New computed tomography-derived indices to predict cardiovascular and insulin-resistance risks in overweight/obese patients
Piernas C., Hernández-Morante JJ., Canteras M., Zamora S., Garaulet M.
Objective: To determine whether a series of new computed tomography (CT)-derived indices are better diagnostic criteria than the classical CT-derived measurements. A second objective is to propose specific or sensitive threshold values of the most accurate criteria for the occurrence of metabolic disturbances. Subjects/Methods: Anthropometric measurements and CT scans were performed in 74 obese subjects. Fat thicknesses, diameters, diagonals and areas were determined. Plasma lipids, insulin, glucose and fat cell size were analyzed. A multivariate regression analysis was performed to determine the most accurate predictor index for metabolic alterations explaining the highest percentage of variance. Results: All the new indices were highly correlated with body mass index, percentage of fat and fat cell size. Subcutaneous thicknesses were greater in women, while internal-coronal and sagittal diameters, visceral adipose tissue (VAT) and internal circumference area were greater in men (P<0.001). Those observations were reinforced by the adipocyte size in both fat depots. Subcutaneous parameters showed the strongest correlation with metabolic alterations, being positively associated with metabolic risk in women and negatively in men. Multivariate regression analysis showed that the best predictor index was the superficial subcutaneous adipose tissue (SSAT) and its relation to visceral area (SSAT/VAT), explaining 42% of total variance for high-density lipoprotein-cholesterol in men and 26% for homeostasis model assessment in women. After receiver operating characteristic-curves analysis, three threshold values for both sexes were proposed to select the most appropriate depending on the clinical situation. Conclusion: For the first time, we have described SSAT and the SSAT/VAT ratio as important indices in obesity-related disturbances. © 2009 Macmillan Publishers Limited. All rights reserved.