Figure 1.
Image demonstration of determining abdominal fat distribution on a CT scan.
Left, sample CT image obtained at the umbilicus level. Right, fat masks created for determining areas of subcutaneous fat (red, “S”), peritoneal fat (blue, “P”) and retroperitoneal fat (green, “R”) using methods described in the Materials and Methods section.
Table 1.
Clinical characteristics of participants with and without metabolic syndrome (MS).
Figure 2.
Clustering of metabolic abnormalities defined by MS in participants with body fat areas in the lowest, middle, or highest tertiles.
(A) Retroperitoneal fat, (B) peritoneal fat, and (C) subcutaneous fat. *p<0.05 vs. lowest tertile, #p<0.05 vs. middle tertile.
Table 2.
The relationship between metabolic syndrome and body fat in logistic regression models, using metabolic syndrome as the dependent variable.
Table 3.
Correlation coefficients (r) between body fat and metabolic variables in participants without medications for hypertension, diabetes, or dyslipidemia (N = 353).
Figure 3.
Different fat compartments to predict the probability of incident hypertension.
Kaplan-Meier failure curves for the probability of developing hypertension in subgroups divided by the median of (A) retroperitoneal fat area, (B) peritoneal fat area, and (C) subcutaneous fat area. P values by log-rank tests are shown.
Table 4.
Hazard ratios (HRs) and 95% confidence interval (95% CI) of different fat components to predict the development of incident hypertension and incident diabetes during follow-up.