Measuring Adiposity in Patients: The Utility of Body Mass Index (BMI), Percent Body Fat, and Leptin

Department of Medicine, New York University School of Medicine, New York, New York, United States of America.
PLoS ONE (Impact Factor: 3.23). 04/2012; 7(4):e33308. DOI: 10.1371/journal.pone.0033308
Source: PubMed


Obesity is a serious disease that is associated with an increased risk of diabetes, hypertension, heart disease, stroke, and cancer, among other diseases. The United States Centers for Disease Control and Prevention (CDC) estimates a 20% obesity rate in the 50 states, with 12 states having rates of over 30%. Currently, the body mass index (BMI) is most commonly used to determine adiposity. However, BMI presents as an inaccurate obesity classification method that underestimates the epidemic and contributes to failed treatment. In this study, we examine the effectiveness of precise biomarkers and duel-energy x-ray absorptiometry (DXA) to help diagnose and treat obesity.
A cross-sectional study of adults with BMI, DXA, fasting leptin and insulin results were measured from 1998-2009. Of the participants, 63% were females, 37% were males, 75% white, with a mean age = 51.4 (SD = 14.2). Mean BMI was 27.3 (SD = 5.9) and mean percent body fat was 31.3% (SD = 9.3). BMI characterized 26% of the subjects as obese, while DXA indicated that 64% of them were obese. 39% of the subjects were classified as non-obese by BMI, but were found to be obese by DXA. BMI misclassified 25% men and 48% women. Meanwhile, a strong relationship was demonstrated between increased leptin and increased body fat.
Our results demonstrate the prevalence of false-negative BMIs, increased misclassifications in women of advancing age, and the reliability of gender-specific revised BMI cutoffs. BMI underestimates obesity prevalence, especially in women with high leptin levels (>30 ng/mL). Clinicians can use leptin-revised levels to enhance the accuracy of BMI estimates of percentage body fat when DXA is unavailable.

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    • "This poor correlation is explained by the fact that adiposity is dependent upon sex and age. It is well known that with the same BMI women and more elderly subjects have a greater percentage of BF15161718. This same fact also explains the improvement in correlation when sex, age, or both are factored into the figures. "
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    ABSTRACT: Background Obesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, thus giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases. Methods We calculated the Pearson correlation coefficient between BMI and CUN-BAE, the Kappa index and the internal validity of the BMI. The risks of arterial hypertension (AHT) and diabetes mellitus (DM) and the AFp for obesity were assessed using both the BMI and CUN-BAE. Results 3888 white subjects were investigated. The overall correlation between BMI and CUN-BAE was R2 = 0.48, which improved when sex and age were taken into account (R2 > 0.90). The Kappa coefficient for diagnosis of obesity was low (28.7 %). The AFp was 50 % higher for DM and double for AHT when CUN-BAE was used. Conclusions The overall correlation between BMI and CUN-BAE was not good. The AFp of obesity for AHT and DM may be underestimated if assessed using the BMI, as may the prevalence of obesity when estimated from the percentage of BF.
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    • "BF% was assessed by BIA (Tanita, Body Composition Analyzer, model TBF-215; Tanita Corp. of America Inc.) with participants in street clothes but shoes and socks removed. BF% of ≥25% in men and ≥30% in women resulted in classification as obese (Shah and Braverman 2012). A subset of adults recorded the amount of food and beverage consumed during their last eating occasion prior to testing. "
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    • "The validity of this measure has previously been challenged (Talma et al. 2013) and should therefore be interpreted with some caution. However, others have suggested body fat measurements to be superior to BMI when examining individual differences (Ode et al. 2007; Shah and Braverman 2012; Ramel et al. 2013). Given that our findings were largely consistent across both BMI and body fat metrics, we believe that a complete reporting of both measurements is worthwhile while the field resolves these assessment methodologies. "
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