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.
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%.
We performed a cross-sectional study.
A total of 6123 (924 lean, 1637 overweight and 3562 obese classified according to BMI) Caucasian subjects (69% females), aged 18–80 years.
BMI, BF% determined by air displacement plethysmography and well-established blood markers of insulin sensitivity, lipid profile and cardiovascular risk were measured.
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−1) as well as in obese BMI-classified individuals (men 4.2±5.5, women 5.1±13.2 mg l−1) compared with lean volunteers with normal body fat amounts (men 0.9±0.5, women 2.1±2.6 mg l−1; P<0.001 for both genders).
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.
The prevalence of obesity is increasing dramatically worldwide.1 Obesity is defined medically as a state of increased adipose tissue of sufficient magnitude to produce adverse health consequences and is associated with increased morbidity and mortality.1 Surprisingly, obesity is a chronic disease that is often neglected and frequently not even thought of as a serious, life-threatening condition.2, 3
Body mass index (BMI) is the most frequently used diagnostic tool in the current classification system of obesity. It has the advantage that a subject's height and weight are easy and inexpensive to measure. The World Health Organization (WHO), the US Preventive Services Task Force and the International Obesity Task Force define overweight as a BMI between 25.0 and 29.9 kg m−2 and obesity as a BMI⩾30.0 kg m−2.4, 5, 6, 7 These cutoffs are very useful in epidemiological studies8 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 diabetes23, 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.18, 25, 26 Other anthropometric measures such as the waist circumference or the waist-to-hip ratio have been shown to better estimate the obesity-associated cardiovascular risk than BMI.27, 28 However, epidemiological studies analyzing the impact of the body fat percentage (BF%) on the levels of cardiometabolic risk factors are scarce.29, 30, 31 It has been suggested that BF% is a better indicator of coronary heart disease risk than waist circumference.32 Furthermore, BF% has been associated with all-cause mortality.33, 34, 35, 36 Therefore, it may be crucial to discriminate the clinical usefulness of measuring BF% to estimate the obesity-associated cardiometabolic risk.
Body composition analysis for determining BF% can be estimated by different techniques encompassing from skin-fold measurement to magnetic resonance imaging.37, 38 Other frequently used methods for measuring body composition include bioelectrical impedance analysis and dual-energy X-ray absorptiometry. More accurate methods include underwater weighing and air displacement plethysmography (ADP). ADP is a validated and reproducible alternative to the gold standard hydrodensitometry.39 It uses the pressure–volume relationship to estimate volume and density and has been shown to predict fat mass and fat-free mass more accurately than dual-energy X-ray absorptiometry and bioelectrical impedance analysis.40 Body fat measured by ADP reportedly correlates better with cardiovascular risk factors than other anthropometric measures, but these findings, although relevant, have been obtained only in relatively small sample size studies.41, 42
Therefore, we conducted a cross-sectional study with two aims: first, to estimate the degree of misclassification regarding the diagnosis of overweight and obesity by using BMI as compared with the direct determination of BF%. Second, to evaluate whether the cardiometabolic risk in non-obese individuals determined by BMI but classified as obese by BF% cutoff points is different to that of BMI-defined obese subjects with a similar BF%, thereby adding relevant information applicable for diagnostic and therapeutic decision-making.
Patients and methods
We conducted a cross-sectional analysis of 6123 Caucasian subjects (4208 females/1915 males), aged 18–80 years including patients visiting the Departments of Endocrinology and Surgery of the University Clinic of Navarra for weight loss treatment as well as hospital and University staff undergoing an annual routine health check-up between January 2005 and December 2009. All patients were weight-stable for the previous 3 months. A subset of 3051 subjects (2213 females/838 males) was included in the analysis designed to compare the cardiovascular and metabolic risk between non-obese and obese individuals classified by BMI with a similar body fat content. The study included as the reference group the subjects classified as lean by both BMI and BF% (560 women and 96 men), and compared it to those classified as non-obese by BMI (<30.0 kg m−2) but obese by BF% (1208 women and 371 men) as well as to a BF%-matched group of individuals classified as obese by both BMI and BF% (445 women and 371 men). Presence of metabolic syndrome was determined following already described criteria.43 The experimental design was approved, from an ethical and scientific standpoint, by the Hospital's Ethical Committee responsible for research and informed consent was obtained from all subjects.
The anthropometric and body composition determinations as well as the blood extraction were performed on a single day. Height was measured to the nearest 0.1 cm with a Holtain stadiometer (Holtain Ltd, Crymych, UK), whereas body weight was measured with the ADP calibrated electronic scale to the nearest 0.1 kg with subjects wearing a swimming suit and cap. BMI was calculated as weight in kg divided by the square of height in meters. Waist circumference was measured at the midpoint between the iliac crest and the rib cage on the midaxillary line. Hip circumference was measured at the point yielding the maximum circumference over the buttocks to the nearest 1 cm. Blood pressure was measured after a 15-min rest in the semi-sitting position with a sphygmomanometer. Blood pressure was determined at least three times at the right upper arm and the mean was used in the analyses.
Body density was estimated by ADP (Bod-Pod, Life Measurements, Concord, CA, USA). Data for estimation of body fat by this plethysmographic method have been reported to agree closely with the traditional gold standard hydrodensitometry underwater weighing.39, 40 Percentage of body fat was estimated from body density using the Siri equation.44 Cutoff points for BF% used for defining overweight (20.1–24.9% for men and 30.1–34.9% for women) and obesity (⩾25.0% for men and ⩾35.0% for women) are those most frequently used in the literature.15, 21, 42, 45, 46, 47, 48
Blood samples were collected after an overnight fast in the morning to avoid potential confounding influences due to hormonal rhythmicity. Plasma glucose and insulin was analyzed as previously described.49, 50 An indirect measure of insulin resistance was calculated by using the homeostatic model assessment.51 Total cholesterol and triglyceride concentrations were determined by enzymatic spectrophotometric methods (Roche, Basel, Switzerland). High-density lipoprotein (HDL-cholesterol) was quantified by a colorimetric method in a Beckman Synchron CX analyzer (Beckman Instruments, Ltd, Bucks, UK). Low-density lipoprotein (low-density lipoprotein-cholesterol) was calculated by the Friedewald formula.
Uric acid and alanine aminotransferase (ALT) were measured by enzymatic tests (Roche) in an automated analyzer (Roche/Hitachi Modular P800). Fibrinogen concentrations were determined according to the method of Clauss using a commercially available kit (Hemoliance, Instrumentation Laboratory, Barcelona, Spain). Measurement of von Willebrand factor (vWF) antigen was performed by a micro-latex immunoassay (Diagnostica Stago, Inc., Parsippany, NJ, USA). A standard curve was prepared with a universal reference (NISBC 91/666) and the results were expressed as percentage of the standard. Intra-and inter-assay coefficients of variation were 4.0% and 8.0%, respectively. High-sensitivity C-reactive protein (CRP) was measured using the Tina-quant CRP (Latex) ultrasensitive assay (Roche). Leptin was quantified by a double-antibody RIA method (Linco Research, Inc., St Charles, MO, USA); intra-and inter-assay coefficients of variation were 5.0% and 4.5%, respectively.
Data are presented as mean±s.d. Differences between groups were analyzed by analysis of variance followed by Scheffé's tests or analysis of covariance. CRP and leptin concentrations were logarithmically transformed, because of their non-normal distribution. The distribution of other variables was adequate for the application of parametric tests. Correlations between two variables were computed by Spearman (ρ) correlation coefficient. Analyses of receiver operating characteristic curves were used to evaluate the overall performance of BMI to detect obesity defined by BF%. Sensitivity and specificity were calculated and used to identify the best gender-specific BMI cutoffs for detecting BF%-defined obesity. General linear model analysis was used to create a predictive equation for BF% (dependent variable) including BMI, sex, age and their interactions as the independent variables. The calculations were performed using the SPSS version 15.0.1 (SPSS, Chicago, IL, USA). A P-value lower than 0.05 was considered statistically significant.
In the sample as a whole, BMI and BF% showed a strong correlation (ρ=0.71; P<0.0001), which was stronger after stratification by gender; (ρ=0.78; P<0.0001 for males; ρ=0.87; P<0.0001 for females) (Figure 1). As expected women exhibited a higher BF% for a given BMI than men. To identify a predictive equation of BF% a stepwise linear regression using BF% determined by ADP as the outcome variable and sex, age, BMI, BMI square and the interactions between the linear BMI and quadratic BMI with age and gender was performed. The equation that better predicts BF% is as follows:
BF%=–44.988+(0.503 × age)+(10.689 × gender)+(3.172 × BMI)–(0.026 × BMI2)+(0.181 × BMI × gender)–(0.02 × BMI × age)–(0.005 × BMI2 × gender)+(0.00021 × BMI2 × age)
wherein male=0 and female=1 for gender. The equation includes both a linear and a quadratic BMI component. This model explains 79% of the variability in BF% with a standard error of the estimate of 4.7%. The correlation coefficient of measured versus predicted BF% values with the equation was ρ=0.88 (P<0.0001).
Our study included 924 lean (15%), 1637 overweight (27%) and 3562 (58%) obese subjects classified according to BMI. Figure 2 shows the classification of the subjects as lean, overweight or obese according to either BMI or BF%. Striking differences were observed depending on the criterion used. According to the actual BF% values, 29% of the lean individuals following the established BMI criteria were obese given their BF%, whereas only 0.2% considered obese by BMI was in reality lean according to BF%. Furthermore, 80% of the BMI overweight-classified individuals were actually obese considering their BF%. This misclassification was higher for women (30% obese by BF% classified as lean by BMI and 84% obese by BF% classified as overweight according to BMI) than for men (25% obese by BF% classified as lean by BMI and 71% obese by BF% classified as overweight according to BMI). The misclassification becomes also evident in the scatter-plots showing BMI vs BF% with the cutoff points marked by reference lines for women and men (Figure 1).
Receiver operating characteristic curves were elaborated (Supplementary Figure 1) to analyze the performance of BMI to detect obesity according to BF% (⩾25.0% in men and ⩾35.0% in women). The recommended BMI cutoff of ⩾30.0 kg m−2 has an excellent specificity (89% in men and 98% in women) but has a poor sensitivity (77% in men and 65% in women). In men, the area under the curve was 0.91, with the best BMI cutoff to detect obesity according to BF% being 29.0 kg m−2, which resulted in a sensitivity (true-positive rate) of 84% and a specificity (true-negative rate) of 85%. In women, the area under the curve was 0.94 and the best BMI cutoff identified was 26.8 kg m−2, which resulted in a sensitivity of 85% and a specificity of 88%.
To evaluate the impact of the observed misclassification on the potential underestimation of the obesity-associated cardiometabolic risk, several risk factors were compared between subjects with a similar BF% within the obesity range but classified either as non-obese (NOOB) or obese (OBOB) by BMI criteria (the subjects included can be observed in Figure 3). According to these matching criteria, although men from the OBOB group exhibited a BMI within the obesity range and those of the NOOB group were classified either as lean or overweight by BMI criteria they both had similar BF% (BF% ∼31%; P=0.269), which were well within the obesity range (Table 1). Both groups exhibited higher waist and hip circumferences and waist-to-hip ratio (P<0.001 for all) as well as systolic blood pressure (SBP) (P<0.001) and diastolic blood pressure (DBP) (P<0.001) than the reference lean group, being higher in the OBOB group (P<0.001, P<0.01 and P<0.05 for waist, hip, waist-to-hip ratio, SBP and DBP, respectively) as compared to NOOB. Although SBP and DBP were significantly higher in NOOB and OBOB groups, they remained within the normal range. Plasma glucose levels were significantly higher in the NOOB group as compared with leans (P<0.05), exhibiting a tendency towards significance in the OBOB group (P=0.081). Insulin concentrations were elevated in both obese groups (P<0.05, and P<0.001 for NOOB and OBOB as compared with leans, respectively), being higher in the OBOB (P<0.01, as compared with NOOB), with only this group exhibiting statistically significant higher homeostatic model assessment values (P<0.01 and P=0.050 vs lean and NOOB, respectively). The lipid profile was overall similarly higher in both the NOOB and OBOB obese groups as compared with the lean volunteers with the exception of total cholesterol that was significantly increased only in the NOOB subjects (P<0.05) and HDL cholesterol levels that were decreased in both obese groups (P<0.001) and further reduced in the OBOB (P<0.001). Interestingly, the markers of inflammation, fibrinogen and CRP, were significantly higher in both obese groups (P<0.05 and P<0.001, respectively) showing no differences between them. In women, as a general tendency almost all the variables analyzed were significantly higher in both obese groups (BF% ∼41%; P=0.104), being higher in the OBOB as compared with the NOOB group. Analogously to what was observed in men, CRP concentrations were higher in the female NOOB and OBOB groups as compared with leans (P<0.001) showing no differences between them, thereby indicating a low-grade chronic inflammation in both obese (NOOB and OBOB) groups regardless of gender. As subjects in the OBOB groups from both genders exhibited a higher waist than those from the NOOB groups, we performed a reanalysis between these two groups adjusting for waist circumference and age. Many of the differences between NOOB and OBOB groups were attributable to waist circumference as DBP, insulin concentrations, uric acid levels and leptin concentrations in men and SBP, DBP, triglyceride levels, low-density lipoprotein, uric acid concentrations and ALT in women showed no significant differences between NOOB and OBOB after adjusting for waist and age.
In addition, we also analyzed the percentage of individuals with normal waist circumference (below 102 and 88 cm in men and women, respectively) with increased cardiometabolic risk factors finding that 5.1, 2.8, 3.8, 4.0 and 11.2% of subjects had elevated fasting plasma glucose (⩾100 mg dl−1), triglycerides (⩾150 mg dl−1), CRP (>3 mg l−1) concentrations, SBP (⩾140 mm Hg) and reduced HDL cholesterol concentrations (<40 mg dl−1 in men and <50 mg dl−1 in women), respectively. When the analysis included subjects with BMI below 30 kg m−2, we found that 7.0, 4.3, 5.2, 7.0 and 14.6% of subjects exhibited elevated fasting plasma glucose, triglycerides, CRP concentrations, SBP and reduced HDL cholesterol concentrations, respectively. However, when the same analysis was performed in individuals with a BF% below the cutoff considered as obesity (below 25.0 and 35.0% in men and women, respectively) only 1.7, 0.8, 1.6, 1.4, and 5.4% of subjects had elevated fasting plasma glucose, triglycerides, CRP concentrations, SBP and reduced HDL cholesterol concentrations, respectively (data not shown). In addition, we also analyzed the advantages of better diagnosing the metabolic syndrome in the obese population with either BF% or BMI. When the presence of the metabolic syndrome was evaluated in the obese population (according to BMI), 77% of patients with the syndrome was detected, whereas 23% of them were undiagnosed. However, when the presence of the metabolic syndrome was measured in the obese population (according to BF%), 96% of patients with the syndrome was diagnosed.
Obesity represents a body fat excess, with the amount of this excess correlating with comorbidity development.17, 22 We found that 29% of subjects classified as lean according to BMI, that is, with a BMI<25.0 kg m−2, and 80% of subjects classified as overweight according to actual BMI, that is, with a BMI⩾25.0 kg m−2 and <30.0 kg m−2, had a BF% well within the obesity range. However, only 0.2 and 1.0% of the subjects with a BF% in the lean or overweight range, respectively, was misclassified as obese according to the BMI value. This datum shows that BMI, although being an extremely valuable tool for epidemiological studies, underestimates BF% especially in the overweight category.3, 17 Thus, our study clearly indicates that there is a high degree of misclassification in the diagnosis of obesity in clinical practice, which results in the underdiagnosis of patients at risk and, therefore, missed opportunities to treat this life-threatening condition. The information derived from receiver operation characteristics analyses suggests that the actual cutoff points that more accurately diagnose obesity are 29.0 kg m−2 in men and 27.0 kg m−2 in women.
In spite of the fact that BF% and obesity increase the risk of cardiovascular diseases and type 2 diabetes,23, 24 large epidemiological studies analyzing the relation between BF% and circulating concentrations of cardiometabolic risk markers are scarce. For the first time, our study provides evidence in a large sample that the levels of cardiometabolic risk factors are similarly higher in lean or overweight BMI-classified subjects with body fat percentages within the obesity range than in obese BMI-classified individuals with matched BF%. Our data show that non-obese subjects by the BMI criterion but obese by BF% exhibit higher blood pressure, glucose, insulin, triglycerides, low-density lipoprotein, fibrinogen and CRP concentrations (see Table 1), with many of them above the cutoff points established for predicting cardiovascular or metabolic risk. For instance, this group of non-obese individuals as defined by BMI but obese according to BF% already exhibit mean fasting glucose levels above 5.6 mmol l−1, which is currently considered as IGT, a homeostatic model assessment index over 2.61, which is the cutoff point indicating insulin resistance,47, 51 and CRP plasma concentrations above 3.0 mg l−1, which is the lower limit of the higher risk range.52 Furthermore, our data provide scientific explanation to the finding that body composition may help to understand the cardiovascular risk in ‘normal-weight’ subjects with high adiposity.29, 53, 54
The few studies analyzing the influence of BF% on cardiometabolic risk factor levels have been predominantly aimed to compare the influence of body fat distribution than to analyze the effect of increased adiposity itself32, 47, 55, 56 or have been performed in volunteers with BMI values within the lean range.20 In this sense, BF% shows lower,56 similar,47 or higher57 correlation with metabolic risk factors than central adiposity measures, but has been recently suggested as a better indicator of coronary heart disease risk than waist circumference.32 The present study emphasizes that anthropometric indicators of body fat distribution such as waist circumference or waist-to-hip ratio are important when evaluating the risk of a subject regardless of its body weight. However, our data further indicate that the actual body fat amount is also having a key role in the increased cardiometabolic risk. In this sense, the use of BF% cutoff points to diagnose obesity allows to detect more individuals with an increased cardiometabolic risk than simple application of the waist circumference classification criteria. In addition, our data suggest that with the new proposed cutoff points a lower number of patients with the metabolic syndrome would be undiagnosed.
In the same way as a progressive decrease in the cutoff points for the diagnosis of hypertension or circulating cholesterol concentrations has been observed in past years, our findings highlight that a reappraisal of the cutoffs to diagnose overweight and obesity may be considered. This is particularly important based on the pathophysiological implications that increased adiposity has in the context of normal- or over-weight. Although BMI is widely used as a proxy measure of body fat, it does not provide information on body composition as evidenced herein. The present study provides evidence for the existence of a similar adverse cardiometabolic risk factor profile already in subjects with a BMI<30.0 kg m−2 but highly increased adiposity compared with obese patients.
One potential limitation of our study pertains to the generalizability to other populations. This study was conducted in Caucasian subjects and would need to be extended to other populations to determine race-specific differences, as regarding BF% ranges for a given BMI and if the significant correlations to the cardiometabolic risk factors are (i) maintained, and (ii) if so, establish to which degree.9, 10, 58 Another potential limitation of the present study is that smoking and medication were generally not available. Accordingly, we could not adjust for the potential confounding effects of these variables. However, we consider that the relatively large sample size may compensate this limitation.
In summary, the use of BMI for obesity diagnosis in clinical practice underestimates the true prevalence of this life-threatening condition given its association with the elevation of well-recognized cardiometabolic risk factors. Our data indicate that a relevant number of obese patients at risk are being underdiagnosed, and, therefore, opportunities for adequate treatment instauration and comorbidity assessment are being missed. The actual cutoff points that more accurately diagnose obesity are 29.0 kg m−2 in men and 27.0 kg m−2 in women. In this sense, 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.
Haslam D, James WPT . Obesity. Lancet 2005; 366: 1197–1209.
Frühbeck G, Diez-Caballero A, Gómez-Ambrosi J, Cienfuegos JA, Salvador J . Preventing obesity. Doctors underestimate obesity. BMJ 2003; 326: 102–103.
Frühbeck G . Screening and interventions for obesity in adults. Ann Intern Med 2004; 141: 245–246.
World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995; 854: 1–452.
World Health Organization. Obesity. Preventing and managing the global epidemic. Report of a WHO consultation on obesity. WHO/NUT/NCD/981. WHO: Geneva, 1998.
US Preventive Services Task Force. Screening for obesity in adults: recommendations and rationale. Ann Intern Med 2003; 139: 930–932.
International Obesity Task Force. 2009. Available: http://www.iotf.org.
Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr 2009; 89: 500–508.
Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC et al. The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes Relat Metab Disord 2002; 26: 789–796.
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163.
Goh VH, Tain CF, Tong TY, Mok HP, Wong MT . Are BMI and other anthropometric measures appropriate as indices for obesity? A study in an Asian population. J Lipid Res 2004; 45: 1892–1898.
Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y . Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694–701.
Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D . Limits of body mass index to detect obesity and predict body composition. Nutrition 2001; 17: 26–30.
Kyle UG, Schutz Y, Dupertuis YM, Pichard C . Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition 2003; 19: 597–604.
Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes 2008; 32: 959–966.
Rothman KJ . BMI-related errors in the measurement of obesity. Int J Obes 2008; 32 (Suppl 3): S56–S59.
Prentice AM, Jebb SA . Beyond body mass index. Obes Rev 2001; 2: 141–147.
Sharma AM, Kushner RF . A proposed clinical staging system for obesity. Int J Obes 2009; 33: 289–295.
Pou KM, Massaro JM, Hoffmann U, Lieb K, Vasan RS, O′Donnell CJ et al. Patterns of abdominal fat distribution: the Framingham Heart Study. Diabetes Care 2009; 32: 481–485.
Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J 2010; 31: 737–746.
Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes 2010; 34: 791–799.
Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 2006; 113: 898–918.
Van Gaal LF, Mertens IL, De Block CE . Mechanisms linking obesity with cardiovascular disease. Nature 2006; 444: 875–880.
Kahn SE, Hull RL, Utzschneider KM . Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006; 444: 840–846.
Sullivan PW, Ghushchyan V, Wyatt HR, Wu EQ, Hill JO . Impact of cardiometabolic risk factor clusters on health-related quality of life in the US. Obesity 2007; 15: 511–521.
Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med 2008; 359: 2105–2120.
Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005; 366: 1640–1649.
Balkau B, Deanfield JE, Després JP, Bassand JP, Fox KAA, Smith JS et al. International Day for the Evaluation of Abdominal obesity (IDEA). A study of waist circumference, cardiovascular disease, and diabetes mellitus in 168 000 primary care patients in 63 countries. Circulation 2007; 116: 1942–1951.
Segal KR, Dunaif A, Gutin B, Albu J, Nyman A, Pi-Sunyer FX . Body composition, not body weight, is related to cardiovascular disease risk factors and sex hormone levels in men. J Clin Invest 1987; 80: 1050–1055.
Gómez-Ambrosi J, Salvador J, Páramo JA, Orbe J, de Irala J, Diez-Caballero A et al. Involvement of leptin in the association between percentage of body fat and cardiovascular risk factors. Clin Biochem 2002; 35: 315–320.
Catalán V, Gómez-Ambrosi J, Ramírez B, Rotellar F, Pastor C, Silva C et al. Proinflammatory cytokines in obesity: impact of type 2 diabetes mellitus and gastric bypass. Obes Surg 2007; 17: 1464–1474.
Dervaux N, Wubuli M, Megnien JL, Chironi G, Simon A . Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Atherosclerosis 2008; 201: 413–417.
Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B . Mortality associated with body fat, fat-free mass and body mass index among 60-year-old Swedish men-a 22-year follow-up. The study of men born in 1913. Int J Obes Relat Metab Disord 2000; 24: 33–37.
Lahmann PH, Lissner L, Gullberg B, Berglund G . A prospective study of adiposity and all-cause mortality: the Malmö Diet and Cancer Study. Obes Res 2002; 10: 361–369.
Bigaard J, Frederiksen K, Tjonneland A, Thomsen BL, Overvad K, Heitmann BL et al. Body fat and fat-free mass and all-cause mortality. Obes Res 2004; 12: 1042–1049.
Flegal KM, Graubard BI . Estimates of excess deaths associated with body mass index and other anthropometric variables. Am J Clin Nutr 2009; 89: 1213–1219.
Pietrobelli A, Heymsfield SB . Establishing body composition in obesity. J Endocrinol Invest 2002; 25: 884–892.
Das SK . Body composition measurement in severe obesity. Curr Opin Clin Nutr Metab Care 2005; 8: 602–606.
Fields DA, Goran MI, McCrory MA . Body-composition assessment via air-displacement plethysmography in adults and children: A review. Am J Clin Nutr 2002; 75: 453–467.
Ginde SR, Geliebter A, Rubiano F, Silva AM, Wang J, Heshka S et al. Air displacement plethysmography: validation in overweight and obese subjects. Obes Res 2005; 13: 1232–1237.
Gómez-Ambrosi J, Salvador J, Silva C, Pastor C, Rotellar F, Gil MJ et al. Increased cardiovascular risk markers in obesity are associated with body adiposity: Role of leptin. Thromb Haemost 2006; 95: 991–996.
Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ, Squires RW et al. Diagnostic performance of body mass index to detect obesity in patients with coronary artery disease. Eur Heart J 2007; 28: 2087–2093.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and international association for the Study of Obesity. Circulation 2009; 120: 1640–1645.
Siri WE . Body composition from fluid spaces and density: analysis of methods. In: Brozek J, Henschel A (eds). Techniques for Measuring Body Composition. National Academy of Sciences, National Research Council: Washington, DC, USA, 1961, pp 223–243.
Deurenberg P, Andreoli A, Borg P, Kukkonen-Harjula K, de Lorenzo A, van Marken Lichtenbelt WD et al. The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations. Eur J Clin Nutr 2001; 55: 973–979.
De Lorenzo A, Deurenberg P, Pietrantuono M, Di Daniele N, Cervelli V, Andreoli A . How fat is obese? Acta Diabetol 2003; 40 (Suppl 1): S254–S257.
Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezenmeir J et al. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes 2006; 30: 475–483.
Wellens RI, Roche AF, Khamis HJ, Jackson AS, Pollock ML, Siervogel RM . Relationships between the body mass index and body composition. Obes Res 1996; 4: 35–44.
Gómez-Ambrosi J, Salvador J, Rotellar F, Silva C, Catalán V, Rodríguez A et al. Increased serum amyloid A concentrations in morbid obesity decrease after gastric bypass. Obes Surg 2006; 16: 262–269.
Gómez-Ambrosi J, Catalán V, Ramírez B, Rodríguez A, Colina I, Silva C et al. Plasma osteopontin levels and expression in adipose tissue are increased in obesity. J Clin Endocrinol Metab 2007; 92: 3719–3727.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC . Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412–419.
Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon III RO, Criqui M et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 2003; 107: 499–511.
De Lorenzo A, Del Gobbo V, Premrov MG, Bigioni M, Galvano F, Di Renzo L . Normal-weight obese syndrome: early inflammation? Am J Clin Nutr 2007; 85: 40–45.
Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med 2008; 168: 1617–1624.
Thomas GN, Ho SY, Lam KS, Janus ED, Hedley AJ, Lam TH . Impact of obesity and body fat distribution on cardiovascular risk factors in Hong Kong Chinese. Obes Res 2004; 12: 1805–1813.
Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity 2006; 14: 727–736.
Rattarasarn C, Leelawattana R, Soonthornpun S, Setasuban W, Thamprasit A, Lim A et al. Relationships of body fat distribution, insulin sensitivity and cardiovascular risk factors in lean, healthy non-diabetic Thai men and women. Diabetes Res Clin Pract 2003; 60: 87–94.
Deurenberg P, Yap M, van Staveren WA . Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 1998; 22: 1164–1171.
This study was supported by grants from the ISCIII (FIS PI061458, PS09/02330 and PI09/91029) and the Departments of Health (20/2005 and 31/2009) and Education of the Gobierno de Navarra. CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERobn) is an initiative of the ISCIII, Spain.
Trial registration: ClinicalTrials.gov NCT01055626.
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on International Journal of Obesity website
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Gómez-Ambrosi, J., Silva, C., Galofré, J. et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes 36, 286–294 (2012). https://doi.org/10.1038/ijo.2011.100
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