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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    • "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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    ABSTRACT: Findings from studies examining interactions between fat taste and dietary fat intake or body weight are mixed. A convenience sample of 735 visitors to the Denver Museum of Nature & Science ≥8 years old rated the taste intensity of edible taste strips impregnated with varying concentrations (%v/v) of linoleic acid (LA) (blank = 0.0, low = 0.06, medium = 0.15, high = 0.38). Percent body fat (BF%) was measured using bioelectrical impedance. Fat taste intensity was rated as significantly different across all concentrations (P < 0.001) except between the blank and low concentrations (P = 0.1). Ratings increased monotonically across concentrations. Children (<18 years; N = 180) rated all concentrations as more intense than adults (P < 0.001 for all). Women and girls rated the highest concentration as more intense than men and boys (P < 0.02 for all). BF% was not correlated with fat taste intensity ratings. Self-reported dietary intake indicated that obese individuals' intensity ratings for medium and high concentrations of LA were inversely related to recent mono- and poly-unsaturated fat exposure (r = -0.19 to -0.27; P < 0.03 for all). No such associations were observed in the nonobese group. Findings suggest that factors other than simple adiposity status influence fat taste intensity ratings, and that participants in fat taste studies should receive standardized meals prior to testing. © The Author 2015. Published by Oxford University Press.
    Chemical Senses 08/2015; DOI:10.1093/chemse/bjv040 · 3.16 Impact Factor
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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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    ABSTRACT: The prevalence of adolescent obesity has increased dramatically over the past 3 decades, and research has documented that the number of television shows viewed during childhood is associated with greater risk for obesity. In particular, considerable evidence suggests that exposure to food marketing promotes eating habits that contribute to obesity. The present study examines neural responses to dynamic food commercials in overweight and healthy-weight adolescents using functional magnetic resonance imaging (fMRI). Compared with non-food commercials, food commercials more strongly engaged regions involved in attention and saliency detection (occipital lobe, precuneus, superior temporal gyri, and right insula) and in processing rewards [left and right nucleus accumbens (NAcc) and left orbitofrontal cortex (OFC)]. Activity in the left OFC and right insula further correlated with subjects' percent body fat at the time of the scan. Interestingly, this reward-related activity to food commercials was accompanied by the additional recruitment of mouth-specific somatosensory-motor cortices-a finding that suggests the intriguing possibility that higher-adiposity adolescents mentally simulate eating behaviors and offers a potential neural mechanism for the formation and reinforcement of unhealthy eating habits that may hamper an individual's ability lose weight later in life. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
    Cerebral Cortex 05/2015; DOI:10.1093/cercor/bhv097 · 8.67 Impact Factor
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    • "In fact, BMI is correlated with fat mass. However, it does not actually disentangle body composition, as it is not able to distinguish fat from fat-free mass (Barbieri et al., 2012; Kyle et al., 2003; Shah and Braverman, 2012; Zaccagni et al., 2014). Furthermore , the percentage of fat mass differs among populations (Norgan, 1994). "
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    American Journal of Human Biology 04/2015; DOI:10.1002/ajhb.22728 · 1.70 Impact Factor
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