Cluster analysis of computed tomography identifies duration-associated abdominal fat deposition in type 2 diabetes mellitus

Figure 1 Flow chart for the inclusion of patients with prediabetes and type 2 diabetes mellitus. T2DM: Type 2 diabetes mellitus; FPG: Fasting plasma glucose; IGT: Impaired glucose tolerance; OGTT: Oral glucose tolerance test; CT: Computed tomography; DMa: Type 2 diabetes mellitus with disease duration < 5 years; DMb: Type 2 diabetes mellitus with disease duration ≥ 5 years.

Figure 2 Schematic diagram of the placement and delineation of regions of interests for various abdominal organs, visceral, subcutaneous, and bilateral renal sinus. A: Regions of interests (ROIs) of the left and right lobes of the liver; B-D: ROIs of the upper, middle, and lower poles of the spleen; E: ROIs of the head of the pancreas; F: ROIs of the body, and tail of the pancreas; G: ROIs of the bilateral renal parenchyma; H: ROI of the L3 lumbar vertebra; I: ROIs of the bilateral psoas major muscles; J: The blue area represents the delineation range of the visceral adipose tissue ROI; K: The purple area represents the delineation range of the subcutaneous adipose tissue ROI; L: The yellow area represents the delineation range of the bilateral renal sinus fat ROI. Yellow circle: Regions of interests placement location for the corresponding structures shown in each panel.

Figure 3 K-means clustering results of the pre-period group, type 2 diabetes mellitus with disease duration < 5 years group, and type 2 diabetes mellitus with disease duration ≥ 5 years group using R. A-C: When k = 2, the decline rate of within-cluster sum of squares slows down and the average silhouette coefficient is the highest. Set as 2 the final number of clusters, and there is no obvious mixing between the two clusters; D-F: When k = 3, the decline rate of WSS significantly slows down and the average silhouette coefficient is the highest. Set 3 as the final number of clusters, and there is no obvious mixing between the three clusters. G-I: When k = 4, the decline rate of WSS significantly slows down and the average silhouette coefficient is the highest. Set 4 as the final number of clusters, and there is no obvious mixing between the four clusters.