Adiposity, measured as increased body mass index (BMI), is associated with reduced all-cause and cardiovascular (CV) mortality in hemodialysis (HD) patients, whereas CV risk increases with BMI in the general population. A major limitation of BMI as a measure of adiposity is its failure to distinguish muscle and fat compartments. In addition, the biology of different adipose compartments is not the same. The visceral adipose tissue (VAT) mass is especially biologically active, secreting a variety of cytokines and adipokines. Alternate methods of estimating body composition were found to have a greater association with CV risk factors than BMI in several populations. We measured total adipose tissue, subcutaneous adipose tissue, and VAT in 48 prevalent HD patients, using magnetic resonance imaging.
Based on these measurements, we developed parsimonious multiple-regression models to estimate these adipose compartments using age, sex, BMI, weight, maximum abdominal circumference (MAC), and race. The parsimonious models for VAT included only age, race, and MAC (adjusted r(2) = 0.776, P < .0001), whereas the subcutaneous adipose tissue model included sex, weight, age, and BMI (adjusted r(2) = 0.91, P < .0001) rather than MAC. The total adipose tissue model included BMI, sex, weight, and age (adjusted r(2) = 0.905, P < .0001).
We propose that measurements of MAC, in addition to height and weight, be included in studies relating body composition to outcomes, because this measure provides a better estimate of the metabolically active VAT pool.