Literature DB >> 27400691

A novel approach for the analysis of longitudinal profiles reveals delayed progression to type 1 diabetes in a subgroup of multiple-islet-autoantibody-positive children.

David Endesfelder1, Michael Hagen1, Christiane Winkler2,3,4,5, Florian Haupt2,3,4, Stephanie Zillmer2,3,4, Annette Knopff2,3, Ezio Bonifacio5,6,7,8, Anette-G Ziegler2,3,4,5, Wolfgang Zu Castell9,10, Peter Achenbach11,12,13,14.   

Abstract

AIMS/HYPOTHESIS: Progression to type 1 diabetes in children and adolescents is not uniform. Based on individual genetic background and environment, islet autoimmunity may develop at variable age, exhibit different autoantibody profiles and progress to clinical diabetes at variable rates. Here, we aimed to quantify the qualitative dynamics of sequential islet autoantibody profiles in order to identify longitudinal patterns that stratify progression rates to type 1 diabetes in multiple-autoantibody-positive children.
METHODS: Qualitative changes in antibody status on follow-up and progression rate to diabetes were analysed in 88 children followed from birth in the prospective BABYDIAB study who developed multiple autoantibodies against insulin (IAA), GAD (GADA), insulinoma-associated antigen-2 (IA-2A) and/or zinc transporter 8 (ZnT8A). An algorithm was developed to define similarities in sequential autoantibody profiles and hierarchical clustering was performed to group children with similar profiles.
RESULTS: We defined nine clusters that distinguished children with respect to their sequential profiles of IAA, GADA, IA-2A and ZnT8A. Progression from first autoantibody appearance to clinical diabetes between clusters ranged from 6% (95% CI [0, 16.4]) to 73% (28.4, 89.6) within 5 years. Delayed progression was observed in children who were positive for only two autoantibodies, and for a cluster of 12 children who developed three or four autoantibodies but were IAA-negative in their last samples, nine of whom lost IAA positivity during follow-up. Among all children who first seroconverted to IAA positivity and developed at least two other autoantibodies (n = 57), the 10 year risk of diabetes was 23% (0, 42.9) in those who became IAA-negative during follow-up compared with 76% (58.7, 85.6) in those who remained IAA-positive (p = 0.004). CONCLUSIONS/
INTERPRETATION: The novel clustering approach provides a tool for stratification of islet autoantibody-positive individuals that has prognostic relevance, and new opportunities in elucidating disease mechanisms. Our data suggest that losing IAA reactivity is associated with delayed progression to type 1 diabetes in multiple-islet-autoantibody-positive children.

Entities:  

Keywords:  Clustering; Islet autoantibody; Longitudinal; Profiles; Progression; Type 1 diabetes

Mesh:

Substances:

Year:  2016        PMID: 27400691     DOI: 10.1007/s00125-016-4050-0

Source DB:  PubMed          Journal:  Diabetologia        ISSN: 0012-186X            Impact factor:   10.122


  48 in total

1.  Proinsulin levels and the proinsulin:c-peptide ratio complement autoantibody measurement for predicting type 1 diabetes.

Authors:  I Truyen; P De Pauw; P N Jørgensen; C Van Schravendijk; O Ubani; K Decochez; E Vandemeulebroucke; I Weets; R Mao; D G Pipeleers; F K Gorus
Journal:  Diabetologia       Date:  2005-10-07       Impact factor: 10.122

2.  Islet autoantibody phenotypes and incidence in children at increased risk for type 1 diabetes.

Authors:  Eleni Z Giannopoulou; Christiane Winkler; Ruth Chmiel; Claudia Matzke; Marlon Scholz; Andreas Beyerlein; Peter Achenbach; Ezio Bonifacio; Anette-G Ziegler
Journal:  Diabetologia       Date:  2015-07-03       Impact factor: 10.122

3.  Infant feeding and the risk of type 1 diabetes.

Authors:  Mikael Knip; Suvi M Virtanen; Hans K Akerblom
Journal:  Am J Clin Nutr       Date:  2010-03-24       Impact factor: 7.045

4.  Autoantibodies to zinc transporter 8 and SLC30A8 genotype stratify type 1 diabetes risk.

Authors:  P Achenbach; V Lampasona; U Landherr; K Koczwara; S Krause; H Grallert; C Winkler; M Pflüger; T Illig; E Bonifacio; A G Ziegler
Journal:  Diabetologia       Date:  2009-07-10       Impact factor: 10.122

5.  Characteristics of rapid vs slow progression to type 1 diabetes in multiple islet autoantibody-positive children.

Authors:  P Achenbach; M Hummel; L Thümer; H Boerschmann; D Höfelmann; A G Ziegler
Journal:  Diabetologia       Date:  2013-03-29       Impact factor: 10.122

6.  Brief communication: early appearance of islet autoantibodies predicts childhood type 1 diabetes in offspring of diabetic parents.

Authors:  Michael Hummel; Ezio Bonifacio; Sandra Schmid; Markus Walter; Annette Knopff; Anette-G Ziegler
Journal:  Ann Intern Med       Date:  2004-06-01       Impact factor: 25.391

7.  Improving prediction of type 1 diabetes by testing non-HLA genetic variants in addition to HLA markers.

Authors:  Andrea K Steck; Fran Dong; Randall Wong; Alexandra Fouts; Edwin Liu; Jihane Romanos; Cisca Wijmenga; Jill M Norris; Marian J Rewers
Journal:  Pediatr Diabetes       Date:  2013-11-08       Impact factor: 4.866

8.  Quantification of islet-cell antibodies and prediction of insulin-dependent diabetes.

Authors:  E Bonifacio; P J Bingley; M Shattock; B M Dean; D Dunger; E A Gale; G F Bottazzo
Journal:  Lancet       Date:  1990-01-20       Impact factor: 79.321

9.  Predictors of Progression From the Appearance of Islet Autoantibodies to Early Childhood Diabetes: The Environmental Determinants of Diabetes in the Young (TEDDY).

Authors:  Andrea K Steck; Kendra Vehik; Ezio Bonifacio; Ake Lernmark; Anette-G Ziegler; William A Hagopian; JinXiong She; Olli Simell; Beena Akolkar; Jeffrey Krischer; Desmond Schatz; Marian J Rewers
Journal:  Diabetes Care       Date:  2015-02-09       Impact factor: 17.152

10.  Blood and islet phenotypes indicate immunological heterogeneity in type 1 diabetes.

Authors:  Sefina Arif; Pia Leete; Vy Nguyen; Katherine Marks; Nurhanani Mohamed Nor; Megan Estorninho; Deborah Kronenberg-Versteeg; Polly J Bingley; John A Todd; Catherine Guy; David B Dunger; Jake Powrie; Abby Willcox; Alan K Foulis; Sarah J Richardson; Emanuele de Rinaldis; Noel G Morgan; Anna Lorenc; Mark Peakman
Journal:  Diabetes       Date:  2014-06-17       Impact factor: 9.461

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  14 in total

Review 1.  Preclinical Autoimmune Disease: a Comparison of Rheumatoid Arthritis, Systemic Lupus Erythematosus, Multiple Sclerosis and Type 1 Diabetes.

Authors:  Giulia Frazzei; Ronald F van Vollenhoven; Brigit A de Jong; Sarah E Siegelaar; Dirkjan van Schaardenburg
Journal:  Front Immunol       Date:  2022-06-30       Impact factor: 8.786

Review 2.  Serum biomarkers for diagnosis and prediction of type 1 diabetes.

Authors:  Lian Yi; Adam C Swensen; Wei-Jun Qian
Journal:  Transl Res       Date:  2018-08-01       Impact factor: 7.012

3.  Insulin-Like Growth Factor Dysregulation Both Preceding and Following Type 1 Diabetes Diagnosis.

Authors:  Melanie R Shapiro; Clive H Wasserfall; Sean M McGrail; Amanda L Posgai; Rhonda Bacher; Andrew Muir; Michael J Haller; Desmond A Schatz; Johnna D Wesley; Matthias von Herrath; William A Hagopian; Cate Speake; Mark A Atkinson; Todd M Brusko
Journal:  Diabetes       Date:  2019-12-11       Impact factor: 9.461

4.  Modeling Disease Progression Trajectories from Longitudinal Observational Data.

Authors:  Bum Chul Kwon; Peter Achenbach; Jessica L Dunne; William Hagopian; Markus Lundgren; Kenney Ng; Riitta Veijola; Brigitte I Frohnert; Vibha Anand
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

5.  Characterising the age-dependent effects of risk factors on type 1 diabetes progression.

Authors:  Michelle So; Colin O'Rourke; Alyssa Ylescupidez; Henry T Bahnson; Andrea K Steck; John M Wentworth; Brittany S Bruggeman; Sandra Lord; Carla J Greenbaum; Cate Speake
Journal:  Diabetologia       Date:  2022-01-18       Impact factor: 10.122

Review 6.  Early prediction of autoimmune (type 1) diabetes.

Authors:  Simon E Regnell; Åke Lernmark
Journal:  Diabetologia       Date:  2017-05-26       Impact factor: 10.122

7.  Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts: the SNAIL study.

Authors:  Anna E Long; Isabel V Wilson; Dorothy J Becker; Ingrid M Libman; Vincent C Arena; F Susan Wong; Andrea K Steck; Marian J Rewers; Liping Yu; Peter Achenbach; Rosaura Casas; Johnny Ludvigsson; Alistair J K Williams; Kathleen M Gillespie
Journal:  Diabetologia       Date:  2018-03-12       Impact factor: 10.122

8.  Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study.

Authors:  Meike Köhler; Andreas Beyerlein; Kendra Vehik; Sonja Greven; Nikolaus Umlauf; Åke Lernmark; William A Hagopian; Marian Rewers; Jin-Xiong She; Jorma Toppari; Beena Akolkar; Jeffrey P Krischer; Ezio Bonifacio; Anette-G Ziegler
Journal:  Acta Diabetol       Date:  2017-08-30       Impact factor: 4.087

9.  Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children.

Authors:  David Endesfelder; Wolfgang Zu Castell; Ezio Bonifacio; Marian Rewers; William A Hagopian; Jin-Xiong She; Åke Lernmark; Jorma Toppari; Kendra Vehik; Alistair J K Williams; Liping Yu; Beena Akolkar; Jeffrey P Krischer; Anette-G Ziegler; Peter Achenbach
Journal:  Diabetes       Date:  2018-10-10       Impact factor: 9.337

Review 10.  What Have Slow Progressors Taught Us About T1D-Mind the Gap!

Authors:  Kathleen M Gillespie; Anna E Long
Journal:  Curr Diab Rep       Date:  2019-09-10       Impact factor: 4.810

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