Replication and cross-validation of T2D subtypes based on clinical variables: an IMI-RHAPSODY study

Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes (T2D). In the current study we replicate and cross-validate these T2D clusters in three large cohorts using readily measured variables in the clinic.MethodsIn this cross-sectional study, 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide and HDL in three independent cohorts. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres.ResultsFive distinct T2D clusters were identified and mapped back to the original four ANDIS clusters. Using C-peptide and HDL instead of HOMA-B and HOMA-S three of the clusters mapped with high sensitivity (80.6 – 90.7%) to the previously identified Severe Insulin Deficient (SIDD), Severe insulin resistant (SIRD) and Obese (MOD) clusters. The previously described ANDIS MARD cluster could be mapped to the two milder groups in our study – one characterised by high HDL, and the other having not any extreme characteristic (MDH cluster). When these two milder groups were combined they mapped well to the previously labelled MARD cluster (sensitivity 79.4%). In the cross-validation between cohorts, particularly the SIDD and MDH cluster cross-validated well with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity ranging from 36.1% to 92.3% where individuals shifted from SIRD to MD and vice versa.Conclusions/interpretationClusters based on C-peptide instead of HOMA measures result in clusters that resemble those based on HOMA measures, especially for SIDD, SIRD and MOD. By adding HDL, the MARD cluster based upon HOMA measures resulted in the current clustering in two clusters with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts.