Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review.
Journal
Communications medicine
ISSN: 2730-664X
Titre abrégé: Commun Med (Lond)
Pays: England
ID NLM: 9918250414506676
Informations de publication
Date de publication:
06 Apr 2024
06 Apr 2024
Historique:
received:
09
05
2023
accepted:
05
03
2024
medline:
7
4
2024
pubmed:
7
4
2024
entrez:
6
4
2024
Statut:
epublish
Résumé
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. 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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
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.
Autres résumés
Type: plain-language-summary
(eng)
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.
Identifiants
pubmed: 38582818
doi: 10.1038/s43856-024-00478-y
pii: 10.1038/s43856-024-00478-y
doi:
Types de publication
Journal Article
Langues
eng
Pagination
66Investigateurs
Deirdre K Tobias
(DK)
Jordi Merino
(J)
Abrar Ahmad
(A)
Catherine Aiken
(C)
Jamie L Benham
(JL)
Dhanasekaran Bodhini
(D)
Amy L Clark
(AL)
Kevin Colclough
(K)
Rosa Corcoy
(R)
Sara J Cromer
(SJ)
Daisy Duan
(D)
Jamie L Felton
(JL)
Ellen C Francis
(EC)
Pieter Gillard
(P)
Véronique Gingras
(V)
Romy Gaillard
(R)
Eram Haider
(E)
Alice Hughes
(A)
Jennifer M Ikle
(JM)
Laura M Jacobsen
(LM)
Anna R Kahkoska
(AR)
Jarno L T Kettunen
(JLT)
Raymond J Kreienkamp
(RJ)
Lee-Ling Lim
(LL)
Jonna M E Männistö
(JME)
Robert Massey
(R)
Niamh-Maire Mclennan
(NM)
Rachel G Miller
(RG)
Mario Luca Morieri
(ML)
Jasper Most
(J)
Rochelle N Naylor
(RN)
Bige Ozkan
(B)
Kashyap Amratlal Patel
(KA)
Scott J Pilla
(SJ)
Katsiaryna Prystupa
(K)
Sridharan Raghavan
(S)
Mary R Rooney
(MR)
Martin Schön
(M)
Zhila Semnani-Azad
(Z)
Magdalena Sevilla-Gonzalez
(M)
Pernille Svalastoga
(P)
Wubet Worku Takele
(WW)
Claudia Ha-Ting Tam
(CH)
Anne Cathrine B Thuesen
(ACB)
Mustafa Tosur
(M)
Amelia S Wallace
(AS)
Caroline C Wang
(CC)
Jessie J Wong
(JJ)
Jennifer M Yamamoto
(JM)
Katherine Young
(K)
Chloé Amouyal
(C)
Mette K Andersen
(MK)
Maxine P Bonham
(MP)
Mingling Chen
(M)
Feifei Cheng
(F)
Tinashe Chikowore
(T)
Sian C Chivers
(SC)
Christoffer Clemmensen
(C)
Dana Dabelea
(D)
Adem Y Dawed
(AY)
Aaron J Deutsch
(AJ)
Laura T Dickens
(LT)
Linda A DiMeglio
(LA)
Monika Dudenhöffer-Pfeifer
(M)
Carmella Evans-Molina
(C)
María Mercè Fernández-Balsells
(MM)
Hugo Fitipaldi
(H)
Stephanie L Fitzpatrick
(SL)
Stephen E Gitelman
(SE)
Mark O Goodarzi
(MO)
Jessica A Grieger
(JA)
Marta Guasch-Ferré
(M)
Nahal Habibi
(N)
Torben Hansen
(T)
Chuiguo Huang
(C)
Arianna Harris-Kawano
(A)
Heba M Ismail
(HM)
Benjamin Hoag
(B)
Angus G Jones
(AG)
Robert W Koivula
(RW)
Aaron Leong
(A)
Gloria K W Leung
(GKW)
Ingrid M Libman
(IM)
Kai Liu
(K)
William L Lowe
(WL)
Robert W Morton
(RW)
Ayesha A Motala
(AA)
Suna Onengut-Gumuscu
(S)
James S Pankow
(JS)
Maleesa Pathirana
(M)
Sofia Pazmino
(S)
Dianna Perez
(D)
John R Petrie
(JR)
Camille E Powe
(CE)
Alejandra Quinteros
(A)
Rashmi Jain
(R)
Debashree Ray
(D)
Mathias Ried-Larsen
(M)
Zeb Saeed
(Z)
Vanessa Santhakumar
(V)
Sarah Kanbour
(S)
Sudipa Sarkar
(S)
Gabriela S F Monaco
(GSF)
Denise M Scholtens
(DM)
Elizabeth Selvin
(E)
Wayne Huey-Herng Sheu
(WH)
Maggie A Stanislawski
(MA)
Nele Steenackers
(N)
Andrea K Steck
(AK)
Norbert Stefan
(N)
Julie Støy
(J)
Rachael Taylor
(R)
Sok Cin Tye
(SC)
Gebresilasea Gendisha Ukke
(GG)
Marzhan Urazbayeva
(M)
Bart Van der Schueren
(B)
Camille Vatier
(C)
Wesley Hannah
(W)
Sara L White
(SL)
Gechang Yu
(G)
Yingchai Zhang
(Y)
Shao J Zhou
(SJ)
Jacques Beltrand
(J)
Michel Polak
(M)
Ingvild Aukrust
(I)
Elisa de Franco
(E)
Sarah E Flanagan
(SE)
Kristin A Maloney
(KA)
Andrew McGovern
(A)
Janne Molnes
(J)
Mariam Nakabuye
(M)
Pål Rasmus Njølstad
(PR)
Hugo Pomares-Millan
(H)
Michele Provenzano
(M)
Cécile Saint-Martin
(C)
Cuilin Zhang
(C)
Yeyi Zhu
(Y)
Sungyoung Auh
(S)
Russell de Souza
(R)
Andrea J Fawcett
(AJ)
Chandra Gruber
(C)
Eskedar Getie Mekonnen
(EG)
Emily Mixter
(E)
Diana Sherifali
(D)
Robert H Eckel
(RH)
John J Nolan
(JJ)
Louis H Philipson
(LH)
Rebecca J Brown
(RJ)
Liana K Billings
(LK)
Kristen Boyle
(K)
Tina Costacou
(T)
John M Dennis
(JM)
Jose C Florez
(JC)
Anna L Gloyn
(AL)
Maria F Gomez
(MF)
Peter A Gottlieb
(PA)
Siri Atma W Greeley
(SAW)
Kurt Griffin
(K)
Andrew T Hattersley
(AT)
Irl B Hirsch
(IB)
Marie-France Hivert
(MF)
Korey K Hood
(KK)
Jami L Josefson
(JL)
Soo Heon Kwak
(SH)
Lori M Laffel
(LM)
Siew S Lim
(SS)
Ruth J F Loos
(RJF)
Ronald C W Ma
(RCW)
Chantal Mathieu
(C)
Nestoras Mathioudakis
(N)
James B Meigs
(JB)
Shivani Misra
(S)
Viswanathan Mohan
(V)
Rinki Murphy
(R)
Richard Oram
(R)
Katharine R Owen
(KR)
Susan E Ozanne
(SE)
Ewan R Pearson
(ER)
Wei Perng
(W)
Toni I Pollin
(TI)
Rodica Pop-Busui
(R)
Richard E Pratley
(RE)
Leanne M Redman
(LM)
Rebecca M Reynolds
(RM)
Robert K Semple
(RK)
Jennifer L Sherr
(JL)
Emily K Sims
(EK)
Arianne Sweeting
(A)
Tiinamaija Tuomi
(T)
Miriam S Udler
(MS)
Kimberly K Vesco
(KK)
Tina Vilsbøll
(T)
Robert Wagner
(R)
Stephen S Rich
(SS)
Paul W Franks
(PW)
Informations de copyright
© 2024. The Author(s).
Références
DiMeglio, L. A., Evans-Molina, C. & Oram, R. A. Type 1 diabetes. Lancet 391, 2449–2462 (2018).
pubmed: 29916386
pmcid: 6661119
doi: 10.1016/S0140-6736(18)31320-5
Insel, R. A. et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38, 1964–1974 (2015).
pubmed: 26404926
pmcid: 5321245
doi: 10.2337/dc15-1419
Nolan, J. J. et al. ADA/EASD precision medicine in diabetes initiative: an international perspective and future vision for precision medicine in diabetes. Diabetes Care 45, 261–266 (2022).
pubmed: 35050364
pmcid: 8914425
doi: 10.2337/dc21-2216
Tobias, D. K. et al. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. 29, 2438–2457 (2023).
pubmed: 37794253
pmcid: 10735053
doi: 10.1038/s41591-023-02502-5
Battaglia, M. et al. Introducing the endotype concept to address the challenge of disease heterogeneity in type 1 diabetes. Diabetes Care 43, 5–12 (2020).
pubmed: 31753960
doi: 10.2337/dc19-0880
Lampasona, V. et al. Islet Autoantibody Standardization Program 2018 Workshop: interlaboratory comparison of glutamic acid decarboxylase autoantibody assay performance. Clin. Chem. 65, 1141–1152 (2019).
pubmed: 31409598
pmcid: 8936135
doi: 10.1373/clinchem.2019.304196
Bonifacio, E. et al. Harmonization of glutamic acid decarboxylase and islet antigen-2 autoantibody assays for national institute of diabetes and digestive and kidney diseases consortia. J. Clin. Endocrinol. Metab. 95, 3360–3367 (2010).
pubmed: 20444913
pmcid: 2928900
doi: 10.1210/jc.2010-0293
Mire-Sluis, A. R., Gaines Das, R. & Lernmark, Å. The World Health Organization international collaborative study for islet cell antibodies. Diabetologia 43, 1282–1292 (2000).
pubmed: 11079747
doi: 10.1007/s001250051524
Bingley, P. J. & Williams, A. J. K. Validation of autoantibody assays in type 1 diabetes: workshop programme. Autoimmunity 37, 257–260 (2004).
pubmed: 15518037
doi: 10.1080/08916930410001710677
Marzinotto, I. et al. Islet autoantibody standardization program: interlaboratory comparison of insulin autoantibody assay performance in 2018 and 2020 workshops. Diabetologia 66, 897–912 (2023).
pubmed: 36759347
pmcid: 10036445
doi: 10.1007/s00125-023-05877-9
Ziegler, A. G. et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. J. Am. Med. Assoc. 309, 2473–2479 (2013).
doi: 10.1001/jama.2013.6285
Steck, A. K. et al. Predictors of progression from the appearance of islet autoantibodies to early childhood diabetes: The Environmental Determinants of Diabetes in the Young (TEDDY). Diabetes Care 38, 808–813 (2015).
pubmed: 25665818
pmcid: 4407751
doi: 10.2337/dc14-2426
Giannopoulou, E. Z. et al. Islet autoantibody phenotypes and incidence in children at increased risk for type 1 diabetes. Diabetologia 58, 2317–2323 (2015).
pubmed: 26138334
doi: 10.1007/s00125-015-3672-y
Vehik, K. et al. Reversion of β-cell autoimmunity changes risk of type 1 diabetes: TEDDY study. Diabetes Care 39, 1535–1542 (2016).
pubmed: 27311490
pmcid: 5001144
doi: 10.2337/dc16-0181
Gorus, F. K. et al. Twenty-year progression rate to clinical onset according to autoantibody profile, age, and HLA-DQ genotype in a registry-based group of children and adults with a first-degree relative with type 1 diabetes. Diabetes Care 40, 1065–1072 (2017).
pubmed: 28701370
doi: 10.2337/dc16-2228
Pöllänen, P. M. et al. Characterisation of rapid progressors to type 1 diabetes among children with HLA-conferred disease susceptibility. Diabetologia 60, 1284–1293 (2017).
pubmed: 28364254
doi: 10.1007/s00125-017-4258-7
Steck, A. K. et al. Predicting progression to diabetes in islet autoantibody positive children. J. Autoimmun. 90, 59–63 (2018).
pubmed: 29395739
pmcid: 5949243
doi: 10.1016/j.jaut.2018.01.006
Jacobsen, L. M. et al. Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. Pediatr. Diabetes 20, 263–270 (2019).
pubmed: 30628751
pmcid: 6456374
doi: 10.1111/pedi.12812
Pöllänen, P. M. et al. Characteristics of slow progression to type 1 diabetes in children with increased HLA-conferred disease risk. J. Clin. Endocrinol. Metab. 104, 5585–5594 (2019).
pubmed: 31314077
doi: 10.1210/jc.2019-01069
Korneva, K. G. et al. Diagnostic capabilities of islet autoantibodies in children with new-onset type 1 diabetes mellitus and healthy siblings. Sovrem. Tekhnologii Med. 12, 29–35 (2021).
pubmed: 34796016
doi: 10.17691/stm2020.12.6.04
Steck, A. K. et al. Age of islet autoantibody appearance and mean levels of insulin, but not GAD or IA-2 autoantibodies, predict age of diagnosis of type 1 diabetes: diabetes autoimmunity study in the young. Diabetes Care 34, 1397–1399 (2011).
pubmed: 21562325
pmcid: 3114355
doi: 10.2337/dc10-2088
Vehik, K. et al. Development of autoantibodies in the TrialNet natural history study. Diabetes Care 34, 1897–1901 (2011).
pubmed: 21750277
pmcid: 3161292
doi: 10.2337/dc11-0560
Parikka, V. et al. Early seroconversion and rapidly increasing autoantibody concentrations predict prepubertal manifestation of type 1 diabetes in children at genetic risk. Diabetologia 55, 1926–1936 (2012).
pubmed: 22441569
doi: 10.1007/s00125-012-2523-3
Bonifacio, E. et al. An age-related exponential decline in the risk of multiple islet autoantibody seroconversion during childhood. Diabetes Care 44, 2260–2268 (2021).
pubmed: 33627366
pmcid: 8929192
doi: 10.2337/dc20-2122
Jacobsen, L. M. et al. The risk of progression to type 1 diabetes is highly variable in individuals with mulitple autoantibodies following screening. Diabetologia 63, 588–596 (2020).
pubmed: 31768570
doi: 10.1007/s00125-019-05047-w
Vehik, K. et al. Hierarchical order of distinct autoantibody spreading and progression to type 1 diabetes in the TEDDY study. Diabetes Care 43, 2066–2073 (2020).
pubmed: 32641373
pmcid: 7440899
doi: 10.2337/dc19-2547
Eising, S. et al. Danish children born with glutamic acid decarboxylase-65 and islet antigen-2 autoantibodies at birth had an increased risk to develop type 1 diabetes. Eur. J. Endocrinol. 164, 247 (2011).
pubmed: 21097569
pmcid: 3022336
doi: 10.1530/EJE-10-0792
Sosenko, J. M. et al. A longitudinal study of GAD65 and ICA512 autoantibodies during the progression to type 1 diabetes in diabetes prevention trial-type 1 (DPT-1) participants. Diabetes Care 34, 2435–2437 (2011).
pubmed: 21911777
pmcid: 3198298
doi: 10.2337/dc11-0981
Krause, S. et al. IA-2 autoantibody affinity in children at risk for type 1 diabetes. Clin. Immunol. 145, 224–229 (2012).
pubmed: 23110943
doi: 10.1016/j.clim.2012.09.010
Achenbach, P. et al. Characteristics of rapid vs slow progression to type 1 diabetes in multiple islet autoantibody-positive children. Diabetologia 56, 1615–1622 (2013).
pubmed: 23539116
doi: 10.1007/s00125-013-2896-y
Gorus, F. K. et al. Screening for insulinoma antigen 2 and zinc transporter 8 autoantibodies: a cost-effective and age-independent strategy to identify rapid progressors to clinical onset among relatives of type 1 diabetic patients. Clin. Exp. Immunol. 171, 82–90 (2013).
pubmed: 23199327
doi: 10.1111/j.1365-2249.2012.04675.x
Mbunwe, E. et al. In antibody-positive first-degree relatives of patients with type 1 diabetes, HLA-A*24 and HLA-B*18, but not HLA-B*39, are predictors of impending diabetes with distinct HLA-DQ interactions. Diabetologia 56, 1964–1970 (2013).
pubmed: 23712485
pmcid: 3918938
doi: 10.1007/s00125-013-2951-8
Xu, P. & Krischer, J. P. Prognostic classification factors associated with development of multiple autoantibodies, dysglycemia, and type 1 diabetes—a recursive partitioning analysis. Diabetes Care 39, 1036–1044 (2016).
pubmed: 27208341
pmcid: 4878220
doi: 10.2337/dc15-2292
Yu, L. et al. Zinc transporter-8 autoantibodies improve prediction of type 1 diabetes in relatives positive for the standard biochemical autoantibodies. Diabetes Care 35, 1213–1218 (2012).
pubmed: 22446173
pmcid: 3357246
doi: 10.2337/dc11-2081
Ilonen, J. et al. Patterns of β-cell autoantibody appearance and genetic associations during the first years of life. Diabetes 62, 3636–3640 (2013).
pubmed: 23835325
pmcid: 3781470
doi: 10.2337/db13-0300
Yu, L. et al. Proinsulin/insulin autoantibodies measured with electrochemiluminescent assay are the earliest indicator of prediabetic islet autoimmunity. Diabetes Care 36, 2266–2270 (2013).
pubmed: 23423694
pmcid: 3714529
doi: 10.2337/dc12-2245
Krischer, J. P. et al. Genetic and environmental interactions modify the risk of diabetes-related autoimmunity by 6 years of age: the TEDDY study. Diabetes Care 40, 1194–1202 (2017).
pubmed: 28646072
pmcid: 5566280
doi: 10.2337/dc17-0238
Bosi, E. et al. Impact of age and antibody type on progression from single to multiple autoantibodies in type 1 diabetes relatives. J. Clin. Endocrinol. Metab. 102, 2881–2886 (2017).
pubmed: 28531305
pmcid: 5546870
doi: 10.1210/jc.2017-00569
Redondo, M. J., Steck, A. K. & Pugliese, A. Genetics of type 1 diabetes. Pediatr. Diabetes 19, 346–353 (2018).
pubmed: 29094512
doi: 10.1111/pedi.12597
Lempainen, J. et al. Effect of the PTPN22 and INS risk genotypes on the progression to clinical type 1 diabetes after the initiation of β-cell autoimmunity. Diabetes 61, 963–966 (2012).
pubmed: 22357962
pmcid: 3314352
doi: 10.2337/db11-0386
Redondo, M. J. et al. A type 1 diabetes genetic risk score predicts progression of islet autoimmunity and development of type 1 diabetes in individuals at risk. Diabetes Care 41, 1887–1894 (2018).
pubmed: 30002199
pmcid: 6105323
doi: 10.2337/dc18-0087
Bender, C., Schlosser, M., Christen, U., Ziegler, A. G. & Achenbach, P. GAD autoantibody affinity in schoolchildren from the general population. Diabetologia 57, 1911–1918 (2014).
pubmed: 24939430
doi: 10.1007/s00125-014-3294-9
Siljander, H. T. et al. Insulin secretion and sensitivity in the prediction of type 1 diabetes in children with advanced β-cell autoimmunity. Eur. J. Endocrinol. 169, 479–485 (2013).
pubmed: 23904276
doi: 10.1530/EJE-13-0206
Xu, P. et al. Prognostic accuracy of immunologic and metabolic markers for type 1 diabetes in a high-risk population: receiver operating characteristic analysis. Diabetes Care 35, 1975–1980 (2012).
pubmed: 22787174
pmcid: 3447832
doi: 10.2337/dc12-0183
Fouts, A. et al. Do electrochemiluminescence assays improve prediction of time to type 1 diabetes in autoantibody-positive TrialNet subjects? Diabetes Care 39, 1738–1744 (2016).
pubmed: 27456836
pmcid: 5033080
doi: 10.2337/dc16-0302
Sosenko, J. M. et al. The use of electrochemiluminescence assays to predict autoantibody and glycemic progression toward type 1 diabetes in individuals with single autoantibodies. Diabetes Technol. Ther. 19, 183–187 (2017).
pubmed: 28177779
pmcid: 5359659
doi: 10.1089/dia.2016.0243
Jia, X. et al. High-affinity ZnT8 autoantibodies by electrochemiluminescence assay improve risk prediction for type 1 diabetes. J. Clin. Endocrinol. Metab. 106, 3455–3463 (2021).
pubmed: 34343303
pmcid: 8864749
Bollyky, J. B. et al. Heterogeneity in recent-onset type 1 diabetes—a clinical trial perspective. Diabetes Metab. Res. Rev. 31, 588–594 (2015).
pubmed: 25689602
pmcid: 4815427
doi: 10.1002/dmrr.2643
Luo, S. et al. Distinct two different ages associated with clinical profiles of acute onset type 1 diabetes in Chinese patients. Diabetes Metab. Res. Rev. 36, e3209 (2020).
pubmed: 31343818
doi: 10.1002/dmrr.3209
Leslie, R. D. et al. Adult-onset type 1 diabetes: current understanding and challenges. Diabetes Care 44, 2449–2456 (2021).
pubmed: 34670785
pmcid: 8546280
doi: 10.2337/dc21-0770
Vicinanza, A., Messaaoui, A., Tenoutasse, S. & Dorchy, H. Diabetic ketoacidosis in children newly diagnosed with type 1 diabetes mellitus: role of demographic, clinical, and biochemical features along with genetic and immunological markers as risk factors. A 20-year experience in a tertiary Belgian center. Pediatr. Diabetes 20, 584–593 (2019).
pubmed: 31038262
Hameed, S. et al. Persistently autoantibody negative (PAN) type 1 diabetes mellitus in children. Pediatr. Diabetes 12, 142–149 (2011).
pubmed: 21518407
doi: 10.1111/j.1399-5448.2010.00681.x
Stoupa, A. & Dorchy, H. HLA-DQ genotypes—but not immune markers—differ by ethnicity in patients with childhood onset type 1 diabetes residing in Belgium. Pediatr. Diabetes 17, 342–350 (2016).
pubmed: 26134450
doi: 10.1111/pedi.12293
Marino, K. R. et al. A predictive model for lack of partial clinical remission in new-onset pediatric type 1 diabetes. PLoS ONE 12, e0176860 (2017).
pubmed: 28459844
pmcid: 5411061
doi: 10.1371/journal.pone.0176860
Ludvigsson, J. et al. Decline of C-peptide during the first year after diagnosis of type 1 diabetes in children and adolescents. Diabetes Res. Clin. Pract. 100, 203–209 (2013).
pubmed: 23529064
doi: 10.1016/j.diabres.2013.03.003
Greenbaum, C. et al. Effect of oral insulin on prevention of diabetes in relatives of patients with type 1 diabetes: a randomized clinical trial. J. Am. Med. Assoc. 318, 1891–1902 (2017).
doi: 10.1001/jama.2017.17070
Wherrett, D. K. et al. Antigen-based therapy with glutamic acid decarboxylase (GAD) vaccine in patients with recent-onset type 1 diabetes: a randomised double-blind trial. Lancet 378, 319–327 (2011).
pubmed: 21714999
pmcid: 3580128
doi: 10.1016/S0140-6736(11)60895-7
Ludvigsson, J. et al. GAD65 antigen therapy in recently diagnosed type 1 diabetes mellitus. N. Engl. J. Med. 366, 433–442 (2012).
pubmed: 22296077
doi: 10.1056/NEJMoa1107096
Christie, M. R., Molvig, J., Hawkes, C. J., Carstensen, B. & Mandrup-Poulsen, T. IA-2 antibody-negative status predicts remission and recovery of C-peptide levels in type 1 diabetic patients treated with cyclosporin. Diabetes Care 25, 1192–1197 (2002).
pubmed: 12087018
doi: 10.2337/diacare.25.7.1192
Herold, K. C. et al. An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes. N. Engl. J. Med. 381, 603–613 (2019).
pubmed: 31180194
pmcid: 6776880
doi: 10.1056/NEJMoa1902226
Pescovitz, M. D. et al. Rituximab, B-lymphocyte depletion, and preservation of beta-cell function. N. Engl. J. Med. 361, 2143–2152 (2009).
pubmed: 19940299
pmcid: 6410357
doi: 10.1056/NEJMoa0904452
Yu, L. et al. Rituximab selectively suppresses specific islet antibodies. Diabetes 60, 2560–2565 (2011).
pubmed: 21831969
pmcid: 3178300
doi: 10.2337/db11-0674
Elsayed, N. A. et al. 2. Classification and diagnosis of diabetes: standards of care in diabetes—2023. Diabetes Care 46, S19–S40 (2023).
pubmed: 36507649
doi: 10.2337/dc23-S002
So, M. et al. Advances in type 1 diabetes prediction using islet autoantibodies: beyond a simple count. Endocr. Rev. 42, 584–604 (2021).
pubmed: 33881515
doi: 10.1210/endrev/bnab013
So, M. et al. Characterising the age-dependent effects of risk factors on type 1 diabetes progression. Diabetologia 65, 684–694 (2022).
pubmed: 35041021
pmcid: 9928893
doi: 10.1007/s00125-021-05647-5
Arif, S. et al. Blood and islet phenotypes indicate immunological heterogeneity in type 1 diabetes. Diabetes 63, 3835–3845 (2014).
pubmed: 24939426
pmcid: 4207393
doi: 10.2337/db14-0365
Viisanen, T. et al. Circulating CXCR5+PD-1+ICOS+ follicular T helper cells are increased close to the diagnosis of type 1 diabetes in children with multiple autoantibodies. Diabetes 66, 437–447 (2017).
pubmed: 28108610
doi: 10.2337/db16-0714
Andrade Lima Gabbay, M., Sato, M. N., Duarte, A. J. S. & Dib, S. A. Serum titres of anti-glutamic acid decarboxylase-65 and anti-IA-2 autoantibodies are associated with different immunoregulatory milieu in newly diagnosed type 1 diabetes patients. Clin. Exp. Immunol. 168, 60–67 (2012).
pmcid: 3390495
doi: 10.1111/j.1365-2249.2011.04538.x
Spanier, J. A. et al. Increased effector memory insulin-specific CD4+ T cells correlate with insulin autoantibodies in patients with recent-onset type 1 diabetes. Diabetes 66, 3051–3060 (2017).
pubmed: 28842400
pmcid: 5697953
doi: 10.2337/db17-0666
Chen, Y. G. et al. Molecular signatures differentiate immune states in type 1 diabetic families. Diabetes 63, 3960–3973 (2014).
pubmed: 24760139
pmcid: 4207392
doi: 10.2337/db14-0214
Anand, V. et al. Islet autoimmunity and HLA markers of presymptomatic and clinical type 1 diabetes: Joint analyses of prospective cohort studies in Finland, Germany, Sweden, and the U.S. Diabetes Care 44, 2269 (2021).
pubmed: 34162665
pmcid: 8929180
doi: 10.2337/dc20-1836
Frohnert, B. I. et al. Late-onset islet autoimmunity in childhood: the Diabetes Autoimmunity Study in the Young (DAISY). Diabetologia 60, 998–1006 (2017).
pubmed: 28314946
pmcid: 5504909
doi: 10.1007/s00125-017-4256-9
Hanna, S. J. et al. Slow progressors to type 1 diabetes lose islet autoantibodies over time, have few islet antigen-specific CD8+ T cells and exhibit a distinct CD95hi B cell phenotype. Diabetologia 63, 1174–1185 (2020).
pubmed: 32157332
pmcid: 7228996
doi: 10.1007/s00125-020-05114-7
Pöllänen, P. M. et al. Dynamics of islet autoantibodies during prospective follow-up from birth to age 15 years. J. Clin. Endocrinol. Metab. 105, e4638–e4651 (2020).
pubmed: 32882033
pmcid: 7686032
doi: 10.1210/clinem/dgaa624
So, M., O’Rourke, C., Bahnson, H. T., Greenbaum, C. J. & Speake, C. Autoantibody reversion: changing risk categories in multiple-autoantibody-positive individuals. Diabetes Care 43, 913–917 (2020).
pubmed: 32019856
pmcid: 7085807
doi: 10.2337/dc19-1731
Chung, W. K. et al. Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia 63, 1671–1693 (2020).
pubmed: 32556613
pmcid: 8185455
doi: 10.1007/s00125-020-05181-w
Bingley, P. J. & Williams, A. J. K. Validation of autoantibody assays in type 1 diabetes: workshop programme. Autoimmunity 37, 257–260 (2009).
Oram, R. A. et al. Utility of diabetes type-specific genetic risk scores for the classification of diabetes type among multiethnic youth. Diabetes Care 45, 1124–1131 (2022).
pubmed: 35312757
pmcid: 9174964
doi: 10.2337/dc20-2872
Tarn, A. C. et al. Predicting insulin-dependent diabetes. Lancet 1, 845–850 (1988).
pubmed: 2895363
doi: 10.1016/S0140-6736(88)91601-7
Bonifacio, E. & Achenbach, P. Birth and coming of age of islet autoantibodies. Clin. Exp. Immunol. 198, 294–305 (2019).
pubmed: 31397889
pmcid: 6857083
doi: 10.1111/cei.13360
Weiss, A. et al. Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening. Diabetologia 65, 2121–2131 (2022).
pubmed: 36028774
pmcid: 9630406
doi: 10.1007/s00125-022-05780-9
Frohnert, B. I. et al. Refining the definition of stage 1 type 1 diabetes: an ontology-driven analysis of the heterogeneity of multiple islet autoimmunity. Diabetes Care https://doi.org/10.2337/dc22-1960 (2023).
Ghalwash, M. et al. Two-age islet-autoantibody screening for childhood type 1 diabetes: a prospective cohort study. lancet. Diabetes Endocrinol. 10, 589–596 (2022).
Kwon, B. C. et al. Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories. Nat. Commun. 13, 1514 (2022).
pubmed: 35314671
pmcid: 8938551
doi: 10.1038/s41467-022-28909-1
Ng, K. et al. Islet autoantibody type-specific titer thresholds improve stratification of risk of progression to type 1 diabetes in children. Diabetes Care 45, 160–168 (2022).
pubmed: 34758977
doi: 10.2337/dc21-0878
Nielsen, L. B. et al. Relationship between ZnT8Ab, the SLC30A8 gene and disease progression in children with newly diagnosed type 1 diabetes. Autoimmunity 44, 616–623 (2011).
pubmed: 21604969
doi: 10.3109/08916934.2011.576724
Andersen, M. L. M. et al. Association between autoantibodies to the Arginine variant of the Zinc transporter 8 (ZnT8) and stimulated C-peptide levels in Danish children and adolescents with newly diagnosed type 1 diabetes. Pediatr. Diabetes 13, 454–462 (2012).
pubmed: 22686132
doi: 10.1111/j.1399-5448.2012.00857.x
Sorensen, J. S. et al. Islet autoantibodies and residual beta cell function in type 1 diabetes children followed for 3-6 years. Diabetes Res. Clin. Pract. 96, 204–210 (2012).
pubmed: 22251574
doi: 10.1016/j.diabres.2011.12.013
Chao, C. et al. Change of glutamic acid decarboxylase antibody and protein tyrosine phosphatase antibody in Chinese patients with acute-onset type 1 diabetes mellitus. Chin. Med. J. 126, 4006–4012 (2013).
pubmed: 24229665
doi: 10.3760/cma.j.issn.0366-6999.20130841
Pecheur, A. et al. Characteristics and determinants of partial remission in children with type 1 diabetes using the insulin-dose-adjusted A1C definition. J. Diabetes Res. 2014, 851378 (2014).
pubmed: 25254220
pmcid: 4164125
doi: 10.1155/2014/851378
Camilo, D. S. et al. Partial remission in Brazilian children and adolescents with type 1 diabetes. Association with a haplotype of class II human leukocyte antigen and synthesis of autoantibodies. Pediatr. Diabetes 21, 606–614 (2020).
pubmed: 32078220
doi: 10.1111/pedi.12999
Steck, A. K. et al. Factors associated with the decline of C-peptide in a cohort of young children diagnosed with type 1 diabetes. J. Clin. Endocrinol. Metab. 106, E1380–E1388 (2021).
pubmed: 33035311
doi: 10.1210/clinem/dgaa715
Gale, E. A. M., Bingley, P. J., Emmett, C. L. & Collier, T. European Nicotinamide Diabetes Intervention Trial (ENDIT): a randomised controlled trial of intervention before the onset of type 1 diabetes. Lancet 363, 925–931 (2004).
pubmed: 15043959
doi: 10.1016/S0140-6736(04)15786-3
JS, S. et al. Effects of oral insulin in relatives of patients with type 1 diabetes: the diabetes prevention trial-type 1. Diabetes Care 28, 1068–1076 (2005).
doi: 10.2337/diacare.28.5.1068
Näntö-Salonen, K. et al. Nasal insulin to prevent type 1 diabetes in children with HLA genotypes and autoantibodies conferring increased risk of disease: a double-blind, randomised controlled trial. Lancet 372, 1746–1755 (2008).
pubmed: 18814906
doi: 10.1016/S0140-6736(08)61309-4
Herold, K. C. et al. Teplizumab (Anti-CD3 mAb) treatment preserves C-peptide responses in patients with new-onset type 1 diabetes in a randomized controlled trial: metabolic and immunologic features at baseline identify a subgroup of responders. Diabetes 62, 3766–3774 (2013).
pubmed: 23835333
pmcid: 3806618
doi: 10.2337/db13-0345
Aronson, R. et al. Low-dose otelixizumab anti-CD3 monoclonal antibody DEFEND-1 study: results of the randomized phase III study in recent-onset human type 1 diabetes. Diabetes Care 37, 2746–2754 (2014).
pubmed: 25011949
pmcid: 4392937
doi: 10.2337/dc13-0327
Demeester, S. et al. Preexisting insulin autoantibodies predict efficacy of otelixizumab in preserving residual β-cell function in recent-onset type 1 diabetes. Diabetes Care 38, 644–651 (2015).
pubmed: 25583753
pmcid: 4370324
doi: 10.2337/dc14-1575
Krischer, J., Schatz, D., Bundy, B., Skyler, J. & Greenbaum, C. Effect of oral insulin on prevention of diabetes in relatives of patients with type 1 diabetes: a randomized clinical trial. JAMA 318, 1891–1902 (2017).
pubmed: 29164254
pmcid: 5798455
doi: 10.1001/jama.2017.17070