Global trends of increasing obesity threaten public health and contribute to the burden of disease as much as smoking does 
. Obesity is associated with increased risk of diabetes, hypertension, heart disease, stroke, cancer, dyslipidemia, liver and gallbladder disease, sleep apnea and respiratory problems, osteoarthritis, abnormal menses and infertility 
. Adiposity in mid-life strongly relates to reduced probability of healthy long term survival in women 
. Obesity has become a priority of national, state and local public health efforts and in the care of individual patients. Thus, clinical detection of obese individuals has commensurately reached critical importance.
With the increasing importance of obesity detection, it is useful to reevaluate how body fat is determined. For adults, the body mass index (BMI) is commonly used. Its popularity stems in part from its convenience, safety, and minimal cost, and its use is widespread, despite not being able to distinguish lean body mass from fat mass 
. The United States Centers for Disease Control and Prevention (CDC) explain: “For adults, overweight and obesity ranges are determined by using weight and height to calculate a number called the “body mass index” (BMI). BMI is used because, for most people, it correlates with their amount of body fat” 
. However, the BMI is actually an indirect surrogate measurement considered imprecise 
Recent estimates from NHANES, a nationally representative health examination survey, project that approximately 34% of adult Americans are overweight (defined as a BMI between 25–30 kg/m2
) and an additional 34% are obese (BMI >30 kg/m2
. In contrast, the CDC estimates rates of obesity over 20% in all 50 states with estimated rates over 30% in 12 states [http://www.cdc.gov/obesity
]. These estimates are fundamental to US policy addressing the epidemic of obesity and are central to designing interventions aimed at curbing its growth, yet they may be flawed because they are based on the BMI.
The outdated BMI formula [BMI
weight in pounds/(height in inches)2
×703], developed nearly 200 years ago by Quetelet, is not a measurement of adiposity, but merely an imprecise mathematical estimate 
. Defining obesity based on percent body fat, as with BMI, also has arbitrary cut-points. In 1995, the World Health Organization (WHO) defined obesity based on a percent body fat ≥25% for men and ≥35% for women 
, while the most recent 2009 guidelines from the American Society of Bariatric Physicians (ASBP), an American Medical Association (AMA) specialty board, used percent body fat ≥25% for men and ≥30% for women. The ASBP percent body fat guidelines identify individuals that are suitable candidates for treatment for obesity with anorectic agents. Most studies comparing BMI with more accurate measures of adiposity used cutoffs of body fat >25% for men and >30% for women 
BMI ignores several important factors affecting adiposity. Greater loss of muscle mass leading to sarcopenic obesity in women occurs increasingly with age. BMI does not acknowledge this factor, exacerbating misclassifications 
. Furthermore, men's BMI also does not consider the inverse relationship between muscular strength and mortality 
. It fails to take into account that men lose less muscle with age than women.
Statistical models have been created to explain variance in leptin with relation to insulin, gender, and BMI, but lack a variable of direct adiposity measurement such as DXA 
. A fully equipped duel-energy x-ray absorptiometry (DXA) provides simultaneous measurements of muscle, bone mass and body adiposity. The ASBP uses both BMI and DXA as criteria for interventions.
Studies comparing DXA-derived percent body fat rates of obesity to BMI have, to date, focused mainly on women 
or imputed data on percent body fat for a substantial proportion of subjects 
. We sought to characterize the degree of misclassification of obesity based on BMI using percent body fat from DXA in a large, unselected population, and to use the more accurate DXA derived measure to identify the optimal cut-points for defining obesity using BMI. Reclassifying obesity cut points is worth considering, as there is a population of individuals with a normal BMI who nonetheless have increased adiposity as determined by more sensitive methods; these are the so-called ‘normal weight obese.’ These individuals may have increased risk for medical comorbidities such as hyperlipidemia, coronary artery disease, hypertension, and diabetes 
. Furthermore, in the intermediate ranges, BMI is not a good discriminator of cardiovascular risk; use of adiposity measures rather than BMI may be a better predictor, but have recently failed 
. Therefore, there is a need to reclassify the obesity epidemic, identify clinically useful biomarkers, and clarify what the medical and scientific communities are measuring with BMI.
Although DXA is a direct measurement of fat and a better measure of adiposity than BMI, it is not a disease correlate. The attempts to find disease correlates to explain disparities between BMI and direct fat measurements have included leptin, insulin, ghrelin, and adiponectin 
. Leptin, a 16 kDa peptide secreted primarily by adipocytes, regulates the body's energy balance by acting as a negative feedback adiposity signal, decreasing food intake and increasing energy expenditure. In individuals with leptin insensitive receptors, neither transport nor action is possible, and leptin levels rise 
. Increased leptin is associated with the inflammatory process and possibly the entire increased morbidity of obesity 
. Individuals with leptin insensitivity and high levels of leptin have parallel comorbidities to normal weight obesity such as chronic inflammation, type II diabetes, hypertension, and myocardial injury [http://www.asbp.org/siterun_data/about_asbp/position_statements/doc7270523281295654373.html
]. Therefore, it was appropriate to investigate whether leptin levels could correct for the disparity between DXA and BMI and be used to create a more accurate measure of obesity.