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AIMS The purpose of this thesis was to fully quantify the dysregulation between body mass index (BMI) and body adiposity in 3 phenotypically different groups of adults for whom BMI may be particularly unreliable. The 3 groups were: 1. The ‘Thin Fat’ , 2. The ‘Fat Fit’ 3. South Asians. A secondary purpose of the thesis was to evaluate the accuracy of 2-C body composition devices and proxy measurements to accurately assess regional and whole body adiposity. METHODS In order to establish a cohort baseline, whole body and regional adiposity were quantified using MRI and MRS. Cohort = 500 healthy adults. Participant’s adiposity data obtained were: TAT, SAT, ASAT, NASAT, IAAT, IHCL, S-IMCL, S-IMCL. Anthropometric data included: height, weight, waist circumference, hip circumference (and skinfolds in some sub-groups). Study 1: (A). In 21 healthy non-obese, males; 4 different 2-C body composition techniques (UWW, BIA, SKF, ADP) were compared to MRI adiposity data. Study 1: (B). In 74 adult Caucasian (40 females and 34 males) abdominal adiposity was measured using an abdominal BIA device (Viscan) and compared to MRI adiposity. Study 2: In 477 participants (343 male & 234 female) an in-depth comparison of BMI was conducted to identify TOFI individuals by developing a clinical index from the abdominal internal fat: subcutaneous abdominal fat (IAAT/ASAT) ratio for a normal range. Study 3: 50 males, fitness tested using VO2 max and then categorized by their fitness (fit vs unfit), and fatness (fat vs slim) according to MRI adiposity data. Study 4: 260 participants (68 Asian & 192 Caucasian) – age and BMI matched. Proxy measures WHR, WC etc compared. Apply study 3, TOFI cut-off to Asians adiposity data. RESULTS From the baseline adiposity data I confirmed that there is a wide range of regional body fat distributions (internal abdominal adipose tissue, IAAT; and abdominal subcutaneous adipose tissue, ASAT) by BMI, and that individuals with similar BMI values can show great variation in IAAT and ASAT. Study 1. (A). When whole cohort data were compared to MRI adiposity data there was no significant difference between the measures derived. However when the cohort was divided by ethnicity (Asian vs Caucasian) differences were more apparent. Caucasian adiposity was overestimated by up to 3% and Asian adiposity was underestimated by up to 11%. BodPod would be best suited to measuring Asian adiposity and BIA devices would be best suited to measuring Caucasian adiposity. Study 1. (B). The abdominal adiposity device (Viscan) using BIA method was not able to accurately measure IAAT in obese males and females. It appeared better at measuring subcutaneous adiposity (ASAT). It also appeared to be influenced by organ volumes in some cases – particularly the liver. Study 2. The ‘Thin on the Outside – Fat on the Inside’ (TOFI) phenotype can be defined using the ratio of IAAT and ASAT (IAAT/ASAT). The resulting TOFI index provides a quantitative means of comparing intra-abdominal fat deposition and thereby identifying “at risk” individuals. In Caucasians, cut-off values of >1.0 in males and >0.45 in females are proposed for TOFI definition. Additionally, anthropometric measurements such as waist circumference (WC) and waist to height ratio (WHtR) are not appropriate for classifying the TOFI phenotype. This is because these surrogates generally correlated more with total and subcutaneous adipose tissue stores than internal or ectopic depots. Study 3. IAAT and liver fat are lower in men who are fat, fit and active than in men who are fat, unfit and inactive. These ‘metabolically healthy’ individuals have the capacity to store excess fat in insulin-sensitive abdominal subcutaneous adipose tissue (ASAT) and this may help explain why the risk of chronic disease is lower in the ‘fat-fit’ than the ‘fat-unfit’. As a consequence, aerobic activity and the pursuit of physical fitness may be more appropriate goals in the battle against chronic disease than weight loss. Study 4. Asian Indian males were found to be significantly ‘fatter’ with significantly higher subcutaneous fat depots compared to similar Caucasian males. Given the increased metabolic risks seen in the Asian population increased IAAT measures were not found to be significantly higher. Additionally, the TOFI classification was not useful in identifying ‘at risk’ individuals in the Asian group. Also, waist circumference measurements did not identify Asian males that had significantly elevated ASAT. However, elevated liver fat stores were seen in Asian males and females compared to Caucasians. Liver fat may therefore be a potential ‘at risk’ identifier in this ethnic group. CONCLUSION The results of this thesis confirm BMI may be an inexpensive, non-invasive measure of obesity for predicting the risk of related complications, but its accuracy is limited by its dysregulation with adiposity. While obesity means excess body fat, the current definition of obesity using BMI is based on body weight regardless of its composition. The studies in this thesis have highlighted that fact that there are several different sub-populations of individuals for whom BMI does not tell the whole story. The Fat-Fit, the TOFI and the Asian Indian are specific phenotypic examples of these sub-populations. This is evidence of the fact that BMI should not be considered as the only measure of obesity. The results of this thesis also confirm that some techniques to measure adiposity are suboptimal for measuring percent body fat. For this reason MRI and other high quality (and high cost) imaging methods are still the best method for health risk based research.