Total and high-molecular weight adiponectin in relation to metabolic variables at baseline and in response to an exercise treatment program -: Comparative evaluation of three assays

OBJECTIVE-Adiponectin, an adipocyte-secreted hormone, circulates in the serum in several multimeric forms. Compared with total adiponectin, high-molecular weight (HMW) adiponectin has been suggested to be a better predictor of metabolic parameters and insulin sensitivity in humans. Our objective was to compare total adiponectin with HMW adiponectin as predictors of metabolic variables and insulin sensitivity at both baseline and after an exercise intervention. RESEARCH DESIGN AND METHODS-We obtained blood samples from 60 men and women with normal glucose tolerance (n = 20), impaired glucose tolerance (IGT) (n = 20), or type 2 diabetes (n = 20) at baseline and after 4 weeks Of training to measure metabolic variables. Using commercially available 'assays, we measured plasma total adiponectin using LINCO, Mediagnost, and ALPCO assays and HMW adiponectin using an ALPCO assay. RESULTS-HMW adiponectin and total adiponectin (ALPCO) had similar ability to predict the presence of insulin resistance. Total adiponectin, as measured by radicimmunoassay (LINCO) and enzyme-linked immunosorbent assay.(ELISA) (Mediagnost), correlated most strongly with measures of insulin sensitivity (P < 0.01) and lipid profile (P < 0.01.) at baseline, showed greater improvements of adiponectin levels (P < 0.001), was more closely associated with improvements of lipid measures with exercise training (P < 0.01), and more accurately predicted insulin resistance and IGT in comparison with total adiponectin or HMW measured with the ALPCO ELISA. CONCLUSIONS-These results do not support the superiority of HMW over total adiponectin (measured using currently available assays) in assessing metabolic variables at baseline or in response to physical training. Moreover, there are significant differences in the ability of commercially available assays for total adiponectin to predict metabolic variables.