Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles
Ghalwash, Mohamed; Anand, Vibha; Ng, Kenney; Dunne, Jessica L; Lou, Olivia; Lundgren, Markus; Hagopian, William A; Rewers, Marian; Ziegler, Anette G; Veijola, Riitta; for the T1DI Study Group (2024-06-11)
American Diabetes Association
11.06.2024
Mohamed Ghalwash, Vibha Anand, Kenney Ng, Jessica L. Dunne, Olivia Lou, Markus Lundgren, William A. Hagopian, Marian Rewers, Anette-G. Ziegler, Riitta Veijola, T1DI Study Group; Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles. Diabetes Care 25 July 2024; 47 (8): 1424–1431.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 by the American Diabetes Association. The final authenticated version is available online at https://doi.org/10.2337/dc24-0198.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 by the American Diabetes Association. The final authenticated version is available online at https://doi.org/10.2337/dc24-0198.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504252914
https://urn.fi/URN:NBN:fi:oulu-202504252914
Tiivistelmä
Abstract
Objective:
To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes
Research Design and Methods:
The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual’s temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis.
Results:
We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0–79.2) was observed in children who first developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9–95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a single, often reverting, autoantibody.
Conclusions:
The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes.
Objective:
To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes
Research Design and Methods:
The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual’s temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis.
Results:
We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0–79.2) was observed in children who first developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9–95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a single, often reverting, autoantibody.
Conclusions:
The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes.
Kokoelmat
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