Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN‐6 cardiovascular outcomes trials

Abstract

AimsTo 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 methodsWe 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.ResultsThe 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).ConclusionsPreviously 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.

The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review

“…The proportion of the SIRD cluster ranged within 7.2% and 23.7% among studies, while 2 similar clusters were revealed by Safai et al such as insulin resistance with short disease duration (21.4%) and insulin resistance with long disease duration (31.7%) [7][8][9]14,[16][17][18]20]. The next most frequent cluster was MOD with varying percentages between studies from 20.4% to 34% [7][8][9]14,17,18,20]. The MARD cluster was the most prevalent among the mentioned five clusters in each study, falling within 34% and 45.4% [7][8][9]14,17,18,20].…”

“…), and 2 studies were conducted on mice. As a result, 14 papers were found to be eligible: 10 articles were included in the review [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] and an additional 4 eligible papers were found after hand-searching of the reference lists of selected articles [ 17 , 18 , 19 , 20 ]. The selection process is presented in Figure 1 .…”

“…The largest sample size was in the study of Karpati et al from Israel, constituting 85,783 patients of whom 60,423 were considered eligible for cluster analysis [ 15 ]. The second largest was the study of Kahkoska et al, with 20,274 DM patients [ 14 ], followed by 8980 individuals from the ANDIS cohort in the study of Ahlgvist et al [ 7 , 20 ] The study with the smallest sample of 33 T1DM patients from several university hospitals was conducted in the UK [ 11 ].…”

“…The variability in population size could be explained by the source of the data, as data were taken from electronic medical records, healthcare databases, from previously conducted longitudinal observational studies and surveys. Disease duration among target populations of reviewed publications, along with newly diagnosed diabetic patients, ranged from 40 days after diagnosis to 12 years or longer [ 14 , 20 ]. The age of the participants varied depending on the type of DM: 5–16 years among T1DM patients, LADA patients were 35 years and older, the age of T2DM patients were between 18–96 years.…”

“…Methods for determining the number of clusters varied from one study to another. Seven papers used the direct silhouette width method [7][8][9]14,17,18,20], one paper had a fixed number of clusters [10], one study determined the number of clusters based on hierarchical clustering with Ward's method [11].…”