Predicted Lean Body Mass and Fat Mass: Novel Insights Into Obesity, Chronic Disease, and Mortality Research

Abstract

Body mass index (BMI) is widely used measure of overall adiposity in epidemiological studies. However, BMI has a critical limitation that it cannot distinguish different body compartments, and therefore fails to capture the true harmful effect of fat mass and the potentially beneficial effect of lean body mass on diverse health outcomes. Unfortunately, assessing body composition in a large epidemiological study is infeasible because it requires expensive and sophisticated technologies. Thus, the independent role of lean body mass and fat mass on health outcomes remains largely underexplored. In this dissertation exercise, I attempted to offer a practical solution to directly assess body composition and examine their associations with major health outcomes. Chapter 1 developed anthropometric prediction equations using simple anthropometric measures to assess lean body mass, fat mass, and percent fat from the National Health and Nutrition Examination Survey 1999-2006. The equations were validated in the independent dataset and using obesity-related biomarkers, suggesting their potential application in epidemiological studies. Using the developed anthropometric equations, Chapter 2 examined the association of predicted lean body mass and fat mass with all-cause and cause-specific mortality in the Health Professional Follow-up Study, and found a strong positive association between predicted fat mass and mortality and a U-shaped association between predicted lean body mass and mortality in men. In particular, I found evidence that low lean body mass may account for the increased risk of mortality in the lower BMI range, suggesting that the ‘obesity paradox’ controversy may be explained by understanding the role of body composition, especially lean body mass, on mortality. Chapter 3 further examined the association between predicted fat mass and type 2 diabetes risk in men, and compared with BMI and other obesity indicators. The predicted fat mass consistently demonstrated the stronger association among all other indicators. Although it is preliminary to conclude that predicted fat mass is a superior measure, our findings show a potential use of the predicted fat mass in advancing our understanding of obesity and type 2 diabetes. Overall, evidence based on the developed anthropometric equations may provide novel insights into obesity, chronic disease, and mortality research.