TY - JOUR
T1 - Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: 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 - Grif, Kurt J.
AU - Sims, Emily K.
AU - Tobias, Deirdre K.
AU - Merino, Jordi
AU - Ahmad, Abrar
AU - Aiken, Catherine
AU - Benham, Jamie L.
AU - Bodhini, Dhanasekaran
AU - Clark, Amy L.
AU - Colclough, Kevin
AU - Corcoy, Rosa
AU - Cromer, Sara J.
AU - Duan, Daisy
AU - Felton, Jamie L.
AU - Francis, Ellen C.
AU - Gillard, Pieter
AU - Gingras, Veronique
AU - Gaillard, Romy
AU - Haider, Eram
AU - Hughes, Alice
AU - Ikle, Jennifer M.
AU - Jacobsen, Laura M.
AU - Kahkoska, Anna R.
AU - Thuesen, Anne Cathrine B.
AU - Andersen, Mette K.
AU - Clemmensen, Christoffer
AU - Hansen, Torben
AU - Ried-Larsen, Mathias
AU - Nolan, John J.
AU - Vilsboll, Tina
AU - ADA/EASD PMDI
PY - 2024/4/6
Y1 - 2024/4/6
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.
UR - http://www.scopus.com/inward/record.url?scp=85197512814&partnerID=8YFLogxK
U2 - 10.1038/s43856-024-00478-y
DO - 10.1038/s43856-024-00478-y
M3 - Journal article
C2 - 38582818
SN - 2730-664X
VL - 4
JO - Communications medicine
JF - Communications medicine
IS - 1
M1 - 66
ER -