Diabetes Obesity and Metabolism · 2020 · 83 citations · 13 references
MedicineCardiologyEndocrinologyClinical EpidemiologyDiabetologyEpidemiologyCardiovascular DiseaseCardiovascular RiskDiabetes ControlDiabetes ManagementDiabetes MellitusMetabolic SyndromeCardiovascular Risk ManagementCardiovascular Risk MarkersDiabetes-associated Cardiac RiskDiabetes-related ComplicationsLong-term Type 2 Diabetes RiskMetabolic Syndrome Risk AssessmentSurvival Endpoints ValidationType 2 Diabetes Risk Assessment
AIMS: To validate the clusters of Swedish individuals with recent-onset diabetes at differential risk of complications, which were identified in a previous study, in three global populations with long-standing type 2 diabetes (T2D) who were at high cardiovascular risk, and to test for differences in the risk of major diabetes complications and survival endpoints. MATERIALS AND METHODS: We assigned participants from recent global outcomes trials (DEVOTE [n = 7637], LEADER [n = 9340] and SUSTAIN-6 [n = 3297]) to the previously defined clusters according to age at diabetes diagnosis, baseline glycated haemoglobin (HbA1c) and body mass index (BMI). Outcomes were assessed using Kaplan-Meier analysis and log-rank tests. RESULTS: The T2D clusters were consistently replicated across the three trial cohorts. The risk of major adverse cardiovascular events and cardiovascular death differed significantly, in all trials, across clusters over a median follow-up duration of 2.0, 3.8 and 2.1 years, respectively, and was highest for the cluster of participants with high HbA1c and low BMI (P < 0.05 in DEVOTE and LEADER). In LEADER and SUSTAIN-6, the risk of nephropathy differed across clusters (P < 0.0001 and P = 0.003, respectively). The risk of severe hypoglycaemia differed in DEVOTE (P = 0.006). CONCLUSIONS: Previously identified clusters can be replicated in three geographically diverse cohorts of long-standing T2D and are associated with cluster-specific risk profiles for additional clinical and survival outcomes, providing further validation of the clustering methodology. The external validity and stability of clusters across cohorts provides a premise for future work to optimize the clustering approach to yield T2D subgroups with maximum predictive validity who may benefit from subtype-specific treatment paradigms.
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