Observational studies suggest a link between diabetes and oral potentially malignant disorders (OPMDs), such as oral lichen planus (OLP) and oral leukoplakia (OLK). The causal relationship, as well as the type of diabetes that promotes OPMDs development, remains unclear. This Mendelian randomization (MR) study estimated the causal effects of diabetes-related traits on OPMDs.

Large-scale genome-wide association study data on type 1 diabetes (T1D), type 2 diabetes (T2D), fasting glucose (FG), fasting insulin (FI), glycated hemoglobin (HbA1c), OLP, OLK, and actinic cheilitis (AC) were used. Causal effects were assessed using inverse-variance weighted (IVW), weighted median, and MR-Egger methods. Multivariable MR analyses evaluated the independent roles of these traits, with extensive sensitivity analyses.

Genetic susceptibility to T1D (IVW OR = 1.09, 95% CI 1.02-1.17, P = 0.007) and T2D (IVW OR = 0.91, 95% CI 0.86-0.97, P = 0.002) showed protective effects against AC. T1D was associated with an increased risk of OLP (IVW OR = 1.09, 95% CI 1.02-1.17, P = 0.007). The effect of T1D on AC and OLP remained robust after adjusting for FI, FG, and HbA1c, while T2D's effect on AC was not significant when considering these glycemic traits. No potential pleiotropy was detected (P > 0.05).

T1D may have a causal role in the development of OLP independent of glycemic traits, emphasizing the need for routine oral examinations in T1D patients. Conversely, genetically predicted T1D and T2D are significantly associated with a reduced risk of AC, challenging previous assumptions and offering new insights into the relationship between diabetes and OPMDs. Further extensive investigations are required to address the limitations of this study and to clarify these associations.

The goal of the TEDDY (The Environmental Determinants of Diabetes in the Young) study is to elucidate factors leading to the initiation of islet autoimmunity (first primary outcome) and those related to progression to type 1 diabetes mellitus (T1DM; second primary outcome). This Review outlines the key findings so far, particularly related to the first primary outcome. The background, history and organization of the study are discussed. Recruitment and follow-up (from age 4 months to 15 years) of 8,667 children showed high retention and compliance. End points of the presence of autoantibodies against insulin, GAD65, IA-2 and ZnT8 revealed the HLA-associated early appearance of insulin autoantibodies (1-3 years of age) and the later appearance of GAD65 autoantibodies. Competing autoantibodies against tissue transglutaminase (marking coeliac disease autoimmunity) also appeared early (2-4 years). Genetic and environmental factors, including enterovirus infection and gastroenteritis, support mechanistic differences underlying one phenotype of autoimmunity against insulin and another against GAD65. Infant growth and both probiotics and high protein intake affect the two phenotypes differently, as do serious life events during pregnancy. As the end of the TEDDY sampling phase is approaching, major omics approaches are in progress to further dissect the mechanisms that might explain the two possible endotypes of T1DM.

Type 1 diabetes (T1D) is a complex autoimmune disorder distinguished by the infiltration of immune cells into pancreatic islets, primarily resulting in damage to pancreatic β-cells. Despite extensive research, the precise pathogenesis of T1D remains elusive, with its etiology linked to a complex interplay of genetic, immune, and environmental factors. While genetic predispositions, such as HLA and other susceptibility genes, are necessary, they do not fully account for disease development. Environmental influences such as viral infections and dietary factors may contribute to the disease by affecting the immune system and epigenetic modifications. Additionally, endogenous retroviruses (ERVs) might play a role in T1D pathogenesis. Current therapeutic approaches, including insulin replacement therapy, immune omodulatory therapy, autoantigen immunotherapy, organ transplantation, and genetic modification, offer potential to alter disease progression but are still constrained by limitations. This review presents updated knowledge on T1D, with a focus on risk factors, predisposing hypotheses, and recent advancements in therapeutic strategies.

Idiopathic pulmonary fibrosis (IPF) is a disease of progressive lung scarring. There is a known association between diabetes mellitus (DM) and IPF, but it is unclear whether a causal relationship exists between these traits.

The objectives of this study are to examine causal relationships among DM, diabetes-associated traits and IPF using a Mendelian randomisation approach.

Two-sample MR approaches, including bidirectional inverse-variance weighted random effects and routine sensitivity models, used genetic variants identified from genome-wide association studies for type 1 diabetes (T1D), type 2 diabetes (T2D), glycated haemoglobin level (HbA1c), fasting insulin level and body mass index (BMI) to assess for causal effects of these traits on IPF. Further analyses using pleiotropy-robust and multivariable MR (MVMR) methods were additionally performed to account for trait complexity.

Results did not suggest that either T1D (OR=1.00, 95% CI 0.93 to 1.07, p=0.90) or T2D (1.02, 0.93 to 1.11, p=0.69) are in the causal pathway of IPF. No effects were suggested of HbA1c (1.19, 0.63 to 2.22, p=0.59) or fasting insulin level (0.60, 0.31 to 1.15, p=0.12) on IPF, but potential effects of BMI on IPF were indicated (1.44, 1.12 to 1.85, p=4.00×10-3). Results were consistent in MVMR, although no independent effects of T2D (0.91, 0.68 to 1.21, p=0.51) or BMI (1.01, 0.94 to 1.09, p=0.82) on IPF were observed when modelled together.

This study suggests that DM and IPF are unlikely to be causally linked. This comorbid relationship may instead be driven by shared risk factors or treatment effects.

To review the genetics of type 1 diabetes (T1D) and T1D genetic risk scores, focusing on their development, research and clinical applications, and future directions.

More than 90 genetic loci have been linked to T1D risk, with approximately half of the genetic risk attributable to the human leukocyte antigen (HLA) locus, along with non-HLA loci that have smaller effects to disease risk. The practical use of T1D genetic risk scores simplifies the complex genetic information, within the HLA and non-HLA regions, by combining the additive effect and interactions of single nucleotide polymorphisms (SNPs) associated with risk. Genetic risk scores have proven to be useful in various aspects, including classifying diabetes (e.g., distinguishing between T1D vs. neonatal, type 2 or other diabetes types), predicting the risk of developing T1D, assessing the prognosis of the clinical course (e.g., determining the risk of developing insulin dependence and glycemic control), and research into the heterogeneity of diabetes (e.g., atypical diabetes). However, there are gaps in our current knowledge including the specific sets of genes that regulate transition between preclinical stages of T1D, response to disease modifying therapies, and other outcomes of interest such as persistence of beta cell function. Several T1D genetic risk scores have been developed and shown to be valuable in various contexts, from classification of diabetes to providing insights into its etiology and predicting T1D risk across different stages of T1D. Further research is needed to develop and validate T1D genetic risk scores that are effective across all populations and ancestries. Finally, barriers such as cost, and training of medical professionals have to be addressed before the use of genetic risk scores can be incorporated into routine clinical practice.

Identified genetic loci for C-peptide and type 1 diabetes (T1D) age at diagnosis (AAD) explain only a small proportion of their variation. We aimed to identify additional genetic loci associated with C-peptide and AAD. Some HLA allele/haplotypes associated with T1D also contributed to variability of C-peptide and AAD, whereas outside the HLA region, T1D loci were mostly not associated with C-peptide or AAD. Genetic variation within CTSH can affect AAD. There is still residual heritability of C-peptide and AAD outside of HLA that could benefit from larger meta-genome-wide association studies.

Type 2 diabetes mellitus (T2DM) is a disease that involves autoimmunity. However, how immune cells function in the peripheral blood remains unclear. Exploring T2DM biomarkers via single-cell RNA sequencing (scRNA-seq) could provide new insights into the underlying molecular mechanisms.

The clinical trial registration number is ChiCTR2100049613. In this study, we included three healthy participants and three T2DM patients. The observed clinical indicators included weight and fasting blood glucose (FBG), glycosylated haemoglobin A1c (HbA1c) and fasting insulin levels. Direct separation and purification of peripheral blood mononuclear cells (PBMCs) were performed via the Ficoll density gradient centrifugation method. Immune cell types were identified via scRNA-seq. The differentially expressed genes, biological functions, cell cycle dynamics, and correlations between blood glucose indicators and genes in different cell types were analysed.

There were differences between the healthy and T2DM groups in terms of FBG and HbA1c (p<0.05 or p<0.01). We profiled 13,591 cells and 3188 marker genes from PBMCs. B cells, T cells, monocytes, and NK cells were grouped into 4 subclusters from PBMCs. CD4+ T cells are mainly in the memory activation stage, and CD8+ T cells are effectors. Monocytes include mainly CD14+ monocytes and FCGR3A+ monocytes. There were 119 differentially expressed genes in T cells and 175 differentially expressed genes in monocytes. Gene set enrichment analysis revealed that the marker genes were enriched in HALLMARK_ INTERFERON_GAMMA_RESPONSE and HALLMARK_TNFA_SIGNALING_VIA_ NFKB. Moreover, TNFRSF1A was identified as the core gene involved in network interactions in T cells.

Our study provides a transcriptional map of immune cells from PBMCs and provides a framework for understanding the immune status and potential immune mechanisms of T2DM patients via scRNA-seq.

http://www.chictr.org.cn, identifier ChiCTR2100049613.

Biological datasets often consist of thousands or millions of variables, e.g. genetic variants or biomarkers, and when sample sizes are large it is common to find many associated with an outcome of interest, for example, disease risk in a GWAS, at high levels of statistical significance, but with very small effects. The False Discovery Rate (FDR) is used to identify effects of interest based on ranking variables according to their statistical significance. Here, we develop a complementary measure to the FDR, the priorityFDR, that ranks variables by a combination of effect size and significance, allowing further prioritisation among a set of variables that pass a significance or FDR threshold. Applying to the largest GWAS of type 1 diabetes to date (15,573 cases and 158,408 controls), we identified 26 independent genetic associations, including two newly-reported loci, with qualitatively lower priorityFDRs than the remaining 175 signals. We detected putatively causal type 1 diabetes risk genes using Mendelian Randomisation, and found that these were located disproportionately close to low priorityFDR signals (p = 0.005), as were genes in the IL-2 pathway (p = 0.003). Selecting variables on both effect size and significance can lead to improved prioritisation for mechanistic follow-up studies from genetic and other large biological datasets.

Type 1 diabetes mellitus (T1D) is a complicated illness marked by the death of insulin- producing pancreatic beta cells, which ultimately leads to insulin insufficiency and hyperglycemia. T lymphocytes are considered to destroy pancreatic beta cells in the etiology of T1D as a result of hereditary and environmental factors. Although the latter factors are very important causes of T1D development, this disease is very genetically predisposed, so there is a significant genetic component to T1D susceptibility. Among the T1D-associated gene mutations, those that affect genes that encode the traditional Human Leukocyte Antigens (HLA) entail the highest risk of T1D development. Accordingly, the results of decades of genetic linkage and association studies clearly demonstrate that mutations in the HLA genes are the most associated mutations with T1D. They can, therefore, be used as biomarkers for prediction strategies and may even prove to be of value for personalized treatments. Other immunity-associated genetic loci are also associated with higher T1D risk. Indeed, T1D is considered an autoimmune disease. Its prevalence is rising globally, especially among children and young people. Given the global rise of, and thus interest in, autoimmune diseases, here we present a short overview of the link between immunity, especially HLA, genes and T1D.

Background: Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. Methods: Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m2) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). Results: Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (P = 0.020 and P = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. Conclusions: A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.

We aimed to investigate the genetic evidence that supports the repurposing of drugs already licensed or in clinical phases of development for prevention of type 1 diabetes.

We obtained genome-wide association study summary statistics for the risk of type 1 diabetes, whole-blood gene expression and serum protein levels and investigated genetic polymorphisms near seven potential drug target genes. We used co-localisation to examine whether the same genetic variants that are associated with type 1 diabetes risk were also associated with the relevant drug target genetic proxies and used Mendelian randomisation to evaluate the direction and magnitude of the associations. Furthermore, we performed Mendelian randomisation analysis restricted to functional variants within the drug target genes.

Co-localisation revealed that the blood expression levels of IL2RA (encoding IL-2 receptor subunit α [IL2RA]), IL6R (encoding IL-6 receptor [IL6R]) and IL6ST (encoding IL-6 cytokine family signal transducer [IL6ST]) shared the same causal variant with type 1 diabetes liability near the corresponding genes (posterior probabilities 100%, 96.5% and 97.0%, respectively). The OR (95% CI) of type 1 diabetes per 1-SD increase in the genetically proxied gene expression of IL2RA, IL6R and IL6ST were 0.22 (0.17, 0.27), 1.98 (1.48, 2.65) and 1.90 (1.45, 2.48), respectively. Using missense variants, genetically proxied TYK2 (encoding tyrosine kinase 2) expression levels were associated with type 1 diabetes risk (OR 0.61 [95% CI 0.54, 0.69]).

Our findings support the targeting of IL-2, IL-6 and TYK2 signalling in prevention of type 1 diabetes.

The analysis code is available at https://github.com/jkoskenniemi/T1DSCREEN , which also includes instructions on how to download the original GWAS summary statistics.

The clinical phenomenon whereby diffuse large B-cell lymphoma (DLBCL) occurs in patients with a history of autoimmune disease (AD) has been noted, but it remains controversial. This study aimed to evaluate the causal associations between nine ADs and DLBCL via a Mendelian randomization (MR) study. Single-nucleotide polymorphism (SNP) obtained from published genome-wide association studies (GWAS) was chosen as instrumental variable (IV). A total of nine ADs of European ancestry including asthma (56,167 cases and 352,255 controls), psoriasis (4,510 cases and 212,242 controls), autoimmune hyperthyroidism (962 cases and 172,976 controls), inflammatory bowel disease (31,665 cases and 33,977 controls), type 1 diabetes (6,683 cases and 12,173 controls), multiple sclerosis (14,498 cases and 24,091 controls), sarcoidosis (2,046 cases and 215,712 controls), ankylosing spondylitis (9,069 cases and 1,550 controls), and celiac disease (12,041 cases and 12,228 controls), were set as the exposure and DLBCL (209 cases and 218,583 controls) of European ancestry as the outcome. Inverse-variance weighted (IVW) was used as the primary analysis method, and the weighted median and MR-Egger method were used as supplementary methods. The sensitivity analyses employed in this study include the MR-Egger intercept, MR-PRESSO global test, Cochran's Q test, leave-one-out analysis, and funnel plot. IVW showed that inflammatory bowel disease (OR = 1.241, 95% CI 1.009-1.526, P = 0.040) and autoimmune hyperthyroidism (OR = 1.464, 95% CI 1.103-1.942, P = 0.008) increased the risk of DLBCL without significant heterogeneity or horizontal pleiotropy, and the results remained stable according to the leave-one-out analysis. The IVW results revealed no associations between the other seven ADs and DLBCL: asthma (OR = 0.782, 95% CI 0.395-1.546, P = 0.159), psoriasis (OR = 0.842, 95% CI 0.669-1.060, P = 0.143), type 1 diabetes (OR = 1.071, 95% CI 0.860-1.334, P = 0.537), multiple sclerosis (OR = 1.331, 95% CI 0.941-1.883, P = 0.105), sarcoidosis (OR = 1.324, 95% CI 0.861-2.038, P = 0.200), ankylosing spondylitis (OR = 1.884, 95% CI 0.776-4.573, P = 0.161), and celiac disease (OR = 1.003, 95% CI 0.854-1.178, P = 0.969). Although no significant heterogeneity or horizontal pleiotropy was detected in these seven ADs and DLBCL, these results did not pass the leave-one-out analysis; therefore, the results need to be interpreted with caution. Inflammatory bowel disease and autoimmune hyperthyroidism may increase the onset of DLBCL. The risk of DLBCL should be considered in specific types of ADs.

Autoimmune disorders (ADs) pose significant health and economic burdens globally, characterized by the body's immune system mistakenly attacking its own tissues. While the precise mechanisms driving their development remain elusive, a combination of genetic predisposition(s) and environmental triggers is implicated. Interleukin-27 (IL-27), among numerous cytokines involved, has emerged as a key regulator, exhibiting dual roles in immune modulation. This review delves into the molecular structure and signaling mechanisms of IL-27, highlighting its diverse effects on various immune cells. Additionally, it explores the involvement of IL-27 in autoimmune diseases, such as multiple sclerosis (MS) and rheumatoid arthritis (RA), offering insights into its potential therapeutic implications. Moreover, its involvement in autoimmune diseases like type 1 diabetes (T1D), inflammatory bowel disease (IBD), myasthenia gravis (MG), Sjögren's syndrome (SS), and Guillain-Barré syndrome (GBS) is multifaceted, with potential diagnostic and therapeutic implications across these conditions. Further research is essential to fully understand IL-27's mechanisms of action and therapeutic potential in autoimmune diseases.

Myasthenia gravis (MG) is a rare autoantibody-mediated disease affecting the neuromuscular junction. We performed a genome-wide association study of 5708 MG cases and 432,028 controls of European ancestry and a replication study in 3989 cases and 226,643 controls provided by 23andMe Inc. We identified 12 independent genome-wide significant hits (P < 5e-8) across 11 loci. Subgroup analyses revealed two of these were associated with early-onset (at age <50) and four with late-onset MG (at age ≥ 50). Imputation of human leukocyte antigen alleles revealed inverse effect sizes for late- and early-onset, suggesting a potential modulatory influence on the time of disease manifestation. We assessed the performance of polygenic risk scores for MG, which significantly predicted disease status in an independent target cohort, explaining 4.21% of the phenotypic variation (P = 5.12e-9). With this work, we aim to enhance our understanding of the genetic architecture of MG.

Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts.

Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD.

There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable.

This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

Age at diagnosis (AAD) of Type 1 diabetes (T1D) is determined by the age at onset of the autoimmune attack and by the rate of beta cell destruction that follows. Twin studies found that T1D AAD is strongly influenced by genetics, notably in young children. In young UK, Finnish, Sardinian patients AAD-associated genomic variants were previously identified, which may vary across populations and with time. In 1956 children of European ancestry born in mainland France in 1980-2008 who declared T1D before 15 years, we tested 94 T1D-associated SNPs for their association with AAD using nonparametric Kruskal-Wallis test. While high-risk HLA genotypes were not found to be associated with AAD, fourteen SNPs located in 12 non-HLA loci showed a strong association (2.9 × 10-12 < P < 1.4 × 10-3 after FDR correction). Four of these loci have been associated with AAD in previous cohorts (GSDMB, IL2, TNFAIP3, IL1), supporting a partially shared genetic influence on AAD of T1D in the studied European populations. In contrast, the association of 8 new loci CLEC16A, TYK2, ERBB3, CCR7, FCRL3, DNAH2, FGF3/4, and HPSE2 with AAD is novel. The 12 protein-coding genes located within these loci are involved in major immune pathways or in predisposition to other autoimmune diseases, which suggests a prominent role for these genes in the early immune mechanisms of beta cell destruction.

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

A series of modifiable lifestyle factors, such as diet quality, physical activity, alcohol intake, and smoking, may drive the rising burden of type 2 diabetes (T2DM) among sub-Saharan Africans globally. It is unclear whether epigenetic changes play a mediatory role in the associations between these lifestyle factors and T2DM. We assessed the associations between a comprehensive lifestyle index, DNA methylation and T2DM among Ghanaian adults.

We used whole-blood Illumina 450 k DNA methylation data from 713 Ghanaians from the Research on Obesity and Diabetes among African Migrants (RODAM) study. We constructed a comprehensive lifestyle index based on established cut-offs for diet quality, physical activity, alcohol intake, and smoking status. In the T2DM-free discovery cohort (n = 457), linear models were fitted to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) associated with the lifestyle index after adjustment for age, sex, body mass index (BMI), and technical covariates. Associations between the identified DMPs and the primary outcome (T2DM), as well as secondary outcomes (fasting blood glucose (FBG) and HbA1c), were determined via logistic and linear regression models, respectively.

In the present study population (mean age: 52 ± 10 years; male: 42.6%), the comprehensive lifestyle index showed a significant association with one DMP annotated to an intergenic region on chromosome 7 (false discovery rate (FDR) = 0.024). Others were annotated to ADCY7, SMARCE1, AHRR, LOXL2, and PTBP1 genes. One DMR was identified and annotated to the GFPT2 gene (familywise error rate (FWER) from bumphunter bootstrap = 0.036). None of the DMPs showed significant associations with T2DM; directions of effect were positive for the DMP in the AHRR and inverse for all the other DMPs. Higher methylation of the ADCY7 DMP was associated with higher FBG (p = 0.024); LOXL2 DMP was associated with lower FBG (p = 0.023) and HbA1c (p = 0.049); and PTBP1 DMP was associated with lower HbA1c (p = 0.002).

In this explorative epigenome-wide association study among Ghanaians, we identified one DMP and DMR associated with a comprehensive lifestyle index not previously associated with individual lifestyle factors. Based on our findings, we infer that lifestyle factors in combination, affect DNA methylation, thereby influencing the risk of T2DM among Ghanaian adults living in different contexts.

Genetic variants associated with autoimmune diseases are highly enriched within putative cis -regulatory regions of CD4 + T cells, suggesting that they alter disease risk via changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. We tested >18,000 autoimmune disease-associated variants for allele-specific expression using massively parallel reporter assays in primary human CD4 + T cells. The 545 expression-modulating variants (emVars) identified greatly enrich for likely causal variants. We provide evidence that many emVars are mediated by common upstream regulatory conduits, and that putative target genes of primary T cell emVars are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis -regulatory elements modulate both known and novel target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk.

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is a member of the TNF protein superfamily and was initially identified as a protein capable of inducing apoptosis in cancer cells. In addition, TRAIL can promote pro-survival and proliferation signaling in various cell types. Subsequent studies have demonstrated that TRAIL plays several important roles in immunoregulation, immunosuppression, and immune effector functions. Type 1 diabetes (T1D) is an autoimmune disease characterized by hyperglycemia due to the loss of insulin-producing β-cells, primarily driven by T-cell-mediated pancreatic islet inflammation. Various genetic, epigenetic, and environmental factors, in conjunction with the immune system, contribute to the initiation, development, and progression of T1D. Recent reports have highlighted TRAIL as an important immunomodulatory molecule with protective effects on pancreatic islets. Experimental data suggest that TRAIL protects against T1D by reducing the proliferation of diabetogenic T cells and pancreatic islet inflammation and restoring normoglycemia in animal models. In this review, we aimed to summarize the consequences of TRAIL action in T1D, focusing on and discussing its signaling mechanisms, role in the immune system, and protective effects in T1D.

Type 1 diabetes (T1D) is an autoimmune disease characterized by the specific destruction of insulin-producing beta cells in the pancreas by the immune system, including CD4 cells which orchestrate the attack and CD8 cells which directly destroy the beta cells, resulting in the loss of glucose homeostasis.

This comprehensive document delves into the complex interplay between the immune system and beta cells, aiming to shed light on the mechanisms driving their destruction in T1D. Insights into the genetic predisposition, environmental triggers, and autoimmune responses provide a foundation for understanding the autoimmune attack on beta cells. From the role of viral infections as potential triggers to the inflammatory response of beta cells, an intricate puzzle starts to unfold. This exploration highlights the importance of beta cells in breaking immune tolerance and the factors contributing to their targeted destruction. Furthermore, it examines the potential role of autophagy and the impact of cytokine signaling on beta cell function and survival.

This review collectively represents current research findings on T1D which offers valuable perspectives on novel therapeutic approaches for preserving beta cell mass, restoring immune tolerance, and ultimately preventing or halting the progression of T1D. By unraveling the complex dynamics between the immune system and beta cells, we inch closer to a comprehensive understanding of T1D pathogenesis, paving the way for more effective treatments and ultimately a cure.

Previous genome-wide association studies (GWAS) have indicated the involvement of shared (population-nonspecific) and nonshared (population-specific) susceptibility genes in the pathogenesis of primary biliary cholangitis (PBC) among European and East-Asian populations. Although a meta-analysis of these distinct populations has recently identified more than 20 novel PBC susceptibility loci, analyses of population-specific genetic architecture are still needed for a more comprehensive search for genetic factors in PBC.

Protein tyrosine phosphatase nonreceptor type 2 ( PTPN2) was identified as a novel PBC susceptibility gene locus through GWAS and subsequent genome-wide meta-analysis involving 2181 cases and 2699 controls from the Japanese population (GWAS-lead variant: rs8098858, p = 2.6 × 10 -8 ). In silico and in vitro functional analyses indicated that the risk allele of rs2292758, which is a primary functional variant, decreases PTPN2 expression by disrupting Sp1 binding to the PTPN2 promoter in T follicular helper cells and plasmacytoid dendritic cells. Infiltration of PTPN2-positive T-cells and plasmacytoid dendritic cells was confirmed in the portal area of the PBC liver by immunohistochemistry. Furthermore, transcriptomic analysis of PBC-liver samples indicated the presence of a compromised negative feedback loop in vivo between PTPN2 and IFNG in patients carrying the risk allele of rs2292758.

PTPN2 , a novel susceptibility gene for PBC in the Japanese population, may be involved in the pathogenesis of PBC through an insufficient negative feedback loop caused by the risk allele of rs2292758 in IFN-γ signaling. This suggests that PTPN2 could be a potential molecular target for PBC treatment.

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Aims: This study is aimed at comparing whole exome sequencing (WES) data with the clinical presentation in children with type 1 diabetes onset ≤ 5 years of age (EOT1D). Methods: WES was performed in 99 unrelated children with EOT1D with subsequent analysis to identify potentially deleterious rare variants in MODY genes. High-resolution HLA class II haplotyping, SNP genotyping, and T1D-genetic risk score (T1D-GRS) were also evaluated. Results: Eight of the ninety-nine EOT1D participants carried a potentially deleterious rare variant in a MODY gene. Rare variants affected five genes: GCK (n = 1), HNF1B (n = 2), HNF4A (n = 1), PDX1 (n = 2), and RFX6 (n = 2). At diagnosis, these children had a mean age of 3.0 years, a mean HbA1c of 10.5%, a detectable C-peptide in 5/8, and a positive islet autoantibody in 6/7. Children with MODY variants tend to exhibit a lower number of pancreatic autoantibodies and a lower fasting C-peptide compared to EOT1D without MODY rare variants. They also carried at least one high-risk DR3-DQ2 or DR4-DQ8 haplotype and exhibited a T1D-GRS similar to the other individuals in the EOT1D cohort, but higher than healthy controls. Conclusions: WES found potentially deleterious rare variants in MODY genes in 8.1% of EOT1D, occurring in the context of a T1D genetic background. Such genetic variants may contribute to disease precipitation by a β-cell dysfunction mechanism. This supports the concept of different endotypes of T1D, and WES at T1D onset may be a prerequisite for the implementation of precision therapies in children with autoimmune diabetes.

The association between autoimmune diseases and chronic rhinosinusitis in observational studies remains unclear. This study aimed to explore the genetic correlation between chronic rhinosinusitis and autoimmune diseases.

We employed Mendelian randomization (MR) analysis and linkage disequilibrium score regression (LDSC) to investigate causal relationships and genetic correlations between autoimmune phenotypes and chronic rhinosinusitis. Additionally, transcriptome-wide association (TWAS) analysis was conducted to identify the shared genes between the two conditions to demonstrate their relationship. The CRS GWAS (genome-wide association study) data and other autoimmune diseases were retrieved from ieuOpenGWAS (https://gwas.mrcieu.ac.uk/), the FinnGen alliance (https://r8.finngen.fi/), the UK Biobank (https://www.ukbiobank.ac.uk/), and the EBI database (https://www.ebi.ac.uk/).

Utilizing a bivariate two-sample Mendelian randomization approach, our findings suggest a significant association of chronic rhinosinusitis with various autoimmune diseases, including allergic rhinitis (p = 9.55E-10, Odds Ratio [OR] = 2,711.019, 95% confidence interval [CI] = 261.83391-28,069.8), asthma (p = 1.81E-23, OR = 33.99643, 95%CI = 17.52439-65.95137), rheumatoid arthritis (p = 9.55E-10, OR = 1.115526, 95%CI = 1.0799484-1.1522758), hypothyroidism (p = 2.08828E-2, OR = 4.849254, 95%CI = 1.7154455-13.707962), and type 1 diabetes (p = 2.08828E-2, OR = 01.04849, 95%CI = 1.0162932-1.0817062). LDSC analysis revealed a genetic correlation between the positive autoimmune phenotypes mentioned above and chronic rhinosinusitis: AR (rg = 0.344724754, p = 3.94E-8), asthma (rg = 0.43703672, p = 1.86E-10), rheumatoid arthritis (rg = 0.27834931, p = 3.5376E-2), and hypothyroidism (rg = -0.213201473, p = 3.83093E-4). Utilizing the Transcriptome-Wide Association Studies (TWAS) approach, we identified several genes commonly associated with both chronic rhinosinusitis and autoimmune diseases. Genes such as TSLP/WDR36 (Chromosome 5, top SNP: rs1837253), ORMDL3 (Chromosome 13, top SNP: rs11557467), and IL1RL1/IL18R1 (Chromosome 2, top SNP: rs12905) exhibited a higher degree of consistency in their shared involvement across atopic dermatitis (AT), allergic rhinitis (AR), and chronic rhinosinusitis (CRS).

Current evidence suggests a genetic correlation between chronic rhinosinusitis and autoimmune diseases like allergic rhinitis, asthma, rheumatoid arthritis, hypothyroidism, and type 1 diabetes. Further research is required to elucidate the mechanisms underlying these associations.

Autoimmune patients have a significantly increased risk of developing coronary artery disease (CAD) compared to the general population. However, autoimmune patients often lack traditional risk factors for CAD and there is increasing recognition of inflammation in CAD development. In this study, we leveraged genome-wide association study (GWAS) data to understand whether there is a genetic relationship between CAD and autoimmunity. Statistical genetic comparison methods were used to identify correlated and causal SNPs between various autoimmune diseases and CAD. Pleiotropic SNPs were identified by cross-phenotype association analysis (CPASSOC) and overlap between GWAS. Causal SNPs were identified using Mendelian Randomization (MR) and Colocalization (COLOC). Using SNP-to-gene mapping, we additionally identified pleiotropic and causal genes and pathways associated between autoimmunity and CAD, which were contextualized by documentation of enrichment in individual cell types identified from coronary atherosclerotic plaques by single-cell RNA sequencing. These results provide insight into potential inflammatory therapeutic targets for CAD.

Spinal muscular atrophy type 3 (juvenile SMA, Kugelberg-Welander disease) is a genetic disease caused by changes in the survival motor neuron 1 (SMN) gene. However, there is increasing evidence of metabolic abnormalities in SMA patients, such as altered fatty acid metabolism, impaired glucose tolerance, and defects in the functioning of muscle mitochondria. Given that data in the literature are scarce regarding this subject, the purpose of this study was to estimate the prevalence of glucose and lipid metabolism disorders in adult patients with SMA type 3.

We conducted a cross-sectional study of 23 adult patients with SMA type 3 who underwent a comprehensive evaluation, including a physical examination, biochemical analysis, and an oral glucose tolerance test during 2020-2023.

At least one lipid abnormality was observed in 60.8% of patients. All four lipid parameters were atypical in 4.3% of patients, three lipid parameters were abnormal in 21.7% of patients, and two lipid parameters were altered in 8.7% patients. A total of 91.3% of SMA3 patients met the HOMA-IR criteria for insulin resistance, with 30.43% having impaired glucose tolerance. None of the patients met the criteria for a diagnosis of overt DM2.

The prevalence of dyslipidemia and altered glucose metabolism in our study sets apart the adult population with SMA3 from the general population, confirming a significant interplay between muscle, liver, and adipose tissue. Ensuring metabolic care for aging patients with SMA 3 is crucial, as they are vulnerable to metabolic derangements and cardiovascular risks.

Endometriosis is a complex and heterogeneous condition affecting 10% of reproductive-age women, and yet, it often goes undiagnosed for several years. Limited observed heritability (7%) of large genetic association studies may be attributable to underlying heterogeneity of disease mechanisms. Therefore, we conducted this study to investigate genetic associations across sub-phenotypes of endometriosis. We performed unsupervised clustering of 4,078 women with endometriosis based on known endometriosis risk factors, symptoms, and concomitant conditions. The clusters were characterized by examining electronic health record (EHR) data and comprehensive chart reviews. We then performed genetic association for each cluster with 39 endometriosis-associated loci (Total Nendometriosis cases = 12,350). We identified five sub-phenotype clusters: (1) pain comorbidities, (2) uterine disorders, (3) pregnancy complications, (4) cardiometabolic comorbidities, and (5) HER-asymptomatic. Bonferroni significant loci included PDLIM5 for the cluster 1, GREB1 for cluster 2, WNT4 for cluster 3, RNLS for cluster 4, and ABO for cluster 5. The difference in associations between the groups suggests complex and varied genetic mechanisms of endometriosis and its symptoms. This study enhances our understanding of the clinical patterns of endometriosis sub-phenotypes, showcasing the innovative approach employed to investigate this complex disease.

Our previous genome‑wide association studies (GWAS) have suggested rs912304 in 14q12 as a suggestive risk variant for type 1 diabetes (T1D). However, the association between this risk region and T1D subgroups and related clinical risk features, the underlying causal functional variant(s), putative candidate gene(s), and related mechanisms are yet to be elucidated.

We assessed the association between variant rs912304 and T1D, as well as islet autoimmunity and islet function, stratified by the diagnosed age of 12. We used epigenome bioinformatics analyses, dual luciferase reporter assays, and expression quantitative trait loci (eQTL) analyses to prioritize the most likely functional variant and potential causal gene. We also performed functional experiments to evaluate the role of the causal gene on islet function and its related mechanisms.

We identified rs912304 as a risk variant for T1D subgroups with diagnosed age ≥ 12 but not < 12. This variant is associated with residual islet function but not islet-specific autoantibody positivity in T1D individuals. Bioinformatics analysis indicated that rs912304 is a functional variant exhibiting spatial overlaps with enhancer active histone marks (H3K27ac and H3K4me1) and open chromatin status (ATAC-seq) in the human pancreas and islet tissues. Luciferase reporter gene assays and eQTL analyses demonstrated that the biallelic sites of rs912304 had differential allele-specific enhancer activity in beta cell lines and regulated STXBP6 expression, which was defined as the most putative causal gene based on Open Targets Genetics, GTEx v8 and Tiger database. Moreover, Stxbp6 was upregulated by T1D-related proinflammatory cytokines but not high glucose/fat. Notably, Stxbp6 over-expressed INS-1E cells exhibited decreasing insulin secretion and increasing cell apoptosis through Glut1 and Gadd45β, respectively.

This study expanded the genomic landscape regarding late-onset T1D risk and supported islet function mechanistically connected to T1D pathogenesis.

Use of genetic risk scores (GRS) may help to distinguish between type 1 diabetes and type 2 diabetes, but less is known about whether GRS are associated with disease severity or progression after diagnosis. Therefore, we tested whether GRS are associated with residual beta cell function and glycaemic control in individuals with type 1 diabetes.

Immunochip arrays and TOPMed were used to genotype a cross-sectional cohort (n=479, age 41.7 ± 14.9 years, duration of diabetes 16.0 years [IQR 6.0-29.0], HbA1c 55.6 ± 12.2 mmol/mol). Several GRS, which were originally developed to assess genetic risk of type 1 diabetes (GRS-1, GRS-2) and type 2 diabetes (GRS-T2D), were calculated. GRS-C1 and GRS-C2 were based on SNPs that have previously been shown to be associated with residual beta cell function. Regression models were used to investigate the association between GRS and residual beta cell function, assessed using the urinary C-peptide/creatinine ratio, and the association between GRS and continuous glucose monitor metrics.

Higher GRS-1 and higher GRS-2 both showed a significant association with undetectable UCPCR (OR 0.78; 95% CI 0.69, 0.89 and OR 0.84: 95% CI 0.75, 0.93, respectively), which were attenuated after correction for sex and age of onset (GRS-2) and disease duration (GRS-1). Higher GRS-C2 was associated with detectable urinary C-peptide/creatinine ratio (≥0.01 nmol/mmol) after correction for sex and age of onset (OR 6.95; 95% CI 1.19, 40.75). A higher GRS-T2D was associated with less time below range (TBR) (OR for TBR<4% 1.41; 95% CI 1.01 to 1.96) and lower glucose coefficient of variance (β -1.53; 95% CI -2.76, -0.29).

Diabetes-related GRS are associated with residual beta cell function in individuals with type 1 diabetes. These findings suggest some genetic contribution to preservation of beta cell function.

In the face of advancements in health care and a shift towards healthy lifestyle, diabetes mellitus (DM) still presents as a global health challenge. This chapter explores recent advancements in the areas of genetic and molecular underpinnings of DM, addressing the revolutionary potential of CRISPR-based genome editing technologies. We delve into the multifaceted relationship between genes and molecular pathways contributing to both type1 and type 2 diabetes. We highlight the importance of how improved genetic screening and the identification of susceptibility genes are aiding in early diagnosis and risk stratification. The spotlight then shifts to CRISPR-Cas9, a robust genome editing tool capable of various applications including correcting mutations in type 1 diabetes, enhancing insulin production in T2D, modulating genes associated with metabolism of glucose and insulin sensitivity. Delivery methods for CRISPR to targeted tissues and cells are explored, including viral and non-viral vectors, alongside the exciting possibilities offered by nanocarriers. We conclude by discussing the challenges and ethical considerations surrounding CRISPR-based therapies for DM. These include potential off-target effects, ensuring long-term efficacy and safety, and navigating the ethical implications of human genome modification. This chapter offers a comprehensive perspective on how genetic and molecular insights, coupled with the transformative power of CRISPR, are paving the way for potential cures and novel therapeutic approaches for DM.

Autoimmune diabetes is a disease characterized by the selective destruction of insulin-secreting β-cells of the endocrine pancreas by islet-reactive T cells. Autoimmune disease requires a complex interplay between host genetic factors and environmental triggers that promote the activation of such antigen-specific T lymphocyte responses. Given the critical involvement of self-reactive T lymphocyte in diabetes pathogenesis, understanding how these T lymphocyte populations contribute to disease is essential to develop targeted therapeutics. To this end, several key antigenic T lymphocyte epitopes have been identified and studied to understand their contributions to disease with the aim of developing effective treatment approaches for translation to the clinical setting. In this review, we discuss the role of pathogenic islet-specific T lymphocyte responses in autoimmune diabetes, the mechanisms and cell types governing autoantigen presentation, and therapeutic strategies targeting such T lymphocyte responses for the amelioration of disease.