Date of Award

5-2014

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Mathematical Sciences

Thesis Sponsor/Dissertation Chair/Project Chair

Diana Thomas

Committee Member

Andrew McDougall

Committee Member

Steven Heymsfield

Abstract

Objective: Trunk shape is a known predictor of amounts of visceral adipose tissue (VAT). The amount of total adipose tissue in the abdomen also predicts visceral adipose tissue mass but it is unknown how much of VAT can be attributed to abdominal shape versus size. Using two new measures derived from external measures such as hip circumference and waist circumference, we investigated how shape and adiposity along with demographic covariates are related to amounts of visceral adipose tissue. These new measures are known as Trunk Shape and Trunk Size. These measures were then used to develop models that predicted the percentage of visceral adipose tissue based on key covariates.

Methods: Subject data were pooled from two studies containing dual energy X-ray absorptiometry measured fat mass, and magnetic resonance imaging measured VAT mass. Eight separate indices: A Body Shape Index (ABSI), BMI, waist circumference (WC), hip circumference (HC), Trunk Size (TSZ), waist/hip ratio (WHR), Trunk Shape (TSA), and body adiposity index (BAI), were examined as predictors of total VAT mass and % of body weight as VAT using multi-linear regression. 192 different regression models were developed that predict VAT mass. Linear models with multiple covariates were then developed and tested to see how well they predicted %VAT.

Results: Adjusted R2 values were consistently higher in males than females. Our new measures indicated that trunk size explains much more of the variation in VAT than trunk shape does. Interestingly, in men, trunk size and shape were correlated, indicating that as men become more obese, they tend to store fat in a “pot-belly” pattern, whereas no correlation between trunk size and shape existed in women. Of all 8 indices tested, WC was found to be the most accurate predictor for VAT and %VAT for both genders, and including age as a covariate improved every adjusted R2 value. Adjusted R2 values for Trunk Size proved to be higher than those for BMI, ABSI, and BAI in both data sets.

Conclusions: Trunk size is a better predictor of VAT than trunk shape even after adjusting for age, gender, and height. Deposits of VAT appear to have a nonlinear relationship with weight gain.

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