Advances in the technological qualities of imaging modalities for assessing human body composition have been stimulated by accumulating evidence that individual components of body composition have significant influences on chronic disease onset, disease progression, treatment response, and health outcomes. Importantly, imaging modalities have provided a systematic method for differentiating phenotypes of body composition that diverge from what is considered normal, that is, having low bone mass (osteopenia/osteoporosis), low muscle mass (sarcopenia), high fat mass (obesity), or high fat with low muscle mass (sarcopenic obesity). Moreover, advances over the past three decades in the sensitivity and quality of imaging not just to discern the amount and distribution of adipose and lean tissue but also to differentiate layers or depots within tissues and cells is enhancing our understanding of distinct mechanistic, metabolic, and functional roles of body composition within human phenotypes. In this review, we focus on advances in imaging technologies that show great promise for future investigation of human body composition and how they are being used to address the pandemic of obesity, metabolic syndrome, and diabetes.

Background and Aims New methods to measure visceral adipose tissue (VAT) by DEXA may help discern sex, race and phenotype differences in the role of VAT in cardiometabolic risk. This study was designed to: a) compare relationships between cardiometabolic risk factors and DEXA-VAT, anthropometric and body composition measures; b) determine thresholds for DEXA-VAT by race; and c) determine the most robust predictors of impaired glucose tolerance (IGT) and metabolic syndrome (MetSx) in obese women. Methods VAT area (cm2) was measured using Lunar iDXA scanner in 229 obese (BMI 30-49.9) women age 21–69 years of European American (EA = 123) and African American (AA = 106) descent. Linear regression modeling and areas under the curve (AUC) compared relationships with cardiometabolic risk. Bootstrapping with LASSO regression modeling determined thresholds and predictors of IGT and MetSx. Results DEXA-VAT explained more of the variance in triglycerides, blood pressure, glucose and HOMA-IR compared to anthropometric and body composition variables. DEXA-VAT had the highest AUC for IGT (0.767) and MetSx (0.749). Including race and interactionXrace terms in modeling did not significantly change results. Thresholds at which probability was ≥ 50% for IGT or MetSx were lower in AA women (IGT: 2120cm2 AA vs 2550cm2 EA; MetSx: 1320cm2 AA vs 1713cm2 EA). The odds for IGT or MetSx was 3-fold greater with each standard deviation increase in DEXA-VAT. Conclusion DEXA-VAT provides robust clinical information regarding cardiometabolic risk in AA and EA women and has great potential in risk reduction efforts.

Background: Metabolic syndrome in football players is a common, yet poorly understood phenomenon. With 88,000 college football players and one million high school football players, there is a large, at-risk population. Hypothesis: We hypothesized that metabolic syndrome in football players is driven by oxidative stress and positive energy balance. Methods: A single site, cross-sectional study was performed at Vanderbilt University Medical Center of high BMI college football players (n=33). Prevalence of metabolic syndrome was determined. Data related to diet composition, oxidative stress, inflammation, body composition, glucose disposition, lipoprotein metabolism, and endothelial function were assessed to identify drivers of the metabolic syndrome in this cohort. Results: Prevalence of clinical metabolic syndrome was 33% (11/33) despite high cardiorespiratory fitness in all players (Table 1). Elevated waist circumference, HDL, and elevated blood pressure were present together in 73% of cases. Cases had increased oxidative stress (F2-isoprostanes) and inflammation (CRP). Insulin resistance was not worse by HOMA. Visceral fat predicted HDL and CRP. Respiratory quotient was identical between groups but metabolomics revealed decreased TCA cycle intermediates in cases. There were no differences in caloric intake but cases had gained more weight (4.2 vs 2.0 kg, p=.06). Conclusion: High BMI collegiate football players have metabolic syndrome at unexpectedly high rates with a unique set of risk factors and unusual pathophysiology. They have low HDL despite normal triglyceride levels and no defects in glucose metabolism. These data suggest deleterious effects of positive energy balance and oxidative stress irrespective of exercise quantity. These results provide strong rationale to conduct larger, longitudinal studies.

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