Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

Authors

Jamie L. Felton, Center for Diabetes and Metabolic Diseases
Maria J. Redondo, Baylor College of Medicine
Richard A. Oram, University of Exeter
Richard A. Oram, University of Exeter
Cate Speake, Benaroya Research Institute at Virginia Mason
S. Alice Long, Benaroya Research Institute at Virginia Mason
Suna Onengut-Gumuscu, University of Virginia School of Medicine
Stephen S. Rich, University of Virginia School of Medicine
Stephen S. Rich, University of Virginia School of Medicine
Gabriela S.F. Monaco, Center for Diabetes and Metabolic Diseases
Arianna Harris-Kawano, Center for Diabetes and Metabolic Diseases
Dianna Perez, Center for Diabetes and Metabolic Diseases
Zeb Saeed, Indiana University School of Medicine
Benjamin Hoag, Sanford School of Medicine
Rashmi Jain, Sanford School of Medicine
Carmella Evans-Molina, Center for Diabetes and Metabolic Diseases
Linda A. DiMeglio, Center for Diabetes and Metabolic Diseases
Heba M. Ismail, Center for Diabetes and Metabolic Diseases
Dana Dabelea, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center
Randi K. Johnson, University of Colorado School of Medicine
Marzhan Urazbayeva, Baylor College of Medicine
John M. Wentworth, Royal Melbourne Hospital
Kurt J. Griffin, Sanford School of Medicine
Kurt J. Griffin, Sanford School of Medicine
Emily K. Sims, Center for Diabetes and Metabolic Diseases
Emily K. Sims, Center for Diabetes and Metabolic Diseases
Paul W. Franks, Harvard T.H. Chan School of Public Health
Robert Wagner, Deutsches Diabetes-Zentrum
Tina Vilsbøll, Steno Diabetes Center Copenhagen
Kimberly K. Vesco, Kaiser Permanente Center for Health Research
Miriam S. Udler, Massachusetts General Hospital
Tiinamaija Tuomi, Helsinki University Hospital
Arianne Sweeting, Faculty of Medicine and Health
Jennifer L. Sherr, Yale School of Medicine

Document Type

Article

Publication Date

12-1-2024

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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