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PubMed ID:
37873281
Public Release Type:
Journal
Publication Year: 2023
Affiliation: 1Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
2University of Exeter, Exeter, UK.
3Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
4Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.
DOI:
https://doi.org/10.1101/2023.10.10.23296375
Authors:
Lu You, Lauric A. Ferrat, Richard A. Oram, Hemang M. Parikh, Andrea K. Steck, Jeffrey Krischer, Maria J. Redondo, Type 1 Diabetes TrialNet Study Group
Request IDs:
22822
Studies:
ATG-GCSF in New Onset Type 1 Diabetes
,
Immune Effects of Oral Insulin in Relatives at Risk for Type 1 Diabetes Mellitus
,
Oral Insulin for Prevention of Diabetes in Relatives at Risk for Type 1 Diabetes Mellitus
,
Preservation of Pancreatic Production of Insulin Through Immunosuppression
,
The Effects of Rituximab on the Progression of Type 1 Diabetes in New Onset Subjects
,
TN10 Anti-CD3 Prevention
,
TrialNet 08: Effects of Recombinant Human Glutamic Acid Decarboxylase (rhGAD65) Formulated in Alum (GAD-alum) on the Progression of Type 1 Diabetes in New Onset Subjects
,
TrialNet 09: Effects of CTLA-4 IG (Abatacept) on the Progression of Type 1 Diabetes in New Onset Subjects
,
TrialNet 14: Effects of Canakinumab on the Progression of Type 1 Diabetes in New Onset Subjects
,
TrialNet 18 -CTLA-4 Ig (Abatacept) for Prevention of abnormal glocose tolerance and diabetes in relatives at-risk for Type 1 Diabetes Mellitus
,
TrialNet Pathway To Prevention (formerly Natural History Study)
Background Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk. Methods We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation. Findings The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics. Interpretation Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.
