Clinical Characterization of Data-Driven Diabetes Clusters of Pediatric Type 2 Diabetes
Authors' conclusion
Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management.
Study context
- Population
- Children and adolescents with autoantibody-negative T2D; clustering on sex, race/ethnicity, age, BMI Z-score and HbA1c at diagnosis, and non-HDL cholesterol within first year
- Participants
- 722 children
- Exposure
- Cluster 1 vs Cluster 2 vs Cluster 3 vs Cluster 4 vs Cluster 5
- Outcome
- Diabetes clusters of pediatric T2D
Methodological detailsdata-driven cluster analysis, K-prototypes clustering
- Data-driven cluster analysis
- K-prototypes clustering
Main finding
As reported in the abstract
- Five distinct clusters identified, with different features, treatment regimens and risk of diabetes complications
- 5
- Cluster 1: highest rate of diabetic ketoacidosis (DKA)
- p = 0.0001
- Cluster 1: most prescribed metformin
- p = 0.06
- Cluster 2: most prone to PCOS
- p = 0.001
- Younger individuals with lowest family history of diabetes: least frequently diagnosed with DKA
- p = 0.001
- Younger individuals with lowest family history of diabetes: least frequently diagnosed with microalbuminuria
- p = 0.06
- Low-BMI individuals with higher A1c: lowest prevalence of acanthosis nigricans
- p = 0.0003
- Low-BMI individuals with higher A1c: lowest prevalence of hypertension
- p = 0.03
Observational data: associations, not effects.
Original publication
Clinical Characterization of Data-Driven Diabetes Clusters of Pediatric Type 2 Diabetes. Pediatr Diabetes. Published 18 July 2023. doi:10.
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