Abstract
Background Cluster analysis may assist in stratifying heterogeneous clinical presentations of type 2 diabetes (T2D). However, the association of cluster-based subgroups with diabetes-related outcomes such as diabetic retinopathy remains unclear. This study was aimed to address this issue with novel clusters of T2D derived from four simple parameters. Method We developed a k-means clustering model in participants with newly diagnosed T2D (N = 1910) from the SENSIBLE and SENSIBLE-Addition studies, based on body mass index (BMI), waist circumference (WC), mean arterial pressure (MAP), and hemoglobin A1c (HbA1c). Diabetic retinopathy was ascertained with the protocol from the Early Treatment of Diabetic Retinopathy Study. Participants (N = 515) without diabetic retinopathy at baseline were followed-up for 3 years. Logistic regression analyses were performed to obtain the odds ratios (ORs) and 95% confidence intervals (CIs). Results Three clusters were identified, with cluster 0, 1 and 2 accounting for 48.2, 8.9 and 42.9%, respectively. Participants with T2D were featured by the lowest BMI, WC, MAP, and HbA1c in cluster 0, poor glycemic condition in cluster 1, and the highest BMI, WC, and MAP in cluster 2. Compared with cluster 0, cluster 1 was associated with increased odds of diabetic retinopathy in both the cross-sectional study (OR 6.25, 95% CI: 3.19-12.23) and the cohort study (OR 9.16, 95% CI: 2.08-40.34), while cluster 2 was not. Moreover, most participants remained their clusters unchanged during follow-up. Conclusions Our cluster-based analysis showed that participants with poor glycemic condition rather than high blood pressure and obesity had higher risk of diabetic retinopathy.
NSTL
Springer Nature