TY - JOUR

T1 - Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes

T2 - a systematic review

AU - Felton, Jamie L.

AU - Redondo, Maria J.

AU - Oram, Richard A.

AU - Speake, Cate

AU - Long, S. Alice

AU - Onengut-Gumuscu, Suna

AU - Rich, Stephen S.

AU - Monaco, Gabriela S. F.

AU - Harris-Kawano, Arianna

AU - Perez, Dianna

AU - Saeed, Zeb

AU - Hoag, Benjamin

AU - Jain, Rashmi

AU - Evans-Molina, Carmella

AU - DiMeglio, Linda A.

AU - Ismail, Heba M.

AU - Dabelea, Dana

AU - Johnson, Randi K.

AU - Urazbayeva, Marzhan

AU - Wentworth, John M.

AU - Griffin, Kurt J.

AU - Sims, Emily K.

AU - ADA/EASD PMDI

A2 - Merino, Jordi

A2 - Thuesen, Anne Cathrine B.

A2 - Andersen, Mette K.

A2 - Clemmensen, Christoffer

A2 - Hansen, Torben

A2 - Nakabuye, Mariam

A2 - Loos, Ruth J. F.

A2 - Guasch-Ferré, Marta

A2 - Vilsbøll, Tina

N1 - © 2024. The Author(s).

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

U2 - 10.1038/s43856-024-00478-y

DO - 10.1038/s43856-024-00478-y

M3 - Journal article

C2 - 38582818

VL - 4

JO - Communications Medicine

JF - Communications Medicine

SN - 2730-664X

IS - 1

M1 - 66

ER -