Article

Diagnostic performance of body mass index to identify obesity as defined by body adiposity: A systematic review and meta-analysis

University of Missouri School of Medicine, Columbia, MO, USA.
International journal of obesity (2005) (Impact Factor: 5). 05/2010; 34(5):791-9. DOI: 10.1038/ijo.2010.5
Source: PubMed

ABSTRACT

We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity.
Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity.
The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43-0.57) and a pooled specificity of 0.90 (CI: 0.86-0.94). Positive LR was 5.88 (CI: 4.24-8.15), I (2)=97.8%; the negative LR was 0.43 (CI: 0.37-0.50), I (2)=98.5%; and the DOR was 17.91 (CI: 12.56-25.53), I (2)=91.7%. Analysis of studies that used BMI cutoffs >or=30 had a pooled sensitivity of 0.42 (CI: 0.31-0.43) and a pooled specificity of 0.97 (CI: 0.96-0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates.
Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.

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    • "In addition to BMI as compared to the percentage of BF, prevalence of obesity estimated with CUN-BAE (61.8 %) was much higher than with BMI (21.35 %). This coincided with other publications in which the prevalences of obesity estimated through the percentage of BF were almost double those yielded by BMI[4,22]or even up to six times higher[23]. Diabetes and AHT are common ailments clearly related to obesity as a risk factor, which is why we studied their association with the two ways of assessing body fat, to find that estimates of BF according to CUN-BAE were more clearly related to AHT and DM than results from BMI, just as was noted byDervaux et al.in the assessment of body fat percentages[24]. "
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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.
    Full-text · Article · Jan 2016 · BMC Public Health
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    • "" Normal Fat " group – normal BMI (<25 kg/m 2 ), normal BF% (≥22%,<30%); " Hidden Fat " profile group – normal BMI (<25 kg/m 2 ), high BF% (≥30%); " Apparent Fat " profile group – high BMI (≥25 kg/m 2 ), high BF% (≥30%) (Oliveros et al. 2014; Okorodudu et al. 2010; "

    Full-text · Article · Jan 2015 · SpringerPlus
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    • "The subject's legs were parted, and the arms were adducted by approximately 30° to prevent skin-to-skin contact. The cut-off used to define obesity was ≥35% BF, which is the most frequently used value reported in the literature.23,24,25,26 "
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    ABSTRACT: Purpose Obesity is a major public health issue and is associated with many metabolic abnormalities. Consequently, the assessment of obesity is very important. A new measurement, the body adiposity index (BAI), has recently been proposed to provide valid estimates of body fat percentages. The objective of this study was to compare the BAI and body mass index (BMI) as measurements of body adiposity and metabolic risk. Materials and Methods This was a cross-sectional analysis performed on Korean women. The weight, height, and hip circumferences of 2950 women (mean age 25±5 years old, 18-39 years) were measured, and their BMI and BAI [hip circumference (cm)/height (m)1.5-18] values were calculated. Bioelectric impedance analysis was used to evaluate body fat content. Glucose tolerance status was assessed with a 75-g oral glucose tolerance test, and insulin sensitivity was estimated with the insulin sensitivity index. Results BMI was more significantly correlated with fat mass and fat percentage. Additionally, BMI was also more significantly associated with metabolic parameters, including fasting glucose, post-load 2-h glucose, fasting insulin, post-load 2-h insulin, triglycerides, and high density lipoprotein cholesterol than BAI. Receiver operating characteristic curve analysis revealed that BMI was a better tool for predicting body fat percentage than BAI. Insulin sensitivity and metabolic syndrome were more significantly associated with BMI than with BAI. Conclusion In Korean women, the current BMI-based classifications for obesity might be superior to BAI-based measurements for determining obesity and predicting metabolic risk.
    Full-text · Article · Jul 2014 · Yonsei Medical Journal
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