Fig 1.
Exemplary segmentation of a coronal T1w Dixon-VIBE-dataset (A). Corresponding fat only images (B). A whole kidney mask is generated using thresholding and active contours (C). Next the renal sinus fat is segmented using thresholding of fat isointense voxels (D).
Fig 2.
N = 366 study subjects were finally included for analysis.
Table 1.
Demographics, cardiovascular risk factors and MRI parameters of the study participants.
Fig 3.
Boxplots with density curves displaying the distribution of renal and sinus fat volumes according to glycemic status.
There was a considerable increase between controls and subjects with prediabetes particularly for renal sinus fat.
Table 2.
Regression model with adjustments for age, gender and glycemic status.
Table 3.
Regression model with adjustments for age, VAT, HDL, LDL, urine albumin/creatinine, liver fat, GFR, gender, hypertension yes/no and glycemic status.
Table 4.
Regression model with adjustments for age, gender and VAT.
Fig 4.
Scatter diagrams showing the correlation of the VAT with the glycemic groups.
There was a significant correlation between VAT and renal sinus fat particularly for healthy controls and individuals with diabetes.
Table 5.
Pearson’s correlation coefficients of VAT and renal volumes with corresponding 95% CI stratified by glycemic status.