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

Okorodudu, Dale O.; Jumean, Marwan; Montori, Víctor M.; Romero‐Corral, Abel; Somers, Virend K.; Erwin, Patricia J.; López-Jiménez, Francisco

doi:10.1038/ijo.2010.5

Cited by 949 publications

(760 citation statements)

References 40 publications

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“…44 Cutoff points for BF% used for defining overweight (20.1-24.9% for men and 30.1-34.9% for women) and obesity (X25.0% for men and X35.0% for women) are those most frequently used in the literature. 15,21,42,[45][46][47][48] hormonal rhythmicity. Plasma glucose and insulin was analyzed as previously described.…”

Section: Body Compositionmentioning

confidence: 99%

“…[4][5][6][7] These cutoffs are very useful in epidemiological studies 8 but in spite of its wide use BMI is only a surrogate measure of body fatness and does not provide an accurate measure of body composition. [8][9][10][11][12][13][14][15][16][17][18][19][20][21] Noteworthy, obesity is defined as an excess accumulation of body fat, with the amount of this excess fat actually being responsible for most obesity-associated health risks. 22 Obesity increases the risk of cardiovascular diseases and type 2 diabetes 23,24 at the same time as imposing functional limitations in a number of subjects, which translate into a reduced quality of life as well as life expectancy.…”

Section: Introductionmentioning

confidence: 99%

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Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity

et al. 2011

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Context: Body mass index (BMI) is widely used as a measure of overweight and obesity, but underestimates the prevalence of both conditions, defined as an excess of body fat. Objective: We assessed the degree of misclassification on the diagnosis of obesity using BMI as compared with direct body fat percentage (BF%) determination and compared the cardiovascular and metabolic risk of non-obese and obese BMI-classified subjects with similar BF%. Design: We performed a cross-sectional study. Subjects: A total of 6123 (924 lean, 1637 overweight and 3562 obese classified according to BMI) Caucasian subjects (69% females), aged 18-80 years. Methods: BMI, BF% determined by air displacement plethysmography and well-established blood markers of insulin sensitivity, lipid profile and cardiovascular risk were measured. Results: We found that 29% of subjects classified as lean and 80% of individuals classified as overweight according to BMI had a BF% within the obesity range. Importantly, the levels of cardiometabolic risk factors, such as C-reactive protein, were higher in lean and overweight BMI-classified subjects with BF% within the obesity range (men 4.3 ± 9.2, women 4.9 ± 19.5 mg l À1 ) as well as in obese BMI-classified individuals (men 4.2 ± 5.5, women 5.1 ± 13.2 mg l À1 ) compared with lean volunteers with normal body fat amounts (men 0.9 ± 0.5, women 2.1 ± 2.6 mg l À1 ; Po0.001 for both genders). Conclusion: Given the elevated concentrations of cardiometabolic risk factors reported herein in non-obese individuals according to BMI but obese based on body fat, the inclusion of body composition measurements together with morbidity evaluation in the routine medical practice both for the diagnosis and the decision-making for instauration of the most appropriate treatment of obesity is desirable.

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Section: Body Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity

et al. 2011

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“…However, BMI does not reflect fat distribution or the proportion of adipose to non-adipose tissue. 13,14 The significance of fat distribution is obvious, with central abdominal obesity (adipose tissue around the trunk) increasing the risk of cardiovascular disease, hypertension and diabetes, [15][16][17][18] whereas peripheral adiposity (adipose tissue around the bottom and thighs) appears to be protective. 17,19 Although impractical for pregnancy, central adiposity can be reliably assessed with computed tomography, magnetic resonance imaging or body densitometry.…”

Section: Introductionmentioning

confidence: 99%

Maternal abdominal subcutaneous fat thickness as a predictor for adverse pregnancy outcome: a longitudinal cohort study

Kennedy

Peek

et al. 2015

BJOG

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Objective To assess maternal abdominal subcutaneous fat thickness (SFT) measured by ultrasound as an independent predictor of adverse pregnancy outcomes.Design A prospective longitudinal cohort study performed on pregnancies delivered between 2012 and 2014.Setting Sydney, Australia.Population About 1510 pregnant women attending routine obstetric ultrasounds.Methods Maternal SFT was measured on routine ultrasounds at 11-14 weeks' gestation (SFT1) and 18-22 weeks' gestation (SFT2). SFT measurements were assessed for estimating risks for obesityrelated pregnancy outcomes using logistic regression modelling adjusted for maternal age, parity, smoking status and body mass index (BMI).Main outcome measures Hypertensive disease, gestational diabetes, caesarean section, low birthweight, preterm delivery, neonatal respiratory distress, Apgar scores, and admission to a neonatal intensive care unit.Results SFT1 and SFT2 were measured on 1461 and 1363 women, respectively. Mean thickness (range) were 21.2 mm (6.9-73.9) for SFT1 and 20.3 mm (7.5-68.0) for SFT2. Complete outcome data were available for 1385 pregnancies. In all, 54% of the women were overweight/obese. The SFT measures decreased from early to mid-pregnancy in overweight/obese women. There was moderate correlation between BMI and SFT1 (R 2 = 0.56) and BMI and SFT2 (R 2 = 0.55). In a multivariate model, SFT1 and SFT2 were better predictors for adverse pregnancy outcomes than BMI.Conclusion Maternal SFT is a significant independent predictor of adverse pregnancy outcomes. Incorporation of SFT into future models for adverse pregnancy outcome may prove valuable.

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“…However, the data for children and adolescents are not clear and there are no established cutoff points for the pediatric population. BMI, despite its limitations on assessment of body fat distribution and the distinction between lean mass and fat mass, is an indicator of adiposity validated for the pediatric population (16). Given the controversy regarding the different methods of assessing adiposity in children and adolescents and their clinical implications, more studies are needed to identify the applicability of these parameters in this specific population.…”

Section: Association Between Adiposity Indicators Metabolic Parametementioning

confidence: 99%

Association between adiposity indicators, metabolic parameters and inflammatory markers in a sample of female adolescents

Coutinho

Leite

Lopes

et al. 2015

Arch. Endocrinol. Metab.

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Objective: The aim of this study was to investigate the association between total and abdominal adiposity with metabolic parameters and inflammatory markers, in female adolescents. Subjects and methods: The sample consisted of 53 adolescents aged 13 to 17 years from a public school in Curitiba, Brazil. The adiposity indicators studied were body mass index (BMI), waist circumference (WC), trunk fat mass (TKFM), total fat mass (TFM) and body fat percentage (BF%) measured by dual-energy X-ray absorptiometry. The metabolic and inflammatory parameters studied were systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, insulin, homeostasis model assessment index for insulin resistance (HOMA-IR), lipids, C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), leptin, adiponectin and resistin. Results: Eighty percent of WC variation, 87% of TKFM and TFM, and 73% of BF% were predicted by BMI variation. There was a significant positive correlation between all indicators of adiposity with SBP, DBP, insulin, HOMA-IR, CRP and leptin. Triglycerides were positively correlated with BMI and WC, and adiponectin correlated negatively with BMI. TNF-α, IL-6, glucose, total cholesterol, and high-and low-density lipoprotein cholesterol did not correlate to the studied variables. Conclusion: BMI showed a significant association with most of the parameters studied, and WC was slightly better than BMI to predict insulin resistance in this specific population. Arch Endocrinol Metab. 2015;59(4):325-34

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

Cited by 949 publications

References 40 publications

Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity

Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity

Maternal abdominal subcutaneous fat thickness as a predictor for adverse pregnancy outcome: a longitudinal cohort study

Association between adiposity indicators, metabolic parameters and inflammatory markers in a sample of female adolescents

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