Biomarkers For Type 1 Diabetes Research Articles

Microbial and metabolomic profiles of type 1 diabetes with depression: A case-control study.

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression. A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model. In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone. T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Factors Governing B Cell Recognition of Autoantigen and Function in Type 1 Diabetes.

Islet autoantibodies predict type 1 diabetes (T1D) but can be transient in murine and human T1D and are not thought to be directly pathogenic. Rather, these autoantibodies signal B cell activity as antigen-presenting cells (APCs) that present islet autoantigen to diabetogenic T cells to promote T1D pathogenesis. Disrupting B cell APC function prevents T1D in mouse models and has shown promise in clinical trials. Autoantigen-specific B cells thus hold potential as sophisticated T1D biomarkers and therapeutic targets. B cell receptor (BCR) somatic hypermutation is a mechanism by which B cells increase affinity for islet autoantigen. High-affinity B and T cell responses are selected in protective immune responses, but immune tolerance mechanisms are known to censor highly autoreactive clones in autoimmunity, including T1D. Thus, different selection rules often apply to autoimmune disease settings (as opposed to protective host immunity), where different autoantigen affinity ceilings are tolerated based on variations in host genetics and environment. This review will explore what is currently known regarding B cell signaling, selection, and interaction with T cells to promote T1D pathogenesis.

Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Hsa_circRNA_405498 and hsa_circRNA_100033 serve as potential biomarkers for differential diagnosis of type 1 diabetes.

The role of circular RNAs (circRNAs) in type 1 diabetes (T1D) is largely unknown. We aimed to identify some circRNAs as differential diagnostic biomarkers for T1D to discriminate patients with latent autoimmune diabetes in adults (LADA), and type 2 diabetes (T2D). The circRNA expression profiles were determined by Arraystar human circRNA microarray in T1D compared to controls (n = 6 each). The differentially expressed circRNAs were validated by RT-qPCR using a validation cohort with 20 T1D and 20 controls. The diagnostic performances of the candidate circRNAs and the clinical parameters were assessed using the logistic least absolute shrinkage and selection operator (LASSO) regression model in a larger cohort with 457 subjects including patients with T1D, T2D and LADA, and controls. We identified 110 differentially expressed circular transcripts (53 upregulated and 57 downregulated) in T1D patients compared with controls. Further analysis showed that the levels of hsa_circRNA_405498 and hsa_circRNA_100033 were significantly downregulated in T1D compared to controls (both P < 0.05). Moreover, the expression levels of these two circRNAs showed sequential downregulation from controls, patients with T2D, LADA, to T1D (P < 0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) plots in logistic LASSO regression model showed high diagnostic accuracy for combination model with the two circRNAs and some clinical parameters in discriminating T1D from LADA (AUC = 0.915), T2D (AUC = 0.993), and controls (AUC = 0.992). Our study demonstrated that hsa_circRNA_405498 and hsa_circRNA_100033 are promising novel differential diagnostic biomarkers for T1D.

Pathological mechanisms of type 1 diabetes in children: investigation of the exosomal protein expression profile.

Type 1 diabetes (T1D) is a serious autoimmune disease with high morbidity and mortality. Early diagnosis and treatment remain unsatisfactory. While the potential for development of T1D biomarkers in circulating exosomes has attracted interest, progress has been limited. This study endeavors to explore the molecular dynamics of plasma exosome proteins in pediatric T1D patients and potential mechanisms correlated with T1D progression. Liquid chromatography-tandem mass spectrometry with tandem mass tag (TMT)6 labeling was used to quantify exosomal protein expression profiles in 12 healthy controls and 24 T1D patients stratified by age (≤ 6 years old and > 6 years old) and glycated hemoglobin (HbA1c) levels (> 7% or > 7%). Integrated bioinformatics analysis was employed to decipher the functions of differentially expressed proteins, and Western blotting was used for validation of selected proteins' expression levels. We identified 1035 differentially expressed proteins (fold change > 1.3) between the T1D patients and healthy controls: 558 in those ≤ 6-year-old and 588 in those > 6-year-old. In those who reached an HbA1c level < 7% following 3 or more months of insulin therapy, the expression levels of most altered proteins in both T1D age groups returned to levels comparable to those in the healthy control group. Bioinformatics analysis revealed that differentially expressed exosome proteins are primarily related to immune function, hemostasis, cellular stress responses, and matrix organization. Western blotting confirmed the alterations in RAB40A, SEMA6D, COL6A5, and TTR proteins. This study delivers valuable insights into the fundamental molecular mechanisms contributing to T1D pathology. Moreover, it proposes potential therapeutic targets for improved T1D management.

Use of a glycomics array to establish the anti-carbohydrate antibody repertoire in type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets.

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Expression-based Genome-wide Association Study Links OPN and IL1-RA With Newly Diagnosed Type 1 Diabetes in Children.

Islet autoantibodies (IAbs) are currently the most reliable indicators of islet autoimmunity. However, IAbs do not fully meet the need for the prediction and intervention of type 1 diabetes (T1D). Serological proteins should be great sources for biomarkers. This work aimed to identify new proteomic biomarkers with the technology of an expression-based genome-wide association study (eGWAS) in children newly diagnosed with T1D. In an attempt to identify additional biomarkers, we performed an eGWAS using microarray data from 169 arrays of the pancreatic islets of T1D rodents (78 T1D cases and 91 controls). We ranked all 16 099 protein-coding genes by the likelihood of differential expression in the pancreatic islets. Our top 20 secreted proteins were screened in 170 children including 100 newly diagnosed T1D, and 50 type 2 diabetes (T2D) and 20 age-matched healthy children. With 6 proteins showing significance, we further conducted a validation study using the second independent set of 400 samples from children including 200 newly diagnosed with T1D, 100 T2D, and 100 age-matched controls. We identified 2 serum proteins that were significantly changed in T1D vs both control and T2D, and 5 serum proteins were significantly changed both in T1D and T2D vs control. Serum osteopontin (OPN) levels were uniquely higher in T1D (T1D vs controls, P = 1.29E-13 ~ 9.38E-7, T1D vs T2D, P = 2.65E-8 ~ 1.58E-7) with no difference between T2D and healthy control individuals. Serum interleukin 1 receptor antagonist (IL-1RA) levels were lower in T1D compared both with T2D (P = 3.36E-9~0.0236) and healthy participants (P = 1.09E-79 ~ 2.00E-12). Our results suggest that OPN and IL1-RA could be candidates for useful biomarkers for T1D in children.

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes.

T cell receptors (TCRs) are unique markers that define antigen specificity for a given T cell. With the evolution of sequencing and computational analysis technologies, TCRs are now prime candidates for the development of next-generation non-cell based T cell biomarkers, which provide a surrogate measure to assess the presence of antigen-specific T cells. Type 1 diabetes (T1D), the immune-mediated form of diabetes, is a prototypical organ specific autoimmune disease in which T cells play a pivotal role in targeting pancreatic insulin-producing beta cells. While the disease is now predictable by measuring autoantibodies in the peripheral blood directed to beta cell proteins, there is an urgent need to develop T cell markers that recapitulate T cell activity in the pancreas and can be a measure of disease activity. This review focuses on the potential and challenges of developing TCR biomarkers for T1D. We summarize current knowledge about TCR repertoires and clonotypes specific for T1D and discuss challenges that are unique for autoimmune diabetes. Ultimately, the integration of large TCR datasets produced from individuals with and without T1D along with computational ‘big data’ analysis will facilitate the development of TCRs as potentially powerful biomarkers in the development of T1D.

1142-P: Update on BCG Clinical Trial Programs in Patients with Advanced Type 1 Diabetes

The bacillus Calmette-Guerin (BCG) vaccine was first introduced a century ago for tuberculosis prevention and is today being tested in clinical trials for treatment of diverse forms of autoimmunity, including type 1 diabetes (T1D). Long-term follow up of a Phase I study of the BCG vaccine in longstanding T1D revealed potential disease-modulating effects after repeated BCG vaccination, including long-term reductions of HbA1c, death of autoreactive cells, modest restoration of insulin secretion and induction of beneficial regulatory T cells (Tregs). Here we provide an update on the BCG clinical trial program current underway at Massachusetts General Hospital (MGH): enrollment numbers, study timelines and interim outcomes for key T1D biomarkers. To date, 236 patients have been enrolled in the randomized and open-label MGH trials and 143 patients have received at least two doses of BCG, 34 of whom are being followed in open-label, unblinded trials. Enrollment of 150 participants in a pediatric study is planned for 2021. Data from ongoing analysis of the open-label BCG trials shows lowered insulin needs and decreases in HbA1c as compared to reference populations. Patients are also being monitored by RNAseq, epigenetics, Treg cell numbers and Treg signature gene expression to better define the mechanism of beneficial effects. We show a gradual and stable 3-year time course of demethylation of signature Treg genes known to be associated with Treg potency, including Foxp3, which was observed to be over-methylated at baseline in T1D and, over 3 years after BCG vaccination, demethylated to a level close to controls without T1D. The clinical time course of BCG-related improvements (i.e., improvement in HbA1C) appears to correlate with the time course of Treg induction through epigenetic modifications. Disclosure W. Kuhtreiber: None. H. Takahashi: None. R. Keefe: None. K. Nelson: None. N. Ng: None. J. Braley: None. H. Zheng: None. D. L. Faustman: None.

Expression-Based Genome-Wide Association Study Links Osteopontin and Interleukin 1 Receptor Antagonist With Newly Diagnosed Type 1 Diabetes in Children

Background: Islet autoantibodies (IAbs) are currently most reliable indicators of islet autoimmunity. However, IAbs do not fully meet the need for the prediction and intervention of type 1 diabetes (T1D). We assumed that secreted proteins associated with T1D are great sources for serum biomarkers. Methods: In an attempt to identify additional biomarkers, we performed an expression-based genome-wide association study (eGWAS) using microarray data from 169 arrays of the pancreatic islets of T1D rodents (78 T1D cases and 91 controls). We ranked all 16,099 protein-coding genes by the likelihood of differential expression in the pancreatic islets. Our top 20 secreted proteins were screened using the serum samples from newly diagnosed children with diabetes. First, we screened 20 selected candidates with 170 sera including 100 T1D, and 50 type 2 diabetes (T2D) and 20 age matched healthy children. With 6 proteins showing significance, we further did validation study using the second independent set of 400 samples from newly diagnosed children with diabetes and age matched controls including 200 T1D, 100 T2D, and 100 age-matched controls. Findings: We identified 2 serum proteins were significantly changed in T1D vs both control and T2D and 5 serum proteins were significantly changed in both T1D and T2D vs control. Serum Osteopontin (OPN) levels were uniquely higher in T1D (p<0.0001) with no difference between T2D and healthy control subjects. Serum Interleukin 1 receptor antagonist (IL-1RA) levels were lower in T1D compared with both T2D (p<0.001) and healthy subjects (p<0.0001).   Interpretation: Our results suggest that OPN and IL1RA could be the candidates of useful biomarkers for T1D in children. Funding Information: Juvenile Diabetes Research Foundation (JDRF) grant (2-SRA-2019-695-S-B), Diabetes Research Center (DRC) grant P30 DK116073. Declaration of Interests: No potential conflicts of interest relevant to this article were reported. The authors declare no conflict of interest associated with this manuscript. Ethics Approval Statement: Signed written informed consents were obtained from participants and the studies were approved by the Institutional Review Board of the University of Colorado.

Estrogenization of insulin by catecholestrogen produced high affinity autoantibodies and altered the normal function of insulin in type 1 diabetes

AimsInsulin (Ins) covalently modified by catecholestrogens (CEs) was commonly found in diabetic patients who have developed insulin resistance. Estrogenization of insulin altered its molecular function and effect carbohydrates metabolisms in these patients. Insulin resistance is a common phenomenon in diabetes but the exact mechanism remains unknown. In this study, binding specificity and affinity of autoantibodies against estrogenized insulin (4-hydroxyestradiol-insulin; 4-OHE2-Ins) were assayed in the serum of type 1 diabetes (T1D) patients in order to explain the phenomena behind insulin resistance. Materials and MethodsSpecificity and affinity of autoantibodies from the sera of 66 T1D patients and 41 controls were analyzed by direct binding, competition ELISA and quantitative precipitin titration. Insulin was also estimated in the serum of T1D patients by ELISA. Key findingEstrogenized insulin (4-OHE2-Ins) exhibited high affinity and specificity to T1D autoantibodies in comparison to Ins (p < .05) or 4-OHE2 (p < .001). Estrogenization of insulin alters its interaction with the insulin receptor (IR). The affinity constant of 4-OHE2-Ins with the T1D autoantibodies was found to be 1.41 × 10−7 M. SignificanceEstrogenization of insulin by catecholestrogen makes these molecules highly antigenic and produced high-affinity autoantibodies in T1D patients. As a result, patients develop insulin resistance and presented this molecule as a potential biomarker for T1D.

2034-P: ß Cell miRNAs Function as Molecular Hubs of Type 1 Diabetes Pathogenesis and Risk

Biomarkers capable of monitoring β cell stress during the evolution of type 1 diabetes (T1D) are currently lacking. MicroRNAs (miRNAs) are a class of small non-coding RNAs ∼22 nucleotides in length that modulate gene expression by binding to the 3’untranslated region of target mRNAs. Given their stability in biological fluids and enrichment in cell-derived EVs, we hypothesized that miRNAs from human islet and islet-derived EVs could identify β-cell stress/death and be leveraged in T1D biomarker strategies. To test this, human islets were obtained from 10 cadaveric donors (5 male/5 female) and treated with or without cytokines (IL-1β and IFN-γ) for 24 hrs, as an ex vivo model of T1D. Small RNA sequencing was performed and identified 1110 and 890 miRNAs in total and 20 and 14 differentially expressed (DE) miRNAs (fold change≥1.5 and p&amp;lt;0.05) from islets and EVs, respectively. These findings were validated in an independent set of cytokine-treated islets and islet-derived EVs (7M and 5F donors). Interestingly, miRNA expression pattern was strikingly different between male and female donors at baseline and under cytokine stress with &amp;lt; 10% overlap among the DE miRNAs. miR-155-5p and miR-146a-5p were the only two miRNAs that were upregulated in cytokine-treated islets and EVs in both the sexes. Functional enrichment analysis of DE miRNAs identified pathways such as insulin signaling, ER stress and apoptosis. Taken together, these data suggest that miRNA expression patterns change dynamically in both human islets and islet-derived EVs in response to pro-inflammatory cytokine stress. EV miRNAs were largely distinct from those in the islet fraction, suggesting that miRNAs are selectively packaged into EVs in response to extrinsic cues. Finally, these data highlight the importance of considering sex as a biological variable when defining T1D biomarkers. Disclosure F. Syed: None. P. Krishnan: None. I. Restrepo: None. R. Mirmira: Advisory Panel; Self; Hibercell, Sigilon Therapeutics, Veralox Therapeutics. Employee; Spouse/Partner; Eli Lilly and Company. C. Evans-Molina: Consultant; Self; Bristol-Myers Squibb. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK093954, UC4-DK104166 to C.E-M., R.M.); U.S. Department of Veterans Affairs (I01BX001733 to C.E-M.); JDRF (3-SRA-2014-41-Q-R); Sigma Beta Sorority; Ball Brothers Foundation; George and Frances Ball Foundation (to C.E-M.); Indiana Diabetes Research Center (P30DK097512)

Chromogranin Serves as Novel Biomarker of Endocrine and Gastric Autoimmunity.

The glycoprotein chromogranin A (CgA) is expressed by endocrine and neuroendocrine cells. High levels of serum CgA serve as markers of neuroendocrine tumors (NET), but its role in autoimmunity has not been assessed. To investigate CgA utility as a marker of endocrine autoimmunity. CgA serum levels were evaluated in 807 consecutive unselected participants (cross-sectional study) with the time-resolved amplified cryptate emission technology. Serum CgA concentrations were increased in 66%, 39%, 38%, and 24% of patients with NET, type 1 diabetes (T1D), autoimmune gastritis (AG) and autoimmune polyendocrinopathy (AP), respectively. Compared with healthy participant controls (C), the odds of positive CgA measurement were up to 28 times higher in the disease groups. In detail, the odds ratios (ORs) for positive CgA levels were 27.98, 15.22, 7.32 (all P < 0.0001) and 3.89 (P = 0.0073) in patients with NET, T1D, AG, and AP, respectively. In AG, CgA and serum gastrin correlated positively (r = 0.55; P < 0.0001). The area under the receiver operating characteristic curve to predict AG was higher for parietal cell antibody (PCA) positivity than for CgA (0.84 vs 0.67; P < 0.0001). However, in combination with PCA and intrinsic factor autoantibodies, CgA independently improved prediction of AG (OR 6.5; P = 0.031). An impact of age on CgA positivity and on CgA value was detected (P < 0.0001) while current smoking significantly increased CgA serum levels by 25% (P = 0.0080). CgA qualifies as a novel biomarker for T1D, AP, and AG.

Islet autoantibodies in disease prediction and pathogenesis.

Type 1 diabetes (T1D) is now predictable by measuring specific islet autoantibodies (IAbs). Almost all children who developed multiple IAbs will progress to T1D with time, while individuals with single IAb have a very low risk although it is an important earlier biomarker. The poor prediction of single IAb has been found to be associated with IAb affinity. Majority of single IAb generated in current standard IAb radio-binding assay (RBA) are of low affinity, which have been demonstrated low risk in T1D development. New generation of nonradioactive IAb assay with electrochemiluminescence (ECL) technology has been shown to discriminate high-affinity from low-affinity IAbs and greatly improve sensitivity and disease specificity. With a high-affinity IAb assay, like ECL assay, single IAb will be expected to be a reliable biomarker for T1D early prediction. Although appearance of IAbs is most reliable biomarkers for T1D, there are no direct evidences that IAbs contribute to β-cell damage. With recent studies on ZnT8, a merging protein on β-cell surface membrane associated with insulin secretion, a subclass of ZnT8 autoantibodies directed to extra-cellular epitopes of ZnT8 on β-cell surface has recently been identified in T1D patients and these cell surface autoantibodies have been found to appear very early, before other IAbs. These findings lead us to a hypothesis that the immunogenic epitopes on β-cell surface might be early targets for autoimmune disease and IAbs to cell surface epitopes might be involved in β-cell destruction, which will change the paradigm of IAbs in T1D pathogenesis.

Highly Sensitive Room-Temperature Sensor Based on Nanostructured K₂W₇O22 for Application in the Non-Invasive Diagnosis of Diabetes.

Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a positive correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in exhaled breath can provide a non-invasive, low risk of infection, low cost, and convenient way to monitor the health condition of diabetics. There has been continuous demand for the improvement of this non-invasive, sensitive sensor system to provide a fast and real-time electronic readout of blood glucose levels. A novel nanostructured K2W7O22 has been recently used to test acetone with concentration from 0 parts-per-million (ppm) to 50 ppm at room temperature. The results revealed that a K2W7O22 sensor shows a sensitive response to acetone, but the detection limit is not ideal due to the limitations of the detection system of the device. In this paper, we report a K2W7O22 sensor with an improved sensitivity and detection limit by using an optimized circuit to minimize the electronic noise and increase the signal to noise ratio for the purpose of weak signal detection while the concentration of acetone is very low.

Immune Mechanisms and Pathways Targeted in Type 1 Diabetes.

The immunosuppressive agent cyclosporine was first reported to lower daily insulin dose and improve glycemic control in patients with new-onset type 1 diabetes (T1D) in 1984. While renal toxicity limited cyclosporine's extended use, this observation ignited collaborative efforts to identify immunotherapeutic agents capable of safely preserving β cells in patients with or at risk for T1D. Advances in T1D prediction and early diagnosis, together with expanded knowledge of the disease mechanisms, have facilitated trials targeting specific immune cell subsets, autoantigens, and pathways. In addition, clinical responder and non-responder subsets have been defined through the use of metabolic and immunological readouts. Herein, we review emerging T1D biomarkers within the context of recent and ongoing T1D immunotherapy trials. We also discuss responder/non-responder analyses in an effort to identify therapeutic mechanisms, define actionable pathways, and guide subject selection, drug dosing, and tailored combination drug therapy for future T1D trials.

Comprehensive Metabolomics Study To Assess Longitudinal Biochemical Changes and Potential Early Biomarkers in Nonobese Diabetic Mice That Progress to Diabetes.

A global nontargeted longitudinal metabolomics study was carried out in male and female NOD mice to characterize the time-profile of the changes in the metabolic signature caused by onset of type 1 diabetes (T1D) and identify possible early biomarkers in T1D progressors. Metabolomics profiling of samples collected at five different time-points identified 676 and 706 biochemicals in blood and feces, respectively. Several metabolites were expressed at significantly different levels in progressors at all time-points, and their proportion increased strongly following onset of hyperglycemia. At the last time-point, when all progressors were diabetic, a large percentage of metabolites had significantly different levels: 57.8% in blood and 27.8% in feces. Metabolic pathways most strongly affected included the carbohydrate, lipid, branched-chain amino acid, and oxidative ones. Several biochemicals showed considerable (>4×) change. Maltose, 3-hydroxybutyric acid, and kojibiose increased, while 1,5-anhydroglucitol decreased more than 10-fold. At the earliest time-point (6-week), differences between the metabolic signatures of progressors and nonprogressors were relatively modest. Nevertheless, several compounds had significantly different levels and show promise as possible early T1D biomarkers. They include fatty acid phosphocholine derivatives from the phosphatidylcholine subpathway (elevated in both blood and feces) as well as serotonin, ribose, and arabinose (increased) in blood plus 13-HODE, tocopherol (increased), diaminopimelate, valerate, hydroxymethylpyrimidine, and dulcitol (decreased) in feces. A combined metabolic signature based on these compounds might serve as an early predictor of T1D-progressors.

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.