Cut-off points for anthropometric indices of adiposity: differential classification in a large population of young women

Abstract

Anthropometric indices of adiposity include body mass index (BMI), waist circumference and waist-to-height ratio. In the recruitment phase of a prospective cohort study carried out between 1998 and 2002 we studied a population sample of 11 786 white Caucasian non-pregnant women in Southampton, UK aged 20-34 y, and explored the extent to which proposed cut-off points for the three indices identified the same or different women and how these indices related to adiposity. Height, weight and waist circumference were measured and body fat was estimated from skinfold thicknesses; fat mass index was calculated as fat mass/height^1.65. 4 869 (42%) women were overweight (BMI ≥ 25) and 1 849 (16%) were obese (BMI ≥ 30). 890 (8%) were not overweight but had a waist circumference ≥ 80 cm and 748 (6%) were overweight but had a waist circumference < 80 cm (6%). 50% of the women had a BMI ≥ 25 or a waist circumference ≥ 80 cm or a waist-to-height ratio ≥ 0.5. 85% of the variation in fat mass index was explained by BMI, 76% by waist circumference and 75% by waist-to-height ratio. Our findings demonstrate that many women are differentially classified depending on which index of adiposity is used. As each index captures different aspects of size in terms of adiposity, there is the need to determine how the three indices relate to function and how they can be of use in defining risk of ill health in women.

Introduction

Overweight (body mass index, BMI, ≥ 25) and obesity (BMI ≥ 30) are major public health issues and significant contributors to the burden of disease worldwide.¹ The prevalence of obesity is rising and projections suggest that 73% of women in the UK will be overweight and 36% obese by 2015.² During pregnancy, women who are overweight or obese are at greater risk of developing gestational diabetes, pre-eclampsia, hypertension and have increased complications during labour.^3-7 Maternal obesity affects fetal growth and development, increasing the risk of neural tube defects and macrosomia.^8;9 Recent evidence has shown that maternal adiposity is associated with increased adiposity in neonates and children at age nine.^10;11

Various anthropometric indices have been proposed to assess adiposity, BMI being the most extensively used in clinical practice. Mortality increases significantly between a BMI of 25 and 30, and more so above 30. A BMI ≥18.5 & <25 is considered normal, ≥ 25 & <30 is overweight and ≥ 30 is obese. However increasing evidence from studies of cardio-metabolic disease shows that waist circumference, an index of central adiposity, may be more closely associated with risk of abnormal metabolic function than BMI.¹² Given that BMI is not easily nor accurately estimated by the general public, waist circumference has been suggested as an alternative measure.^13;14 Cut-off points have been suggested to identify individuals for whom weight management would be recommended. For women, it has been proposed that a waist circumference of ≥ 80 cm would identify almost all women with a BMI ≥ 25 and a waist circumference of ≥ 88 cm those with a BMI ≥ 30.¹³ An alternative index, waist circumference expressed relative to height, the waist-to-height ratio, has also been suggested as a screening tool.¹⁵ Individuals with values of ≥ 0.5 are advised to “Take care” and ≥ 0.6 to take “Action”. A simple public health message, “Keep your waist circumference to less than half your height” has been put forward. Waist-to-height ratio predicts intra-abdominal fat and two studies have shown that it is a better predictor of death than BMI.^16;17 Proponents suggest that the cut-off points for waist-to-height ratio would apply to both men and women, different ethnic groups and children, in contrast to those for waist circumference.^15;18

The extent to which proposed cut-off points for body mass index (BMI) waist circumference and waist-to-height ratio identify the same or different women is unclear and there are few large studies of anthropometry in young women of reproductive age. It is of considerable importance to identify individuals “at risk”, and to determine whether these indices mark the same or differing risk of ill health. Here, we present results from a large general population sample of young women living in Southampton, UK on whom we have detailed anthropometric measurements. We sought to explore whether high BMI, high waist circumference and high waist-to-height ratio occur in the same or different women and how these indices relate to total and regional adiposity.

Methods

The data were collected as part of the Southampton Women’s Survey, which is a study of a population sample of non-pregnant women aged 20-34 years living in the city of Southampton and registered with a general practitioner. The survey was carried out between 1998 and 2002. A profile of the cohort has been published.¹⁹ The Southampton Women’s Survey was approved by the Southampton and South West Hampshire Local Research Ethics Committee.

Trained research nurses visited the women at home, administered a questionnaire and took anthropometric measurements. The questionnaire included details of the women’s current smoking and previous obstetric history. Height was measured with a stadiometer to the nearest 0.1 cm with the head in the Frankfort plane. Weight was measured with calibrated electronic scales to the nearest 0.1 kg and the women were asked to remove their shoes and any heavy items of clothing or jewellery. Waist circumference was measured midway between the lower rib margin and the iliac crest (both palpated in the mid axillary line) at the end of expiration over bare skin.²⁰ Hip circumference was measured as the maximum circumference over the buttocks over thin clothing.²¹ Both circumferences were measured to the nearest 0.1 cm using a fibreglass tape-measure. Four skinfold thicknesses (triceps, biceps, subscapular and supra-iliac) were measured in triplicate on the non-dominant side using Harpenden skinfold calipers to the nearest 0.1 mm.²² Up to two further readings were taken if necessary. The mean of the three closest readings was used in the statistical analysis.

Statistical analysis

The data were analysed using STATA version 8. Data were available for 12 551 women, but we restricted our analysis to 11 786 white Caucasian women (94% of the population), because of the known differences in body composition between different ethnic groups.²³ We excluded 22 women from the analysis because we did not know their ethnic group. We used the maximum number of observations available for each analysis.

BMI was calculated as weight/height² (kg/m²). Fat mass was estimated from skinfold thickness measurements using the method of Durnin & Womersley.²⁴ The appropriate equation was used depending how many and which skinfold thickness measurements were available. 11 594 women had all four skinfold thickness measurements available, 50 had three, 15 had two and 3 had only the triceps available. Percent body fat was calculated as fat mass (kg)/weight (kg) × 100. Fat mass index (FMI) was calculated as fat mass (kg) / heightⁿ (m). We determined n following log-log regression analysis ^25;26. The value for n of 1.65 gave the least correlation between fat mass index and height. To assess fat distribution, we calculated subscapular to triceps skinfold ratio.

To present the findings graphically, kernel density plots were used to estimate the probability density functions for FMI. A kernel density plot can be considered a refinement of a histogram or frequency plot and is a graphical summary of the shape of the data. Separate plots were produced within the three categories defined by the cut-off points of each of the three indices. For BMI the categories were 1: < 25, 2: ≥ 25 & < 30 and 3: ≥ 30, for waist circumference 1: < 80, 2: ≥ 80 & < 88 and 3: ≥ 88 and for waist-to-height ratio 1: < 0.5, 2: ≥ 0.5 & ≥ 0.6 and 3: ≥ 0.6.

Results

Table 1 shows the general characteristics of the women. 47% of the women had had one or more children (including stillbirths) and 32% were smokers at the time of the interview. 42% of the women had a BMI ≥ 25 and 16% a BMI ≥ 30 (Table 2).

Table 1. Characteristics of women in the Southampton Women’s Survey.

	Median	Interquartile range	5th centile	95th centile
Age y*	28	4.2	21	34
Height cm*	163.4	6.3	153.3	173.9
Weight kg	64.7	57.7, 74.2	50.2	95.7
Body mass index kg/m2	24.1	21.8, 27.6	19.4	35.7
Waist circumference cm	78.1	72.2, 86.3	66.2	103.6
Hip circumference cm	101.5	96.4, 108.2	90.0	122.2
Waist-to-height ratio	0.48	0.44, 0.53	0.41	0.64
Waist hip ratio	0.77	0.74, 0.81	0.69	0.89
Triceps skinfold thickness mm	19.1	14.9, 24.3	10.2	34.0
Biceps skinfold thickness mm	9.7	6.8, 14.0	4.3	23.3
Subscapular skinfold thickness mm	16.4	11.4, 24.4	7.9	40.7
Upper suprailiac skinfold thickness mm	20.0	13.4, 28.5	7.6	40.7
Subscapular to triceps skinfold ratio	0.89	0.72, 1.11	0.53	1.50
Fat mass kg	20.0	15.8, 26.0	11.3	38.6
Percent body fat*	31.3	6.0	21.6	41.1

^*values are mean and SD

Table 2. Numbers of women in categories of BMI according to World Health Organisation definitions.

BMI category		Number of women	Percent
< 18.5	(underweight)	226	1.9
18.5-24.9	(normal weight)	6 573	56.3
25.0-29.9	(overweight)	3 020	25.9
30.0- 39.9	(obese)	1 642	14.1
≥ 40	(morbidly obese)	207	1.8

Table 3 shows the number of women divided into four groups based on their BMI and waist circumference. Some 49% of women had a BMI ≥ 25, a waist circumference ≥ 80 cm or both, and 14% of women were classified differently according to the two indices, by either having the combination of a BMI < 25 and a waist circumference ≥ 80 cm, or having a BMI ≥ 25 but a waist circumference < 80 cm. Using the higher cut-off points, 23% of women had a BMI ≥ 30, a waist circumference ≥ 88 cm or both, and 9% of women were differently classified by the cut-offs with 7% of women having the combination of a BMI < 30 and a waist circumference ≥ 88 cm, and 2% having a BMI ≥ 30 but a waist circumference < 88 cm.

Table 3. Numbers of women in 4 groups according to BMI and waist circumference.

Key:
frequency
percentage

A: BMI cut-off 25kg/m2 and waist circumference cut-off 80cm
	BMI < 25kg/m2	BMI ≥ 25kg/m2	Total
Waist < 80cm	5,879	748	6,627
	51%	6%	57%
Waist ≥ 80cm	890	4,094	4,984
	8%	35%	43%
Total	6,769	4,842	11,611
	58%	42%	100%

B: BMI cut-off 30kg/m2 and waist circumference cut-off 88cm
	BMI < 30kg/m2	BMI ≥ 30kg/m2	Total
Waist < 88cm	8,925	177	9,102
	77%	2%	78%
Waist ≥ 88cm	854	1,655	2,509
	7%	14%	22%
Total	9,779	1,832	11,611
	84%	16%	100%

Table 4 shows the number of women divided into four groups based on their BMI and waist-to-height ratio. We used cut-offs for waist-to-height ratio of 0.5 and 0.6.¹⁵ 46% of women had a BMI ≥ 25, a waist-to-height ratio ≥ 0.5 or both. 13% of women were classified differently according to the 2 indices. Using higher cut-off points, only 1% of women had the combination of a BMI < 30 and a waist-to-height ratio of ≥ 0.6, while 8% had the combination of a BMI ≥ 30 but a waist-to-height ratio of < 0.6.

Table 4. Numbers of women in 4 groups according to BMI and waist-to-height ratio.

Key:
frequency
percentage

	BMI < 25kg/m2	BMI ≥ 25kg/m2	Total
Waist-to-height ratio < 0.5	6,269	988	7,257
	54%	9%	63%
Waist-to-height ratio ≥ 0.5	500	3,854	4,354
	4%	33%	38%
Total	6,769	4,842	11,611
	58%	42%	100%

	BMI < 30kg/m2	BMI ≥ 30kg/m2	Total
Waist-to-height ratio < 0.6	9,723	885	10,608
	84%	8%	91%
Waist-to-height ratio ≥ 0.6	56	947	1,003
	0.5%	8%	9%
Total	9,779	1,832	11,611
	84%	16%	100%

Women with the combination of a BMI < 25 and a waist circumference ≥ 80 cm were taller than those with the combination of a BMI ≥ 25 and a waist circumference < 80 cm (167 vs 160 cm respectively). For every 1 cm increase in height, waist circumference increased by 0.25%; (95% CI 0.21 to 0.29; P <0.001) and BMI decreased by 0.2%; (95% CI −0.25 to −0.14; P<0.001).

Table 5 shows the proportion of women who would be classified as “at risk” according to height for each of the three measures. More women in the lowest third of height had a BMI ≥ 25 compared with those in the highest (45% vs 39%). Conversely fewer women in the lowest third of height had a waist circumference ≥ 80 cm compared with those in the highest third of height (38% vs. 48%). Adjusting waist circumference for height by using waist-to-height ratio overcompensated for the excess of women in the highest third as determined by waist circumference, resulting in a larger proportion of women exceeding the cut-off point in the lowest third: 44% in the lowest third of height had a waist-to-height ratio ≥ 0.5 compared with only 32% in the highest third of height. Differences in height accounted for 0.5%, 1.4% and 2.7% of the variation in BMI, waist circumference and waist-to-height ratio respectively.

Table 5. Proportion of women with BMI of 25 kg/m² or more, waist circumference of 80 cm or more and waist-to-height ratio of 0.5 or more according to height.

	Thirds of height
	1 (Lowest)	2	3 (Highest)
BMI ≥ 25kg/m2	45%	41%	39%
Waist circ ≥ 80cm	38%	42%	48%
Waist-to-height ratio ≥ 0.5	44%	37%	32%

To establish the most appropriate exponent needed to make weight or waist circumference completely independent of height, we regressed log₁₀ body weight on log₁₀ height, and log₁₀ waist on log₁₀ height. The linear regression coefficient is the power to which height should be raised to achieve no association between the derived variable and height. The optimal power index was 1.68 for weight/height and 0.41 for waist/height, i.e. in this population there was no statistically significant association between height and weight/height^1.68 and height and waist/height^0.41. Rounding these powers indicates that, for this population, the standard BMI formula (i.e. using a power of 2) only approximates a height-independent measure of weight but is probably the most appropriate one, and that to adjust waist circumference for height, waist circumference should be divided by the square root of height rather than height itself.

To explore how well the three indices of adiposity reflected fatness we looked at the association with percent body fat, the subscapular to triceps skinfold ratio and FMI. BMI, waist circumference and waist-to-height ratio explained 66, 63 and 63% of the variation in percent body fat respectively. These indices predicted less of the variation in the subscapular to triceps skinfold ratio (19, 24 and 25% respectively) but were more strongly associated with FMI, explaining 85, 76 and 75% of the variation respectively.

Figure 1 shows the kernel density estimation of the probability density function for FMI for the three anthropometric indices of adiposity. Each index has been divided into three categories according to their particular cut-off points. Regardless of the index used, mean FMI was higher for each progressively higher category. However, there was considerable overlap such that, for example, women whose waist circumference was ≥80 & <88 cm could have a FMI in the same range as some women whose waist circumference was <80 cm. For the lowest category (three left-most lines in blue), the probability density functions using the three different indices were remarkably similar. For each progressively higher category, however, greater differences between the probability density functions for each index were observed, shown by the lines not overlapping so closely.

Figure 1. Kernel density estimation of the probability density function for fat mass index for three anthropometric indices of adiposity.

Category 1 Far left lines (medium grey)

BMI < 25 kg/m²

Waist circumference < 80 cm

Waist-to-height ratio < 0.5

Category 2 Middle lines (black)

BMI 25.0 - 29.9 kg/m²

Waist circumference 80.0 – 87.9 cm

Waist-to-height ratio 0.5 – 0.59

Category 3 Far right lines (pale grey)

BMI ≥ 30 kg/m²

Waist circumference ≥ 88 cm

Waist-to-height ratio ≥ 0.6

Table 6 shows the number of women who might be at risk of ill health according to which of the three indices or combination of indices is used. These data are for 11 611 women on whom complete data were available. Of the single indices, waist-to-height ratio identified the fewest women and waist circumference the most. Some 50% of the women in this study had a BMI ≥ 25 or a waist circumference ≥ 80 cm or a waist-to-height ratio ≥ 0.5.

Table 6. Number of women (%) who might be at risk of ill health according to which index or combination of indices were used. (11 611 women on whom complete data were available).

Index/combination of indices	Number of women (%)
BMI ≥ 25 kg/m2	4 842 (42%)
Waist circ ≥ 80 cm	4 984 (43%)
Waist-to-height ratio ≥ 0.5	4 354 (38%)
BMI ≥ 25 kg/m2 or waist circ ≥ 80 cm	5 732 (49%)
BMI ≥ 25 kg/m2 or waist-to-height ratio ≥ 0.5	5 342 (46%)
Waist circ ≥ 80 cm or waist-to-height ratio ≥ 0.5	5 220 (45%)
BMI ≥ 25 kg/m2 or waist circ ≥ 80 cm or waist-to-height ratio ≥ 0.5	5 835 (50%)

Discussion

We determined body mass index, waist circumference and waist-to-height ratio in young women aged 20 to 34 years living in Southampton, UK. BMI, waist circumference and waist-to-height ratio were positively associated with adiposity. Women were differentially classified depending on which index of adiposity was used. Half the women in the study would be categorised as being “at risk” using the proposed cut-off points for one or other index: a BMI ≥ 25, a waist circumference ≥ 80 cm or a waist-to-height ratio ≥ 0.5.

The demographic profile of the women in this study was similar to women of the same age, although no study can claim to be wholly representative of the general population.¹⁹ However, the Southampton Women’s Survey is one of the largest studies of its kind to date, making it a valuable resource in providing extensive information about the body size and shape of young women. 42% of our women had a BMI ≥ 25, while in the UK National Diet and Nutrition Survey 2000-01 (NDNS) 44% of women aged 25-34 y had a BMI > 25 and in the Health Survey for England 2004, 48% of women had a BMI > 25.^27;28 Wells et al report the prevalence of overweight and obesity (BMI ≥ 25) as 24% in women aged 21-30 years and 40% in women aged 31-40 years who took part in the UK National Sizing Survey, carried out in 2000-2001.²⁹

We found that BMI had the least dependence on height of the three indices. For the population in the present study, the exponent that was most suitable for expressing weight independent of height was 1.68. By convention BMI is expressed as weight relative to height squared and clearly this is an approximation which can have important implications when used uncritically.³⁰ In five groups of women, Han et al found that raising height to the power 0.87-1.74 corrected weight for height.³¹ We also found a positive statistically significant association between height and waist circumference (r = 0.11). This is in contrast to findings from Han et al who found a non-significant association between height and waist circumference (r = −0.036). When we divided waist circumference by height the association remained but became negative, so that fewer women in the highest third of height had a waist-to-height ratio ≥ 0.5 compared with the lowest third. In exploring the optimal index power of height in the relation waist/height, Han et al reported powers of between 0.02 and 0.58. The optimal index power in our study was 0.41, within the range reported by Han et al and which for practical purposes would be approximated by taking the square root of height as the denominator. Although expressing waist circumference divided by √height provides a measure of waist that is approximately independent of height, this ratio is not straightforward to calculate and seems unlikely to find widespread use in practice.

With the rising prevalence of obesity in populations, it is important to identify simple markers of the risk of ill health, and anthropometric indices have considerable utility in this regard. Clearly, excessive adiposity is disadvantageous and abnormal fat patterning carries additional risk. We considered how comparable each index was in reflecting adiposity by examining the probability distributions of FMI. FMI is preferred over percent body fat as a measure of adiposity as it better reflects the metabolic load imposed by fat mass^25;32. It is possible to achieve a high percent body fat by having a low lean mass. Furthermore the relation between BMI and percent body fat is a curvilinear one, such that at high BMI, percent body fat fails to reflect increases in adiposity. We divided the data into categories according to the particular cut-off points for each index. In the lowest category, the probability distributions of FMI were almost identical for each of the three indices. However, the distributions were more variable when comparing the three indices in the middle and upper categories. This suggests that each index captures different aspects of increased size in terms of adiposity. The extensive overlap of the probability distributions of FMI for the different categories for each index emphasises the limited specificity of anthropometry for identifying those women with greatest adiposity. Using skinfold thickness measurements, an indirect method, to estimate body fat, will have misclassified some individuals. However, this method is widely used and the most appropriate in large-scale studies such as this. If the degree of adiposity itself carries risk of ill health, it will be important to determine which of the three indices best reflects functional state, and hence best identifies disease-risk.

It is arguable whether waist circumference or waist-to-height ratio should replace BMI as a predictor of ill health. In our study, using the cut-offs for waist circumference misclassified fewer women than those for waist-to-height ratio. 6% of women had a BMI ≥ 25 but a waist circumference < 80cm, whereas 9% had a BMI ≥ 25 but a waist-to-height ratio < 0.5. This would favour waist circumference over waist-to-height ratio. In a study by Wells et al, women with a BMI of 24-25 had waist circumferences ranging from 73 to 114 cm.²⁹ There is a trend for increasing waist circumference over time, which is not matched by a similar increase in BMI.^33;34 This would support the use of waist circumference over BMI as a predictor of risk. We showed that waist circumference explained more of the variation in the distribution of fat, reflected by the subscapular to triceps skinfold ratio, whereas BMI better explained variation in total adiposity (percent body fat and fat mass index). Studies show that waist circumference is useful in predicting risk of disease associated with central adiposity such as cardiovascular disease, type 2 diabetes, levels of blood lipids and blood pressure.^35-39. However, BMI might better predict the risk of morbidities associated with overall adiposity, such as musculo-skeletal disorders.⁴⁰ Furthermore, while people do not accurately estimate their BMIs, measuring waist circumference with reliability and reproducibility may not be straightforward either. Less is known about usefulness of anthropometric indices in predicting reproductive outcomes and thus the sole use of one or other index is questionable. However, in this study, we have collected information from a large population of young women, which, in time, will enable us to determine associations between these indices and aspects of reproductive health.

We have shown that different women are identified depending on which index of adiposity is used. Currently, BMI and waist circumference are commonly used in defining risk. Waist-to-height ratio appears to identify a small additional number of women. Each index captures different aspects of size in terms of adiposity and we need to determine which one or combination best identifies those at specific risk. Of note is that 50% of the young women in this study had excess body fat according to one or other index. This serves to emphasise the magnitude of the public health problem. It is clearly inadequate simply to target individuals at the extreme upper end of the distribution and there is the need to shift the adiposity distribution of this half of the population downwards.