Autoantibodies Directed Toward a Novel IA-2 Variant Protein Enhance Prediction of Type 1 Diabetes

Understanding Islet Autoantibodies in Prediction of Type 1 Diabetes

As screening studies and preventive interventions for type 1 diabetes (T1D) advance rapidly, the utility of islet autoantibodies (IAbs) in T1D prediction comes with challenges for early and accurate disease progression prediction. Refining features of IAbs can provide more accurate risk assessment. The advances in islet autoantibodies assay techniques help to screen out islet autoantibodies with high efficiency and high disease specificity. Exploring new islet autoantibodies to neoepitopes/neoantigens remains a hot research field for improving prediction and disease pathogenesis. We will review the recent research progresses of islet autoantibodies to better understand the utility of islet autoantibodies in prediction of T1D.

CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study

OBJECTIVE

Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes.

RESEARCH DESIGN AND METHODS

One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).

RESULTS

Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability.

CONCLUSIONS

CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes.

Identification of a Five-mRNA Signature as a Novel Potential Prognostic Biomarker for Glioblastoma by Integrative Analysis

Despite the availability of advanced multimodal therapy, the prognosis of patients suffering from glioblastoma (GBM) remains poor. We conducted a genome-wide integrative analysis of mRNA expression profiles in 302 GBM tissues and 209 normal brain tissues from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project to examine the prognostic and predictive value of specific mRNAs in GBM. A total of 26 mRNAs were identified to be closely related to GBM patients’ OS (p < 0.05). Utilizing survival analysis and the Cox regression model, we discovered a set of five mRNAs (PTPRN, ABCC3, MDK, NMB, and RALYL) from these 26 mRNAs that displayed the capacity to stratify patients into high- and low-risk groups with statistically different overall survival in the training set. The model of the five-mRNA biomarker signature was successfully verified on a testing set and independent sets. Moreover, multivariate Cox regression analysis revealed that the five-mRNA biomarker signature was a prognostic factor for the survival of patients with GBM independent of clinical characteristics and molecular features (p < 0.05). Gene set enrichment analysis indicated that the five-mRNA biomarker signature might be implicated in the incidence and development of GBM through its roles in known cancer-related pathways, signaling molecules, and the immune system. Moreover, consistent with the bioinformatics analysis, NMB, ABCC3, and MDK mRNA expression was considerably higher in four human GBM cells, and the expression of PTPRN and RALYL was decreased in GBM cells (p < 0.05). Our study developed a novel candidate model that provides new prospective prognostic biomarkers for GBM.

Demographic and diagnostic markers in new onset pediatric type 1 and type 2 diabetes: differences and overlaps

PURPOSE

Type 1 diabetes (T1D) is the most common type of diabetes in children, but the frequency of type 2 diabetes (T2D) is increasing rapidly. Classification of diabetes is based on a constellation of features that vary by type. We aimed to compare demographic, clinical, and laboratory characteristics at diagnosis of pediatric T1D and T2D.

METHODS

We studied children who visited a large academic hospital in Houston, Texas (USA) with a new diagnosis of T2D (n=753) or T1D (n=758). We compared age, sex, race/ethnicity, presence of obesity, glucose, hemoglobin A1c, islet autoantibody positivity, C-peptide, and presence of diabetic ketoacidosis (DKA) at diabetes diagnosis.

RESULTS

At diagnosis, children with T2D, compared with those with T1D, were older (13.6 years vs. 9.7 years), more likely female (63.2% vs. 47.8%), of racial/ethnic minority (91.1% vs. 42.3%), and obese (90.9% vs. 19.4%) and were less likely to have DKA (7.8% vs. 35.0%) and diabetes autoantibodies (5.5% vs. 95.4%). Children with T2D also had significantly lower glucose, lower hemoglobin A1c and lower C-peptide level (all comparisons, p<0.0001). In multiple logistic regression analysis, older age, racial/ethnic minority, obesity, higher C-peptide, and negative islet autoantibodies were independently associated with T2D (all, p<0.05), while sex, glucose, hemoglobin A1c, and DKA were not (model p<0.0001).

CONCLUSION

There are important demographic, clinical, and laboratory differences between T1D and T2D in children. However, none of the characteristics were unique to either diabetes type, which poses challenges to diabetes classification at diagnosis.

Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies: Beyond a Simple Count

Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject's age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

Normal and defective pathways in biogenesis and maintenance of the insulin storage pool

Both basal and glucose-stimulated insulin release occur primarily by insulin secretory granule exocytosis from pancreatic β cells, and both are needed to maintain normoglycemia. Loss of insulin-secreting β cells, accompanied by abnormal glucose tolerance, may involve simple exhaustion of insulin reserves (which, by immunostaining, appears as a loss of β cell identity), or β cell dedifferentiation, or β cell death. While various sensing and signaling defects can result in diminished insulin secretion, somewhat less attention has been paid to diabetes risk caused by insufficiency in the biosynthetic generation and maintenance of the total insulin granule storage pool. This Review offers an overview of insulin biosynthesis, beginning with the preproinsulin mRNA (translation and translocation into the ER), proinsulin folding and export from the ER, and delivery via the Golgi complex to secretory granules for conversion to insulin and ultimate hormone storage. All of these steps are needed for generation and maintenance of the total insulin granule pool, and defects in any of these steps may, weakly or strongly, perturb glycemic control. The foregoing considerations have obvious potential relevance to the pathogenesis of type 2 diabetes and some forms of monogenic diabetes; conceivably, several of these concepts might also have implications for β cell failure in type 1 diabetes.

Checking the Checkpoint Inhibitors: A Case of Autoimmune Diabetes After PD-1 Inhibition in a Patient with HIV

Immune checkpoint inhibitor-associated diabetes mellitus (ICI-DM) is a known immune-related adverse event (irAE) following treatment with programmed cell death protein 1 (PD-1), with a reported 0.9% incidence. We hereby present the first case, to our knowledge, of ICI-DM following ICI use in a human immunodeficiency virus (HIV) patient. In this case, a 48-year-old man with HIV stable on highly active antiretroviral therapy (HAART) was diagnosed with Hodgkin lymphoma and initiated treatment with the PD-1 inhibitor nivolumab. His lymphoma achieved complete response after 5 months. However, at month 8, he reported sudden polydipsia and polyuria. Labs revealed a glucose level of 764 mg/dL and glycated hemoglobin A1c (HbA1c) of 7.1%. Low C-peptide and elevated glutamic acid decarboxylase 65 (GAD65) antibody levels confirmed autoimmune DM, and he was started on insulin. Major histocompatibility complex class II genetic analysis revealed homozygous HLA DRB1*03-DQA1*0501-DQB1*02 (DR3-DQ2), which is a known primary driver of genetic susceptibility to type 1 DM. Autoimmune DM has been reported as an ICI-associated irAE. However, patients with immunocompromising conditions such as HIV are usually excluded from ICI trials. Therefore, little is known about such irAEs in this population. In this case, risk of ICI-DM as an irAE was likely increased by several factors including family history, a high-risk genetic profile, islet-related immunologic abnormalities, active lymphoma, and HIV infection with a possible immune reconstitution event. Clinicians should maintain a high index of suspicion for development of irAEs associated with ICI, particularly as use of these therapies broadens. Thorough investigation for presence of higher-risk features should be conducted and may warrant inclusion of pre-therapy genetic and/or autoantibody screening.

Failed Genetic Protection: Type 1 Diabetes in the Presence of HLA-DQB1*06:02

Certain HLA class II genes increase the risk for type 1 diabetes (T1D) development while others provide protection from disease development. HLA class II alleles encode MHC proteins on antigen-presenting cells, which function to present peptides and activate CD4 T cells. The DRB1*15:01 (DR15)-DQA1*01:02-DQB1*06:02 (DQ6) haplotype provides dominant protection across all stages of T1D and is a common haplotype found in Caucasians. However, it is present in <1% of people with T1D. Knowing which metabolic, immunologic, and genetic features are unique to individuals who fail genetic protection and develop T1D is important for defining the underlying mechanisms of DQB1*06:02-mediated protection. We describe a T1D cohort with DQB1*06:02 (n = 50) and compare them to individuals with T1D and without DQB1*06:02 (n = 2,759) who were identified over the last 26 years at the Barbara Davis Center for Diabetes. The age at diagnosis was similar between the cohorts and normally distributed throughout childhood and early adulthood. The average hemoglobin A_1c was 10.8 ± 2.8% (95 ± 7 mmol/mol) at diagnosis in those DQB1*06:02 positive. The majority of T1D DQB1*06:02 ⁺ individuals were positive for one or more islet autoantibodies; however, there was a greater proportion who were islet autoantibody negative compared with those T1D DQB1*06:02 ^- individuals. Interestingly, DQB1*03:02, which confers significant T1D risk, was present in only those DQB1*06:02 ⁺ individuals with islet autoantibodies. This is one of the largest studies examining patients presenting with clinical T1D in the presence of DQB1*06:02, which provides a population to study the mechanisms of failed genetic protection against T1D.

Molecular biomarkers in diabetes mellitus (DM)

Background: Diabetes mellitus (DM) is a growing epidemic metabolic syndrome, which affects near 5.6% of the world's population. Almost 12% of health expenditure is dedicated to this disorder. Discovering and developing biomarkers as a practical guideline with high specificity and sensitivity for the diagnosis, prognosis, and clinical management of DM is one of the subjects of great interest among DM researchers due to the long-lasting asymptomatic clinical manifestation of DM. In this study, we described a recently identified molecular biomarker involved in DM. Methods: This review study was done at the Diabetes Research Center affiliated to Shahid Sadoughi University of Medical Sciences. PubMed, Scopus, Google Scholar, and Web of Science were searched using the following keywords: "diabetes mellitus", "biomarker", "microRNA", "diagnostic tool" and "clinical manifestation." Results: A total of 107 studies were finally included in this review. After evaluating numerous articles, including original, metaanalysis, and review studies, we focused on molecular biomarkers involved in DM diagnosis and management. Conclusion: Increasing interest in biomarkers associated with DM goes back to its role in decreasing diabetes-related morbidity and mortality. This review focused on major molecular biomarkers such as proteomic and microRNA (miRNAs) as novel and interesting DM biomarkers that can help achieve timely diagnosis of DM.