Factors Associated with Risk of Diabetic Complications in Novel Cluster-Based Diabetes Subgroups: A Japanese Retrospective Cohort Study

Abstract

<jats:p>Diabetes is a complex and heterogeneous disease, making the prediction of the risks of diabetic complications challenging. Novel adult-onset diabetes subgroups have been studied using cluster analysis, but its application in East Asians remains unclear. We conducted a retrospective cohort study to elucidate the clinical utility of cluster-based subgroup analysis in the Japanese population. Cluster analysis based on anti-glutamate decarboxylase antibody (GAD antibody) levels, age at diagnosis, body mass index (BMI), hemoglobin A1c (A1c), and homeostatic model assessment 2 estimates of β-cell function and insulin resistance was performed in 1520 diabetic patients. The risk of developing diabetic complications was analyzed using Kaplan–Meier analysis and the Cox proportional hazards model. By cluster analysis, we identified five distinct subgroups of adult-onset diabetes in the Japanese population. The risk of diabetic complications varied greatly among the clusters. Patients with severe autoimmune diabetes or severe insulin deficiency diabetes were at an increased risk of diabetic retinopathy, and those with severe insulin resistant diabetes (SIRD) had the highest risk of developing diabetic kidney disease (DKD). After adjusting for uncorrectable and correctable risk factors, SIRD was found to be an independent risk factor for DKD. In conclusion, we identified five subgroups of adult-onset diabetes and the risk factors for diabetic complications in the Japanese population. This new classification system can be effective in predicting the risk of diabetic complications and for providing optimal treatment.</jats:p>

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].…”

“…Liver diseases such as NAFLD and hepatic fibrosis were found to be associated with the SIRD cluster in studies of Zaharia et al and both studies of Ahlgvist et al [7,9,20] At the same time, neuropathies identified in the Zaharia et al study among SIDD individuals, were not associated with any cluster after adjustment for disease duration or age at onset in the study of Safai et al [9,16] In the study of Kahkoska et al, unadjusted analysis showed that CVDs were associated with the SIDD cluster, which is characterized by low BMI and insulin deficiency [14]. However, CVDs did not differ among clusters after adjustment for known modifiable and non-modifiable risk factors in the studies of Safai et al and Tanabe et al [8,16] Amato et al phenotyped diabetic patients based on fasting incretin levels into two independent clusters: cluster 1 (65.6%) with lower incretin levels and cluster 2 (34.4%) with higher incretin levels [10]. Thus, cluster 1 differed by a lower glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and, consequently, with higher levels of HbA1c and fasting plasma glucose (FPG) compared to cluster 2, which was explained by possible increased a-cell activity and its effect on the reduction in b-cell function.…”

“…), 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 .…”

“…Only two studies have indicated reducing the dimensionality of the data prior to CA [12,13]. The widespread method for clustering among included publications was k-means clustering [7,8,13,[15][16][17][18]20]. Several studies performed k-means analysis only for GADA-negative individuals [7,8,18].…”

“…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].…”