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Felton JL, Redondo MJ, Oram RA, Speake C, Long SA, Onengut-Gumuscu S, Rich SS, Monaco GSF, Harris-Kawano A, Perez D, Saeed Z, Hoag B, Jain R, Evans-Molina C, DiMeglio LA, Ismail HM, Dabelea D, Johnson RK, Urazbayeva M, Wentworth JM, Griffin KJ, Sims EK. Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2024; 4:66. [PMID: 38582818 PMCID: PMC10998887 DOI: 10.1038/s43856-024-00478-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 03/05/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.
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Affiliation(s)
- Jamie L Felton
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Richard A Oram
- NIHR Exeter Biomedical Research Centre (BRC), Academic Kidney Unit, University of Exeter, Exeter, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianna Harris-Kawano
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Dianna Perez
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Zeb Saeed
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Rashmi Jain
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | | | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, VIC, Australia
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
- University of Melbourne Department of Medicine, Parkville, VIC, Australia
| | - Kurt J Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Emily K Sims
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA.
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA.
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Jia X, Yu L. Understanding Islet Autoantibodies in Prediction of Type 1 Diabetes. J Endocr Soc 2023; 8:bvad160. [PMID: 38169963 PMCID: PMC10758755 DOI: 10.1210/jendso/bvad160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Indexed: 01/05/2024] Open
Abstract
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.
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Affiliation(s)
- Xiaofan Jia
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO 80045, USA
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Xu D, Jiang C, Xiao Y, Ding H. Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy. Front Genet 2023; 14:1287613. [PMID: 38028597 PMCID: PMC10658004 DOI: 10.3389/fgene.2023.1287613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN. Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN. Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples. Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue.
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Affiliation(s)
- Danping Xu
- School of Medicine, University of Electronic Science and Technology of China, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Chonghao Jiang
- Affiliated Hospital of North China University of Science and Technology, Tangshan, China
| | - Yonggui Xiao
- North China University of Science and Technology, Tangshan, China
| | - Hanlu Ding
- Renal Division and Institute of Nephrology, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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4
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Jia X, Yu L. Effective assay technologies fit for large-scale population screening of type 1 diabetes. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 3:1034698. [PMID: 36992730 PMCID: PMC10012058 DOI: 10.3389/fcdhc.2022.1034698] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/30/2022] [Indexed: 01/24/2023]
Abstract
While worldwide prevention efforts for type 1 diabetes (T1D) are underway to abrogate or slow progression to diabetes, mass screening of islet autoantibodies (IAbs) in the general population is urgently needed. IAbs, the most reliable biomarkers, play an essential role in prediction and clinical diagnosis of T1D. Through laboratory proficiency programs and harmonization efforts, a radio-binding assay (RBA) has been well established as the current 'gold' standard assay for all four IAbs. However, in view of the need for large-scale screening in the non-diabetic population, RBA consistently faces two fundamental challenges, cost-efficiency and disease specificity. While all four IAbs are important for disease prediction, the RBA platform, with a separate IAb test format is laborious, inefficient and expensive. Furthermore, the majority of IAb positivity in screening, especially from individuals with single IAb were found to be low risk with low affinity. It is well documented from multiple clinical studies that IAbs with low affinity are low risk with less or no disease relevance. At present, two non-radioactive multiplex assays, a 3-assay ELISA combining three IAbs and a multiplex ECL assay combining all four IAbs, have been successfully used as the primary methods for general population screenings in Germany and the US, respectively. Recently, the TrialNet Pathway to Prevention study has been organizing an IAb workshop which aims to analyze the 5-year T1D predictive values of IAbs. A T1D-specific assay with high efficiency, low cost and requiring low volume of sample will definitely be necessary to benefit general population screening.
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Affiliation(s)
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States
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Li B, Zhao X, Xie W, Hong Z, Zhang Y. Integrative analyses of biomarkers and pathways for diabetic nephropathy. Front Genet 2023; 14:1128136. [PMID: 37113991 PMCID: PMC10127684 DOI: 10.3389/fgene.2023.1128136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.
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Affiliation(s)
- Bo Li
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Xu Zhao
- Emergency and Critical Care Center, Renmin Hospital, Hubei University of Medicine, Shiyan, China
| | - Wanrun Xie
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Zhenzhen Hong
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Yi Zhang
- Department of Endocrinology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
- *Correspondence: Yi Zhang,
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So M, Speake C, Steck AK, Lundgren M, Colman PG, Palmer JP, Herold KC, Greenbaum CJ. Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies: Beyond a Simple Count. Endocr Rev 2021; 42:584-604. [PMID: 33881515 DOI: 10.1210/endrev/bnab013] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Indexed: 02/06/2023]
Abstract
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.
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Affiliation(s)
- Michelle So
- Diabetes Clinical Research Program, and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA 98101, USA
| | - Cate Speake
- Diabetes Clinical Research Program, and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA 98101, USA
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Markus Lundgren
- Department of Clinical Sciences Malmö, Lund University, Malmö 22200, Sweden
| | - Peter G Colman
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Melbourne, Victoria 3050, Australia
| | - Jerry P Palmer
- VA Puget Sound Health Care System, Department of Medicine, University of Washington, Seattle, WA 98108, USA
| | - Kevan C Herold
- Department of Immunobiology, and Department of Internal Medicine, Yale University, New Haven, CT 06520, USA
| | - Carla J Greenbaum
- Diabetes Clinical Research Program, and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA 98101, USA
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7
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Gu Y, Jia X, Vartak T, Miao D, Dong F, Jerram ST, Rewers M, Ferrara A, Lawrence JM, Yu L, Leslie RD. Improving clinical utility of GAD65 autoantibodies by electrochemiluminescence assay and clinical phenotype when identifying autoimmune adult-onset diabetes. Diabetologia 2021; 64:2052-2060. [PMID: 34272582 PMCID: PMC8382643 DOI: 10.1007/s00125-021-05492-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/19/2021] [Indexed: 01/01/2023]
Abstract
AIMS/HYPOTHESIS It is important to differentiate the two major phenotypes of adult-onset diabetes, autoimmune type 1 diabetes and non-autoimmune type 2 diabetes, especially as type 1 diabetes presents in adulthood. Serum GAD65 autoantibodies (GADA) are the most sensitive biomarker for adult-onset autoimmune type 1 diabetes, but the clinical value of GADA by current standard radiobinding assays (RBA) remains questionable. The present study focused on the clinical utility of GADA differentiated by a new electrochemiluminescence (ECL) assay in patients with adult-onset diabetes. METHODS Two cohorts were analysed including 771 diabetic participants, 30-70 years old, from the Action LADA study (n = 6156), and 2063 diabetic participants, 20-45 years old, from the Diabetes in Young Adults (DiYA) study. Clinical characteristics of participants, including requirement of early insulin treatment, BMI and development of multiple islet autoantibodies, were analysed according to the status of RBA-GADA and ECL-GADA, respectively, and compared between these two assays. RESULTS GADA was the most prevalent and predominant autoantibody, >90% in both cohorts. GADA positivity by either RBA or ECL assay significantly discriminated clinical type 1 from type 2 diabetes. However, in both cohorts, participants with ECL-GADA positivity were more likely to require early insulin treatment, have multiple islet autoantibodies, and be less overweight (for all p < 0.0001). However, clinical phenotype, age at diagnosis and BMI independently improved positive predictive value (PPV) for the requirement of insulin treatment, even augmenting ECL-GADA. Participants with GADA detectable by RBA, but not confirmed by ECL, had a phenotype more similar to type 2 diabetes. These RBA-GADA positive individuals had lower affinity GADA compared with participants in which GADA was confirmed by ECL assay. CONCLUSIONS/INTERPRETATION Detection of GADA by ECL assay, given technical advantages over RBA-GADA, identified adult-onset diabetes patients at higher risk of requiring early insulin treatment, as did clinical phenotype, together allowing for more accurate clinical diagnosis and management.
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Affiliation(s)
- Yong Gu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
- Endocrinology and Metabolism, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaofan Jia
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
- Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Tanwi Vartak
- Centre for Immunobiology, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Dongmei Miao
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Fran Dong
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Samuel T Jerram
- Centre for Immunobiology, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Assiamira Ferrara
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Jean M Lawrence
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA.
| | - R David Leslie
- Centre for Immunobiology, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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Industry Development of Derivative Functionalized Gold Nanomaterials and Their Application in Chemiluminescence Bioanalysis: Based on the Industrial Practice of China's Central Yunnan Urban Agglomeration. J CHEM-NY 2020. [DOI: 10.1155/2020/5474506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Electrochemiluminescence biosensor is an analytical method combining electrochemiluminescence technology with biosensor. Using nanomaterials as electrochemical luminescence sensor platform can not only immobilize a large number of biomolecules but also improve the performance of the sensor to realize the supersensitive detection of biomacromolecules. Although these methods have high sensitivity for bioanalysis, there are still some shortcomings which limit the practical application. Therefore, this paper discusses the development of functional gold nanomaterials industry and its application in chemiluminescence bioanalysis. In this paper, two methods of synthesizing luminescent functional gold nanomaterials at room temperature were studied by using chemiluminescent reagents as reducing agent and protective agent. Based on luminescent functionalized gold nanoparticles, immunoassay and DNA bioanalysis probes were constructed, and their applications in chemiluminescence and electrochemiluminescence bioanalysis were discussed. Finally, the simulation results show that the relative deviation between the experimental results and the existing clinical methods is less than 17%. The sensor has good stability and selectivity and can be used for the determination of CEA in human serum. The gold nanomaterials synthesized by further research have excellent chemiluminescence activity and can be used to label biomolecules and prepare biological probes. This article aims to explore the application of chemical methods in the transformation of new industries, to achieve breakthroughs in new products in industrial innovation, and to achieve the cross-fusion of management science and engineering disciplines and chemical disciplines. The industrial development of derivative functionalized gold nanomaterials has broad application prospects in biological analysis.
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Abstract
PURPOSE OF REVIEW Although type 1 diabetes (T1D) is characterized by destruction of the pancreatic beta cells by self-reactive T cells, it has become increasingly evident that B cells also play a major role in disease development, likely functioning as antigen-presenting cells. Here we review the biology of islet antigen-reactive B cells and their participation in autoimmune diabetes. RECENT FINDINGS Relative to late onset, individuals who develop T1D at an early age display increased accumulation of insulin-reactive B cells in islets. This B-cell signature is also associated with rapid progression of disease and responsiveness to B-cell depletion therapy. Also suggestive of B-cell participation in disease is loss of anergy in high-affinity insulin-reactive B cells. Importantly, loss of anergy is seen in patient's healthy first-degree relatives carrying certain T1D risk alleles, suggesting a role early in disease development. SUMMARY Recent studies indicate that islet-reactive B cells may play a pathogenic role very early in T1D development in young patients, and suggest utility of therapies that target these cells.
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Affiliation(s)
- Mia J. Smith
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - John C. Cambier
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO
| | - Peter A. Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
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Jacobsen LM, Bocchino L, Evans-Molina C, DiMeglio L, Goland R, Wilson DM, Atkinson MA, Aye T, Russell WE, Wentworth JM, Boulware D, Geyer S, Sosenko JM. The risk of progression to type 1 diabetes is highly variable in individuals with multiple autoantibodies following screening. Diabetologia 2020; 63:588-596. [PMID: 31768570 PMCID: PMC7229995 DOI: 10.1007/s00125-019-05047-w] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 10/11/2019] [Indexed: 12/30/2022]
Abstract
AIMS/HYPOTHESIS Young children who develop multiple autoantibodies (mAbs) are at very high risk for type 1 diabetes. We assessed whether a population with mAbs detected by screening is also at very high risk, and how risk varies according to age, type of autoantibodies and metabolic status. METHODS Type 1 Diabetes TrialNet Pathway to Prevention participants with mAbs (n = 1815; age, 12.35 ± 9.39 years; range, 1-49 years) were analysed. Type 1 diabetes risk was assessed according to age, autoantibody type/number (insulin autoantibodies [IAA], glutamic acid decarboxylase autoantibodies [GADA], insulinoma-associated antigen-2 autoantibodies [IA-2A] or zinc transporter 8 autoantibodies [ZnT8A]) and Index60 (composite measure of fasting C-peptide, 60 min glucose and 60 min C-peptide). Cox regression and cumulative incidence curves were utilised in this cohort study. RESULTS Age was inversely related to type 1 diabetes risk in those with mAbs (HR 0.97 [95% CI 0.96, 0.99]). Among participants with 2 autoantibodies, those with GADA had less risk (HR 0.35 [95% CI 0.22, 0.57]) and those with IA-2A had higher risk (HR 2.82 [95% CI 1.76, 4.51]) of type 1 diabetes. Those with IAA and GADA had only a 17% 5 year risk of type 1 diabetes. The risk was significantly lower for those with Index60 <1.0 (HR 0.23 [95% CI 0.19, 0.30]) vs those with Index60 values ≥1.0. Among the 12% (225/1815) ≥12.0 years of age with GADA positivity, IA-2A negativity and Index60 <1.0, the 5 year risk of type 1 diabetes was 8%. CONCLUSIONS/INTERPRETATION Type 1 diabetes risk varies substantially according to age, autoantibody type and metabolic status in individuals screened for mAbs. An appreciable proportion of older children and adults with mAbs appear to have a low risk of progressing to type 1 diabetes at 5 years. With this knowledge, clinical trials of type 1 diabetes prevention can better target those most likely to progress.
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Affiliation(s)
- Laura M Jacobsen
- Division of Pediatric Endocrinology, Department of Pediatrics, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA.
| | - Laura Bocchino
- Health Informatics Institute, University of South Florida, Tampa, FL, USA
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Linda DiMeglio
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Robin Goland
- Division of Pediatric Endocrinology, Diabetes, and Metabolism, Columbia University Medical Center, New York, NY, USA
| | - Darrell M Wilson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Mark A Atkinson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, USA
| | - Tandy Aye
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - William E Russell
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John M Wentworth
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - David Boulware
- Health Informatics Institute, University of South Florida, Tampa, FL, USA
| | - Susan Geyer
- Health Informatics Institute, University of South Florida, Tampa, FL, USA
| | - Jay M Sosenko
- Division of Endocrinology, University of Miami, Miami, FL, USA
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11
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Jia X, Gu Y, High H, Yu L. Islet autoantibodies in disease prediction and pathogenesis. Diabetol Int 2020; 11:6-10. [PMID: 31949998 PMCID: PMC6942067 DOI: 10.1007/s13340-019-00414-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 10/03/2019] [Indexed: 01/01/2023]
Abstract
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.
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Affiliation(s)
- Xiaofan Jia
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045 USA
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Yong Gu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045 USA
| | - Hilary High
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045 USA
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045 USA
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12
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Gu Y, Zhao Z, Waugh K, Miao D, Jia X, Cheng J, Michels A, Rewers M, Yang T, Yu L. High-throughput multiplexed autoantibody detection to screen type 1 diabetes and multiple autoimmune diseases simultaneously. EBioMedicine 2019; 47:365-372. [PMID: 31447394 PMCID: PMC6796526 DOI: 10.1016/j.ebiom.2019.08.036] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 08/16/2019] [Accepted: 08/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Islet autoantibodies (IAbs) are the most reliable biomarkers to assess risk of progression to clinical type 1 diabetes (T1D). There are four major biochemically defined IAbs currently used in clinical trials that are equally important for disease prediction. The current screening methods use a radio-binding assay (RBA) for single IAb measurement, which are laborious and inefficient for large-scale screening. More importantly, up to 40% of patients with T1D have other autoimmune conditions that can be identified through relevant autoantibody testing. Thus, there is a need to screen for T1D and other autoimmune diseases simultaneously. METHODS Based on our well-established electrochemiluminescence (ECL) assay platform, we developed a multiplexed ECL assay that combines 7 individual autoantibody assays together in one single well to simultaneously screen T1D, and three other autoimmune diseases including celiac disease, autoimmune thyroid disease and autoimmune poly-glandular syndrome-1 (APS-1). The 7-Plex ECL assay was extensively validated against single antibody measurements including a standard RBA and single ECL assay. FINDINGS The 7-Plex ECL assay was well correlated to each single ECL autoantibody assay and each RBA. INTERPRETATION The multiplexed ECL assay provides high sensitivity and disease specificity, along with high throughput and a low cost for large-scale screenings of T1D and other relevant autoimmune diseases in the general population. FUND: JDRF grants 2-SRA-2015-51-Q-R, 2-SRA-2018-533-S-B, NIH grants DK32083 and DK32493. NSFC grants 81770777.
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Affiliation(s)
- Yong Gu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America,Department of Endocrinology, First Affiliated Hospital of Nanjing Medical University, China
| | - Zhiyuan Zhao
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Kathleen Waugh
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Dongmei Miao
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Xiaofan Jia
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Jeremy Cheng
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Aaron Michels
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Tao Yang
- Department of Endocrinology, First Affiliated Hospital of Nanjing Medical University, China
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, United States of America,Corresponding author at: Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045, United States of America.
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13
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Abstract
PURPOSE OF REVIEW 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. RECENT FINDINGS 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.
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Affiliation(s)
- Laura M Jacobsen
- Department of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Brittney N Newby
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, 1275 Center Drive, Biomedical Sciences Building J-589, Box 100275, Gainesville, FL, 32610, USA
| | - Daniel J Perry
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, 1275 Center Drive, Biomedical Sciences Building J-589, Box 100275, Gainesville, FL, 32610, USA
| | - Amanda L Posgai
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, 1275 Center Drive, Biomedical Sciences Building J-589, Box 100275, Gainesville, FL, 32610, USA
| | - Michael J Haller
- Department of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Todd M Brusko
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, 1275 Center Drive, Biomedical Sciences Building J-589, Box 100275, Gainesville, FL, 32610, USA.
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14
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Greenbaum CJ, Speake C, Krischer J, Buckner J, Gottlieb PA, Schatz DA, Herold KC, Atkinson MA. Strength in Numbers: Opportunities for Enhancing the Development of Effective Treatments for Type 1 Diabetes-The TrialNet Experience. Diabetes 2018; 67:1216-1225. [PMID: 29769238 PMCID: PMC6014559 DOI: 10.2337/db18-0065] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 04/20/2018] [Indexed: 12/12/2022]
Abstract
The early to mid-1980s were an inflection point in the history of type 1 diabetes research. Two landmark events occurred: the initiation of immune-based interventions seeking to prevent type 1 diabetes and the presentation of an innovative model describing the disorder's natural history. Both formed the basis for hundreds of subsequent studies designed to achieve a dramatic therapeutic goal-a means to prevent and/or reverse type 1 diabetes. However, the need to screen large numbers of individuals and prospectively monitor them using immunologic and metabolic tests for extended periods of time suggested such efforts would require a large collaborative network. Hence, the National Institutes of Health formed the landmark Diabetes Prevention Trial-Type 1 (DPT-1) in the mid-1990s, an effort that led to Type 1 Diabetes TrialNet. TrialNet studies have helped identify novel biomarkers; delineate type 1 diabetes progression, resulting in identification of highly predictable stages defined by the accumulation of autoantibodies (stage 1), dysglycemia (stage 2), and disease meeting clinical criteria for diagnosis (stage 3); and oversee numerous clinical trials aimed at preventing disease progression. Such efforts pave the way for stage-specific intervention trials with improved hope that a means to effectively disrupt the disorder's development will be identified.
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Affiliation(s)
- Carla J Greenbaum
- Clinical Research Center, Diabetes Research Program, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Cate Speake
- Clinical Research Center, Diabetes Research Program, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Jeffrey Krischer
- Diabetes Center and Pediatric Epidemiology Center, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Jane Buckner
- Clinical Research Center, Diabetes Research Program, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Peter A Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Desmond A Schatz
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL
| | - Kevan C Herold
- Departments of Immunobiology and Internal Medicine, Yale University, New Haven, CT
| | - Mark A Atkinson
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL
- Department of Pathology, College of Medicine, University of Florida, Gainesville, FL
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15
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Gu Y, Zhao Z, High H, Yang T, Yu L. Islet Autoantibody Detection by Electrochemiluminescence (ECL) Assay. ACTA ACUST UNITED AC 2017; 8. [PMID: 29487479 PMCID: PMC5796772 DOI: 10.4172/2155-9899.1000531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Yong Gu
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO, USA.,Department of Endocrinology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhiyuan Zhao
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO, USA
| | - Hilary High
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO, USA
| | - Tao Yang
- Department of Endocrinology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Liping Yu
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO, USA
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16
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Insel R, Dutta S, Hedrick J. Type 1 Diabetes: Disease Stratification. Biomed Hub 2017; 2:111-126. [PMID: 31988942 PMCID: PMC6945911 DOI: 10.1159/000481131] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 08/30/2017] [Indexed: 12/13/2022] Open
Abstract
Type 1 diabetes, a disorder characterized by immune-mediated loss of functional pancreatic beta cells, is a disease continuum with specific presymptomatic stages with defined risk of progression to symptomatic disease. Prognostic biomarkers have been developed for disease staging and for stratification of subjects that address the heterogeneity in rate of disease progression. Using biomarkers for stratification of subjects at different stages of type 1 diabetes will enable smaller and shorter intervention clinical trials with greater effect size. Addressing the heterogeneity of the disease will allow precision medicine-based approaches to prevention and interception of presymptomatic stages of disease and treatment and cure of symptomatic disease.
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Affiliation(s)
| | | | - Joseph Hedrick
- Disease Interception Accelerator - T1D, Janssen Research & Development, LLC, Raritan, NJ, USA
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17
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Abstract
Type 1 diabetes mellitus (T1DM) is an autoimmune disorder that affects an estimated 30 million people worldwide. It is characterized by the destruction of pancreatic β cells by the immune system, which leads to lifelong dependency on exogenous insulin and imposes an enormous burden on patients and health-care resources. T1DM is also associated with an increased risk of comorbidities, such as cardiovascular disease, retinopathy, and diabetic kidney disease (DKD), further contributing to the burden of this disease. Although T cells are largely considered to be responsible for β-cell destruction in T1DM, increasing evidence points towards a role for B cells in disease pathogenesis. B cell-depletion, for example, delays disease progression in patients with newly diagnosed T1DM. Loss of tolerance of islet antigen-reactive B cells occurs early in disease and numbers of pancreatic CD20+ B cells correlate with β-cell loss. Although the importance of B cells in T1DM is increasingly apparent, exactly how these cells contribute to disease and its comorbidities, such as DKD, is not well understood. Here we discuss the role of B cells in the pathogenesis of T1DM and how these cells are activated during disease development. Finally, we speculate on how B cells might contribute to the development of DKD.
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18
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Proteoliposome-based full-length ZnT8 self-antigen for type 1 diabetes diagnosis on a plasmonic platform. Proc Natl Acad Sci U S A 2017; 114:10196-10201. [PMID: 28874568 DOI: 10.1073/pnas.1711169114] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Identified as a major biomarker for type 1 diabetes (T1D) diagnosis, zinc transporter 8 autoantibody (ZnT8A) has shown promise for staging disease risk and disease diagnosis. However, existing assays for ZnT8 autoantibody (ZnT8A) are limited to detection by soluble domains of ZnT8, owing to difficulties in maintaining proper folding of a full-length ZnT8 protein outside its native membrane environment. Through a combined bioengineering and nanotechnology approach, we have developed a proteoliposome-based full-length ZnT8 self-antigen (full-length ZnT8 proteoliposomes; PLR-ZnT8) for efficient detection of ZnT8A on a plasmonic gold chip (pGOLD). The protective lipid matrix of proteoliposomes improved the proper folding and structural stability of full-length ZnT8, helping PLR-ZnT8 immobilized on pGOLD (PLR-ZnT8/pGOLD) achieve high-affinity capture of ZnT8A from T1D sera. Our PLR-ZnT8/pGOLD exhibited efficient ZnT8A detection for T1D diagnosis with ∼76% sensitivity and ∼97% specificity (n = 307), superior to assays based on detergent-solubilized full-length ZnT8 and the C-terminal domain of ZnT8. Multiplexed assays using pGOLD were also developed for simultaneous detection of ZnT8A, islet antigen 2 autoantibody, and glutamic acid decarboxylase autoantibody for diagnosing T1D.
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19
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Abstract
PURPOSE OF REVIEW Type 1 diabetes (T1D) is now predictable by measuring major islet autoantibodies (IAbs) against insulin and other pancreatic β cells proteins including GAD65 (GADA), islet antigen 2 (IA-2A), and zinc transporter 8 (ZnT8A). The assay technology for IAbs has made great progress; however, several important aspects still need to be addressed and improved. RECENT FINDINGS Currently a radio-binding assay has been well established as the 'gold' standard assay for all four IAbs. New generation of nonradioactive IAb assay with electrochemiluminescence technology has been shown to further improve sensitivity and disease specificity. Recently, multiplexed assays have opened the possibility of more efficient screening in large populations. Identification of potential new autoantibodies to neo-antigens or neo-epitopes posttranslational modification is a new important field to be explored. SUMMARY Individuals having a single positive autoantibody are at low risk for progression to T1D, whereas individuals expressing two or more positive autoantibodies, especially on multiple tests over time, have nearly 100% risk of developing clinical T1D when followed for over two decades. More efficient and cost effective IAb assays will hopefully lead to point-of-care screening in the general population.
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Affiliation(s)
- Liping Yu
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
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20
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Abstract
Two fundamental aspects for precisely predicting the risk of developing type 1 diabetes by islet autoantibodies are assay sensitivity and disease specificity. We have recently developed electrochemiluminescent (ECL) insulin autoantibody (IAA) and GAD65 autoantibody (GADA) assays. ECL assays are sensitive, able to identify the initiation of islet autoimmunity earlier in life among high-risk young children before clinical onset of diabetes and are more disease specific because they are able to discriminate high-affinity, high-risk diabetes specific islet autoantibodies from low-affinity, low-risk autoantibodies.
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Affiliation(s)
- Liping Yu
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Building 500-13001 E. 17th Place, Campus Box C290, Room E1354, Aurora, CO, 80045, USA.
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