Gut microbiota, behavior, and nutrition after type 1 diabetes diagnosis: A longitudinal study for supporting data in the metabolic control

A bibliometric study of global trends in T1DM and intestinal flora research

Background

Type 1 diabetes mellitus (T1DM) is a chronic metabolic disease that seriously jeopardizes human physical and mental health and reduces quality of life. Intestinal flora is one of the critical areas of exploration in T1DM research.

Objective

This study aims to explore the research hotspot and development trend of T1DM and intestinal flora to provide research direction and ideas for researchers.

Methods

We used the Web of Science (WOS) Core Collection and searched up to 18 November 2023, for articles on studies of the correlation between T1DM and intestinal flora. CiteSpace, VOSviewers and R package "bibliometrix" were used to conduct this bibliometric analysis.

Results

Eventually, 534 documents met the requirements to be included, and as of 18 November 2023, there was an upward trend in the number of publications in the field, with a significant increase in the number of articles published after 2020. In summary, F Susan Wong (UK) was the author with the most publications (21), the USA was the country with the most publications (198), and the State University System of Florida (the United States) was the institution with the most publications (32). The keywords that appeared more frequently were T cells, fecal transplants, and short-chain fatty acids. The results of keywords with the most robust citation bursts suggest that Faecalibacterium prausnitzii and butyrate may become a focus of future research.

Conclusion

In the future, intestinal flora will remain a research focus in T1DM. Future research can start from Faecalibacterium prausnitzii and combine T cells, fecal bacteria transplantation, and short-chain fatty acids to explore the mechanism by which intestinal flora affects blood glucose in patients with T1DM, which may provide new ideas for the prevention and treatment of T1DM.

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

Introduction

A growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.

Methods

In this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. To obtain unobserved associations in the microbe and disease views, we extract different latent features based on the geometrical structure of microbes and diseases, and project multi-modal latent features into a common subspace. Next, we introduce graph regularization to preserve the local manifold structure of Gaussian interaction profile kernel similarity and add L p , q -norms to the projection matrix to ensure the interpretability and sparsity of the model.

Results

The AUC values for global leave-one-out cross-validation and 5-fold cross validation implemented by MLFLHMDA are 0.9165 and 0.8942+/-0.0041, respectively, which perform better than other existing methods. In addition, case studies of different diseases have demonstrated the superiority of the predictive power of MLFLHMDA. The source code of our model and the data are available on https://github.com/LiangzheZhang/MLFLHMDA_master.

Important denominator between autoimmune comorbidities: a review of class II HLA, autoimmune disease, and the gut

Human leukocyte antigen (HLA) genes are associated with more diseases than any other region of the genome. Highly polymorphic HLA genes produce variable haplotypes that are specifically correlated with pathogenically different autoimmunities. Despite differing etiologies, however, many autoimmune disorders share the same risk-associated HLA haplotypes often resulting in comorbidity. This shared risk remains an unanswered question in the field. Yet, several groups have revealed links between gut microbial community composition and autoimmune diseases. Autoimmunity is frequently associated with dysbiosis, resulting in loss of barrier function and permeability of tight junctions, which increases HLA class II expression levels and thus further influences the composition of the gut microbiome. However, autoimmune-risk-associated HLA haplotypes are connected to gut dysbiosis long before autoimmunity even begins. This review evaluates current research on the HLA-microbiome-autoimmunity triplex and proposes that pre-autoimmune bacterial dysbiosis in the gut is an important determinant between autoimmune comorbidities with systemic inflammation as a common denominator.

Clostridia and Enteroviruses as Synergistic Triggers of Type 1 Diabetes Mellitus

What triggers type 1 diabetes mellitus (T1DM)? One common assumption is that triggers are individual microbes that mimic autoantibody targets such as insulin (INS). However, most microbes highly associated with T1DM pathogenesis, such as coxsackieviruses (COX), lack INS mimicry and have failed to induce T1DM in animal models. Using proteomic similarity search techniques, we found that COX actually mimicked the INS receptor (INSR). Clostridia were the best mimics of INS. Clostridia antibodies cross-reacted with INS in ELISA experiments, confirming mimicry. COX antibodies cross-reacted with INSR. Clostridia antibodies further bound to COX antibodies as idiotype-anti-idiotype pairs conserving INS-INSR complementarity. Ultraviolet spectrometry studies demonstrated that INS-like Clostridia peptides bound to INSR-like COX peptides. These complementary peptides were also recognized as antigens by T cell receptor sequences derived from T1DM patients. Finally, most sera from T1DM patients bound strongly to inactivated Clostridium sporogenes, while most sera from healthy individuals did not; T1DM sera also exhibited evidence of anti-idiotype antibodies against idiotypic INS, glutamic acid decarboxylase, and protein tyrosine phosphatase non-receptor (islet antigen-2) antibodies. These results suggest that T1DM is triggered by combined enterovirus-Clostridium (and possibly combined Epstein-Barr-virus-Streptococcal) infections, and the probable rate of such co-infections approximates the rate of new T1DM diagnoses.