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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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Guertin KA, Repaske DR, Taylor JF, Williams ES, Onengut-Gumuscu S, Chen WM, Boggs SR, Yu L, Allen L, Botteon L, Daniel L, Keating KG, Labergerie MK, Lienhart TS, Gonzalez-Mejia JA, Starnowski MJ, Rich SS. Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project. Genome Med 2024; 16:31. [PMID: 38355597 PMCID: PMC10865687 DOI: 10.1186/s13073-024-01305-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Population screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing. METHODS Children were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at "high genetic risk" were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D. RESULTS A total of 3818 children aged 2-16 years were recruited, with 14.2% (n = 542) having a "high genetic risk." Of children with "high genetic risk" and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a "high genetic risk." In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure. CONCLUSIONS Minimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.
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Affiliation(s)
- Kristin A Guertin
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Department of Public Health Sciences, UConn School of Medicine, UConn Health, 263 Farmington Avenue, MC 6325, Farmington, CT, 06030, USA
| | - David R Repaske
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Julia F Taylor
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Eli S Williams
- Department of Pathology, Division of Medical Genetics, UVAHealth, University of Virginia, 21 Hospital Drive, Charlottesville, VA, 22903, USA
| | - Suna Onengut-Gumuscu
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Wei-Min Chen
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Sarah R Boggs
- Department of Pediatrics, Division of Pediatric Diabetes & Endocrinology, University of Virginia, UVAHealth, 1204 W Main Street, 6th Floor, Charlottesville, VA, 22903, USA
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, 1774 Aurora Court, Suite A140, Aurora, CO, 80045, USA
| | - Luke Allen
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Lacey Botteon
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Louis Daniel
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Katherine G Keating
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Mika K Labergerie
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Tyler S Lienhart
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Jorge A Gonzalez-Mejia
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Matt J Starnowski
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA
| | - Stephen S Rich
- Department of Public Health Sciences, University of Virginia, 1300 Jefferson Park Avenue, 3182 West Complex, Charlottesville, VA, 22903, USA.
- Center for Public Health Genomics, University of Virginia, 1335 Lee Street, 3235 West Complex, Charlottesville, VA, 22903, USA.
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He L, Jia X, Rasmussen CG, Waugh K, Miao D, Dong F, Frohnert B, Steck AK, Simmons KM, Rewers M, Yu L. High-Throughput Multiplex Electrochemiluminescence Assay Applicable to General Population Screening for Type 1 Diabetes and Celiac Disease. Diabetes Technol Ther 2022; 24:502-509. [PMID: 35238620 PMCID: PMC9464081 DOI: 10.1089/dia.2021.0517] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Objective: Large-scale screening of the general population for islet autoantibodies (IAbs) to detect type 1 diabetes (T1D) has started worldwide. The standard screening method of separate radio-binding assay (RBA) for each IAb is an inefficient bottleneck. Furthermore, most positive results by RBA in screening of general population individuals without a clinical diagnosis of T1D are low-affinity and not predictive of future diabetes. Research Design and Methods: We have developed and validated a novel 6-Plex assay based on electrochemiluminescence (ECL) technology that combines in a single well high-affinity IAbs (to insulin, GAD, IA-2, and ZnT8), transglutaminase autoantibodies for celiac disease, and severe acute respiratory syndrome coronavirus 2 antibodies. The Autoimmunity Screening for Kids (ASK) provided 880 serum samples, from 828 children aged 1-17 years without diabetes who were previously tested for IAbs using single ECL assays and RBA assays. Results: Levels of all six antibodies in the 6-Plex ECL assay correlated well with respective single ECL assay levels. Similar to single ECL assays, the 6-Plex ECL assay positivity was congruent with the RBA in 95% (35/37) of children who later developed T1D and in 88% (105/119) high-risk children with multiple IAbs. In contrast, only 56% (86/154, P < 0.0001) of children with persistent single IAb by RBA were found to be positive by 6-Plex ECL assay. Of 555 samples negative for all IAbs by RBA, few (0.2%-0.5%) were positive at low levels in the 6-Plex ECL assay. Conclusions: The study demonstrated that the 6-Plex ECL assay compares favorably to the standard RBAs in terms of disease specificity for general population screening in children. The 6-Plex ECL assay was therefore adopted as the primary screening tool in the general population screening ASK program with advantages of high efficiency, low cost, and low serum volume.
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Affiliation(s)
- Ling He
- Department of Endocrinology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Xiaofan Jia
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Cristy Geno Rasmussen
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Kathleen Waugh
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Dongmei Miao
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Fran Dong
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Brigitte Frohnert
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Andrea K. Steck
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Kimber M. Simmons
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA
- Address correspondence to: Marian Rewers, MD, PhD, 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, Aurora, Colorado, USA
- Address correspondence to: Liping Yu, MD, Barbara Davis Center for Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, B140, Aurora, CO 80045, USA
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