiota of Groups 1 and 2, decreasing phylogenetic diversity in comparison to Group 3. The relative abundance of Oscillospiraceae UCG_002, Parabacteroides, Akkermansia, and Alistipes was higher in Group 3 compared to Groups 1 and 2. Moreover, Oscillospiraceae UCG_002 and Parabacteroides were protectors of the autoantibodies' positivity (RRR = 0.441 and RRR = 0.034, respectively). Conversely, Agathobacter was higher in Group 2, and Lachnospiraceae was in both Groups 1 and 2. Lachnospiraceae correlated positively with the sucrose degradation pathway, while the principal genera in Group 3 were associated with amino acid biosynthesis pathways. In summary, HLA and familial risk influence microbiota composition and functionality in children predisposed to CD or T1D, increasing their autoimmunity risk.

Spatial transcriptomes and microbiota reveal immune mechanism that respond to pathogen infection in the posterior intestine of Sebastes schlegelii

The intestine is a site of immune cell priming at birth. Therefore, spatial transcriptomes were performed to define how the transcriptomic landscape was spatially organized in the posterior intestine of Sebastes schlegelii following Edwardsiella piscicida infection. In the healthy condition, we identified a previously unappreciated molecular regionalization of the posterior intestine. Following bacterial infection, most immune-related genes were identified in mucosa layer. Moreover, investigation of immune-related genes and genes in immune-related KEGG pathways based on spatial transcriptomes shed light on which sections of these genes are in the posterior intestine. Meanwhile, the high expression of genes related to regeneration also indicated that the posterior intestine was responding to the invasion of pathogens by constantly proliferating new cells. In addition, the increasing microbiota communities indicated that these bacteria maintained posterior intestine integrity and shaped the mucosal immune system. Taken together, spatial transcriptomes and microbiota compositions have significant implications for understanding the immune mechanism that responds to E. piscicida infection in the posterior intestine of S. schlegelii, which also provides a theoretical basis for the spatial distribution of immune genes and changes in bacterial flora in other teleosts in the process of resisting pathogens.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

BACKGROUND

Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.

OBJECTIVE

We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.

METHODS

We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes.

RESULTS

We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes.

DISCUSSION

The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Functional and Taxonomic Traits of the Gut Microbiota in Type 1 Diabetes Children at the Onset: A Metaproteomic Study

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic β-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients' GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of β-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

Pathophysiology of Type 1 Diabetes and Gut Microbiota Role

Type 1 diabetes (T1D) is a multifactorial autoimmune disease driven by T-cells against the insulin-producing islet β-cells, resulting in a marked loss of β-cell mass and function. Although a genetic predisposal increases susceptibility, the role of epigenetic and environmental factors seems to be much more significant. A dysbiotic gut microbial profile has been associated with T1D patients. Moreover, new evidence propose that perturbation in gut microbiota may influence the T1D onset and progression. One of the prominent features in clinically silent phase before the onset of T1D is the presence of a microbiota characterized by low numbers of commensals butyrate producers, thus negatively influencing the gut permeability. The loss of gut permeability leads to the translocation of microbes and microbial metabolites and could lead to the activation of immune cells. Moreover, microbiota-based therapies to slow down disease progression or reverse T1D have shown promising results. Starting from this evidence, the correction of dysbiosis in early life of genetically susceptible individuals could help in promoting immune tolerance and thus in reducing the autoantibodies production. This review summarizes the associations between gut microbiota and T1D for future therapeutic perspectives and other exciting areas of research.

Safety and efficacy of fecal microbiota transplantation for autoimmune diseases and autoinflammatory diseases: A systematic review and meta-analysis

Objective

To evaluate the safety and efficacy of fecal microbiota transplantation for autoimmune diseases and autoinflammatory diseases.

Methods

Relevant literature was retrieved from the PubMed database, Embase database, Cochrane Library database, etc. The search period is from the establishment of the database to January 2022. The outcomes include clinical symptoms, improvement in biochemistry, improvement in intestinal microbiota, improvement in the immune system, and adverse events. Literature screening and data extraction were independently carried out by two researchers according to the inclusion and exclusion criteria, and RevMan 5.3 software was used for statistics and analysis.

Results

Overall, a total of 14 randomized controlled trials (RCTs) involving six types of autoimmune diseases were included. The results showed the following. 1) Type 1 diabetes mellitus (T1DM): compared with the autologous fecal microbiota transplantation (FMT) group (control group), the fasting plasma C peptide in the allogenic FMT group at 12 months was lower. 2) Systemic sclerosis: at week 4, compared with one of two placebo controls, three patients in the experimental group reported a major improvement in fecal incontinence. 3) Ulcerative colitis, pediatric ulcerative colitis, and Crohn's disease: FMT may increase clinical remission, clinical response, and endoscopic remission for patients with ulcerative colitis and increase clinical remission for patients with Crohn's disease. 4) Psoriatic arthritis: there was no difference in the ratio of ACR20 between the two groups.

Conclusion

Based on current evidence, the application of FMT in the treatment of autoimmune diseases is effective and relatively safe, and it is expected to be used as a method to induce remission of active autoimmune diseases.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021235055, identifier CRD42021235055.

Gut Microbiota Functional Traits, Blood pH, and Anti-GAD Antibodies Concur in the Clinical Characterization of T1D at Onset

Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and ¹H-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium, Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia, Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA_1c levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D.

Alterations of the Gut Microbiota in Patients with Diabetic Nephropathy

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease. Accumulating studies have implied a critical role for the gut microbiota in diabetes mellitus (DM) and DN. However, the precise roles and regulatory mechanisms of the gut microbiota in the pathogenesis of DN remain largely unclear. In this study, metagenomics sequencing was performed using fecal samples from healthy controls (CON) and type 2 diabetes mellitus (T2DM) patients with or without DN. Fresh fecal samples from 15 T2DM patients without DN, 15 DN patients, and 15 age-, gender-, and body mass index (BMI)-matched healthy controls were collected. The compositions and potential functions of the gut microbiota were estimated. Although no difference of gut microbiota α and β diversity was observed between the CON, T2DM, and DN groups, the relative abundances of butyrate-producing bacteria (Clostridium, Eubacterium, and Roseburia intestinalis) and potential probiotics (Lachnospira and Intestinibacter) were significantly reduced in T2DM and DN patients. Besides, Bacteroides stercoris was significantly enriched in fecal samples from patients with DN. Moreover, Clostridium sp. 26_22 was negatively associated with serum creatinine (P < 0.05). DN patients could be accurately distinguished from CON by Clostridium sp. CAG_768 (area under the curve [AUC] = 0.941), Bacteroides propionicifaciens (AUC = 0.905), and Clostridium sp. CAG_715 (AUC = 0.908). DN patients could be accurately distinguished from T2DM patients by Pseudomonadales, Fusobacterium varium, and Prevotella sp. MSX73 (AUC = 0.889). Regarding the potential bacterial functions of the gut microbiota, the citrate cycle, base excision repair, histidine metabolism, lipoic acid metabolism, and bile acid biosynthesis were enriched in DN patients, while selenium metabolism and branched-chain amino acid biosynthesis were decreased in DN patients.

IMPORTANCE Gut microbiota imbalance is found in fecal samples from DN patients, in which Roseburia intestinalis is significantly decreased, while Bacteroides stercoris is increased. There is a significant correlation between gut microbiota imbalance and clinical indexes related to lipid metabolism, glucose metabolism, and renal function. The gut microbiota may be predictive factors for the development and progression of DN, although further studies are warranted to illustrate their regulatory mechanisms.

The Effects of Time-Restricted Eating on Metabolism and Gut Microbiota: A Real-Life Study

The metabolic benefits of time-restricted eating (TRE) in humans are statistically significant but not clinically relevant. Few data are available about the effects of TRE on the gut microbiota. We compared the effects of a TRE regimen (<12 h feeding; n = 25) with a time-unrestricted (TUE) regimen (>12 h feeding; n = 24), on the clinical and dietary variables and gut-microbiota composition in patients with obesity, who were subjected for 12 weeks to the same caloric restriction. Median weight loss was 4.0 kg and 2.2 kg in the TRE and TUE groups, respectively, with a between-group borderline difference (p = 0.049). No significant between-group difference was found in other dietary, anthropometric, or laboratory variables. There were no substantial between-group differences in alpha and beta diversity or gut-microbiota composition. The TRE group showed a significant increase in the frequency of Lachnospiraceae, Parasutterella, and Romboutsia at the study’s end. A TRE regimen induced small changes both in metabolic/dietary variables and in the gut-microbiota composition, with respect to the TUE. The microbial changes we have found were of uncertain clinical significance.

Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes

The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic β cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.

Dietary Conjugated Linoleic Acid Reduces Body Weight and Fat in Snord116m+/p- and Snord116m-/p- Mouse Models of Prader-Willi Syndrome

Prader–Willi Syndrome (PWS) is a human genetic condition that affects up to 1 in 10,000 live births. Affected infants present with hypotonia and developmental delay. Hyperphagia and increasing body weight follow unless drastic calorie restriction is initiated. Recently, our laboratory showed that one of the genes in the deleted locus causative for PWS, Snord116, maintains increased expression of hypothalamic Nhlh2, a basic helix–loop–helix transcription factor. We have previously also shown that obese mice with a deletion of Nhlh2 respond to a conjugated linoleic acid (CLA) diet with weight and fat loss. In this study, we investigated whether mice with a paternal deletion of Snord116 (Snord116^m+/p−) would respond similarly. We found that while Snord116^m+/p− mice and mice with a deletion of both Snord116 alleles were not significantly obese on a high-fat diet, they did lose body weight and fat on a high-fat/CLA diet, suggesting that the genotype did not interfere with CLA actions. There were no changes in food intake or metabolic rate, and only moderate differences in exercise performance. RNA-seq and microbiome analyses identified hypothalamic mRNAs, and differentially populated gut bacteria, that support future mechanistic analyses. CLA may be useful as a food additive to reduce obesity in humans with PWS.

Taxonomic and Functional Fecal Microbiota Signatures Associated With Insulin Resistance in Non-Diabetic Subjects With Overweight/Obesity Within the Frame of the PREDIMED-Plus Study

OBJECTIVE

An altered gut microbiota has been associated with insulin resistance, a metabolic dysfunction consisting of cellular insulin signaling impairment. The aim of the present study is to determine the taxonomic and functional fecal microbiota signatures associated with HOMA-IR index in a population with high cardiovascular risk.

METHODS

A total of 279 non-diabetic individuals (55-75 years aged) with overweight/obesity and metabolic syndrome were stratified according to tertiles of HOMA-IR index. Blood biochemical parameters, anthropometric measurements and fecal samples were collected at baseline. Fecal microbial DNA extraction, 16S amplicon sequencing and bioinformatics analysis were performed.

RESULTS

Desulfovibrio, Odoribacter and Oscillospiraceae UCG-002 were negatively associated with HOMA-IR index, whereas predicted total functional abundances revealed gut metabolic modules mainly linked to amino acid degradation. Butyricicoccus, Erysipelotrichaceae UCG-003, Faecalibacterium were positively associated with HOMA-IR index, whereas predicted total functional abundances revealed gut metabolic modules mainly linked to saccharide degradation. These bacteria contribute differentially to the gut metabolic modules, being the degree of contribution dependent on insulin resistance. Both taxa and gut metabolic modules negatively associated to HOMA-IR index were linked to mechanisms involving sulfate reducing bacteria, improvement of intestinal gluconeogenesis and production of acetate. Furthermore, both taxa and gut metabolic modules positively associated to HOMA-IR index were linked to production and mechanisms of action of butyrate.

CONCLUSIONS

Specific taxonomic and functional fecal microbiota signatures associated with insulin resistance were identified in a non-diabetic population with overweight/obesity at high cardiovascular risk. These findings suggest that tailoring therapies based on specific fecal microbiota profiles could be a potential strategy to improve insulin sensitivity.

Impact of type 1 diabetes on the composition and functional potential of gut microbiome in children and adolescents: possible mechanisms, current knowledge, and challenges

Diabetes prevalence and incidence among youth have been increasing globally. Type 1 Diabetes (T1D) in children or adolescents accounts for 5-10% of all diagnosed cases of diabetes. Emerging evidence indicates that genetic factors, especially genes in the human leukocyte antigen region, are not the only factors involved in the predisposition of an individual to T1D. The pathogenesis and development of T1D is driven by both genetic predisposition and environmental factors. Studies indicate that gut microbiota is one of the potential environmental influencers involved in the pathophysiology of TID. Gut microbiota mediates the development of diabetes by altering intestinal permeability, modifying intestinal immunity, and molecular mimicry. The gut microbial diversity, taxonomic profile, and functional potential of gut microbes are significantly altered in individuals with T1D as compared to healthy individuals. However, studies are still needed to identify the specific microbes and microbial metabolites that are involved in the development and pathogenesis of T1D. This will help the development of microbiome-based therapeutic strategies for the prevention and treatment of T1D. The present review article highlights the following: (i) the current knowledge and knowledge gaps in understanding the association between T1D and gut microbiome specifically focusing on the composition and functional potential of gut microbiome in children and adolescents, (ii) the possible mechanisms involved in gut microbiome-mediated T1D pathogenesis, and (iii) challenges and future direction in this field.Abbreviations: B/F ratio: Bacteroidetes to Firmicutes ratio; F/B ratio: Firmicutes to Bacteroidetes ratio; FDR: First-degree relatives; GPR: G protein-coupled receptors; HLA: human leucocyte antigen; IL: interleukin; IFN- γ: interferon-γ; KEGG: Kyoto Encyclopedia of Genes and Genomes; LPS: lipopolysaccharide; mTOR: mammalian target of rapamycin; PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; SCFA: short chain fatty acids; T1D: Type 1 diabetes; T2D: Type 2 diabetes; TJ: tight junction; T_regs: regulatory T cells.

The Effects of Erchen Decoction on Gut Microbiota and Lipid Metabolism Disorders in Zucker Diabetic Fatty Rats

Obesity is a chronic metabolic disease caused by genetic and environmental factors that has become a serious global health problem. There is evidence that gut microbiota is closely related to the occurrence and development of obesity. Erchen Decoction (ECD), a traditional Chinese medicine, has been widely used for clinical treatment and basic research of obesity and related metabolic diseases in recent years. It can significantly improve insulin resistance (IR) and lipid metabolism disorders. However, there is no microbiological study on its metabolic regulation. In this study, we investigated the effects of ECD on obesity, especially lipid metabolism and the composition and function of gut microbiota in Zucker diabetic fatty (ZDF) rats, and explored the correlation between the biomarkers of gut microbiota and metabolite and host phenotype. The results showed that ECD could reduce body weight, improve IR and lipid metabolism, and reduce the concentration of free fatty acids (FFA) released from white adipose tissue (WAT) due to excessive lipolysis by interfering with the insulin receptor substrate 1 (IRS1)/protein kinase B (AKT)/protein kinase A (PKA)/hormone-sensitive triglyceride lipase (HSL) signaling pathway in ZDF rats. Additionally, ECD gradually adjusted the overall structure of changed gut microbiota, reversed the relative abundance of six genera, and changed the function of gut microbiota by reducing the content of propionic acid, a metabolite of gut microbiota, in ZDF rats. A potentially close relationship between biomarkers, especially Prevotella, Blautia, and Holdemania, propionic acid and host phenotypes were demonstrated through correlation analysis. The results suggested that the beneficial effects of ECD on obesity, especially lipid metabolism disorders, are related to the regulation of gut microbiota in ZDF rats. This provides a basis for further research on the mechanism and clinical application of ECD to improve obesity via gut microbiota.

Insights from Bacteroides Species in Children with Type 1 Diabetes

In our previous study the enrichment of the intestinal proteome of type 1 diabetes (T1D) children with Bacteroides proteins was observed, which led us to our current study that aimed to isolate and characterize Bacteroides species from fecal samples of T1D and control children. Repetitive sequence-based PCR (rep-PCR) was used for typing the isolated Bacteroides species. The antibiotic susceptibility and mucinolytic activity of the isolates was determined. The quantification of specific bacterial groups in the fecal samples was determined by qPCR. The ability to adhere and invade the human colonic cell line HT29-MTX-E12 of strains of P. dorei, B. uniformis and P. distasonis was determined and their whole genome sequencing was performed. The results showed similar numbers of Bacteroides species in T1D and control samples, but unique Bacteroides species and a higher recovery of P. distasonis from T1D samples was observed. Rep-PCR grouped the different Bacteroides species, but no discrimination by origin was achieved. T1D children showed a significant increase in Proteobacteria and a depletion in Lactobacillus sp. All tested P. dorei, B. uniformis and P. distasonis were able to adhere to HT29-MTX-E12 cells but significant differences (p < 0.05) in the ability to invade was observed. The highest ability to invade was exhibited by P. distasonis PtF D14MH1 and P. dorei PtFD16P1, while B. uniformis strains were unable to invade. The damage to tight junctions was also observed. The presence of Lactobacillus sp. inhibited the invasion ability of P. distasonis PtF D14MH1 but not P. dorei PtFD16P1. Sequences of agonist peptides of the human natural preproinsulin and the insulin B chain insB:9-23 peptide mimics were identified. The results reported in our study stresses the continued efforts required to clarify the link between T1D and gut microbiota.

Intestinal Microbiota in Common Chronic Inflammatory Disorders Affecting Children

The incidence and prevalence rate of chronic inflammatory disorders is on the rise in the pediatric population. Recent research indicates the crucial role of interactions between the altered intestinal microbiome and the immune system in the pathogenesis of several chronic inflammatory disorders in children, such as inflammatory bowel disease (IBD) and autoimmune diseases, such as type 1 diabetes mellitus (T1DM) and celiac disease (CeD). Here, we review recent knowledge concerning the pathogenic mechanisms underlying these disorders, and summarize the facts suggesting that the initiation and progression of IBD, T1DM, and CeD can be partially attributed to disturbances in the patterns of composition and abundance of the gut microbiota. The standard available therapies for chronic inflammatory disorders in children largely aim to treat symptoms. Although constant efforts are being made to maximize the quality of life for children in the long-term, sustained improvements are still difficult to achieve. Additional challenges are the changing physiology associated with growth and development of children, a population that is particularly susceptible to medication-related adverse effects. In this review, we explore new promising therapeutic approaches aimed at modulation of either gut microbiota or the activity of the immune system to induce a long-lasting remission of chronic inflammatory disorders. Recent preclinical studies and clinical trials have evaluated new approaches, for instance the adoptive transfer of immune cells, with genetically engineered regulatory T cells expressing antigen-specific chimeric antigen receptors. These approaches have revolutionized cancer treatments and have the potential for the protection of high-risk children from developing autoimmune diseases and effective management of inflammatory disorders. The review also focuses on the findings of studies that indicate that the responses to a variety of immunotherapies can be enhanced by strategic manipulation of gut microbiota, thus emphasizing on the importance of proper interaction between the gut microbiota and immune system for sustained health benefits and improvement of the quality of life of pediatric patients.

The role of the gut microbiota on the metabolic status of obese children

Background

The term “metabolically healthy obese (MHO)” denotes a hale and salutary status, yet this connotation has not been validated in children, and may, in fact, be a misnomer. As pertains to obesity, the gut microbiota has garnered attention as conceivably a nosogenic or, on the other hand, protective participator.

Objective

This study explored the characteristics of the fecal microbiota of obese Chinese children and adolescents of disparate metabolic statuses, and the associations between their gut microbiota and circulating proinflammatory factors, such as IL-6, TNF-α, lipopolysaccharide-binding protein (LBP), and a cytokine up-regulator and mediator, leptin.

Results

Based on weight and metabolic status, the 86 Chinese children (ages 5–15 years) were divided into three groups: metabolically healthy obese (MHO, n = 42), metabolic unhealthy obese (MUO, n = 23), and healthy normal weight controls (Con, n = 21). In the MUO subjects, the phylum Tenericutes, as well as the alpha and beta diversity, were significantly reduced compared with the controls. Furthermore, Phylum Synergistetes and genus Bacteroides were more prevalent in the MHO population compared with controls. For the MHO group, Spearman’s correlation analysis revealed that serum IL-6 positively correlated with genus Paraprevotella, LBP was positively correlated with genus Roseburia and Faecalibacterium, and negatively correlated with genus Lactobacillus, and leptin correlated positively with genus Phascolarctobacterium and negatively with genus Dialister (all p < 0.05).

Conclusion

Although there are distinct differences in the characteristic gut microbiota of the MUO population versus MHO, dysbiosis of gut microsystem is already extant in the MHO cohort. The abundance of some metabolism-related bacteria associates with the degree of circulating inflammatory compounds, suggesting that dysbiosis of gut microbiota, present in the MHO children, conceivably serves as a compensatory or remedial response to a surfeit of nutrients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01548-9.