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Waist to height ratio as an indicator of ‘early health risk’: superior to current NICE matrix practice using BMI and

waist circumference

Journal: BMJ Open

Manuscript ID bmjopen-2015-010159

Article Type: Research

Date Submitted by the Author: 02-Oct-2015

Complete List of Authors: Ashwell, Margaret; Ashwell Associates, Gibson, Sigrid; Sig-Nurture, ; UK,

<b>Primary Subject

Heading</b>: Public health

Secondary Subject Heading: Global health, Health policy

Keywords: PUBLIC HEALTH, Coronary heart disease < CARDIOLOGY, Health policy < HEALTH SERVICES ADMINISTRATION & MANAGEMENT

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Waist to height ratio as an indicator of ‘early health risk’:

superior to current NICE matrix practice using BMI and

waist circumference

Running head: Waist-to-height ratio versus NICE matrix

Margaret Ashwell1,2,*

* Corresponding author

Email: [email protected]

Sigrid Gibson3


1 Ashwell Associates, Ashwell Street, Ashwell, Herts SG7 5PZ, UK

2 Honorary Senior Visiting Fellow, Cass Business School, City University London EC1Y 8TZ,

3 Sig-Nurture Ltd, Guildford, Surrey GU1 2TF, UK

KEYWORDS

Waist-to-height ratio; obesity; central obesity ; NICE matrix; BMI

WORD COUNT

1561

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ABSTRACT

Objectives

There is now overwhelming evidence that central obesity, as opposed to total obesity, carries more

health risks. The National Institute for Health and Care Excellence (NICE) have therefore suggested

that waist circumference should be included with BMI within a matrix to categorise health risk. We

wanted to compare how the adult population is classified using this NICE matrix, against the

distribution using a boundary value of 0.5 for waist-to-height ratio (WHtR), a proxy for central

obesity.

Participants

We have used recent data from four years of the UK National Diet and Nutrition Survey (NDNS)

(2008 to 2012). Total subjects, n= 1453 ; men, n= 631 and women, n= 822, all aged 19y and over.

Results

35% of the group who were judged to be at ‘no increased risk’, using the NICE matrix, had WHtR

equal or greater than 0.5. They are ‘missed’ because they would not be flagged up as ‘early health

risk’ using the NICE matrix. On the other hand, only 3% of the group who would be at ‘increased

risk’ using the NICE matrix were judged to be ‘no increased risk’ using WHtR.

Conclusions

Use of a simple boundary value for WHtR (0.5) identifies more people at ‘early health risk’ than does

the more complex NICE matrix and may be a better and simpler indicator of the metabolic risks of

central obesity.

Strengths

• The use of the very simple waist-to-height ratio (WHtR )addresses a current dilemma of how

to best identify early health risk with a very simple low cost anthropometric measure.

• The predictive value of waist-to-height ratio is backed by systematic reviews and meta-

analyses in many different populations

• The research in this paper suggests a limitation of the current NICE matrix, which requires

both BMI and WC to be over threshold, and is therefore not as good at picking up early health

risk as WHtR

Limitations

• Very few studies have addressed the superiority of WHtR over the NICE matrix for

identifying cardiometabolic risk and we have only ben able to compare our results directly

with data from one other country.

• We hope this ‘breakthrough’ paper will act as a stimulus for further research. In virtually all

cases this will be possible on data sets which have already been collected and the research

will not be costly.

BACKGROUND

It is now acknowledged that central obesity, as opposed to total obesity, assessed by Body Mass Index

(BMI), is associated with, and predicts, the most health risks in populations of all ages [1-5]. Waist-

to-height ratio (WHtR) has been shown to be a good proxy for visceral adipose tissue [6, 7].

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A boundary value of WHtR 0.5 as a risk assessment tool was suggested twenty years ago and this

translates into the simple message "Keep your waist to less than half your height" [8-11]. This

boundary value has been used in populations around the world and findings have supported the

premise that WHtR is a simple and effective anthropometric index to identify metabolic risks. For

example see [12-18]. As well as its close relationship with morbidity, WHtR also has a clearer

relationship with mortality than BMI does [19].

We have shown previously that continuing to use BMI as a sole indicator of risk would mean that

10% of the whole UK population, and more than 25% of the UK population who are judged to be

normal weight using BMI, are misclassified and might not be alerted to the need to take care or to

take action [11].

The National Institute for Health and Care Excellence (NICE) has tried to overcome this limitation of

BMI by suggesting that waist circumference is measured alongside BMI and has therefore produced a

complex cross-classification matrix to categorise risk [20]. Further, the Director of NICE’s Clinical

Guidelines Programmes is reported to have asked his Public Health team to study research on WHtR

for Guidance due to be published in 2017 [21]. Our aim was to assist NICE by comparing risk

estimated by the NICE matrix with that estimated by WHtR.

METHODS

We have used recent data from four years of the UK National Diet and Nutrition Survey (NDNS)

(2008 to 2012) [22] to illustrate how the adult population (n=1453 aged 19 years and over,) is split

using the NICE matrix and the WHtR boundary values of 0.5 and 0.6. All data were weighted for non-

response to the nurse visit.

Categories for waist circumference within the NICE matrix were: Low (men: <94cm, women:

<80cm); High (men: 94-102cm women: 80-88cm); Very high (men: >102cm Women: >88cm). For

BMI, Underweight( <18.5kg/m2 );Healthy weight (18.5-24.9kg/m2); Overweight (25-29.9kg/m2);

Obese (30-39.9kg/m2); Very obese (>40kg/m

2) . The NICE matrix of waist circumference x BMI

categorises health risk as: ‘no increased risk’, ‘increased risk’, ‘high risk’ and ‘very high risk’. We

combined the NICE categories of ‘increased risk’ and ‘high risk’ to obtain 3 groups with similar

numbers of adults, for comparison with our 3 categories of WHtR: ‘no increased risk’ (WHtR <0.5),

‘increased risk’ (WHtR =0.5 and < 0.6 ) and ‘very high risk’ ( WHtR = or >0.6).

RESULTS

The NICE matrix categorised 41% of the NDNS adult population as ‘no increased risk’, 32% as

‘increased risk’ or ‘high risk’ and 26% as ‘very high risk (Fig1); values for men and women

respectively 42%, 34%, 24% and 40%, 30%, 28%, (not shown). Waist-to-height ratio categorised the

adult population as 29% ‘no increased risk’, 44% ‘increased risk’ and 27% of the population as ‘very

high risk’(values for men and women respectively 24%, 48%, 28% and 34%, 40%, 26%, not shown).

FIGURE 1 NEAR HERE

Of greater importance, the cross tables analysis in the Table (fourth column) showed that more than

one third (35%) of the adult group who were judged to be at ‘no increased risk’ according to the NICE

matrix had WHtR equal or greater than 0.5 and might not be alerted to the need to take care or to take

action (44% and 26% for men and women, respectively, not shown). On the other hand, the Table

(third column) shows that only 3% of the group who would be at ‘increased risk’ according to the

NICE matrix were judged to be at ‘no increased risk’ using WHtR (values for men and women

respectively 1% and 6%, not shown). Figure 2 shows this cross tables analysis for all adults.

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TABLE NEAR HERE

FIGURE 2 NEAR HERE

DISCUSSION

Our analysis showed WHtR 0.5 categorised more subjects as being at ‘early increased’ risk than the

NICE matrix. This is because the NICE matrix only classifies those with a combination of Tier 2 high

waist circumference and overweight by BMI as being at ‘increased’ risk, whereas with WHtR, having

a value between 0.5 and 0.6 carries an ‘increased risk’ Men were more likely than women to fall into

this early ‘increased risk’ category; probably because of their greater likelihood of having central

obesity.

We are unaware of any other UK data where risk identified by WHtR has been compared to the NICE

matrix. However the New Zealand Ministry of Health performed a similar comparison with their

National Survey data. [23]. They showed that, whereas 48% of men were identified as at ‘no

increased risk’ by the NICE matrix, only 29% were classed as ‘no increased risk’ by WHtR <0.5. The

comparable values for women were 44% by NICE matrix and 41% by WHtR<0.5. In other words,

the NZ data also showed that WHtR 0.5 categorised more people, particularly men, as being at ‘ early

increased risk’ than the NICE matrix did.

To stress the metabolic implications of the misclassification by the NICE matrix, it is important to

show that those ‘misclassified’ people have raised cardiometabolic risk factors. We have unpublished

data from this NDNS adult sample to show that triglycerides and total to HDL cholesterol ratio are

raised in the group who would be misclassified by the NICE matrix but are unaware of any other

study which has investigated this aspect of misclassification using the NICE matrix. However, a

study of adults in Singapore found that WHtR >0.5 identified the highest proportion for all the CVD

risk factors in both genders, even higher than a combination of BMI and WC [24].

There is good evidence for the metabolic implications of misclassification by BMI alone, particularly

in younger age groups: The Bogalusa heart study of children from 4 to 18y showed that nearly 10% of

the children who were normal by BMI had WHtR > 0.5 and that these children had raised

cardiometabolic risk factors [1]. A study using NHANES data also showed that children from 5 to

18y with normal BMIs exhibited raised cardiometabolic risk factors if their WHtR was above 0.5

[14]. The Korean National Health and Nutrition Examination Survey showed there to be more

medical concerns for the normal weight group with central obesity (defined on the basis of WHtR)

than the normal weight group without central obesity, even in adolescents [25]. Further, prospective

data from the ALSPAC study has shown that WHtR in children aged 7-9y predicts adolescent

cardiometabolic risk better than BMI [26].

We have also investigated this ‘misclassified’ category of adults in the Health Survey for England

(HSE) data and shown that both men and women in this group (normal weight ‘apples’) have raised

cholesterol and glycated haemoglobin, even compared with overweight subjects, as measured by

BMI, who have WHtR <0.5 (overweight ‘pears’)[27].

The results from the cross tables analysis of our data (see last column of Table) can be extrapolated to

estimate that 14% of the whole UK adult population would be ‘missed’ using the NICE matrix. In

terms of numbers of people, and estimating the UK adult population as 48 million, this translates to

nearly 7 million adults who would be classified as at ‘no increased risk’ by the NICE matrix, but

would have ‘early increased risk’ identified by WHtR >0.5.

CONCLUSIONS AND IMPLICATIONS

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WHtR is a simple primary screening risk assessment tool which identifies more people at ‘early health

risk’ than the complex NICE matrix, which uses a combination of BMI and waist circumference. Of

course, any anthropometric measure is only the first step in identifying people at risk. More complex

risk scores (e.g. for diabetes) include further risk factors such as sex, age, ethnicity, socioeconomic

status, and family history. Clinical screening for more complex risk factors will, of course, then

follow for those deemed at risk by these simpler measures.

WHtR should be particularly useful for monitoring ‘early health risk’ in children. There are

indications that waist circumference in children has increased more rapidly than BMI [28] and future

predictions are that this gap will widen further [29] reflecting the increase in central, rather than

total, obesity. There is now a strong body of evidence supporting a boundary value of WHtR> 0.5 to

identify ‘early risk’ in children [30]. Ensuring that a child’s waist circumference does not exceed half

his/her height can be monitored in the community and by parents. They will not even need a tape

measure or weighing scales: a piece of string will suffice [11]. In fact, this simple method is already

recommended for self monitoring in Thailand where the Royal Thai Ministry of Public Health has

launched a campaign for using WHtR for health promotion and prevention programs[17]. It is

therefore timely that, in UK, NICE intends to investigate the potential use of WHtR.

COMPETING INTERESTS

The authors declare no financial competing interests. MA devised and copyrighted the

Ashwell® Shape Chart which is distributed to health professionals on a non-profit making

basis.

AUTHORS CONTRIBUTIONS

Both authors conceived the article and drafted the manuscript. SG analysed data from the

NDNS. Both authors agreed the final manuscript.

DATA SHARING STATEMENT

All unpublished data from the study is available to all on request from the authors

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Table: Categorisation of risk category in all subjects by NICE matrix, WHtR and by both

NICE matrix classification

Base n

WHtR <= 0.5

(as % of those in NICE

category)

WHtR more than 0.5 and less than 0.6 (as % in NICE category)

WHtR more than 0.6 (as % in NICE category)

At risk by NICE

matrix but at ‘no

increased risk’ by WHtR (<0.5)

At ‘no increased

risk’ by NICE

matrix but at

‘increased risk’ by WHtR (>=0.5)

n/a (underweight) 17 100% 0 0

NICE ‘no increased risk’ 576 65% 35% 0

NICE ‘increased’ and ‘high’

risk 470 3% 78% 19%

NICE ‘very high risk’ 390 0 18% 82%

Total subjects 1453 29% 44% 27% 1% 14%

The numbers of subjects in column 1 are actual base numbers (unweighted). Percentages are calculated using weighted data.

The NICE matrix of waist circumference x BMI categorised health risk as: ‘not applicable’/underweight, ‘no increased risk’, ‘increased risk’,’ high risk’ and ‘very high risk’.

NB The NICE matrix only classifies those with a combination of Tier 2 high waist circumference and overweight by BMI as being at ‘increased’ risk.

We combined the NICE categories of ‘increased risk’ and ‘high risk’ to obtain 3 groups with similar numbers of adults, for comparison with our 3 categories of WHtR.

Categories for waist circumference within the NICE matrix were: Low (men: <94cm, women: <80cm), High (men: 94-102cm women: 80-

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88cm); Very high (men: >102cm Women: >88cm).

Categories for BMI within the NICE matrix were: Underweight (<18.5kg/m2); Healthy weight (18.5-24.9kg/m2); Overweight (25-29.9kg/m2); Obese (30-39.9kg/m2) Very obese (>40kg/m2)

Categories for WHtR were: ‘no increased risk’ (WHtR <0.5),’ increased risk’ (WHtR 0.5 to < 0.6) and ‘very high risk’ ( WHtR 0.6+).

Columns 3 to 5 in the Table refer to subjects in the survey who had WHtR less than 0.5, more than 0.5 but less than 0.6, and >0.6, expressed as a percentage of the total in each NICE category, and for the total sample (bottom row).

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Figure 1: Categorisation of risk category of all

subjects by WHtR and by NICE matrix

WHtR: Green = ‘’no increased risk, WHtR <0.5,(29%), Yellow= ‘increased risk’,WHtR 0.5 to < 0.6,) (44%) and Red= ‘very high risk’, WHtR 0.6+, (27%).

NICE matrix: Blue= ‘not applicable/underweight]’ (1%) Green = ‘no increased risk’ (41%),

Yellow=’increased’ and’ high risk’ (32%), and Red= ‘very high risk’ (26%).

41

29

32

44

26

27

0% 20% 40% 60% 80% 100%

NICE

WHtR

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Figure 2: Percentage of subjects who have WHtR >0.5

and >0.6 within different categories of the NICE matrix

Vertical columns denote risk by NICE matrix: ‘not applicable’; ‘no increased risk’, ‘

increased’ and ‘high risk’ and ‘very high risk’ .

Colours within vertical columns denote risk by WHtR : Green = ‘no increased risk’ (WHtR <0.5), Yellow= ‘increased risk’ (WHtR 0.5 to < 0.6 ) and Red= ‘very high risk’ ( WHtR

0.6+) .

100%

65%

35%

78%

18%

19%

82%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

n/a No increased risk Increased/high risk Very high risk

3%

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Waist to height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on

BMI and waist circumference

Journal: BMJ Open

Manuscript ID bmjopen-2015-010159.R1


Date Submitted by the Author: 03-Dec-2015


<b>Primary Subject





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simpler and more predictive than using a ‘matrix’ based

on BMI and waist circumference

Running head: Waist-to-height ratio as an indicator of ‘early

health risk’




Sigrid Gibson3





KEYWORDS

Waist-to-height ratio; central obesity; obesity; BMI, matrix , Public Health England; NICE

WORD COUNT

3185 (text)

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ABSTRACT

Objective

There is now good evidence that central obesity carries more health risks compared to total obesity

assessed by BMI. It has therefore been suggested that waist circumference (WC), a proxy for central

obesity, should be included with BMI in a ‘matrix’ to categorise health risk. We wanted to compare

how the adult UK population is classified using such a ‘matrix’ with the classification using another proxy for central obesity, waist-to-height ratio (WHtR), using a boundary value of 0.5. Further, we

wished to compare cardiometabolic risk factors in the population with ‘healthy’ BMI divided

according to whether they have WHtR below or above 0.5.

Methods

Recent data from four years (2008 to 2012) of the UK National Diet and Nutrition Survey (NDNS)

(n= 1453 adults aged 19 y and over) were used to cross-classify respondents on anthropometric

indices. Regression was used to examine differences in levels of risk factors (TG, TC, LDL, HDL,

TC:HDL, HbA1c, fasting glucose, SBP and DBP) according to WHtR below and above 0.5, with

adjustment for confounders (age, sex and BMI).

Results

35% of the group who were judged to be at ‘no increased risk’ using the ‘matrix’ had WHtR equal or

greater than 0.5. However, the ‘matrix’ did not assign ‘increased risk’ to those with a ‘healthy’ BMI

and ‘high’ waist circumference. In fact, our analysis showed that the group with ‘healthy’ BMI, but

WHtR >= 0.5, had some significantly higher cardiometabolic risk factors than the group with

‘healthy’ BMI but WHtR below 0.5.

Conclusions


a more complex ‘matrix’ using traditional boundary values for BMI and WC. WHtR may be a

simpler and more predictive indicator of the ‘early heath risks’ associated with central obesity.

Strengths and Limitations

• The use of the waist-to-height ratio (WHtR ) addresses a current dilemma of how to best

identify ‘early health risk’ with a very simple, low cost, anthropometric measure.


analyses in many different populations.

• The analysis of cardiometabolic risk factors within the group with ‘healthy’ BMI shows that

some of these factors are significantly increased if WHtR >=0.5, thus supporting the

definition of WHtR>0.5 as an indicator of ‘early health risk’.

• Very few studies have addressed the comparison of WHtR with any ‘matrix’ based on BMI

and WC for identifying cardiometabolic risk. We have only been able to compare our results

directly with data from one other country. We hope this paper will act as a stimulus for

further cost-effective analysis of existing datasets.

BACKGROUND

Anthropometric proxies for central obesity, as opposed to total obesity, assessed by Body Mass Index

(BMI), are usually associated with, and are slightly better predictors, of increased levels of health risk

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factors in populations of all ages [1-4]. However, there have been some studies which showed that

anthropometric indicators for total and central obesity did not differ in their predictive abilities, for

example [5] [6] [7, 8]. In most of the above studies, waist circumference (WC) and waist to hip ratio (WHR) [9] have been

used as proxies for central obesity. Waist-to-height ratio (WHtR) is a proxy for central (visceral) adipose tissue [10, 11], which has recently received attention as a marker of ‘early health risk’.

A boundary value of WHtR 0.5 as a risk assessment tool was first suggested twenty years ago and this

translates into the simple message "Keep your waist to less than half your height" [12-15]. This boundary value has been used around the world and findings in many populations have supported the

premise that WHtR is a simple and effective anthropometric index to identify health risks e.g. [16-22]

As well as its close relationship with morbidity, WHtR also has a clearer relationship with mortality

compared to BMI [23].

We have previously shown that using BMI as a sole indicator of risk would mean that 10% of the

whole UK population, and more than 25% of the UK population who are judged to be ‘healthy’

weight using BMI, are ‘misclassified’ and might not be alerted to the need to take care or to take

action [24] [15].

The National Institute for Health and Care Excellence (NICE) tried to overcome this limitation of

BMI by suggesting that waist circumference is measured alongside BMI [25]. Public Health England

has built on this suggestion to produce a comprehensive cross-classification matrix to categorise risk

[26]. For simplicity and clarity, we will refer to this as the ‘matrix’. In more recent guidance, NICE

has advised ‘Think about using waist circumference, in addition to BMI, in people with a BMI less

than 35 kg/m2 [27]. Earlier in 2015, indications were given that NICE wish to study research on

WHtR for guidance due to be published in 2017 [28]. Our aim was to assist NICE by comparing risk

estimated by the ‘matrix’ with that estimated by WHtR.

METHODS

We used recent data from four years of the UK National Diet and Nutrition Survey (NDNS) (2008 to

2012) [29]. The NDNS is the most authoritative source of quantitative information on the food habits and nutrient intake of the UK population. Jointly funded by the Department of Health in England

(now Public Health England) and the Food Standards Agency, the results are used by Government to

develop policy and monitor trends in diet and nutrient intakes. Households were sampled from the UK

Postcode Address File, with one adult and one child (18 months or older), or one child selected for inclusion. Participants completed a detailed computer assisted personal interview to obtain

background information (age, gender, ethnicity, region) and eating and lifestyle behaviours such as

smoking, dieting to lose weight, medication and supplement use. Anthropometric measurements

(weight, height, waist circumference) were taken by trained nurses. Weight (in bare feet and minimal

clothes) was measured to the nearest 100g using calibrated scales. Height was measured with a

portable stadiometer with the head in horizontal Frankfort plane. Waist circumference was measured

with a tape measure at the point midway between the iliac crest and the costal margin (lower rib).

Fasting blood samples were obtained and two tubes for each participant were sent to Cambridge

Addenbrooke’s for immediate analysis. Further detail of blood sampling and analysis are detailed in

Appendix O of survey documentation. Data files from 4 years (2008-2012) of the NDNS Rolling Programme were obtained under license from the UK Data Archive (http://www.esds.ac.uk).

Classification of respondents by anthropometric indices (BMI, WC, WHtR)

Boundary values for WC within the ‘matrix’ were: Low (men: <94cm, women: <80cm); High (men:

94-102cm women: 80-88cm); Very high (men: >102cm Women: >88cm). For BMI, Underweight

(<18.5kg/m2 );Healthy weight (18.5-24.9kg/m

2); Overweight (25-29.9kg/m

2); Obese (30-39.9kg/m

2);

Very obese (>40kg/m2) .

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The ‘matrix’ of WC and BMI categorises health risk as: ‘no increased risk’, ‘increased risk’, ‘high

risk’ and ‘very high risk’. ‘No increased risk’ was assigned to healthy weight combined with low or

high WC and also to overweight combined with low WC. ‘Increased risk’ was assigned to healthy

weight combined with very high WC, to overweight combined with high WC, to overweight

combined with high WC and to obese combined with low WC. ‘High risk’ was assigned to overweight combined with very high WC and to obese combined with high WC. ‘Very high risk was

assigned to obese combined with very high WC and also to very obese with any category of WC.

We combined the ‘matrix’ categories of ‘increased risk’ and ‘high risk’ to obtain 3 tiers with similar numbers of adults, for comparison with the 3 tiers of WHtR based on the following boundary values:

‘no increased risk’ (WHtR <0.5), ‘increased risk’ (WHtR >=0.5 and < 0.6) and ‘very high risk’

(WHtR >=0.6).

The classification was based on all adults with data on BMI and WC (and WHtR) (n=1453). Data

were weighted to take account of differential response to the nurse visit

Linear models to assess independent association of WHtR among ‘healthy’ weight adults.

Regression models (GLM procedure in SPSS) were used to estimate the independent effect of WHtR

on CVD risk factors as outcome variables. WHtR was entered as a bivariate (<0.5 vs. >=0.5) and the

models were first adjusted for age and sex, and then additionally for BMI. Interactions were not

significant and were excluded from the final models. Numbers were as follows: adults with BMI in the ‘healthy’ range (n=490) (333/157 for WHtR<0.5 vs. >=0.5). Two thirds of these provided samples

for analysis (213/114 for TC, HDL and LDL), other numbers are shown in Tables.

RESULTS

Classification of subjects

The ‘matrix’ categorised 41% of the NDNS adult population sampled as ‘no increased risk’, 32% as

‘increased risk’ or ‘high risk’ and 26% as ‘very high risk (Figure1 and column b in Table 1); values

for men and women respectively 42%, 34%, 24% and 40%, 30%, 28%, (not shown). Waist-to-height ratio categorised the adult population as 29% ‘no increased risk’, 44% ‘increased risk’ and 27% of the

population as ‘very high risk’(see Figure 1). Values for men and women respectively were 24%, 48%,

28% and 34%, 40%, 26%, not shown in Figure 1.

FIGURE 1 NEAR HERE

Of greater importance, the cross tables analysis in Table 1 (column d) showed that more than one

third (35%) of the adult group who were judged to be at ‘no increased risk’ according to the ‘matrix’ had WHtR equal or greater than 0.5 and might not be alerted to the need to take care or to take action

(44% and 26% for men and women, respectively, not shown). On the other hand, Table 1 (column c)

shows that only 3% of the group, who would be at ‘increased risk’ according to the ‘matrix’, were judged to be at ‘no increased risk’ using WHtR. Values for men and women respectively were 1% and

6%, not shown in Figure 1. Figure 2 shows this cross tables analysis graphically for all adults.

TABLE 1 NEAR HERE

FIGURE 2 NEAR HERE

Cardiometabolic risk factors of subjects with ‘healthy BMI’

divided according to WHtR below and above 0.5

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The classification analysis reported above showed WHtR 0.5 classified more subjects as being at

‘early increased’ risk than the ‘matrix’. However, the ‘matrix’ only classifies those with a

combination of very high waist circumference and overweight by BMI as being at ‘increased’ risk.

People with ‘healthy’ BMI and high WC as defined as being at ‘no increased’ risk. On the other hand,

classification on the basis of WHtR designates those with WHtR equal to or above 0.5 as carrying an ‘increased risk’, irrespective of BMI status.

We were therefore keen to investigate the people within the ‘healthy’ BMI range (18.5-24.9kg/m2)

and we did this by comparing cardiometabolic risk factors in these adults according to their WHtR status.

TABLE 2 NEAR HERE

Table 2 shows the characteristics of the adults with BMI in the ‘healthy’ range when they were

divided according to WHtR below and above 0.5. Those with WHtR >=0.5 were older than those with

WHtR<0.5 (p<0.001) and had higher mean BMI (p<0.001). Hence we adjusted for age and BMI (and

also for sex) in regression models.

Table 3 shows that all of the cardiometabolic risk factors studied were the same or indicated lower

risk in the group with WHtR<0.5. The differences for four of the risk factors, (HDL-cholesterol, total

cholesterol to HDL-cholesterol ratio, triglycerides and systolic blood pressure (SBP)) reached

statistical significance (p<0.05) when adjustment was made for age and sex. When further adjusted for BMI, as well as age and sex, three of these cardiometabolic risk factors retained statistical

significance (p<0.05). This showed that the differences in cardiometabolic risk factors were not due to

the higher BMI in the group with WHtR >=0.5.

TABLE 3 NEAR HERE

FIGURE 3 NEAR HERE-

DISCUSSION

Although BMI, WC and WHtR are, by their very nature, strongly correlated [30, 24], the more important question is to ask which anthropometric proxy measure is the simplest and most accurate in

helping to indicate ‘early health risk’?

Our classification analysis showed WHtR>= 0.5 classified more subjects as being at ‘early increased’

risk than the ‘matrix’. Men were more likely than women to fall into this early ‘increased’ risk

category, probably because of their greater propensity to central obesity.

We are unaware of any other UK study where risk identified by WHtR has been compared to the

same ‘matrix’. The New Zealand (NZ) Ministry of Health performed a similar comparison with their

National Survey data [31]. They showed that, whereas 48% of men were identified as at ‘no increased risk’ by the ‘matrix’, only 29% were classed as ‘no increased risk’ by WHtR <0.5. The comparable

values for women were 44% by ‘matrix’ and 41% by WHtR<0.5. In other words, the NZ data also

showed that WHtR 0.5 classified more people, particularly men, as being at ‘ early increased risk’

compared to the ‘matrix’.

Our analysis of the cardiometabolic risk factors in the ‘healthy’ BMI group made it clear that the

people who have a BMI in the ‘healthy’ range, but have WHtR >0.5, had risk factor levels that were

less favourable than those in the ‘healthy’ BMI range with WHtR <0.5 (Table 3 and Figure 3). These results have prompted us to suggest that WHtR is an independent indicator of ‘early health risk’ after

adjustment for age, sex and BMI. Given the small sample size, not all differences were statistically

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significant but future rounds of the NDNS rolling programme will offer increased power.

Additionally, larger datasets could be used to test the validity and generalisability of our conclusions.

There is good evidence from around the world for the metabolic implications of misclassification by

BMI alone: A study of adults in Singapore found that WHtR >=0.5 identified the highest proportion

of all the cardiometabolic risk factors in men and women, even higher than a combination of BMI and

WC [32]. In USA, the Bogalusa heart study of children aged from 4 to 18y showed that nearly 10% of

the children, who were ‘healthy’ by BMI, had WHtR > 0.5 and that these children had raised

cardiometabolic risk factors [33]. A study using NHANES data also showed that children from 5 to 18y with ‘healthy’ BMIs exhibited raised cardiometabolic risk factors if their WHtR was above 0.5

[18]. In Korea, the National Health and Nutrition Examination Survey showed there to be more

medical concerns for the adolescents in the ‘healthy’ weight group with central obesity (defined on the basis of WHtR) than the ‘healthy’ weight group without central obesity [34]. Further, prospective

data from the ALSPAC study in UK has shown that WHtR in children aged 7-9y predicts adolescent


Our results in Table 3 included an analysis where the values for the cardiometabolic risk factors were

adjusted for BMI as well as age and sex. Most studies, for example [18, 33] [24], do not take this

‘belt and braces’ approach when comparing those with WHtR below and above 0.5. However, it was reassuring that three out of four risk factors remained statistically different (p<0.05) even when this

approach was taken. This gives confidence that the above-cited papers, and many others, contain valid

results.

We have previously investigated adults in the NDNS [24] and also in the Health Survey for

England[36] and shown that both men and women in the group who have BMI in the ‘healthy’ range but who have WHtR >=0.5, have increased cardiometabolic risk factors, not only when they are

compared to subjects with ‘healthy’ BMI and WHtR <0.5, but even when they are compared with

subjects, classified as overweight by BMI, who have WHtR <0.5.

The results from the cross tables analysis of our data in Table 1 can be extrapolated to estimate that

14% of the whole UK adult population would be ‘missed’ using the ‘matrix’. In terms of numbers of

people, and estimating the UK adult population as 48 million, this translates to nearly 7 million adults

who would be classified as at ‘no increased risk’ by the ‘matrix’, but would have ‘early increased

cardiometabolic risk’ identified by WHtR >=0.5.


WHtR is a simple primary screening risk assessment tool that identifies more people at ‘early health

risk’ than a ‘matrix’, which uses a combination of BMI and waist circumference. We recommend that

the ‘matrix’ be amended to show that having a high waist circumference even in the ‘healthy’ range of

BMI, carries ‘increased’ risk. However, we believe that serious consideration should be given to the

use of WHtR to replace the ‘matrix’.

Of course, any anthropometric measure is only the first step in identifying people at ‘early health

risk’. More complex risk scores (e.g. for diabetes) include further risk factors such as sex, age,

ethnicity, socioeconomic status, and family history. Further screening for clinical risk factors should


Although our own analysis was on adults, many other studies suggesting the potential use of WHtR as

an indicator of ‘early health risk’ have been performed on children. There are indications that waist

circumference has increased more rapidly than BMI [37] and future predictions are that this gap will widen further [38] reflecting the increase in central, rather than total, obesity. Ensuring that a child’s

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waist circumference does not exceed half his/her height can be monitored in the community and by

parents. They will not even need a tape measure or weighing scales: a piece of string will suffice [15] [39]. In fact, this simple method is already recommended for self monitoring in Thailand where the

Royal Thai Ministry of Public Health has launched a campaign for adults and children to use WHtR

for health promotion and prevention programmes [21]. It is therefore timely that, in UK, NICE intends to investigate the potential use of WHtR.

Our results fully support an opinion expressed very recently: “..clinicians should look beyond BMI.

Although assessing for total fat mass with BMI to identify patients at greater cardiovascular risk is a good start, it is not sufficient.” [40]

ACKNOWLEDGEMENTS

The authors wish to thank Dr Kirsten Rennie for additional statistical advice on the manuscript

COMPETING INTERESTS

The authors declare no financial competing interests. MA devised and copyrighted the Ashwell®

Shape Chart which is distributed to health professionals on a non-profit making basis.


Sigrid Gibson and Margaret Ashwell conceived the article and drafted the manuscript. SG analysed

data from the NDNS. Sigrid Gibson and Margaret Ashwell agreed the final manuscript.


No additional data available.

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Table 1: Classification of risk category in all subjects by the ‘matrix’, based on BMI and waist circumference, by WHtR and by both

a b c d e

‘Matrix’ classification Base (n) WHtR < 0.5 (as % of those in ‘matrix’ category)

WHtR>= 0.5 and < 0.6 (as % in ‘matrix’ category)

WHtR >=0.6 (as % in ‘matrix’ category)

n/a (underweight) 17 100% 0 0

‘no increased risk’ 576 65% 35% 0

‘increased’ and ‘high’ risk 470 3% 78% 19%

‘very high risk’ 390 0 18% 82%

Total subjects 1453 29% 44% 27%

The numbers of subjects in column b are actual base numbers (unweighted). Percentages are calculated using weighted data.

The ‘matrix’ of waist circumference x BMI classified health risk as: ‘not applicable’/underweight, ‘no increased risk’, ‘increased risk’,’ high risk’ and ‘very high risk’. NB The ‘matrix’ only classifies those with a combination of high waist circumference and overweight by BMI as being at ‘increased’ risk.

We combined the ‘matrix’ categories of ‘increased risk’ and ‘high risk’ to obtain three groups with similar numbers of adults, for comparison with our three categories of WHtR.

Categories for waist circumference within the ‘matrix’ were: Low (men: <94cm, women: <80cm), High (men: 94-102cm women: 80-88cm); Very high (men: >102cm Women: >88cm).

Categories for BMI within the ‘matrix’ were: Underweight (<18.5kg/m2); Healthy weight (18.5-24.9kg/m2); Overweight (25-29.9kg/m2); Obese (30-39.9kg/m2) Very obese (>40kg/m2)

Categories for WHtR were: ‘no increased risk’ (WHtR <0.5),’ increased risk’ (WHtR 0.5 to < 0.6) and ‘very high risk’ ( WHtR 0.6+). Columns c,d and e in the Table refer to subjects in the survey who had WHtR less than 0.5, equal or more than 0.5 but less than 0.6, and equal or more than 0.6, expressed as a percentage of the total in each ‘matrix’ category.

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Table 2; Characteristics of adults* with ‘healthy’ BMI#

according to WHtR 0.5

WHtR <0.5

WHtR =>0.5

p

Number of subjects* 213 114

Sex (% men/ %women) 43/57 40/60

Age (years) (mean, SE) 39.7 (0.9) 54.7 (1.5) <0.001

BMI (kg/m2) (mean, SE) 22.1 (0.09) 23.5 (0.09) <0.001

WHtR (mean, SE) 0.46 (0.001) 0.53 (0.002) <0.001

*with lipid values

## BMI ‘healthy’ range defined as BMI 18.5 to 24.9 kg/m2

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Table 3: Cardiometabolic risk factors for those with ‘healthy’ BMI # according to WHtR 0.5

Adjusted for age and sex Adjusted for age, sex and BMI

WHtR <0.5 (mean with 95% CI)

WHtR= >0.5 (mean with 95% CI)

Difference in means

P WHtR <0.5 (mean with 95% CI)


Difference in means

P

Total cholesterol (mmol/l) 4.93 (4.8 to 5.05) 4.98 (4.79 to 5.17) 0.052 0.665 4.97 (4.84 to 5.10) 4.87 (4.66 to 5.08) -0.10 0.452

HDL cholesterol (mmol/l) 1.67 (1.62 to 1.73) 1.49 (1.40 to 1.57) -0.181 0.001 1.67 (1.61 to 1.73) 1.49 (1.40 to 1.59) -0.175 0.003

Total cholesterol: HDL cholesterol 3.05 (2.93 to 3.17) 3.46 (3.28 to 3.64) 0.412 0.001 3.09 (2.97 to 3.21) 3.37 (3.18 to 3.57) 0.282 0.024

Triglycerides (mmol/l) 0.99 (0.92 to 1.07) 1.15 (1.04 to 1.27) 0.162 0.021 1.02(0.94 to 1.09) 1.09 (0.97 to 1.21) 0.072 0.353

LDL cholesterol (mmol/l) 2.84 (2.74 to 2.96) 3.00 (2.83 to 3.16) 0.148 0.151 2.88 (2.77 to 2.99) 2.91 (2.73 to 3.09) 0.029 0.799

HbA1c (%) 5.50 (5.43 to 5.58) 5.40 (5.28 to 5.51) -0.104 0.147 5.48 (5.40 to 5.55) 5.46 (5.33 to 5.58) -0.02 0.798

Fasting glucose (mmol/l) 5.02 (4.86 to 5.19) 4.96 (4.70 to 5.22) -0.063 0.693 4.98 (4.81 to 5.15) 5.07(4.82 to 4.79) 0.083 0.630

SBP (mm Hg) 121.3 (119.6 to 122.9) 124.8 (122.2 to 127.3) 3.5 0.03 121.3 (119.6 to 123.0) 124.7 (122.0 to 127.5) 3.45 0.049

DBP (mm Hg) 69.5 (68.2 to 70.7) 71.2(69.3 to 73.1) 1.78 0.133 69.4 (68.1 to 70.6) 71.5 (69.4 to 73.5) 2.09 0.105

# BMI ‘healthy’ range defined as BMI 18.5 to 24.9 kg/m

2

Estimated means were adjusted for age, sex (and BMI) as indicated. Number of subjects (n): For total cholesterol, HDL cholesterol, triglycerides = 213 with WHtR < 0.5 and 114 with WHtR >0.5; For LDL, n= 213/113; HbA1c, n= 208/112; glucose, n= 206/105 and for SBP and DBP, n= 246/119. P values in bold signify those which are statistically significant i.e. below 0.05

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31. Ministry of Health. Understanding Excess Body Weight.New Zealand Health Survey. Wellington: Ministry of Health; 2015.

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adult population in Singapore. PLoS One. 2015;10(4):e0122985. doi:10.1371/journal.pone.0122985.

33. Mokha JS, Srinivasan SR, Dasmahapatra P, Fernandez C, Chen W, Xu J et al. Utility of waist-to-height ratio in assessing the status of central

obesity and related cardiometabolic risk profile among normal weight and overweight/obese children: the Bogalusa Heart Study. BMC Pediatr.

2010;10:73. doi:10.1186/1471-2431-10-73.

34. Cho WK, Kim H, Lee HY, Han KD, Jeon YJ, Jung IA et al. Insulin Resistance of Normal Weight Central Obese Adolescents in Korea

Stratified by Waist to Height Ratio: Results from the Korea National Health and Nutrition Examination Surveys 2008-2010. Int J Endocrinol.

2015;2015:158758. doi:10.1155/2015/158758.

35. Graves L, Garnett SP, Cowell CT, Baur LA, Ness A, Sattar N et al. Waist-to-height ratio and cardiometabolic risk factors in adolescence:

findings from a prospective birth cohort. Pediatric obesity. 2014;9(5):327-38. doi:10.1111/j.2047-6310.2013.00192.x.

36. Gibson S, Ashwell M. Non-overweight ‘apples’ have higher cardiometabolic risk factors than overweight ‘pears’: waist-to-height ratio is a

better screening tool than BMI for blood levels of cholesterol and glycated haemoglobin. Obes Facts 2015;8(Supplement 1):139.

37. Mindell JS, Dinsdale H, Ridler C, Rutter HR. Changes in waist circumference among adolescents in England from 1977-1987 to 2005-2007.

Public Health. 2012;126(8):695-701. doi:10.1016/j.puhe.2012.05.004.

38. Shaw A, Retat L, Brown M, Divajeva D, Webber L. Beyond BMI: Projecting The Future Burden Of Obesity In England Using Different

Measures Of Adiposity


39. Wise J. Piece of string can assess cardiovascular risk, study finds. British Medical Journal. 2015;350:h2434

40. Poirier P. The Many Paradoxes of Our Modern World: Is There Really an Obesity Paradox or Is It Only a Matter of Adiposity Assessment?

Ann Intern Med. 2015. doi:10.7326/M15-2435.

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Figure 1 :Categorisation of risk category of all subjects by WHtR and by the ‘matrix’

WHtR: Green = ‘’no increased risk, WHtR <0.5,(29%), Yellow= ‘increased risk’, WHtR >=0.5 to < 0.6,)

(44%) and Red= ‘very high risk’, WHtR 0.6+, (27%).

The‘ matrix’: Blue= ‘not applicable/underweight]’ (1%) Green = ‘no increased risk’ (41%), Yellow=’increased’ and’ high risk’ (32%), and Red= ‘very high risk’ (26%).

187x141mm (72 x 72 DPI)

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Figure 2: Percentage of subjects who have WHtR >=0.5 and >=0.6 within different categories of the ‘matrix’

Vertical columns denote risk by the ’matrix’: ‘not applicable’; ‘no increased risk’, ‘ increased’ and ‘high risk’ and ‘very high risk’ .

Colours within vertical columns denote risk by WHtR : Green = ‘no increased risk’ (WHtR <0.5), Yellow=

‘increased risk’ (WHtR>= 0.5 to < 0.6 ) and Red= ‘very high risk’ ( WHtR >=0.6) .

157x158mm (72 x 72 DPI)

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Figure 3:Subjects with ‘healthy’ BMI divided according to WHtR >= 0.5.

Means for HDL-cholesterol and the ratio of total cholesterol: HDL cholesterol were statistically different (P<0.05) when adjusted for sex, age and BMI.

246x189mm (72 x 72 DPI)

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Waist to height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on

BMI and waist circumference

Journal: BMJ Open

Manuscript ID bmjopen-2015-010159.R2


Date Submitted by the Author: 06-Jan-2016


<b>Primary Subject





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simpler and more predictive than using a ‘matrix’ based

on BMI and waist circumference

Running head: Waist-to-height ratio as an indicator of ‘early

health risk’




Sigrid Gibson3





KEYWORDS

Waist-to-height ratio; central obesity; obesity; BMI, matrix , Public Health England; NICE

WORD COUNT

3185 (text)

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ABSTRACT

Objectives

There is now good evidence that central obesity carries more health risks compared to total obesity

assessed by BMI. It has therefore been suggested that waist circumference (WC), a proxy for central

obesity, should be included with BMI in a ‘matrix’ to categorise health risk. We wanted to compare

how the adult UK population is classified using such a ‘matrix’ with that using another proxy for central obesity, waist-to-height ratio (WHtR), using a boundary value of 0.5. Further, we wished to

compare cardiometabolic risk factors in adults with ‘healthy’ BMI divided according to whether they

have WHtR below or above 0.5.

Setting, participants and outcome measures

Recent data from four years (2008 to 2012) of the UK National Diet and Nutrition Survey (NDNS)

(n= 1453 adults) were used to cross-classify respondents on anthropometric indices. Regression was

used to examine differences in levels of risk factors (TG, TC, LDL, HDL, TC:HDL, HbA1c, fasting

glucose, SBP and DBP) according to WHtR below and above 0.5, with adjustment for confounders

(age, sex and BMI).

Results

35% of the group who were judged to be at ‘no increased risk’ using the ‘matrix’ had WHtR equal or

greater than 0.5. However, the ‘matrix’ did not assign ‘increased risk’ to those with a ‘healthy’ BMI

and ‘high’ waist circumference. In fact, our analysis showed that the group with ‘healthy’ BMI, and

WHtR >= 0.5, had some significantly higher cardiometabolic risk factors compared to the group with

‘healthy’ BMI but WHtR below 0.5.

Conclusions


a more complex ‘matrix’ using traditional boundary values for BMI and WC. WHtR may be a

simpler and more predictive indicator of the ‘early heath risks’ associated with central obesity.

Strengths and Limitations

• The use of the waist-to-height ratio (WHtR ) addresses a current dilemma of how to best

identify ‘early health risk’ with a very simple, low cost, anthropometric measure.


analyses in many different populations.

• The analysis of cardiometabolic risk factors within the group with ‘healthy’ BMI shows that

some of these factors are significantly increased if WHtR >=0.5, thus supporting the

definition of WHtR>0.5 as an indicator of ‘early health risk’.

• Very few studies have addressed the comparison of WHtR with any ‘matrix’ based on BMI

and WC for identifying cardiometabolic risk. We have only been able to compare our results

directly with data from one other country. We hope this paper will act as a stimulus for

further cost-effective analysis of existing datasets.

BACKGROUND

Anthropometric proxies for central obesity, as opposed to total obesity, assessed by Body Mass Index

(BMI), are usually associated with, and are slightly better predictors, of increased levels of health risk

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factors in populations of all ages [1-4]. However, there have been some studies which showed that

anthropometric indicators for total and central obesity did not differ in their predictive abilities, for

example [5] [6] [7, 8]. In most of the above studies, waist circumference (WC) and waist to hip ratio (WHR) [9] have been

used as proxies for central obesity. Waist-to-height ratio (WHtR) is a proxy for central (visceral) adipose tissue [10, 11], which has recently received attention as a marker of ‘early health risk’.

A boundary value of WHtR 0.5 as a risk assessment tool was first suggested twenty years ago and this

translates into the simple message "keep your waist to less than half your height" [12-15]. This boundary value has been used around the world and findings in many populations have supported the

premise that WHtR is a simple and effective anthropometric index to identify health risks e.g. [16-22]

As well as its close relationship with morbidity, WHtR also has a clearer relationship with mortality

compared to BMI [23].

We have previously shown that using BMI as a sole indicator of risk would mean that 10% of the

whole UK population, and more than 25% of the UK population who are judged to be ‘healthy’

weight using BMI, are ‘misclassified’ and might not be alerted to the need to take care or to take

action [24] [15].

The National Institute for Health and Care Excellence (NICE) tried to overcome this limitation of

BMI by suggesting that waist circumference is measured alongside BMI [25]. Public Health England

has built on this suggestion to produce a comprehensive cross-classification matrix to categorise risk

[26]. For simplicity and clarity, we will refer to this as the ‘matrix’. In more recent guidance, NICE

has advised “Think about using waist circumference, in addition to BMI, in people with a BMI less

than 35 kg/m2” [27]. Earlier in 2015, indications were given that NICE wish to study research on

WHtR for guidance due to be published in 2017 [28]. Our aim was to assist NICE by comparing risk

estimated by the ‘matrix’ with that estimated by WHtR.

METHODS

We used recent data from four years of the UK National Diet and Nutrition Survey (NDNS) (2008 to

2012) [29]. The NDNS is the most authoritative source of quantitative information on the food habits and nutrient intake of the UK population. Jointly funded by the Department of Health in England

(now Public Health England) and the Food Standards Agency, the results are used by Government to

develop policy and monitor trends in diet and nutrient intakes. Households were sampled from the UK

Postcode Address File, with one adult and one child (18 months or older), or one child selected for inclusion.

We used the nurse weights for the sample (variable wt_Y1234), which adjust for unequal selection,

non-response to the household/MFP and individual interviews and non-response to the nurse visit. Full details are given in Appendix B of the survey documentation:

http://doc.ukdataservice.ac.uk/doc/6533/mrdoc/pdf/6533_ndns_rp_yr1-4_userguide.pdf.

To derive the nurse interview non-response weight, logistic regression modelling was used to generate

a predicted probability for each participant that they would take part in the nurse interview based on

their personal and household characteristics. These predicted probabilities were then used to generate

a set of non-response weights; participants with a low predicted probability got a larger weight,

increasing their representation in the sample.

Participants completed a detailed computer assisted personal interview to obtain background

information (age, gender, ethnicity, region) and eating and lifestyle behaviours such as smoking,

dieting to lose weight, medication and supplement use. Anthropometric measurements (weight,

height, waist circumference) were taken by trained nurses. Weight (in bare feet and minimal clothes)

was measured to the nearest 100g using calibrated scales. Height was measured with a portable stadiometer with the head in horizontal Frankfort plane. Waist circumference was measured with a

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tape measure at the point midway between the iliac crest and the costal margin (lower rib). Fasting

blood samples were obtained and two tubes for each participant were sent to Cambridge

Addenbrooke’s for immediate analysis. Further detail of blood sampling and analysis are detailed in

Appendix O of survey documentation. Data files from 4 years (2008-2012) of the NDNS Rolling

Programme were obtained under license from the UK Data Archive (http://www.esds.ac.uk).

Classification of respondents by anthropometric indices (BMI, WC, WHtR)

Boundary values for WC within the ‘matrix’ were: Low (men: <94cm, women: <80cm); High (men: 94-102cm women: 80-88cm); Very high (men: >102cm Women: >88cm). For BMI, Underweight

(<18.5kg/m2 );Healthy weight (18.5-24.9kg/m2); Overweight (25-29.9kg/m2); Obese (30-39.9kg/m2);

Very obese (>40kg/m2) .

The ‘matrix’ of WC and BMI categorises health risk as: ‘no increased risk’, ‘increased risk’, ‘high

risk’ and ‘very high risk’. ‘No increased risk’ was assigned to healthy weight combined with low or

high WC and also to overweight combined with low WC. ‘Increased risk’ was assigned to healthy

weight combined with very high WC, to overweight combined with high WC, to overweight

combined with high WC and to obese combined with low WC. ‘High risk’ was assigned to

overweight combined with very high WC and to obese combined with high WC. ‘Very high risk was

assigned to obese combined with very high WC and also to very obese with any category of WC.

We combined the ‘matrix’ categories of ‘increased risk’ and ‘high risk’ to obtain 3 tiers with similar

numbers of adults, for comparison with the 3 tiers of WHtR based on the following boundary values:

‘no increased risk’ (WHtR <0.5), ‘increased risk’ (WHtR >=0.5 and < 0.6) and ‘very high risk’

(WHtR >=0.6).

The classification was based on all adults with data on BMI and WC (and WHtR) (n=1453). Data

were weighted to take account of differential response to the nurse visit

Linear models to assess independent association of WHtR among ‘healthy’ weight adults.

Regression models (GLM procedure in SPSS) were used to estimate the independent effect of WHtR

on CVD risk factors as outcome variables. WHtR was entered as a bivariate (<0.5 vs. >=0.5) and the models were first adjusted for age and sex, and then additionally for BMI. Interactions were not

significant and were excluded from the final models. Numbers were as follows: adults with BMI in

the ‘healthy’ range (n=490) (333/157 for WHtR<0.5 vs. >=0.5). Two thirds of these provided samples

for analysis (213/114 for TC, HDL and LDL), other numbers are shown in Tables.

RESULTS

Classification of subjects

The ‘matrix’ categorised 41% of the NDNS adult population sampled as ‘no increased risk’, 32% as

‘increased risk’ or ‘high risk’ and 26% as ‘very high risk (Figure1 and column b in Table 1); values

for men and women respectively 42%, 34%, 24% and 40%, 30%, 28%, (not shown). Waist-to-height

ratio categorised the adult population as 29% ‘no increased risk’, 44% ‘increased risk’ and 27% of the population as ‘very high risk’(see Figure 1). Values for men and women respectively were 24%, 48%,

28% and 34%, 40%, 26%, not shown in Figure 1.

FIGURE 1 NEAR HERE

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Of greater importance, the cross tables analysis in Table 1 (column d) showed that more than one

third (35%) of the adult group who were judged to be at ‘no increased risk’ according to the ‘matrix’

had WHtR equal or greater than 0.5 and might not be alerted to the need to take care or to take action

(44% and 26% for men and women, respectively, not shown). On the other hand, Table 1 (column c)

shows that only 3% of the group, who would be at ‘increased risk’ according to the ‘matrix’, were judged to be at ‘no increased risk’ using WHtR. Values for men and women respectively were 1% and

6%, not shown in Figure 1. Figure 2 shows this cross tables analysis graphically for all adults.

TABLE 1 NEAR HERE

FIGURE 2 NEAR HERE

Cardiometabolic risk factors of subjects with ‘healthy BMI’ divided according to WHtR below and above 0.5

The classification analysis reported above showed WHtR 0.5 classified more subjects as being at ‘early increased’ risk than the ‘matrix’. However, the ‘matrix’ only classifies those with a

combination of very high waist circumference and overweight by BMI as being at ‘increased’ risk.

People with ‘healthy’ BMI and high WC as defined as being at ‘no increased’ risk. On the other hand, classification on the basis of WHtR designates those with WHtR equal to or above 0.5 as carrying an

‘increased risk’, irrespective of BMI status.

We were therefore keen to investigate the people within the ‘healthy’ BMI range (18.5-24.9kg/m2)

and we did this by comparing cardiometabolic risk factors in these adults according to their WHtR

status.

TABLE 2 NEAR HERE

Table 2 shows the characteristics of the adults with BMI in the ‘healthy’ range when they were

divided according to WHtR below and above 0.5. Those with WHtR >=0.5 were older than those with

WHtR<0.5 (p<0.001) and had higher mean BMI (p<0.001). Hence we adjusted for age and BMI (and

also for sex) in regression models.

Table 3 shows that all of the cardiometabolic risk factors studied were the same or indicated lower

risk in the group with WHtR<0.5. The differences for four of the risk factors, (HDL-cholesterol, total

cholesterol to HDL-cholesterol ratio, triglycerides and systolic blood pressure (SBP)) reached statistical significance (p<0.05) when adjustment was made for age and sex. When further adjusted

for BMI, as well as age and sex, three of these cardiometabolic risk factors retained statistical

significance (p<0.05). This showed that the differences in cardiometabolic risk factors were not due to

the higher BMI in the group with WHtR >=0.5.

TABLE 3 NEAR HERE

FIGURE 3 NEAR HERE-

DISCUSSION

Although BMI, WC and WHtR are, by their very nature, strongly correlated [30, 24], the more important question is to ask which anthropometric proxy measure is the simplest and most accurate in helping to indicate ‘early health risk’?

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Our classification analysis showed WHtR>= 0.5 classified more subjects as being at ‘early increased’

risk than the ‘matrix’. Men were more likely than women to fall into this early ‘increased’ risk

category, probably because of their greater propensity to central obesity.

We are unaware of any other UK study where risk identified by WHtR has been compared to the

same ‘matrix’. The New Zealand (NZ) Ministry of Health performed a similar comparison with their

National Survey data [31]. They showed that, whereas 48% of men were identified as at ‘no increased

risk’ by the ‘matrix’, only 29% were classed as ‘no increased risk’ by WHtR <0.5. The comparable

values for women were 44% by ‘matrix’ and 41% by WHtR<0.5. In other words, the NZ data also showed that WHtR 0.5 classified more people, particularly men, as being at ‘ early increased risk’

compared to the ‘matrix’.

Our analysis of the cardiometabolic risk factors in the ‘healthy’ BMI group made it clear that the

people who have a BMI in the ‘healthy’ range, but have WHtR >0.5, had risk factor levels that were

less favourable than those in the ‘healthy’ BMI range with WHtR <0.5 (Table 3 and Figure 3). These

results have prompted us to suggest that WHtR is an independent indicator of ‘early health risk’ after

adjustment for age, sex and BMI. Given the small sample size, not all differences were statistically

significant but future rounds of the NDNS rolling programme will offer increased power.

Additionally, larger datasets could be used to test the validity and generalisability of our conclusions.

There is good evidence from around the world for the metabolic implications of misclassification by

BMI alone: A study of adults in Singapore found that WHtR >=0.5 identified the highest proportion

of all the cardiometabolic risk factors in men and women, even higher than a combination of BMI and

WC [32]. In USA, the Bogalusa heart study of children aged from 4 to 18y showed that nearly 10% of

the children, who were ‘healthy’ by BMI, had WHtR > 0.5 and that these children had raised

cardiometabolic risk factors [33]. A study using NHANES data also showed that children from 5 to 18y with ‘healthy’ BMIs exhibited raised cardiometabolic risk factors if their WHtR was above 0.5 [18]. In Korea, the National Health and Nutrition Examination Survey showed there to be more

medical concerns for the adolescents in the ‘healthy’ weight group with central obesity (defined on

the basis of WHtR) than the ‘healthy’ weight group without central obesity [34]. Further, prospective

data from the ALSPAC study in UK has shown that WHtR in children aged 7-9y predicts adolescent


Our results in Table 3 included an analysis where the values for the cardiometabolic risk factors were

adjusted for BMI as well as age and sex. Most studies, for example [18, 33] [24], do not take this

‘belt and braces’ approach when comparing those with WHtR below and above 0.5. However, it was

reassuring that three out of four risk factors remained statistically different (p<0.05) even when this approach was taken. The risk factors which retained significance were HDL cholesterol, total

cholesterol: HDL cholesterol and SBP. This gives confidence that the above-cited papers, and many

others, contain valid results.

We have previously investigated adults in the NDNS [24] and also in the Health Survey for

England[36] and shown that both men and women in the group who have BMI in the ‘healthy’ range

but who have WHtR >=0.5, have increased cardiometabolic risk factors, not only when they are

compared to subjects with ‘healthy’ BMI and WHtR <0.5, but even when they are compared with

subjects, classified as overweight by BMI, who have WHtR <0.5.

The results from the cross tables analysis of our data in Table 1 can be extrapolated to estimate that

14% of the whole UK adult population would be ‘missed’ using the ‘matrix’. In terms of numbers of

people, and estimating the UK adult population as 48 million, this translates to nearly 7 million adults

who would be classified as at ‘no increased risk’ by the ‘matrix’, but would have ‘early increased

cardiometabolic risk’ identified by WHtR >=0.5.

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Although our own analysis was on data from adults, many other studies suggesting the potential use of WHtR as an indicator of ‘early health risk’ have been performed on children or adolescents [33-

35]. There are indications that waist circumference has increased more rapidly than BMI [37] and future predictions are that this gap will widen further [38] reflecting the increase in central, rather than

total, obesity. Ensuring that a child’s waist circumference does not exceed half his/her height can be

monitored in the community and by parents. They will not even need a tape measure or weighing

scales: a piece of string will suffice [15] [39]. In fact, this simple method is already recommended for self monitoring in Thailand where the Royal Thai Ministry of Public Health has launched a campaign

for adults and children to use WHtR for health promotion and prevention programmes [21].

In terms of cost effectiveness, measuring BMI requires weighing scales as well as stadiometer for

measuring height; waist-to-height ratio requires a tape measure and stadiometer. Since a tape measure

is cheaper and more portable than weighing scales, we assume that the use of waist-to-height ratio

will be more cost effective. If the assessor only wishes to know if the subject has a waist-to-height

ratio at or below 0.5, the tape can be replaced by a simple piece of string [21] and the methodology

becomes even more cost effective. .


WHtR is a simple primary screening risk assessment tool that identifies more people at ‘early health

risk’ than a ‘matrix’, which uses a combination of BMI and waist circumference. We recommend that

the ‘matrix’ be amended to show that having a high waist circumference even in the ‘healthy’ range of

BMI, carries ‘increased’ risk. However, we believe that serious consideration should be given to the

use of WHtR to replace the ‘matrix’.

Of course, any anthropometric measure is only the first step in identifying people at ‘early health

risk’. More complex risk scores (e.g. for diabetes) include further risk factors such as sex, age, ethnicity, socioeconomic status, and family history. Further screening for clinical risk factors should


Our results fully support an opinion expressed very recently: “..clinicians should look beyond BMI.

Although assessing for total fat mass with BMI to identify patients at greater cardiovascular risk is a

good start, it is not sufficient.” [40]. It is therefore timely that, in UK, NICE intends to investigate the potential use of WHtR [28].

ACKNOWLEDGEMENTS

The authors wish to thank Dr Kirsten Rennie for additional statistical advice on the manuscript

COMPETING INTERESTS

The authors declare no financial competing interests. MA devised and copyrighted the Ashwell®

Shape Chart which is distributed to health professionals on a non-profit making basis.


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Both authors conceived the article and drafted the manuscript. SG analysed data from the NDNS.

Both authors agreed the final manuscript.


No additional data available.

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Table 1: Classification of risk category in all subjects by the ‘matrix’, based on BMI and waist circumference, by WHtR and by both

a b c d e

‘Matrix’ classification Base (n) WHtR < 0.5 (as % of those in ‘matrix’ category)

WHtR>= 0.5 and < 0.6 (as % in ‘matrix’ category)

WHtR >=0.6 (as % in ‘matrix’ category)

n/a (underweight) 17 100% (17) 0 0

‘no increased risk’ 576 65% (367) 35% (209) 0

‘increased’ and ‘high’ risk 470 3% (15) 78% (366) 19% (89)

‘very high risk’ 390 0 18% (68) 82% 322)

Total subjects 1453 29% (399) 44% (643) 27% (411)

The numbers of subjects in column b are actual base numbers (unweighted). Percentages are calculated using weighted data.

The ‘matrix’ of waist circumference x BMI classified health risk as: ‘not applicable’/underweight, ‘no increased risk’, ‘increased risk’,’ high risk’ and ‘very high risk’. NB The ‘matrix’ only classifies those with a combination of high waist circumference and overweight by BMI as being at ‘increased’ risk.

We combined the ‘matrix’ categories of ‘increased risk’ and ‘high risk’ to obtain three groups with similar numbers of adults, for comparison with our three categories of WHtR.

Categories for waist circumference within the ‘matrix’ were: Low (men: <94cm, women: <80cm), High (men: 94-102cm women: 80-88cm); Very high (men: >102cm Women: >88cm).

Categories for BMI within the ‘matrix’ were: Underweight (<18.5kg/m2); Healthy weight (18.5-24.9kg/m2); Overweight (25-29.9kg/m2); Obese (30-39.9kg/m2) Very obese (>40kg/m2)

Categories for WHtR were: ‘no increased risk’ (WHtR <0.5),’ increased risk’ (WHtR 0.5 to < 0.6) and ‘very high risk’ ( WHtR 0.6+). Columns c,d and e in the Table refer to subjects in the survey who had WHtR less than 0.5, equal or more than 0.5 but less than 0.6, and equal or more than 0.6, expressed as a percentage of the total in each ‘matrix’ category.

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Table 2; Characteristics of adults* with ‘healthy’ BMI#

according to WHtR 0.5

WHtR <0.5

WHtR =>0.5

p

Number of subjects* 213 114

Sex (% men/ %women) 43/57 40/60

Age (years) (mean, SE) 39.7 (0.9) 54.7 (1.5) <0.001

BMI (kg/m2) (mean, SE) 22.1 (0.09) 23.5 (0.09) <0.001

WHtR (mean, SE) 0.46 (0.001) 0.53 (0.002) <0.001

*with lipid values

## BMI ‘healthy’ range defined as BMI 18.5 to 24.9 kg/m2

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Table 3: Cardiometabolic risk factors for those with ‘healthy’ BMI # according to WHtR 0.5

Adjusted for age and sex Adjusted for age, sex and BMI

WHtR <0.5 (mean with 95% CI)


Difference in means

P WHtR <0.5 (mean with 95% CI)


Difference in means

P

Total cholesterol (mmol/l) 4.93 (4.8 to 5.05) 4.98 (4.79 to 5.17) 0.052 0.665 4.97 (4.84 to 5.10) 4.87 (4.66 to 5.08) -0.10 0.452

HDL cholesterol (mmol/l) 1.67 (1.62 to 1.73) 1.49 (1.40 to 1.57) -0.181 0.001 1.67 (1.61 to 1.73) 1.49 (1.40 to 1.59) -0.175 0.003

Total cholesterol: HDL cholesterol 3.05 (2.93 to 3.17) 3.46 (3.28 to 3.64) 0.412 0.001 3.09 (2.97 to 3.21) 3.37 (3.18 to 3.57) 0.282 0.024

Triglycerides (mmol/l) 0.99 (0.92 to 1.07) 1.15 (1.04 to 1.27) 0.162 0.021 1.02(0.94 to 1.09) 1.09 (0.97 to 1.21) 0.072 0.353

LDL cholesterol (mmol/l) 2.84 (2.74 to 2.96) 3.00 (2.83 to 3.16) 0.148 0.151 2.88 (2.77 to 2.99) 2.91 (2.73 to 3.09) 0.029 0.799

HbA1c (%) 5.50 (5.43 to 5.58) 5.40 (5.28 to 5.51) -0.104 0.147 5.48 (5.40 to 5.55) 5.46 (5.33 to 5.58) -0.02 0.798

Fasting glucose (mmol/l) 5.02 (4.86 to 5.19) 4.96 (4.70 to 5.22) -0.063 0.693 4.98 (4.81 to 5.15) 5.07(4.82 to 4.79) 0.083 0.630

SBP (mm Hg) 121.3 (119.6 to 122.9) 124.8 (122.2 to 127.3) 3.5 0.03 121.3 (119.6 to 123.0) 124.7 (122.0 to 127.5) 3.45 0.049

DBP (mm Hg) 69.5 (68.2 to 70.7) 71.2(69.3 to 73.1) 1.78 0.133 69.4 (68.1 to 70.6) 71.5 (69.4 to 73.5) 2.09 0.105

# BMI ‘healthy’ range defined as BMI 18.5 to 24.9 kg/m

2

Estimated means were adjusted for age, sex (and BMI) as indicated. Number of subjects (n): For total cholesterol, HDL cholesterol, triglycerides = 213 with WHtR < 0.5 and 114 with WHtR >0.5; For LDL, n= 213/113; HbA1c, n= 208/112; glucose, n= 206/105 and for SBP and DBP, n= 246/119. P values in bold signify those which are statistically significant i.e. below 0.05

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Figure 1 :Categorisation of risk category of all subjects by WHtR and by the ‘matrix’

WHtR: Green = ‘’no increased risk, WHtR <0.5,(29%), Yellow= ‘increased risk’, WHtR >=0.5 to < 0.6,)

(44%) and Red= ‘very high risk’, WHtR 0.6+, (27%).

The‘ matrix’: Blue= ‘not applicable/underweight]’ (1%) Green = ‘no increased risk’ (41%), Yellow=’increased’ and’ high risk’ (32%), and Red= ‘very high risk’ (26%).

187x141mm (72 x 72 DPI)

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Figure 2: Percentage of subjects who have WHtR >=0.5 and >=0.6 within different categories of the ‘matrix’

Vertical columns denote risk by the ’matrix’: ‘not applicable’; ‘no increased risk’, ‘ increased’ and ‘high risk’ and ‘very high risk’ .

Colours within vertical columns denote risk by WHtR : Green = ‘no increased risk’ (WHtR <0.5), Yellow=

‘increased risk’ (WHtR>= 0.5 to < 0.6 ) and Red= ‘very high risk’ ( WHtR >=0.6) .

157x158mm (72 x 72 DPI)

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Figure 3:Subjects with ‘healthy’ BMI divided according to WHtR >= 0.5.

Means for HDL-cholesterol and the ratio of total cholesterol: HDL cholesterol were statistically different (P<0.05) when adjusted for sex, age and BMI.

246x189mm (72 x 72 DPI)

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