A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review

Type 1 diabetes (T1D) is an autoimmune condition characterized by the destruction of insulin-producing pancreatic beta cells, leading to lifelong insulin dependence and significant complications. Early detection of T1D is essential to delay disease onset and improve outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for predicting and diagnosing T1D. This systematic review evaluates the current landscape of AI/ML-based approaches for early T1D detection. A comprehensive search across PubMed, EMBASE, Science Direct, and Scopus identified 1447 studies, of which 10 met the inclusion criteria for narrative synthesis after screening and full-text review. The studies utilized diverse ML models, including logistic regression, support vector machines, random forests, and artificial neural networks. The datasets encompassed clinical parameters, genetic risk markers, continuous glucose monitoring (CGM) data, and proteomic and metabolomic biomarkers. The included studies involved a total of 49,172 participants and employed case-control, retrospective cohort, and prospective cohort designs. Models integrating multimodal data achieved the highest predictive accuracy, with area under the curve (AUC) values reaching up to 0.993 in sex-specific models. CGM data and plasma biomarkers, such as CXCL10 and IL-1RA, also emerged as valuable tools for identifying at-risk individuals. While the results highlight the potential of AI/ML in revolutionizing T1D risk stratification and diagnosis, challenges remain. Data heterogeneity and limited model generalizability present barriers to widespread implementation. Future research should prioritize the development of universal frameworks and real-world validation to enhance the reliability and clinical integration of these tools. Ultimately, AI/ML technologies hold transformative potential for clinical practice by enabling earlier diagnosis, guiding targeted interventions, and improving long-term patient outcomes. These advancements could support clinicians in making more informed, timely decisions, thus reducing diagnostic delays and paving the way for personalized prevention strategies in both pediatric and adult populations.

The Impact of Stress on Autoimmune Disorders: Type 1 Diabetes Mellitus and Systemic Lupus Erythematosus

Autoimmune disorders, including type 1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE), are influenced by a combination of genetic, environmental, and immunological factors. Among these, stress, both physical and psychological, has been increasingly recognized as a significant contributor to disease onset and progression. This review explores the current literature on the relationship between stress and autoimmune diseases, focusing on the neuroendocrine pathways, such as the hypothalamic-pituitary-adrenal (HPA) axis, and the effects of glucocorticoids on immune modulation. These mechanisms contribute to clinical manifestations, such as disease flares or progression, highlighting the impact of stress on patient outcomes. Evidence suggests that psychological stress can precipitate the onset of T1DM in genetically predisposed individuals, with immune disruptions occurring before diagnosis. In SLE, both acute and chronic stress, particularly trauma-induced stress, has been linked to increased disease activity and flare-ups, largely due to stress-induced immune dysregulation that disrupts the balance between pro-inflammatory and anti-inflammatory cytokines. Despite the substantial evidence supporting the role of stress in autoimmune disease exacerbation, further research is necessary to fully understand the mechanisms by which stress influences autoimmune diseases and to develop effective stress management.

How do immune cells shape type 1 diabetes? Insights from Mendelian randomization

Objective

The role of immune cells in type 1 diabetes (T1D) is unclear. The aim of this study was to assess the causal effect of different immune cells on T1D using Mendelian randomization (MR).

Methods

A dataset of immune cell phenotypes (numbered from GCST0001391 to GCST0002121) was obtained from the European Bioinformatics Institute, while a T1D dataset (numbered finngen_R10_T1D) was obtained from FinnGen. Single nucleotide polymorphisms meeting the conditions were screened stepwise according to the assumptions of association, independence, and exclusivity. Inverse variance weighted was used as the main method for the MR analysis. MR-Egger was used to assess the horizontal pleiotropy of the results. Cochran's Q and the leave-one-out method were respectively used for the heterogeneity analysis and the sensitivity analysis of the results.

Results

MR analysis showed that effector memory (EM) double-negative (DN) (CD4-CD8-) %T cells [odds ratio (OR) = 1.157, 95% confidence interval (95% CI) = 1.016-1.318, p = 0.028, false discovery rate (FDR) = 0.899], EM CD8br %T cells (OR = 1.049, 95% CI = 1.003-1.098, p = 0.037, FDR = 0.902), CD28 on CD28+CD45RA+CD8br (OR = 1.334, 95% CI = 1.132-1.571, p = 0.001, FDR = 0.044), IgD+CD38dim %lymphocytes (OR = 1.045, 95% CI = 1.002-1.089, p = 0.039, FDR = 0.902), CD80 on monocytes (OR = 1.084, 95% CI = 1.013-1.161, p = 0.020, FDR = 0.834), SSC-A on plasmacytoid dendritic cells (pDCs) (OR = 1.174, 95% CI = 1.004-1.372, p = 0.044, FDR = 0.902), and FSC-A on pDCs (OR = 1.182, 95% CI = 1.011-1.382, p = 0.036, FDR = 0.902) were associated with an increased genetic susceptibility to T1D. Cochran's Q showed that there was heterogeneity for CD28 on the CD28+CD45RA+CD8br results (p = 0.043), whereas there was no heterogeneity for the other results (p ≥ 0.05). The sensitivity analysis showed that the MR analysis results were robust.

Conclusion

The MR analysis demonstrated that seven immune cell phenotypes were associated with an increased genetic susceptibility to T1D. These findings provide a new direction for the pathogenesis of and the drug development for T1D.

Vitamin D in Primary Sjogren's Syndrome (pSS) and the Identification of Novel Single-Nucleotide Polymorphisms Involved in the Development of pSS-Associated Diseases

Sjögren's syndrome (SS) is a chronic autoimmune disorder characterised by lymphocytic infiltration of the exocrine glands, which leads to dryness of the eyes and mouth; systemic manifestations such as arthritis, vasculitis, and interstitial lung disease; and increased risks of lymphoma and cardiovascular diseases. SS predominantly affects women, with a strong genetic component linked to sex chromosomes. Genome-wide association studies (GWASs) have identified numerous single-nucleotide polymorphisms (SNPs) associated with primary SS (pSS), revealing insights into its pathogenesis. The adaptive and innate immune systems are crucial to SS's development, with viral infections implicated as environmental triggers that exacerbate autoimmune responses in genetically susceptible individuals. Moreover, recent research has highlighted the role of vitamin D in modulating immune responses in pSS patients, suggesting its potential therapeutic implications. In this review, we focus on the recently identified SNPs in genes like OAS1, NUDT15, LINC00243, TNXB, and THBS1, which have been associated with increased risks of developing more severe symptoms and other diseases such as fatigue, lymphoma, neuromyelitis optica spectrum disorder (NMOSD), dry eye syndrome (DES), and adverse drug reactions. Future studies should focus on larger, multi-ethnic cohorts with standardised protocols to validate findings and identify new associations. Integrating genetic testing into clinical practise holds promise for improving SS management and treatment strategies, enabling personalised interventions based on comprehensive genetic profiles. By focusing on specific SNPs, vitamin D, and their implications, future research can lead to more effective and personalised approaches for managing pSS and its complications.

Teplizumab's immunomodulatory effects on pancreatic β-cell function in type 1 diabetes mellitus

This review explores the immunomodulatory potential of Teplizumab and its impact on pancreatic β-cell function in T1D. Characterized by the autoimmune destruction of insulin-producing beta cells, T1D's management involves maintaining glycemic control through exogenous insulin. Teplizumab, a humanized monoclonal antibody targeting the CD3 antigen, has shown promise in delaying T1D onset and preserving residual β-cell function. The review employs a narrative approach, synthesizing evidence from diverse clinical trials and studies gathered through a meticulous literature search. It scrutinizes Teplizumab's mechanisms of action, including its influence on autoreactive CD8 + T cells and regulatory T cells, offering insights into its immunological pathways. The synthesis of findings from various trials demonstrates Teplizumab's efficacy in preserving C-peptide levels and reducing exogenous insulin requirements, particularly in recent-onset T1D. Considering Teplizumab's real-world implications, the paper addresses potential obstacles, including side effects, patient selection criteria, and logistical challenges. It also emphasizes exploring combination therapies and personalized treatment strategies to maximize Teplizumab's benefits. The review contributes a nuanced perspective on Teplizumab's clinical implications and future directions in T1D management, bridging theoretical understanding with practical considerations.

Association of HLA Haplotypes with Autoimmune Pathogenesis in Newly Diagnosed Type 1 Romanian Diabetic Children: A Pilot, Single-Center Cross-Sectional Study

BACKGROUND

The increasing incidence of autoimmune diseases in type 1 diabetes mellitus (T1DM) patients highlights the influence of human leukocyte antigen (HLA) haplotypes on their development. This study aims to determine genetic predisposition to autoimmune diseases in T1DM patients, including thyroid disease and celiac diseases, and explore its correlation with vitamin D deficiency.

METHODS

A cross-sectional study involving thirty-six T1DM children was conducted. Typing was performed for the HLA A, B, C, DP, DR, and DQ loci. Regression analysis linked DR-DQ haplotypes to T1DM and the associated conditions.

RESULTS

The most frequent predisposing alleles and haplotypes were HLA-DR3 (70.27%), DQ2 (70.27%), DR3-DQ2 (70.27%), DQB1*02:01 (70.27%), A02 (54.05%), whereas the most prevalent protecting allele was DPB1*04:01 (52.63%). Positive correlations were observed between positive anti-thyroid peroxidase antibodies and the absence of protective alleles (DPB1*04:02, p = 0.036; DPB1*04:01, p = 0.002). Associations were found between the absence of DPB1*04:01 and anti-thyroglobulin antibodies (p = 0.03). HLA allele DPB1*03:01 was linked with vitamin D deficiency (p = 0.021). Positive anti-transglutaminase antibodies correlated with C03:03 (p = 0.026) and DRB1*04:01-DQA1*03-DQB1*03:01 (p < 0.0001) and the lack of DQA1*01:03-DQB1*06:03-DRB1*13:01 (p < 0.0001).

CONCLUSIONS

The predisposing T1DM haplotypes were associated with the presence of anti-transglutaminase and anti-thyroid antibodies, indicating a genetic predisposition to autoimmune diseases.

Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem

Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.

Protein kinase D (PKD) on the crossroad of lipid absorption, synthesis and utilization

Inappropriate lipid levels in the blood, as well as its content and composition in different organs, underlie multiple metabolic disorders including obesity, non-alcoholic fatty liver disease, type 2 diabetes, and atherosclerosis. Multiple processes contribute to the complex metabolism of triglycerides (TGs), fatty acids (FAs), and other lipid species. These consist of digestion and absorption of dietary lipids, de novo FAs synthesis (lipogenesis), uptake of TGs and FAs by peripheral tissues, TGs storage in the intracellular depots as well as lipid utilization for β-oxidation and their conversion to lipid-derivatives. A majority of the enzymatic reactions linked to lipogenesis, TGs synthesis, lipid absorption, and transport are happening at the endoplasmic reticulum, while β-oxidation takes place in mitochondria and peroxisomes. The Golgi apparatus is a central sorting, protein- and lipid-modifying organelle and hence is involved in lipid metabolism as well. However, the impact of the processes taking part in the Golgi apparatus are often overseen. The protein kinase D (PKD) family (composed of three members, PKD1, 2, and 3) is the master regulator of Golgi dynamics. PKDs are also a sensor of different lipid species in distinct cellular compartments. In this review, we discuss the roles of PKD family members in the regulation of lipid metabolism including the processes executed by PKDs at the Golgi apparatus. We also discuss the role of PKDs-dependent signaling in different cellular compartments and organs in the context of the development of metabolic disorders.

Teplizumab in Type 1 Diabetes Mellitus: An Updated Review

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin. Despite the advancements in insulin delivery methods, the suboptimal outcomes of these methods have triggered the search for therapies that may prevent or reverse the disease. Given the autoimmune aetiology of T1DM, therapies counteracting the immune-mediated destruction of the β-cells are the obvious target. Although several treatment strategies have been attempted to target cellular, humoral and innate immunity, very few have had a clinically meaningful impact. Of all the available immunomodulatory agents, cluster of differentiation (CD) 3 antibodies have exhibited the most promising preclinical and clinical results. Muromonab-CD3, which also happened to be a murine CD3 antibody, was the first monoclonal antibody approved for clinical use and was primarily indicated for graft rejection. The adverse effects associated with muromonab-CD3 led to its withdrawal. Teplizumab, a newer CD3 antibody, has a better side-effect profile because of its humanized nature and non-Fc-receptor-binding domain. In November 2022, teplizumab became the first immunomodulatory agent to be licensed by the US Food and Drug Administration for delaying the onset of T1DM in high-risk adults and children over 8 years old. The mechanism seems to be enhancing regulatory T-cell activity and promoting immune tolerance. This article reviews the mechanism of action and the clinical trials of teplizumab in individuals with T1DM or at risk of developing the disease.

Nature vs. nurture: FOXP3, genetics, and tissue environment shape Treg function

The importance of regulatory T cells (Tregs) in preventing autoimmunity has been well established; however, the precise alterations in Treg function in autoimmune individuals and how underlying genetic associations impact the development and function of Tregs is still not well understood. Polygenetic susceptibly is a key driving factor in the development of autoimmunity, and many of the pathways implicated in genetic association studies point to a potential alteration or defect in regulatory T cell function. In this review transcriptomic control of Treg development and function is highlighted with a focus on how these pathways are altered during autoimmunity. In combination, observations from autoimmune mouse models and human patients now provide insights into epigenetic control of Treg function and stability. How tissue microenvironment influences Treg function, lineage stability, and functional plasticity is also explored. In conclusion, the current efficacy and future direction of Treg-based therapies for Type 1 Diabetes and other autoimmune diseases is discussed. In total, this review examines Treg function with focuses on genetic, epigenetic, and environmental mechanisms and how Treg functions are altered within the context of autoimmunity.

Modulation of autoimmune diabetes by N-ethyl-N-nitrosourea- induced mutations in non-obese diabetic mice

Genetic association studies of type 1 diabetes (T1D) in humans, and in congenic non-obese diabetic (NOD) mice harboring DNA segments from T1D-resistant mice, face the challenge of assigning causation to specific gene variants among many within loci that affect disease risk. Here, we created random germline mutations in NOD/NckH mice and used automated meiotic mapping to identify mutations modifying T1D incidence and age of onset. In contrast with association studies in humans or congenic NOD mice, we analyzed a relatively small number of genetic changes in each pedigree, permitting implication of specific mutations as causative. Among 844 mice from 14 pedigrees bearing 594 coding/splicing changes, we identified seven mutations that accelerated T1D development, and five that delayed or suppressed T1D. Eleven mutations affected genes not previously known to influence T1D (Xpnpep1, Herc1, Srrm2, Rapgef1, Ppl, Zfp583, Aldh1l1, Col6a1, Ccdc13, Cd200r1, Atrnl1). A suppressor mutation in Coro1a validated the screen. Mutagenesis coupled with automated meiotic mapping can detect genes in which allelic variation influences T1D susceptibility in NOD mice. Variation of some of the orthologous/paralogous genes may influence T1D susceptibility in humans.

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Roles of extracellular vesicles associated non-coding RNAs in Diabetes Mellitus

Extracellular vesicles (EVs), especially exosomes (50 to 150 nm), have been shown to play important roles in a wide range of physiological and pathological processes, including metabolic diseases such as Diabetes Mellitus (DM). In the last decade, several studies have demonstrated how EVs are involved in cell-to-cell communication. EVs are enriched in proteins, mRNAs and non-coding RNAs (miRNAs, long non-coding RNAs and circRNAS, among others) which are transferred to recipient cells and may have a profound impact in either their survival or functionality. Several studies have pointed out the contribution of exosomal miRNAs, such as miR-l42-3p and miR-26, in the development of Type 1 and Type 2 DM (T1DM and T2DM), respectively. In addition, some miRNA families such as miR-let7 and miR-29 found in exosomes have been associated with both types of diabetes, suggesting that they share common etiological features. The knowledge about the role of exosomal long non-coding RNAs in this group of diseases is more immature, but the exosomal lncRNA MALAT1 has been found to be elevated in the plasma of individuals with T2DM, while more than 169 lncRNAs were reported to be differentially expressed between healthy donors and people with T1DM. Here, we review the current knowledge about exosomal non-coding RNAs in DM and discuss their potential as novel biomarkers and possible therapeutic targets.