Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles
(2024) In Diabetes Care 47(8). p.1424-1431- 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 au-toantibody: 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... (More)
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 au-toantibody: 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 auto-antibodies. 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.
(Less)
- author
- Ghalwash, Mohamed ; Anand, Vibha ; Ng, Kenney ; Dunne, Jessica L. ; Lou, Olivia ; Lundgren, Markus LU ; Hagopian, William A. ; Rewers, Marian ; Ziegler, Anette G. and Veijola, Riitta LU
- author collaboration
- organization
- publishing date
- 2024-08
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Diabetes Care
- volume
- 47
- issue
- 8
- pages
- 8 pages
- publisher
- American Diabetes Association
- external identifiers
-
- pmid:38861550
- scopus:85199813006
- ISSN
- 0149-5992
- DOI
- 10.2337/dc24-0198
- language
- English
- LU publication?
- yes
- id
- 5f96fc9c-208c-4e91-a675-058289418501
- date added to LUP
- 2024-09-09 16:02:33
- date last changed
- 2024-09-10 03:00:05
@article{5f96fc9c-208c-4e91-a675-058289418501,
abstract = {{<p>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 au-toantibody: 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 auto-antibodies. 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.</p>}},
author = {{Ghalwash, Mohamed and Anand, Vibha and Ng, Kenney and Dunne, Jessica L. and Lou, Olivia and Lundgren, Markus and Hagopian, William A. and Rewers, Marian and Ziegler, Anette G. and Veijola, Riitta}},
issn = {{0149-5992}},
language = {{eng}},
number = {{8}},
pages = {{1424--1431}},
publisher = {{American Diabetes Association}},
series = {{Diabetes Care}},
title = {{Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles}},
url = {{http://dx.doi.org/10.2337/dc24-0198}},
doi = {{10.2337/dc24-0198}},
volume = {{47}},
year = {{2024}},
}