Introduction: Obesity is associated with insulin resistance and type 2 diabetes. Dual-energy X-ray absorptiometry (DXA) is a means of determining body composition and body fat distribution at different sites including whole body and trunk-locations where there tends to be high correlation at an individual level.
Methods: We performed an analysis of DXA-derived metrics of adiposity (truncal fat %,subtotal fat % and total fat %) from the NHANES database and then correlated the findings with markers of insulin resistance. We analyzed the data from DXA scans in NHANES 1999-2004. Homeostatic model assessment-insulin resistance and HOMA-β (beta-cell function) were estimated. Spearman correlation coefficients were calculated (ρ) between HOMA-IR,HOMA-β and different measures of obesity (Waist circumference(in cm), Body Mass Index (kg/m2), truncal fat %, subtotal fat % as well as total fat %) to gauge the relationship between markers of glucose homeostasis and DXA derived metrics of obesity. We also performed logarithmic transformation of HOMA-IR as well as HOMA-β to ensure normality of distribution and to meet the criteria for regression analysis. A forward selection model (by outcome and gender) was performed to predict log transformed insulin resistance (log HOMA-IR) as well as log transformed HOMA-β (log HOMA-β,measure of beta cell function) from age, serum triglycerides, HDL, trunk fat % and the SBP (in both males and females separately), after reviewing the spearman correlation coefficients.
Results: There were a total of 6147 men and 6369 women who were part of the study cohort. There was a positive correlation between markers of adiposity and log HOMA-IR and log HOMA-β in both males and females.Truncal fat % had the highest nonparametric correlation coefficent with log HOMA-IR among the DXA derived fat% (0.54 in males and 048 in females). In the multivariate analysis, truncal fat % was an independent predictor of logHOMA-IR as well as logHOMA-β. In males, the significant predictors of log HOMA-IR were; age, truncal fat % and HDL cholesterol (Adjusted R square of 0.325 (±0.66), F(3,207) = 34.63, p < .01). In females, the significant predictors of log HOMA-IR were; age, truncal fat %, SBP, Serum triglyceride and HDL cholesterol (Adjusted R square of 0.307 (±0.65),F(5,198) = 18.9, p < .01). In both males and females, the significant predictors of log HOMA-β were; age, and truncal fat % (Males; adjusted R square of 0.25 (±0.63), F (2,208) = 36.4, p < .01, Females; adjusted R square of 0.27 (±0.62), F (2,201) = 38.4, p < .01).
Conclusions: Body fat % on DXA is an imaging biomarker for insulin resistance. Incorporating this important information into DXA acquisitions and reporting frameworks may allow for this information to be available to providers who refer patients for these imaging studies.