Diabetes type 1: Can it be treated as an autoimmune disorder?

Tea drinking effectively improves symptoms of diabetes and prevents hepatorenal damage in mice

Since type 2 diabetic patients often develop resistance to metformin as the progresses of diabetes, and almost all type 1 diabetic patients need receive insulin injection for hyperglycemia control. It is important to explore novel strategies with different mechanisms for diabetes management. Glucose-induced osmotic diuresis, known as polyuria, is the first clinical symptom in severe type 2 diabetes mellitus (T2DM) and type 1 diabetes mellitus (T1DM). Drinking green tea or black tea effectively mitigates diabetic symptoms including polyuria, polydipsia, polyphagia and hyperglycemia in db/db mice via regulating renal aquaporin 2 and urine transporter A1 (UT-A1), in favor renal water reabsorption. This unique mechanism of action of tea could be useful for the treatment of diabetes in humans. In this study, we found that drinking Large-leaf yellow tea (LYT) for 5 weeks effectively ameliorated polyuria, polydipsia, polyphagia, hyperglycemia and excessive body weight gain, as well as upregulated renal water reabsorption associated proteins, including protein kinase C-alpha (PKC-α), membrane PKC-α and glycosylated UT-A1 in db/db mice. Four-days experiment were also confirmed the rapidly response of these proteins in favor renal water reabsorption and the amelioration of diabetic symptoms by LYT. We also found that green tea drinking effectively mitigated symptoms of diabetes in a mouse model for T1DM via upregulating these proteins. Moreover, green tea drinking prevented hepatorenal damage caused by hyperglycemia as suggested by the reduced levels of aspartate aminotransferase and creatinine in serum and the enhanced antioxidant defense system in liver and kidney. These results suggest the possible application of tea or tea constitutes in the clinical treatment of severe T2DM and T1DM, and the kidney is the target organ.

Inflammatory Mediators in the Oral Fluids and Blood Samples of Type 1 Diabetic Patients, According to Periodontal Status-A Systematic Review

There is currently little information on the immune profile of adult type 1 diabetes patients diagnosed with periodontal disease. The aim of this systematic review is to bring together the known evidence of which inflammatory markers, measured in salivary flow or gingival crevicular fluid and serum blood, are present in both pathologies. Following the Preferred Reporting Items for Systematic reviews and Meta-Analys guidelines, we systematically searched in the PubMed, Web of Science, Scopus and Cochrane Library databases for studies on the associations of different chemokines with type 1 diabetes mellitus and periodontal disease. From a total of 703 patients, of which 526 were patients diagnosed with type 1 diabetes and 215 were controls without diabetes, multiple inflammatory mediators, such as interleukin 8, which showed higher concentrations in the crevicular fluid in several studies of type 1 diabetes patients and a greater severity in its effects on the periodontal status, as well as osteoprotegerin and tumor necrosis factor alpha, have been found elevated in diabetic patients with poor periodontal control. The results suggest that interleukin 8, tumor necrosis factor alpha and osteoprotegerin may be promising novel biomarkers of type 1 diabetes mellitus, and may also indicate the susceptibility profile in these individuals for the worsening of the patient's periodontal status.

Serum and urinary levels of MIF, CD74, DDT and CXCR4 among patients with type 1 diabetes mellitus, type 2 diabetes and healthy individuals: Implications for further research

Background

Macrophage migration inhibitory factor (MIF) is a highly conserved cytokine with pleiotropic properties, mainly pro-inflammatory. MIF seems to exert its pro-inflammatory features by binding to its transmembrane cellular receptor CD74. MIF also has CXCR4, which acts as a co-receptor in this inflammatory process. Apart from MIF, D-dopachrome tautomerase (DDT) or MIF2, which belongs to the MIF superfamily, also binds to receptor CD74. Therefore, these molecules, MIF, CD74, DDT and CXCR4 are suggested to work together orchestrating an inflammatory process. Diabetes mellitus is characterised by chronic low-grade inflammation. Therefore, the aim of the present study was to evaluate serum and urinary levels of the aforementioned molecules among patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) and among healthy controls.

Methods

We enrolled 13 patients with T1DM, 74 patients with T2DM and 25 healthy individuals as controls. Levels of CD74, CXCR4, DDT, and MIF were measured using ELISA Kits according to the manufacturer's instructions.

Results

We documented increased serum MIF levels together with higher urinary CD74 levels among patients with T1DM, when compared to patients with T2DM and healthy adults. In particular, patients with T1DM showed significantly increased levels of MIF compared to T2DM (p = 0.011) and healthy controls (p = 0.0093). CD74 in urine were significantly higher in patients with T1DM compared to those affected with T2DM (p = 0.0302) and healthy group (p = 0.0099). On the contrary, serum CD74 were similar among the three groups. No statistical differences were identified in CXCR4 levels both in serum and in urine of all groups. Patients with T2DM and overweight/obesity had increased urinary levels of CD74, when compared to lean patients with T2DM.

Conclusion

The increased serum MIF levels and urinary CD74 levels among patients with T1DM may be attributed to the autoimmune milieu, which characterises patients with T1DM, when compared to patients with T2DM. These two findings merit further attention as they could pave the way for further research regarding the potential beneficial effects of inhibitors of MIF among patients with T1DM, especially in the early stages of T1DM. Finally, the role of inhibitors of MIF could be further explored in the context of obesity among patients with T2DM.

PLAGL1 overexpression induces cytoplasmic DNA accumulation that triggers cGAS/STING activation

Pancreatic β-cell damage mediated by apoptosis is believed to be a main trigger of type 1 diabetes mellitus (T1DM), which is proposed as an organ-specific autoimmune disease mediated by T cells. Nonetheless, the fundamental origins of T1DM remain uncertain. Here, we illustrate that an increase in PLAGL1 expression induces β-cell apoptosis, as evidenced by mitochondrial membrane impairment and nucleolar degradation. The gene expression levels from cDNA samples were determined using qRT-PCR method. Western blot and Co-immunoprecipitation were applied for protein expression and interactions, respectively. Flow cytometry and TUNEL assay were used to detect pancreatic β cell apoptosis. Female NOD/LtJ mice with recent-onset T1DM has been used in in vivo studies. Glucose-stimulated insulin secretion (GSIS) and glucose tolerance test (GTT) method is used for islet function assessment. Haematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) were performed to evalute histological improvement of islet beta. Subsequent cytoplasmic DNA accumulation triggers DNA senser, the cyclic guanosine monophosphate-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway. STING activation further stimulates downstream IRF3 and NF-kB pathways, thus boost type-I interferon signalling and NF-kB mediated inflammation. These findings elucidate a molecular mechanism linking PLAGL1 induced cell apoptosis to type-I interferon signalling and suggest a potential benefit for targeting cGAS/STING in T1DM treatment.

Macroglossia in endocrine and metabolic disorders: current evidence, perspectives and challenges

Macroglossia is an uncommon condition characterized by chronic, painless and abnormal enlargement of the tongue. A multitude of medical conditions can cause macroglossia. Major endocrine and metabolic disorders associated with macroglossia include genetic, congenital and acquired conditions, such as mucopolysaccharidoses; acquired and congenital hypothyroidism and myxedema; transient neonatal diabetes mellitus; acromegaly and amyloidosis. Macroglossia is often associated (~57-60%) with all types of mucopolysaccharidoses, particularly type I (Hurler syndrome) and type II (Hunter syndrome), being a prominent feature of the disorder. It may also occur in patients with acquired and congenital hypothyroidism and myxedema, being a common sign of congenital hypothyroidism with an approximate prevalence of 12-25% at the time of diagnosis. Macroglossia is a predominant oral finding in subjects with transient neonatal diabetes mellitus (~44%), acromegaly (54-69%) and amyloidosis (10-25%), particularly AL amyloidosis (20-40%) whereas is considered a hallmark of the disease. Secondary to macroglossia various disturbances may occur, such as difficulty in speech or eating, orthodontic anomalies or even more serious conditions including upper airway obstruction or obstructive sleep apnea. Until now, no comprehensive review has been conducted focusing on macroglossia in endocrine and metabolic disorders. The objective of this review is to summarize literature on the etiology and epidemiology of macroglossia in major endocrine and metabolic disorders. It highlights key aspects such as pathophysiology, clinical presentation, diagnostic evaluation, management and prognosis of macroglossia in the context of endocrine and metabolic disorders.

Diet patterns, gut microbiota and metabolic disorders: Perspectives and challenges

The worldwide surge in obesity and associated metabolic disorders is emerging as a significant public health issue for societies and healthcare systems. Available evidence has shown that alterations in the gut microbiota could be implicated in the pathogenesis of obesity and associated disorders. A healthy gut microbiome is characterized by richness and high microbial diversity. Gut microbiota affect how the host responds to diet, and conversely, the host may modify the gut microbiota through changes in dietary habits. Diet can impact and alter the composition, diversity, and species richness of the gut microbiota over time. An unhealthy diet, high in fat and sugar, may lead to decreased microbial diversity, reduced synthesis of metabolites that maintain gut permeability, damage to the mucus layer, increased bacterial translocation and lipopolyssacharide which can trigger endotoxemia, chronic subclinical inflammation and metabolic disorders. Currently, the impact of diet on gut microbial composition and its involvement in the pathogenic mechanisms underlying metabolic disorders is one of the most promising areas of research in nutrition. This special issue has gathered original research articles in topics related to diet patterns, gut microbiota, obesity and associated metabolic disorders as well as brief reports, reviews and perspectives in the wider field of translational and clinical metabolic research. In particular, the aim of this Special Issue was to present evidence connecting gut microbiota with metabolic disorders, explore the underlying mechanisms of this association, and examine how diet patterns may influence this relationship.

Clinical metabolomics: Useful insights, perspectives and challenges

Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.

Causal associations between type 1 diabetes mellitus and cardiovascular diseases: a Mendelian randomization study

BACKGROUND

The presence of type 1 diabetes mellitus (T1DM) has been demonstrated to pose an increased risk for developing cardiovascular diseases (CVDs). However, the causal relationships between T1DM and CVDs remain unclear due to the uncontrolled confounding factors and reverse causation bias of the observational studies.

METHODS

Summary statistics of T1DM and seven CVDs from the largest available genome-wide association studies (GWAS) of European ancestry and FinnGen biobank were extracted for the primary MR analysis, and the analysis was replicated using UK biobank (UKBB) for validation. Three complementary methods: inverse variance weighted (IVW), weighted median, and MR-Egger were used for the MR estimates. The potential pleiotropic effects were assessed by MR-Egger intercept and MR-PRESSO global test. Additionally, multivariable MR (MVMR) analysis was performed to examine whether T1DM has independent effects on CVDs with adjustment of potential confounding factors. Moreover, a two-step MR approach was used to assess the potential mediating effects of these factors on the causal effects between T1DM and CVDs.

RESULTS

Causal effects of T1DM on peripheral atherosclerosis (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.02-1.10; p = 0.002)] and coronary atherosclerosis (OR = 1.03, 95% CI: 1.01-1.05; p = 0.001) were found. The results were less likely to be biased by the horizontal pleiotropic effects (both p values of MR-Egger intercept and MR-PRESSO Global test > 0.05). In the following MVMR analysis, we found the causal effects of T1DM on peripheral atherosclerosis and coronary atherosclerosis remain significant after adjusting for a series of potential confounding factors. Moreover, we found that hypertension partly mediated the causal effects of T1DM on peripheral atherosclerosis (proportion of mediation effect in total effect: 11.47%, 95% CI: 3.23-19.71%) and coronary atherosclerosis (16.84%, 95% CI: 5.35-28.33%). We didn't find significant causal relationships between T1DM and other CVDs, including heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), myocardial infarction (MI) and stroke. For the reverse MR from CVD to T1DM, no significant causal relationships were identified.

CONCLUSION

This MR study provided evidence supporting the causal effect of T1DM on peripheral atherosclerosis and coronary atherosclerosis, with hypertension partly mediating this effect.

Advanced Delivery Strategies for Immunotherapy in Type I Diabetes Mellitus

Type 1 diabetes mellitus (T1DM) has been defined as an autoimmune disease characterised by immune-mediated destruction of the pancreatic β cells, leading to absolute insulin deficiency and hyperglycaemia. Current research has increasingly focused on immunotherapy based on immunosuppression and regulation to rescue T-cell-mediated β-cell destruction. Although T1DM immunotherapeutic drugs are constantly under clinical and preclinical development, several key challenges remain, including low response rates and difficulty in maintaining therapeutic effects. Advanced drug delivery strategies can effectively harness immunotherapies and improve their potency while reducing their adverse effects. In this review, we briefly introduce the mechanisms of T1DM immunotherapy and focus on the current research status of the integration of the delivery techniques in T1DM immunotherapy. Furthermore, we critically analyse the challenges and future directions of T1DM immunotherapy.

Characteristic gene prognostic model of type 1 diabetes mellitus via machine learning strategy

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM.

METHODS

The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO)-support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM.

RESULTS

The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4⁺ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4⁺ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM.

CONCLUSIONS

The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

Obesity in Patients with Type 1 Diabetes: Links, Risks and Management Challenges

Obesity affects large numbers of patients with type 1 diabetes (T1D) across their lifetime, with rates ranging between 2.8% and 37.1%. Patients with T1D and obesity are characterized by the presence of insulin resistance, of high insulin requirements, have a greater cardiometabolic risk and an enhanced risk of developing chronic complications when compared to normal-weight persons with T1D. Dual treatment of obesity and T1D is challenging and no specific guidelines for improving outcomes of both glycemic control and weight management have been established for this population. Nevertheless, although evidence is scarce, a comprehensive approach based on a balanced hypocaloric diet, physical activity and cognitive behavioral therapy by a multidisciplinary team, expert in both obesity and diabetes, remains as the best clinical practice. However, weight loss responses with lifestyle changes alone are limited, so in the "roadmap" of the treatment of obesity in T1D, it will be helpful to include anti-obesity pharmacotherapy despite at present there is a lack of evidence since T1D patients have been excluded from anti-obesity drug clinical trials. In case of severe obesity, bariatric surgery has proven to be of benefit in obtaining a substantial and long-term weight loss and reduction in cardiovascular risk. The near future looks promising with the development of new and more effective anti-obesity treatments and strategies to improve insulin resistance and oxidative stress. Advances in precision medicine may help individualize and optimize the medical management and care of these patients. This review, by gathering current evidence, highlights the need of solid knowledge in all facets of the treatment of patients with obesity and T1D that can only be obtained through high quality well-designed studies.