Peer Review Articles on the Limitations of Bmi in Defining Overweight and Obesity

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Peer-reviewed

Research Article

Prevalence of Obesity and the Human relationship betwixt the Torso Mass Index and Torso Fat: Cantankerous-Exclusive, Population-Based Data

• Julie A. Pasco,
• Geoffrey C. Nicholson,
• Sharon L. Brennan,
• Mark A. Kotowicz

x

• Published: Jan xiii, 2012
• https://doi.org/10.1371/journal.pone.0029580

Figures

Abstruse

Groundwork

Anthropometric measures such as the torso mass index (BMI) and waist circumference are widely used every bit convenient indices of adiposity, yet at that place are limitations in their estimates of trunk fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the human relationship between the BMI and trunk fat.

Methodology/Principal Findings

This population-based, cross-sectional study was conducted as office of the Geelong Osteoporosis Study. A random sample of one,467 men and 1,076 women aged 20–96 years was assessed 2001–2008. Overweight and obesity were identified according to BMI (overweight 25.0–29.9 kg/mⁱⁱ; obesity ≥thirty.0 kg/yardⁱⁱ) and waist circumference (overweight men 94.0–101.ix cm; women eighty.0–87.9 cm; obesity men ≥102.0 cm, women ≥88.0 cm); body fat mass was assessed using dual free energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4–47.9) of men and xxx.two% (95%CI 27.4–33.0) of women were overweight and a further 20.2% (95%CI 18.0–22.4) of men and 28.6% (95%CI 25.8–31.3) of women were obese. Using waist circumference, 27.five% (95%CI 25.1–30.0) of men and 23.3% (95%CI 20.8–25.9) of women were overweight, and 29.3% (95%CI 26.9–31.7) of men and 44.1% (95%CI 41.2–47.1) of women, obese. Both criteria indicate that approximately sixty% of the population exceeded recommended thresholds for salubrious body habitus. In that location was no consistent pattern apparent betwixt BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose backlog weight was largely owing to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.

Conclusions/Significance

Every bit the BMI does not account for differences in torso limerick, nosotros suggest that gender- and historic period-specific thresholds should be considered when the BMI is used to point adiposity.

Commendation: Pasco JA, Nicholson GC, Brennan SL, Kotowicz MA (2012) Prevalence of Obesity and the Relationship between the Body Mass Index and Trunk Fat: Cross-Sectional, Population-Based Data. PLoS 1 vii(1): e29580. https://doi.org/10.1371/journal.pone.0029580

Editor: Guoying Wang, Johns Hopkins Bloomberg School of Public Health, U.s. of America

Received: July 15, 2011; Accepted: Nov 30, 2011; Published: January 13, 2012

Copyright: © 2012 Pasco et al. This is an open-access article distributed under the terms of the Creative Eatables Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The study was supported by grants awarded by the Victorian Health Promotion Foundation (http://world wide web.vichealth.vic.gov.au/), the National Health and Medical Research Council (NHMRC, Australia) (http://www.nhmrc.gov.au/) and Perpetual Trustees (http://www.perpetual.com.au/philanthropic-services.aspx). The funders had no role in report pattern, information collection and assay, decision to publish, or grooming of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In the nineteenth century, the mathematician and social statistician, Adolphe Quetelet, observed that the weight of the boilerplate man was proportional to the square of the summit [i]. The ratio of body weight measured in kilograms divided by the square of the pinnacle measured in metres was termed the Quetelet'south Index and later renamed the Torso Mass Index (BMI) [2]. Had Quetelet based his studies on human growth in the adult globe during the 21^st century, the continuance of the ratio may accept been obscured by the widespread prevalence of obesity. As in the US [3] and United kingdom of great britain and northern ireland [4], at that place has been a marked rising in the prevalence of obesity in Australia [5], particularly since the 1980s. BMI data from the nationwide AusDiab study conducted 1999–2000 indicated that the prevalence of obesity in urban Commonwealth of australia had risen ii.v-fold since the 1980 National Heart Foundation of Australia survey [5]. This alarming ascent is of public wellness business concern, because obesity is associated with an increased risk for developing hypertension, lipid disorders, type 2 diabetes, center disease [6], stroke [seven], osteoarthritis [eight] and certain cancers [9]. The cost of obesity to the Australian health system exceeded $8 billion in 2008, and this included costs associated with consequent metabolic disease, cardiac affliction and surgical complications [ten]. Furthermore, obesity is associated with a modestly increased adventure for early all-cause mortality [eleven]. It seems probable that wellness advantages inspired past modern medicine are being eroded by the current obesity epidemic.

Although the BMI is widely utilised every bit an anthropometric estimate of full general adiposity, the failure to identify differences in body composition and body fat distribution limit its usefulness. In recognition that visceral fatty accumulation increases the risk for metabolic disease, waist circumference was promoted as an culling surrogate measure out of obesity [12] and ratios such as waist-to-hip [13], [14] and waist-to-height [15] have also been investigated equally markers of risk for metabolic disease. All the same, few studies have reported population characteristics based on waist circumference.

In this cross-exclusive written report of men and women enrolled in the Geelong Osteoporosis Study (GOS), we aimed to depict the prevalence of overweight and obesity in Australian adults according to current criteria based on the BMI and waist circumference. Furthermore, we have investigated the human relationship between the BMI and body fat determined by dual energy x-ray absorptiometry (DXA).

Methods

Ethics statement

The Barwon Health Human Research Ideals Committee approved the study. All participants gave written, informed consent.

Subjects

The GOS is a population-based study of adult men and women aged 20 years and over, randomly-selected from the Commonwealth electoral rolls for the Barwon Statistical Partitioning in south-eastern Australia. This region is ideal for epidemiological research because the population is big enough (259,013 full population; 91,078 men and 98,740 women aged 20 years and over) and suitably diverse to be representative of the nation [16]. Baseline assessments for the GOS commenced in 1993 for women (one,494 recruited, 77% response) and in 2001 for men (i,467 recruited, 67% response). This analysis utilises data collected at the x-year follow-upward assessment for women (882 of the eligible women were assessed at the 10-yr follow-upwards with 82% response, 2003–2008) and baseline assessment for men (2001–2006) [16]. A farther sample of 194 women anile 20–29 year was too randomly generated (2005–2008, 82% response) and incorporated into the female cohort. Thus, data from 1,467 men and 1,076 women were included in analyses for determining prevalence of overweight and obesity, and for assessing the associations between BMI, waist circumference and lifestyle; virtually all of the cohort (99%) was white.

A sub-group of 1,299 (89%) men and 855 (79%) women had whole body scans using DXA that provided valid data on trunk fat mass; 167 men and 221 women were excluded from statistical modelling for predicting fat mass and percentage body fat (%BF) from BMI considering they were unable to assume the right position for authentic scanning (n = 189; 34 of these individuals weighed 120 kg or more than, which exceeded the scanner upper load), had prostheses or implants such every bit pacemakers, stents or breast augmentation (n = 135) or jewellery that could non be removed (northward = 13), or they did not take a whole body DXA scan (n = 52).

Measures

Continuing height without shoes was measured to the nearest 0.1 cm using a wall-mounted stadiometer; weight was measured to the nearest 0.i kg using electronic scales; waist (smallest circumference between the lower rib and iliac crest) and hip (maximal gluteal) circumferences were measured in a horizontal aeroplane with a narrow, non-elastic tape measure [17]. Subjects were categorised as underweight if BMI <xviii.v kg/m², overweight if BMI was in the range 25.0–29.9 kg/grandⁱⁱ, and obese if BMI ≥30.0 kg/m² [12]. Obesity was also grouped as form I if BMI 30.0–34.nine kg/g², grade Ii if BMI 35.0–39.9 kg/k², and form III if BMI ≥twoscore.0 kg/yard². Waist circumferences of 94.0–101.9 and ≥102.0 cm for men, and 80.0–87.9 and ≥88.0 cm for women, were used to classify overweight and obesity, respectively [12]. Whole torso scans were performed using DXA (Lunar DPX-50 or Prodigy Pro), which provided estimates of body fat mass, 'lean' mass (comprising muscle, peel, connective tissue and the lean component of adipose tissue - h2o and protein [18]) and bone mineral content (BMC). The %BF was calculated as body fat mass expressed equally a percentage of the whole torso mass from DXA (sum of body fat mass, lean mass and BMC). All clinical measures were performed past trained personnel.

Dietary intakes, including full energy intake (EI) from food and booze, were estimated using a food frequency questionnaire developed past the Cancer Council of Victoria [nineteen] and validated for assessing dietary intakes in the Australian population [20]. The questionnaire recorded the individual's habitual consumption of 74 foods and six alcoholic beverages over the preceding 12-month period on a 10-point frequency scale. These responses were supplemented with information about portion sizes and food types. Hateful daily booze and energy intakes were computed from the dietary data by means of the nutrient composition tables in the NUTTAB95 database (Food Standards Commonwealth of australia New Zealand, Canberra, 1995). Basal metabolic rate (BMR) was predicted from age, weight and gender [21] and EI/BMR calculated with both estimates expressed in megajoules; individuals with depression ratios were identified as EI/BMR <0.nine.

Statistics

Waist measurements were not obtained for 21 men and 22 women; values were computed using prediction equations based on gender, age and weight and included in prevalence estimates for overweight and obesity based on waist circumference. To determine the prevalence of obesity according to BMI, historic period-stratified samples of men and women were standardised to national age-profiles (Australian Agency of Statistics, Cat. No. 2068.0 – 2006 Census Tables). Gender differences in prevalence of obesity based on BMI and waist circumference were determined using age-stratified data; tests of homogeneity across age strata were performed in developing these models. For direct comparing with the AusDiab report, prevalence data for obesity were standardised to the 1998 Australian population aged ≥25 years (Australian Bureau of Statistics, Cat. No. 3201.0 – June 1997 to June 1998).

Multiple regression techniques were used to make up one's mind gender differences in the linear relationships between BMI and body fat mass and second club polynomial relationship between BMI and %BF. Polynomial relationships were centred nigh the mean to reduce collinearity. In validating the models, interaction terms between exposure variables were considered, to place event modifiers; interaction terms were retained in the models if p<0.05. Prediction equations for %BF from BMI were developed taking into account the effects of gender and historic period, and subsequently lean mass and BMC. Statistical analyses were performed using Stata (release 9.0 Statacorp, College Station, TX, USA) and Minitab (version xv; Minitab, State Higher, PA, United states).

Results

BMI for determining prevalence of obesity

Our data indicate that, co-ordinate to BMI criteria, 1.0% of individuals were underweight. Our data besides betoken that 37.5% of individuals were overweight and a further 24.v% were obese; prevalence figures for each gender are listed in Table i. The prevalence of obesity according to BMI was lower for men than for women (RR = 0.71, 95% CI 0.62–0.81). Pooled overweight and obesity information prove that 65.iii% men and 58.eight% women had BMI values to a higher place the recommended threshold (62.0% overall). The prevalence of obesity defined by BMI and historic period-standardised to the 1998 Australian population for ages ≥25 years has risen from twenty.8% (95%CI xviii.4–23.1) in 1999–2000 [5] to 25.3% (95%CI 23.four–27.two) in 2001–2008.

BMI was correlated to other indices of adiposity in both men and women; Pearson'due south correlation was 0.88 for weight, 0.88 for waist, 0.49 for waist/hip ratio and 0.85 for waist/superlative in men, and 0.92 for weight, 0.87 for waist, 0.32 for waist/hip ratio and 0.86 for waist/height in women (all p<0.001). Gender-specific characteristics of the study population are presented in Tables 2 and iii, for the whole group and co-ordinate to categories of BMI; because of small numbers, underweight individuals were included in the category BMI <25.0 kg/thousand² and obesity grades I, Two and III were pooled to form the BMI ≥xxx kg/m^two category. Men and women with BMI <25.0 kg/k² were younger and taller than those who were overweight and obese. The waist/hip and waist/meridian ratios both increased across increasing categories of BMI. Waist/hip ratio ≥0.80 was present in all but nine men and in 798 (74.2%) of women.

Information from dietary analyses advise no consistency in the pattern of total energy intake across BMI categories; however, there was an increase in the proportion of individuals with low EI/BMR (<0.9) with increasing BMI. No association was observed between self-reported alcohol intake and BMI for men, just amidst women, cocky-reported alcohol intake decreased with increasing BMI. A smaller proportion of obese men were smokers compared to those who were overweight or for whom BMI <25.0 kg/g^two, but this pattern was non axiomatic amid women.

Age-specific prevalence of overweight and obesity every bit defined by BMI criteria are shown for men and women in Effigy 1. Relatively low prevalence of obesity was observed for young men and women aged 20–29 years, and among the elderly anile ≥eighty years; peaks of 29.7% (95%CI 23.five–35.9) occurred for ages 60–69 years for men and 38.7% (95%CI 31.7–45.7) for women aged l–59 years. In contrast to women, the prevalence of overweight men exceeded the prevalence of obesity across the full developed age range.

Figure i. Age-specific prevalence of overweight and obesity.

Historic period-specific prevalence (%) of overweight (body mass index, BMI, 25.0–29.9 kg/m²) and obesity (BMI ≥thirty.0 kg/grand²) for (a) men and (b) women by age decades (20 = 20–29 years, etc). Information are shown as hateful and 95% conviction intervals.

https://doi.org/10.1371/periodical.pone.0029580.g001

BMI compared with waist circumference for determining prevalence of obesity

According to waist circumference, our information indicate that 25.iv% of individuals were overweight and a farther 36.9% were obese; prevalence figures for each gender are listed in Tabular array ane. The prevalence of obesity based on waist circumference was lower for men than for women (RR = 0.69, 95%CI 0.63–0.76). Pooled overweight and obese data indicate that 56.8% men and 67.5% women had waist measurements that exceeded the recommended threshold (62.3% overall).

There was exact agreement using BMI and waist circumference criteria for categorising normal, overweight and obese groups for 66.1% men and 67.8% women; agreement to inside one category was observed for 99.5% men and 96.4% women. Obesity defined by waist circumference identified a larger proportion of the population than did obesity defined by BMI. Whereas 95.iii% of the women and xc.viii% of the men who were classed every bit obese by BMI were too obese by waist circumference, but 60.4% women and 55.5% men who were classed equally obese using waist circumference were also obese using BMI.

BMI and body fat

A linear relationship was observed between BMI and body fat mass (Figure 2a). For any given BMI in the range, mean body fat mass was six.3 kg greater for women than for men (p<0.001). For BMI 25.0 kg/thou^two, the mean predicted body fat mass was 17.7 kg (95%CI 17.5–17.nine) for men and 24.0 kg (95%CI 23.7–24.3) for women; for BMI 30.0 kg/m², the mean predicted trunk fat mass was 27.2 kg (95%CI 27.0–27.5) for men and 33.v kg (95%CI 33.2–33.eight) for women.

Figure 2. Body mass index and the relationship with body fat.

Scatter plot of trunk mass index (BMI) against (a) body fat (kg) for men and women and (b) body fat (%). Predicted values are represented past dashed lines.

https://doi.org/10.1371/periodical.pone.0029580.g002

Gender was identified every bit an effect modifier in the second order polynomial relationship between BMI and %BF (Figure 2b). Over the range of BMI, for any item BMI, %BF was greater for women than for men. For BMI 25.0 kg/1000², the mean predicted %BF was 22.vii% (95%CI 22.4–23.0) for men and 36.vii% (95%CI 36.four–37.0) for women; for BMI 30.0 kg/m², the mean predicted %BF was 29.nine% (95%CI 29.six–30.2) for men and 44.ii% (95%CI 43.8–44.6) for women.

Table 4 lists regression coefficients for regression models for predicting the dependent variable, %BF, by sequentially including the contained variables gender, BMI and age. The prediction equation combining these variables explained 0.825 of the variance in %BF. When lean mass and BMC were also included as contained variables in the models, the contribution of age was no longer significant (p>0.05). The regression coefficients for the parsimonious model for predicting %BF are shown in model five (Tabular array 4); the model includes gender, BMI, lean mass and BMC as the independent variables, which explains 0.889 of the variance in %BF. Gender was identified as an effect modifier, so interaction terms are also included.

Table iv. Regression coefficients (95%CI) for models predicting the dependent variable, %BF, by sequentially including gender (model ane), BMI (model 2), age (model 3), lean mass (model 4) and BMC (model 5) as contained variables.

https://doi.org/10.1371/journal.pone.0029580.t004

Discussion

According to internationally accepted thresholds for BMI, 37.5% of individuals (45.one% of men and thirty.ii% of women) were overweight and a further 24.5% were obese (20.ii% of men and 28.half dozen% of women). Prevalence figures using waist circumference were 25.4% overweight (27.5% of men and 23.3% of women) and 36.9% obese (29.3% of men and 44.1% of women). Both criteria indicated that approximately 60% of the population exceeded the recommended threshold for healthy body habitus. An increase in the proportion of men and women with low EI/BMR was observed with increasing BMI and this could reflect deliberate caloric restriction practised by overweight and obese individuals and/or underreporting of dietary intake that is accentuated with greater BMI [22], [23].

Our estimated 24.5% (95%CI 23.4–27.2) prevalence of obesity for the menstruation 2001–2008 suggests an increase since 1999–2000, when the nationwide AusDiab study reported a prevalence of 20.viii% (95%CI xviii.4–23.1) [xi]. This comparison is tempered, however, by differences in sampling strategies that exist betwixt the studies. In contrast to the AusDiab study, nosotros observed a greater prevalence of obesity in women than in men for estimates based on both BMI and waist circumference. Consistent with that earlier report, and as well based on cross-sectional data, we have documented that the prevalence of obesity increased with age until later middle historic period and declined in old age. This turn down may be due partly to obesity-related bloodshed [11].

Determining BMI does not require sophisticated equipment and information technology is easy to calculate; nevertheless, nosotros have highlighted limitations in its use equally a measure of adiposity. Gender-specific differences in the relationship betwixt BMI and torso fatty were largely explained by the greater lean mass associated with a muscular male body build and bone mass. Excess weight-for-meridian attributable to lean and bone tissue rather than torso fat may, in part, account for the observed loftier prevalence of overweight men according to BMI criteria. We also report an effect of age on %BF that was contained of BMI. Thus, in that location is a likely underestimation of adiposity past BMI in elderly people for whom there is a loss of lean tissue, specially skeletal muscle, and bone. An age-related decline in DXA lean mass, particularly in men, has been observed in apparently healthy individuals, in the absence of accompanying weight loss [24]. The observed age-related changes in trunk composition are consistent with reported effects of growth hormone deficiency and the increasing prevalence of growth hormone deficiency with age [25] together with an historic period-related turn down in sex steroids, especially testosterone in men [26].

Our reported non-linear relationship between BMI and %BF, merely linear human relationship between BMI and torso fat mass, underscores an before observation by Garrow and Webster that the correlation between of BMI with %BF is not as strong as the correlation with body fat mass [27]. Studies in children and adolescents [28], [29] take similarly reported that BMI is more poorly correlated with DXA estimates of %BF than with body fat mass. BMI is indicative of trunk fat mass not the relative measure out expressed as a percentage of body weight; increments in body weight result in diminishing increments in %BF.

Nosotros recognise several strengths and weaknesses in our study. The age-stratified random sampling technique used to generate our sample is a strength that ensured a skillful representation of all ages across the adult historic period spectrum. We acknowledge that prevalence data for obesity may be influenced past incomplete participation in the study and a bias related to body composition cannot exist excluded; no torso composition data were available for not-participants. The employ of a os densitometer for measuring body fatty, lean and os tissue precludes big individuals as the scanner bed has a safety load limit and the bed dimensions do not accommodate very large bodies. Thus, relationships between BMI and body fat have not included the very obese. Similarly, a frailty bias may have been introduced by excluding individuals with prostheses or pacemaker implants. The sample was substantially white and, as the relationship between trunk fatty and BMI differs by ethnicity [30], our findings may not be generalisable to other populations. Different thresholds for overweight and obesity divers by anthropometry have been developed to cater for recognised ethnic differences [12].

In conclusion, we written report a high prevalence of overweight and obese men and women in Australia, based on BMI and waist circumference criteria. In the absence of sophisticated imaging technologies such as DXA in about clinical settings, torso fat is difficult to measure, supporting a role for using BMI to guess wellness risk. However, we take demonstrated that excess body weight-for-meridian may not necessarily exist indicative of excess body fatty. Our data support an before contention that, to improve estimates of backlog trunk fat, BMI thresholds for defining overweight and obesity should be gender- and age-specific to account for differences in body build in men and women and the effects of age on body composition [31], [32]. Adiposity is a continuous trait and nosotros recognise that optimal thresholds will remain dependent on risk assessment for morbidity and mortality.

Acknowledgments

The authors admit the men and women who participated in the written report.

Author Contributions

Conceived and designed the experiments: JAP GCN SLB MAK. Performed the experiments: JAP. Analyzed the data: JAP. Wrote the paper: JAP GCN SLB MAK.

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