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. 2024 Apr 6;4(1):66.
doi: 10.1038/s43856-024-00478-y.

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

Jamie L Felton  1   2 , Maria J Redondo  3   4 , Richard A Oram  5   6   7 , Cate Speake  8 , S Alice Long  9 , Suna Onengut-Gumuscu  10 , Stephen S Rich  10 , Gabriela S F Monaco  1   2 , Arianna Harris-Kawano  1 , Dianna Perez  1 , Zeb Saeed  11 , Benjamin Hoag  12 , Rashmi Jain  12 , Carmella Evans-Molina  1   2   11   13 , Linda A DiMeglio  1   2 , Heba M Ismail  1   2 , Dana Dabelea  14 , Randi K Johnson  15   16 , Marzhan Urazbayeva  3 , John M Wentworth  17   18   19 , Kurt J Griffin #  12   20 , Emily K Sims #  21   22 ; ADA/EASD PMDI
Collaborators, Affiliations

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

Jamie L Felton et al. Commun Med (Lond). .

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.

Plain language summary

Islet autoantibodies are markers found in the blood when insulin-producing cells in the pancreas become damaged and can be used to predict future development of type 1 diabetes. We evaluated published literature to determine whether characteristics of islet antibodies (type, levels, numbers) could improve prediction and help understand differences in how individuals with type 1 diabetes respond to treatments. We found existing evidence shows that islet autoantibody type and number are most useful to predict disease progression before diagnosis. In addition, the age when islet autoantibodies first appear strongly influences rate of progression. These findings provide important information for patients and care providers on how islet autoantibodies can be used to understand future type 1 diabetes development and to identify individuals who have the potential to benefit from intervention or prevention therapy.

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Conflict of interest statement

E.K.S. has received compensation for educational lectures from Medscape, ADA, and MJH Life Sciences and as a consultant for DRI Healthcare. C.E.M. reported serving on advisory boards for Provention Bio, Isla Technologies, MaiCell Technologies, Avotres, DiogenyX, and Neurodon; receiving in-kind research support from Bristol Myers Squibb and Nimbus Pharmaceuticals; and receiving investigator initiated grants from Lilly Pharmaceuticals and Astellas Pharmaceuticals. L.A.D. reports research support to institution from Dompe, Lilly, Mannkind, Provention, Zealand and consulting relationships with Abata and Vertex. R.A.O. had a UK MRC Confidence in concept grant to develop a T1D GRS biochip with Randox Ltd, and has ongoing research funding from Randox R&D. No other authors report any relevant conflicts of interest.

Figures

Fig. 1
Fig. 1. PRISMA diagram.
For study classification, “Prior to diagnosis” refers to studies that assessed differences in rates of progression and clinical features during the period leading up to stage 3 T1D diagnosis. “At diagnosis” refers to studies that assessed heterogeneity in clinical features at the time of stage 3 T1D diagnosis. “After diagnosis” refers to studies that used features before or at the time of stage 3 T1D diagnosis to characterize subsequent metabolic decline (and preservation of endogenous insulin production). “Treatment response” refers to Studies that assessed heterogeneity in responses to disease-modifying therapies tested in clinical trials in subjects at or before stage 3 T1D diagnosis.
Fig. 2
Fig. 2. Quality assessment of literature search results.
Graphical heat map of quality assessment questions surrounding (a) autoantibody measurements, (b) study design and analysis, (c) outcome assessments, and (d) study follow-up.
Fig. 3
Fig. 3. Questions to consider to improve the quality of and applicability of islet autoantibody precision medicine research.
Based on findings of this review, this is a suggested checklist for improving the quality of islet autoantibody precision medicine research.
See this image and copyright information in PMC

References

    1. DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet. 2018;391:2449–2462. doi: 10.1016/S0140-6736(18)31320-5. - DOI - PMC - PubMed
    1. Insel RA, et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015;38:1964–1974. doi: 10.2337/dc15-1419. - DOI - PMC - PubMed
    1. Nolan JJ, et al. ADA/EASD precision medicine in diabetes initiative: an international perspective and future vision for precision medicine in diabetes. Diabetes Care. 2022;45:261–266. doi: 10.2337/dc21-2216. - DOI - PMC - PubMed
    1. Tobias DK, et al. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. 2023;29:2438–2457. doi: 10.1038/s41591-023-02502-5. - DOI - PMC - PubMed
    1. Battaglia M, et al. Introducing the endotype concept to address the challenge of disease heterogeneity in type 1 diabetes. Diabetes Care. 2020;43:5–12. doi: 10.2337/dc19-0880. - DOI - PMC - PubMed