A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes
eBioMedicine, 2022
Achenbach P., Hippich M., Zapardiel-Gonzalo J., Karges B., Holl R., Petrera A., Bonifacio E., Ziegler A.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases | Patient Stratification | Plasma |  Olink Target 96 |
Abstract
Background
Diabetes in childhood and adolescence includes autoimmune and non-autoimmune forms with heterogeneity in clinical and biochemical presentations. An unresolved question is whether there are subtypes, endotypes, or theratypes within these forms of diabetes.
Methods
The multivariable classification and regression tree (CART) analysis method was used to identify subgroups of diabetes with differing residual C-peptide levels in patients with newly diagnosed diabetes before 20 years of age (n=1192). The robustness of the model was assessed in a confirmation and prognosis cohort (n=2722).
Findings
The analysis selected age, haemoglobin A1c (HbA1c), and body mass index (BMI) as split parameters that classified patients into seven islet autoantibody-positive and three autoantibody-negative groups. There were substantial differences in genetics, inflammatory markers, diabetes family history, lipids, 25-OH-Vitamin D3, insulin treatment, insulin sensitivity and insulin autoimmunity among the groups, and the method stratified patients with potentially different pathogeneses and prognoses. Interferon-ɣ and/or tumour necrosis factor inflammatory signatures were enriched in the youngest islet autoantibody-positive groups and in patients with the lowest C-peptide values, while higher BMI and type 2 diabetes characteristics were found in older patients. The prognostic relevance was demonstrated by persistent differences in HbA1c at 7 years median follow-up.
Interpretation
This multivariable analysis revealed subgroups of young patients with diabetes that have potential pathogenetic and therapeutic relevance.