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Waist-to-Height Ratio Is More Predictive of Years of Life
Lost than Body Mass Index
Margaret Ashwell
1
*, Les Mayhew
2
, Jon Richardson
2
, Ben Rickayzen
2
1Ashwell Associates, Ashwell, UK and Visiting Research Fellow, Oxford Brookes University, Oxfordshire, United Kingdom, 2Cass Business School, City University London,
Faculty of Actuarial Science and Insurance, London, United Kingdom
Abstract
Objective:
Our aim was to compare the effect of central obesity (measured by waist-to-height ratio, WHtR) and total obesity
(measured by body mass index, BMI) on life expectancy expressed as years of life lost (YLL), using data on British adults.
Methods:
A Cox proportional hazards model was applied to data from the prospective Health and Lifestyle Survey (HALS)
and the cross sectional Health Survey for England (HSE). The number of years of life lost (YLL) at three ages (30, 50, 70 years)
was found by comparing the life expectancies of obese lives with those of lives at optimum levels of BMI and WHtR.
Results:
Mortality risk associated with BMI in the British HALS survey was similar to that found in US studies. However, WHtR
was a better predictor of mortality risk. For the first time, YLL have been quantified for different values of WHtR. This has
been done for both sexes separately and for three representative ages.
Conclusion:
This study supports the simple message ‘‘Keep your waist circumference to less than half your height’’. The use
of WHtR in public health screening, with appropriate action, could help add years to life.
Citation: Ashwell M, Mayhew L, Richardson J, Rickayzen B (2014) Waist-to-Height Ratio Is More Predictive of Years of Life Lost than Body Mass Index. PLoS
ONE 9(9): e103483. doi:10.1371/journal.pone.0103483
Editor: Martin Young, University of Alabama at Birmingham, United States of America
Received November 12, 2013; Accepted June 30, 2014; Published September 8, 2014
Copyright: ß2014 Ashwell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The initial work on this project was funded by a research grant from the Institute and Faculty of Actuaries in the UK. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Author Margaret Ashwell is employed by Ashwell Associates. There are no patents, products in development or marketed products to
declare. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
* Email: margaret@ashwell.uk.com
Introduction
Obesity is a condition used to describe high levels of body fat
and is associated with increased risk of morbidity and mortality.
The measure of obesity most commonly used is the Body Mass
Index (BMI), defined as weight/height
2
. Individuals are classified
as overweight if their BMI is between 25 kg/m
2
and 30 kg/m
2
,
obese if it is between 30 and 40 kg/m
2
and morbidly obese if it is
greater than 40 kg/m2. Using BMI to measure obesity, the Health
Survey for England (HSE) shows that the proportion of adults
classed as obese has increased in the UK from 15% in 1993 to
26% in 2010 [1]. Obesity in men and women is at its highest level
since records began, with 26.2% men and 26.1% women classified
as obese [2].
In recent years, indices of central obesity (first waist-hip ratio
‘‘WHpR’’ and then waist circumference ‘‘WC’’) have increasingly
been associated with higher cardiometabolic risk than BMI in both
cross-sectional and prospective studies. The use of waist-to-height
ratio (WHtR) for detecting central obesity, and health risks
associated with it, was first proposed in the mid 1990s by one of
the authors and others [3] [4] [5]. Using regression analysis on
ten-year follow-up Health and Lifestyle Survey (HALS) data, Cox
and Whichelow found that BMI was not a significant predictor of
death from all causes. By contrast, they found that WHtR was
significant in predicting all-cause mortality [6]. Since then interest
in the effectiveness of this measure has risen in both adults and
children in many different ethnic groups and countries.
A recent meta-analysis [7] concluded that robust statistical
evidence from studies, involving more than 300, 000 adults in
several ethnic groups, showed the superiority of WHtR over WC
and BMI for detecting cardiometabolic risk factors in both sexes.
Most studies support a boundary value (a preferred term to ‘cut-off
value’) for minimal risk of WHtR of 0.5 [8] and this value is being
rapidly adopted in many studies [9]. Given that WHtR is a better
risk measure than both BMI and WC, but that BMI has been the
traditional measure of obesity, we focused our analysis on WHtR
and BMI (ie we did not include WC in our study).
The UK government is concerned that the cost of obesity will
be felt by every single part of society, not just in headline financial
or health terms but in very personal ways, describing obesity as the
equivalent of the ;climate change’ of public health [9]. The reasons
for this public and government concern are clear to see. Obesity is
associated with an increased risk of various life threatening diseases
such as cancer, cardio-vascular diseases and diabetes [10,11]. For
example, an obese woman, compared with a healthy weight
woman, is almost thirteen times more likely to develop type 2
diabetes [12]. These diseases lead to reduced life expectancy. The
most recent UK Government policy document [13] outlines a call
to action for just this reason. Studies into the relationship between
BMI and mortality have found that the risk of death increases
when BMI is less than 20 kg/m
2
, is minimal between 20 kg/m
2
PLOS ONE | www.plosone.org 1 September 2014 | Volume 9 | Issue 9 | e103483
and 25 kg/m
2
and increases for BMI categories above this level
[14] [15].
Much of the research into the effects of obesity on life
expectancy has been done using BMI as the measure of obesity
and has focused on the US population [21]. In this paper we argue
that, based on the evidence, WHtR is a better measure of obesity
to use and, for the first time, we are able to quantify the years of
life lost (YLL) due to obesity as measured by WHtR. We also
quantify the YLL through being obese as measured by BMI in
order to be able to compare our results (which are based on the
British population) with previous studies using BMI which are
based on the US population. We find that the results using BMI
are consistent between the two countries.
As mentioned above, we contend that WHtR is a better
measure of the health risks due to obesity than BMI. BMI
overestimates fat in muscular people and cannot give information
about fat distribution. In contrast, WHtR is a better proxy for
central fat, which has greater associated health risks than fat stored
in other parts of the body.
To bring these strands together, the aim of the research was to
estimate YLL in an obese individual where obesity was measured
either by BMI or WHtR. We calculated the effects of obesity on
life expectancy at representative ages and for each gender
separately. We found that central obesity, as measured by WHtR,
was a better predictor of mortality and YLL than BMI. We
conclude that, compared with BMI, WHtR is more valuable for
health screening purposes, for delivering public health policies and
for estimating the burden of obesity on society.
Methods
Sources of information
YLL is defined as the difference in life expectancy between an
individual whose anthropometric indices are optimally healthy
and an individual with a sub-optimal measurement. To calculate
YLL, we combined information from three sources: the Health
Survey of England [16] the Health and Lifestyle Survey 1985 [17]
and 2006 UK interim life tables [18,19].
The Health Survey for England (HSE) provides cross-sectional
information on the prevalence of obesity in the population by age
and gender. This survey is conducted annually for the National
Health Service (NHS) in order to monitor the nation’s health
through surveying the population for specified health issues [20].
The Health and Lifestyle Survey (HALS) is a longitudinal study
of health and behaviour based on a representative random sample
of the British population (England, Wales and Scotland). It was
initiated in 1985 and now contains information on more than 20
years of follow-up data on participants who have died [17]. HALS
is similar to the National Health and Nutrition Survey (NHANES)
used for the US population, so the methodology used in the US
study [21] is replicated in this paper, allowing direct comparison of
results between Britain and the US.
HALS provides a long term picture of the British population’s
health and physiological characteristics including the following
attributes: weight, height and waist measurements, smoking
behaviour, alcohol consumption, diet and physical exercise;
associated health implications; beliefs and perceptions about
health and lifestyle; relationships between lifestyle and physiolog-
ical status and the effect of cognitive ability on these issues.
HALS data were collected by interview. Physiological informa-
tion, including measures of obesity, was collected through a nurse
visit. The dataset included 7,414 respondents from the age of 18.
Almost the entire sample is linked with the NHS central register
allowing death information for these respondents to be updated
periodically. Our results are based on the survey results up to
2005. Nearly 2000 deaths have been recorded as shown in
Table 1. It should be noted that, since our work was carried out,
the survey results have been updated to 2009.
Overall strategy and calculation of years of life lost (YLL)
Our strategy was to identify the pure effects of obesity on years
of life lost (YLL) by excluding smokers from our analyses. Smoking
is a competing cause of death. Therefore, by excluding smokers,
we removed any distortion in our YLL results which would come
from the risks associated with smoking, rather than those from
obesity. Although it is possible to carry out a similar analysis for
smokers, it is difficult to present a clear set of conclusions since,
apart from the competing risk point, the amount that smokers
smoke (ie whether an individual is a habitual heavy or light
smoker) is not captured as accurately as we would wish and so will
affect the results.
For a comparison of the relative impact which obesity and
smoking has on life expectancy, the reader is referred to a recent
review [22]. The authors note that, certainly in the US (and this is
particularly true of the UK), the proportion of the population that
smokes has been decreasing over a long period. It is hence
arguably more pertinent that we concentrate on non-smokers in
this study.
There were four steps in the calculation of YLL:
1. An estimate of the distribution of anthropometric indices of
interest (BMI and WHtR) within the total population was
made for each year of adult life from age 18 to 85 years using
data from HSE published in 2006.
2. Using HALS data, estimates of the Cox proportional hazards
ratio for death, based on BMI and WHtR values and age, were
obtained. This was done by investigating the association
between the obesity of participants at the start of the study and
their subsequent mortality. The role of the Cox model is to
identify the relationships between risk factors that affect a
subject’s survival.
3. For each age, the relative risks associated with different levels of
obesity were combined with the distribution of obesity in the
Table 1. HALS population samples and number of deaths.
Measure Smoking Status Male Subjects Male Deaths Female Subjects Female Deaths
BMI Non-smokers 1,685 473 2,609 608
BMI All 3,297 952 3,979 893
WHtR Non-smokers 1,678 472 2,597 603
WHtR All 3,281 949 3,958 887
doi:10.1371/journal.pone.0103483.t001
Increased Waist-to-Height Ratio Reduces Life Expectancy
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population. This was then used to decompose the population
life table into ‘impaired life’ tables for obese groups.
4. Using the resulting ‘impaired life’ tables, the mean life
expectancy for each age was derived and compared with the
corresponding figure from the non-obese life table to calculate
YLL. The mean was then used to allow direct comparison with
previous research [21] [23]. The population life table used was
the 2006 interim life table for the United Kingdom, produced
by the Office for National Statistics [18].
Analysing the distribution of anthropometric indices
Using the HSE data, the proportion of the population in the
following BMI categories were estimated: under 17 kg/m
2
,17to
,18, 18 to ,19, 19 to 20 etc up to 44 to ,45, and over 45 kg/m
2
.
The categories for WHtR were: under 0.36, 0.36 to ,0.38, 0.38 to
,0.40 etc up to 0.78 to ,0.80, and over 0.80.
The BMI and WHtR data was smoothed to remove distortions
across ages caused by sample error. We adopted the same
smoothing procedure as used in [21] on the HSE data. The
probability of being in the following 32 overlapping BMI
categories was estimated: 13 kg/m
2
to ,18 kg/m
2
,14to,19,
15 to ,20 up to 44 to ,49. The categories for WHtR were: 0.28
to ,0.36, 0.30 to ,0.38, 0.32 to,0.40 etc up to 0.80 to ,0.88.
Researchers using US data [21] [24] found that a third degree
polynomial accurately characterised the convex relationship
between change in age and BMI. Since we found no contradictory
evidence from Britain, the same approach was followed for BMI
and WHtR in this study. The probability of being in each interval
was estimated for each age from 18 to 85 years using the resulting
equations. Then, within each age, the probability of being in each
one unit interval was estimated as the moving average of the wider
intervals containing the one unit interval. For example, the
percentage of the population for BMI of 18 would be the average
of BMI 14 to ,19, 15 to ,20, 16 to ,21, 17 to ,22 and 18,23.
A smoothed distribution of BMI and WHtR for ages 18 to 85
years was obtained. Having carried out this procedure, we verified
that the smoothed total population obesity distribution was very
close to the unsmoothed distribution. The mean values of BMI
and WHtR for each interval were then calculated from the HSE
data. For the purpose of projecting YLL, all the individuals in any
particular obesity category are assumed to have the average
measurement for that category.
Estimating Hazard Ratios by Age of Adult Life
To prepare the data for the Cox proportional hazards model,
the following adjustments were made to the HALS data.
Individuals with missing height, weight or waist values were
removed from the dataset. Pregnant females were also excluded.
Cox proportional hazards models were estimated for non-
smokers, using the following predictor variables: BMI, BMI
2
,
WHtR, WHtR
2
, Age and Age
2
plus certain combinations thereof.
The inclusion of quadratic terms allows the effect of a predictor
variable to change over the range of inputs.
Obesity measures might have a disproportionately large effect
on mortality at very high levels and it is necessary to allow for this.
SPSS 16.0 was used to fit the parameters to the model using the
built in maximum likelihood estimation algorithm. This approach
is explained in more detail in [26]. Interaction terms (BMI x Age,
BMI
2
6Age etc.) were also tested and incorporated where the
model fit was enhanced as a result.
The Cox proportional hazards model took the form:
hi(t)~h0(t) exp ( X
n
bnxni)
where:
hi(t)
= hazard (force of mortality) of i
th
individual at time t,
h0(t)
= baseline hazard at time t,
xni
are covariates,
bn
are covariate coefficients.
For choice of covariates, we tested a wide range of options
including interaction terms involving combinations of two
covariates (see Table 2). Not surprisingly, age was a common
factor in all specifications of the model since age and mortality are
so strongly linked.
In this model, the log of the mortality rate is estimated as a
function of age and obesity. This model assumes that the effect of
the covariates (explanatory factors) is constant over time. In this
case, the hazard rate can be interpreted as the force of mortality
(or the instantaneous rate of mortality). It should be noted that this
model cannot be used to assess the likely lifespan of a particular
individual of a certain age and physiology as it does not allow for
the likely change in the individual’s physiology over their lifetime
(i.e. we assume that each individual’s BMI or WHtR remains
constant over their lifetime).
Estimating Expected Years of Life Lost
The 2006 interim life tables for the United Kingdom produced
by the Office for National Statistics were used to provide the
conditional probability of deaths for the total population [18]. The
smoothed BMI distribution, together with the hazard ratios and
the life tables, were used to produce estimates of YLL. Results
based on WHtR were produced in a similar way. The steps
involved in this method were the same as those used by other
authors [21].
YLL for a person aged, say 30 years, in a particular BMI
category relative to a person with optimum BMI (in this case
24 kg/m
2
for males and 26 kg/m
2
for females) is found by
subtracting the expected age at death for that BMI category from
the expected age at death in the optimum BMI category. Similarly
YLL was estimated for WHtR by comparing to optimum values of
WHtR (in this case 0.5 for males and 0.46 for females). The
optimal measurements are found by comparing the life expectan-
cies for differing WHtR/BMI measurements within the same age.
Those measurements that produce the largest life expectancies are
deemed to be the optimal ones.
Increased Waist-to-Height Ratio Reduces Life Expectancy
PLOS ONE | www.plosone.org 3 September 2014 | Volume 9 | Issue 9 | e103483
Results
Mortality rates and obesity based on HALS
We repeated the work of Cox and Whichelow [6] to see if the
relationships of BMI and WHtR with mortality had changed with
the collection of ten years’ more HALS data.
Figures 1 and 2 show the 20 year all-cause mortality rate by
deciles (tenths) based on BMI and WHtR for males and females.
The mortality rate is the number of deaths in a decile of BMI (or
WHtR) divided by the number of people originally in that decile,
expressed as a percentage.
For each measure, a clear upward trend is apparent for both
sexes; obesity tends to affect the mortality rates of males more than
females. Logistic regression analysis of the probability of death
versus BMI or WHtR category confirms a statistically significant
gender difference (p,0.01).
Further inspection of both Figures shows that WHtR is a more
sensitive predictor of mortality than BMI for males and females
(i.e. based on a steeper mortality gradient across the deciles for
WHtR). Use of regression analysis showed that the difference in
slopes between mortality rate and BMI (Figure 1) and WHtR
(Figure 2) was statistically different (p,0.01). The mortality rate
for males, based on BMI, increases from 1.5% to 4.5% across the
deciles. In contrast, the mortality rate, based on WHtR, increases
from 0.5% to just under 7%. Similarly for females, the mortality
rate based on BMI increases from 1.5% to just under 4% whereas
the mortality rate based on WHtR increases from 0.6% to just
under 6% across the deciles. In addition, the relationship between
mortality rate and WHtR deciles (Figure 2) has a smoother
gradient than that for BMI (Figure 1), particularly for males.
In terms of the correlation between mortality rate and within
decile median value of either measure, the results show that both
BMI and WHtR perform well, but that the correlation between
mortality rate and WHtR is higher than that between mortality
rate and BMI for both genders. For males, we obtained a Pearson
correlation coefficient of 0.98+/20.07 where 0.07 is the standard
error for WHtR and 0.91+/20.15 for BMI. For females the
equivalent results were 0.97+/20.10 and 0.96+/20.11.
Thus, the 20-year follow up data from HALS not only confirms
the results from the 10-year data, but lends further support to the
premise that WHtR is a superior predictor of mortality than BMI,
particularly in the case of males [21].
Fit of proportional hazards models
We used the Cox proportional hazards model to estimate the
mortality rates of individuals at different ages. We fitted two
stepwise regression models for males and females separately using
SPSS: one model for BMI and the other for WHtR. We examined
the change in the log likelihood to decide whether to include a
variable or not based on standard criteria.
Table 2 shows the coefficients and 95% confidence intervals for
each model. For the WHtR model the best combination of
covariates was Age, (WHtR)
2
and a composite variable (WHtR x
Age) for both males and females. For the male BMI model, the
inclusion of BMI, (BMI)
2
and (Age)
2
proved to be the best
covariate combination. For the female BMI model, BMI and
(BMI)
2
did not significantly improve the model fit over age alone.
Nevertheless, although the fitted parameters did not pass the
inclusion test they did produce results that were consistent with
past research [21] and so have been retained.
It would appear counter intuitive that, for both genders, the
BMI coefficient is negative. However, the main relationship
between BMI and mortality is modelled by the (BMI)
2
parameter;
the negative BMI coefficient has the effect of dampening the
mortality rate increase, particularly for people at the lower BMI
values.
On the basis of our analysis, we concluded that WHtR was
better than BMI at predicting mortality, because it resulted in a
better fit for both males and females and the results were more
intuitive. We then used the predicted mortality rates from each
model to estimate the YLL at different ages as described in the
next section.
YLL results according to different anthropometric indices
In this section we report the years of life lost (YLL) for different
values of BMI and WHtR. Using the results of the Cox
Table 2. Cox proportional hazard model coefficients for BMI and WHtR.
Variable BMI coefficients WHtR coefficients
Sex Male Female Male Female
Age 0.1725 0.1087 0.1886 0.1518
(0.1125, 0.2324)* (0.1018, 0.1156)* (0.128, 0.2493)* (0.1077, 0.196)*
(Age)
2
20.0005105 n.a. n.a. n.a.
(20.0009845, 20.0000365)*
BMI 20.1949 20.1605 n.a. n.a.
(20.3519, 20.03784)* (20.2795, 20.04143)
(BMI)
2
0.004038 0.00302 n.a. n.a.
(0.001299, 0.006777)* (0.0009275, 0.005112)
WHtR n.a. n.a. n.a. n.a.
(WHtR)
2
n.a. n.a. 11.43 7.439
(5.038, 17.83)* (2.469, 12.41)*
(WHtR x Age) n.a. n.a. 20.1534 20.09009
(20.2619, 20.04493)* (20.1726, 20.00762)*
*Coefficients passed the likelihood ratio test for inclusion into the final model.
Note: ‘‘n.a.’’ means not applicable. Figures in brackets are 95% confidence intervals. Table excludes smokers.
doi:10.1371/journal.pone.0103483.t002
Increased Waist-to-Height Ratio Reduces Life Expectancy
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proportional hazards model, we converted predicted mortality
rates at different ages and obesity values into life expectancies. We
calculated YLL by subtracting the life expectancy of an individual
having particular BMI or WHtR measurements from the life
expectancy of a person of the same age with optimal measure-
ments. For this purpose, we derived an optimal value of BMI of 24
Figure 1. Mortality rate by BMI decile (source: HALS). A clear upward trend is apparent for both sexes and obesity tends to affect the mortality
rates of males more than females. Logistic regression analysis of the probability of death versus BMI category confirms a statistically significant
gender difference (p,0.01).
doi:10.1371/journal.pone.0103483.g001
Figure 2. Mortality rate by WHtR decile (source: HALS). WHtR is a significantly better predictor of mortality than BMI for males and females (i.e.
steeper mortality gradient across the deciles for WHtR). Use of regression analysis showed that the difference in slopes between mortality rate and
BMI (Figure 1) and WHtR (Figure 2) was statistically different (p,0.01).
doi:10.1371/journal.pone.0103483.g002
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for males (26 for females) and optimal value of WHtR of 0.5 for
males (0.46 for females).
A summary of the results is shown in Table 3 where results for
both males and females are given at the representative ages of 30,
50 and 70 years. For each category, the table shows the percentage
of the whole population above the stated value of the anthropo-
metric index and the expected future years of life lost (YLL) at the
given age. Figures 3 to 6 show the results graphically for male and
female non-smokers for the three representative ages. The results
assume that each individual stays with their particular anthropo-
metric index throughout the rest of their life.
General results relevant to both BMI and WHtR (Table 3
and Figures 3 to 6). There is a clear J-shaped association
between the two anthropometric indices and YLL (see Figures 3 to
6). Males and females at older ages lose fewer years from obesity
(total or central) than those at younger ages (see Table 3). For
example a 30 year old female non-smoker with WHtR 0.7 has
YLL of 4.6 whereas her 70 year old counterpart has YLL of 2.7.
The simple reason for this is that older people have fewer years to
lose relative to normal life expectancy since life expectancy
decreases with age attained.
For both BMI and WHtR, there is more variation in YLL
between the three age groups for males than for females (eg range
of 20.2 to 6.7 YLL for males at highest WHtR compared to 10.6 to
5.9 YLL for females at highest WHtR - see Table 3).
YLL would be lower if the group of lives included smokers
(results not shown). This is because smokers have a lower life
expectancy than non-smokers. The group as a whole would
therefore have a lower life expectancy compared to a group
containing only non-smokers. This combined group would
therefore have fewer years of life to lose, on average, from the
effects of obesity. The effect of including smokers would therefore
be to dampen the YLL results shown in Table 3.
Results relevant to BMI (Table 3 and Figures 3 and
4). The accepted BMI ‘normal’ range (i.e for optimal health) is
from 18.5 to less than 25 [14]. The optimal YLL derived in this
study is at BMI 24 for males and 26 for females; the YLL values in
Figures 3 and 4 relate to these reference BMI values. Surprisingly,
both males and females have some increased YLL in part of the
‘normal’ BMI category. The region where the increased YLL
occurs tends to be between 18.5 and 22.
Males have increased YLL compared with females in the
‘overweight’ category (ie BMI from 25 to 30 kg/m
2
) at all three of
the representative ages (Figs. 3 and 4). YLL increases markedly
after BMI 30 kg/m
2
(the ‘obese’ category) in both sexes. However,
obese males have greater YLL than obese females at all three of
the representative ages. For example, a 30 year old male with a
BMI of 35 is expected to lose 5 years compared with 2.2 years for
the corresponding 30 year old female (see Table 3).
Results relevant to WHtR (Table 3 and Figures 5 and
6). One of the authors has previously proposed that the WHtR
range of 0.4 to 0.5 should be considered as ‘OK’ [25]. Many other
researchers in this field use WHtR 0.5 as a boundary value [8]
with increased risk starting above 0.5 (‘Consider Action’ category)
and substantially increased risk starting at WHtR over 0.6 - the
‘Take Action’ category [25].
In our study, the minimal value of YLL for males was at WHtR
0.5 and for females was at 0.46; the YLL values in Figures 5 and 6
relate to these reference values of WHtR. There is minimal
increased mortality risk for either sex in the ‘OK’ range of WHtR
i.e from 0.4 to 0.5.
Both sexes, certainly at the lower two representative ages, have
an increased risk of mortality if they are in the ‘Consider Action’
(WHtR 0.5 to 0.6) range. YLL increases markedly after WHtR 0.6
in both sexes (the ‘Take Action’ category). However, males have
greater YLL than corresponding females at all three of the ages.
For example, a 30 year old male with a WHtR 0.7 is expected to
lose 7.2 years compared with 4.6 years for the equivalent female
(see Table 3).
Figure 3. YLL relative to BMI 24 in male non-smokers. There is a J-shaped association between BMI and YLL at all three of the representative
ages. The optimal YLL is at BMI 24 kg/m
2
for males and YLL figures relate to this reference BMI value. The ‘accepted normal’ BMI category ranges from
18.5 to less than 25 kg/m
2
. Surprisingly, males have slight increased YLL in part of the ‘normal’ BMI category (ie BMI from 18.5 to to 22 kg/m
2
). Males
have increased YLL compared with females in the ‘overweight’ category (ie BMI from 25 to 30 kg/m
2
) at all three of the representative ages.
doi:10.1371/journal.pone.0103483.g003
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Table 3. Summary of YLL results based on BMI and WHtR for males and females at three representative ages: 30, 50 and 70 y.
Age 30 years male female
BMI value(kg/m
2
) 2530354025303540
% above BMI value
1
59.3% 21.2% 5.3% 0.8% 48.4% 19.6% 6.8% 1.9%
YLL 0.1 1.6 5.0 10.5 0 0.4 2.2 5.3
WHtR 0.5 0.6 0.7 $0.8 0.5 0.6 0.7 $0.8
% above WHtR value 62.1% 11.9% 1.3% 0.2% 41.7% 8.9% 1.6% 0.3%
YLL 0 1.7 7.2 20.2 0.1 1.4 4.6 10.6
Age 50 years
BMI value(kg/m
2
) 2530354025303540
% above BMI value
1
75.3% 32.4% 8.0% 1.4% 63.3% 29.2% 10.9% 3.0%
YLL 0.1 1.5 4.7 9.7 0 0.4 2.1 5.1
WHtR 0.5 0.6 0.7 $0.8 0.5 0.6 0.7 $0.8
% above WHtR value 85.1% 27.1% 3.1% 0.5% 65.9% 20.5% 4.0% 0.7%
YLL 0 1.4 5.8 14.3 0.1 1.3 4.1 9.2
Age 70 years
BMI value(kg/m
2
) 2530354025303540
% above BMI value
1
74.8% 31.7% 7.6% 1.4% 69.6% 33.8% 12.1% 3.1%
YLL 0.1 1.2 3.6 6.9 0 0.3 1.6 4.0
WHtR 0.5 0.6 0.7 $0.8 0.5 0.6 0.7 $0.8
% above WHtR value 93.0% 42.4% 5.4% 0.7% 80.8% 33.7% 7.2% 1.4%
YLL 0 0.5 2.9 6.7 0 0.8 2.7 5.9
1
HSE percentages for whole population.
doi:10.1371/journal.pone.0103483.t003
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Discussion
BMI results
Comparison between Britain and USA. The J-shaped
association between BMI and YLL is similar to those found by
others for the white US population when BMI is plotted against
YLL or increased mortality risk [21,26,27] and to that found in a
collaborative analysis of 57 prospective studies in western Europe
and the US [28].
However, our results for Britain suggest a steeper growth in
YLL as BMI increases and smaller YLL from BMI categories in
the unhealthy low range (BMI less than 20). As in the US [21],
males were observed to have higher YLL for a specific BMI than
females and ‘overweight’ was not found to be a serious health issue
- with US females only showing signs of increased YLL in the
‘overweight’ BMI category of 28. Recently, a systematic review
has been published of reported hazard ratios for all cause mortality
and BMI. In a sample size of nearly 3 million US individuals,
overweight was actually associated with a significant lower all
cause mortality than normal weight. Only Grade 2 and 3 obesity
were associated with higher all-cause mortality [29].
US researchers also considered YLL and mortality separately
for black males and females [26] and found a much flatter
relationship between YLL and BMI for the white population.
Figure 4. YLL relative to BMI 26 in female non-smokers. There is a J-shaped association between BMI and YLL at all three of the representative
ages. The optimal YLL is at BMI 26 kg/m
2
for females and YLL figures relate to this reference BMI value. The ‘accepted normal’ BMI category ranges
from 18.5 to less than 25 kg/m
2
. Surprisingly, females have slight increased YLL in part of the ‘normal’ BMI category (ie BMI from 18.5 to 24 kg/m
2
).
doi:10.1371/journal.pone.0103483.g004
Figure 5. YLL relative to WHtR 0.5 in male non-smokers. There is a J-shaped association between WHtR and YLL at all three of the
representative ages. The optimal YLL is at WHtR 0.5 and YLL figures relate to this reference value. There is minimal increased mortality risk in the ‘OK’
range of WHtR i.e from 0.4 to 0.5. At the lower two representative ages, males have an increased risk of mortality if they are in the ‘Consider Action’
(WHtR 0.5 to 0.6) range. YLL increases markedly after WHtR 0.6 (the ‘Take Action’ category) at all three of the representative ages.
doi:10.1371/journal.pone.0103483.g005
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Therefore, if the US studies had combined the results for the white
and black populations, their overall results would have been flatter.
By implication, it is possible that the results demonstrated in our
study might have been diluted by the effect of combining ethnic
groupings. There is the possibility that significant differences exist
between different ethnicities within Britain. However, there were
insufficient data on ethnic mix within the data to examine this
possibility further.
Fontaine et al [21] controlled for smoking as a potential
confounder of BMI and YLL, whereas we chose to present results
for non-smokers alone because of this acknowledged confounding
influence and because of concern about the suitability of the data.
Waist-to-height ratio results
Although several prospective studies have shown that WHtR is
a better predictor of morbidity than BMI e.g. [30] [31] [32], we
are aware of only one study which has looked at all cause mortality
in relation to BMI and WHtR [6] and none which have calculated
YLL for both anthropometric indices.
The 20-year follow-up results from the HALS data presented
here are consistent with those previously calculated using the 10-
year follow-up HALS data [6]. The previous study carried out
logistic regression analysis, with adjustment for age and smoking,
in 2,184 men and 2,730 women aged 30–79 years. This showed
that, whereas BMI did not significantly predict death from all
causes of cardiovascular death, WHtR was a significant predictor
(P,0.01) of both death from all causes and from cardiovascular
causes. Our results, based on nearly 2,000 deaths from the same
cohort, support and strengthen the earlier findings.
Overall, our results on WHtR suggest that YLL increases
dramatically from categories in excess of WHtR 0.52 for both
males and females (see Figures 5 and 6). A recent systematic
review collated global studies using specificity and sensitivity
analyses in cross-sectional studies to estimate prediction of
cardiometabolic risk for waist-to-height ratio. Based on optimal
specificity and sensitivity, the review has suggested a boundary
value of WHtR 0.5 [8]. Many authors have used this simple
WHtR boundary value to indicate first level of risk - not least
because it converts to the simple message ‘‘Keep your waist
circumference to less than half your height’’. Substantially
increased risk has been suggested to start at WHtR 0.6 but this
value has only been set pragmatically. Similar pragmatic reasoning
has been used for the boundary value of WHtR 0.4 [25]. Our
results using YLL not only lend support to all these proposed
boundary values but they also help to quantify the effects in terms
of reduced life expectancy. The results from this method of
quantification could be used to try to persuade obese people to
reduce the fat around their waist.
Limitations of our study
Use of HALS data. The following points should be noted
about our analysis of the prospective HALS data:
NSome research [33] suggests that participants who die early
may distort the results because terminal illness is associated
with low body fat. Previous research on HALS data [6]
suggests that this effect is marginal. No account in our analysis
was taken of other kinds of risk factors (such as diabetes or high
blood pressure) in estimating between the predictor variables
and mortality. This is, therefore, potentially an area for further
research.
NThe sample size at very high obesity levels is small in this
dataset. The consequence is that there will be more
uncertainty around the results obtained for the proportional
hazard ratios at the highest levels of obesity.
NUnlike the US study on which our methodology is based [21],
we took no account of ethnicity. This was because our British
dataset was too sparse to distinguish between ethnic groupings.
The US findings were that the influence of obesity on mortality
was much greater in the white compared with the black
population. This was discussed in more detail above.
NWe are aware that, during the period while we have been
carrying out our research, another HALS dataset has been
released (which extends the follow-up period to 2009). We
Figure 6. YLL relative to WHtR 0.46 in female non-smokers. There is a J-shaped association between WHtR and YLL at all three of the
representative ages. The optimal YLL is at WHtR 0.46 and YLL figures relate to this reference value. There is minimal increased mortality risk in the ‘OK’
range of WHtR i.e from 0.4 to 0.5. At the lower two representative ages, females have an increased risk of mortality if they are in the ‘Consider Action’
(WHtR 0.5 to 0.6) range. YLL increases markedly after WHtR 0.6 (the ‘Take Action’ category) at all three of the representative ages.
doi:10.1371/journal.pone.0103483.g006
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intend to carry out a similar analysis on the later dataset in due
course.
Calculation of YLL. The following points should be noted
about our calculation of YLL:
NThe proportional hazards model assumes that the coefficients
are constant over time. To check that this assumption was met
in practice, we plotted Shoenfield residuals against survival
time for each independent variable. We found them to be
independent of time and so concluded that this assumption was
acceptable (as it was in the US study [21]).
NThe relatively small sample size of individuals in the severest
obesity categories means that as both BMI and WHtR
increase, the derived hazard ratios become less precise. These
have a large influence on the YLL figures. It would be helpful
to calculate confidence intervals. However, as noted in [21],
this is very difficult to do in practice since the data used in the
study come from 3 different sources. Our results support the
results of other US studies [21,26,27]. To clarify this
uncertainty it would be helpful if a similar analysis could be
applied to a much larger dataset containing a higher
proportion of overweight and obese individuals.
NThe YLL calculations assume that BMI or WHtR remains
constant over the individual’s future lifetime. Therefore, the
YLL results described earlier are always a result of comparing
an individual in (and remaining in) a certain BMI (or WHtR)
category with an individual in (and remaining in) the optimum
BMI (or WHtR) category. In extending this research it would
be preferable to use longitudinal data in which there would be
included continuous measures of body fat at every age,
although clearly this would have implications in terms of both
cost and time needed to do the research.
Strengths of our approach
NWe have used 20-year prospective data from the HALS
survey. Although others have used data on many more
individuals from NHANES, their prospective data is only over
a period of 5 years [34].
NWe have used British data to quantify and compare YLL using
BMI and WHtR. To the best of our knowledge we are the first
to use WHtR to quantify YLL.
Generalisability and policy implications
Growth in Britain of the obese and morbidly obese categories of
the population has been substantial since the start of the HALS
investigation (1985). A similar study repeated now would have a
larger sample of such individuals but it would take time before a
suitable period of follow-up had elapsed to provide reliable results.
This paper covers a topic that is important to the planning of
health care, social policy and insurance in Britain. Through
further analysis of the HALS prospective data [17], our study
suggests that the mortality risk associated with obesity in Britain is
similar to that found in US studies. It finds that a 30 year-old male
non-smoker with a BMI of 40 is expected to live 10.5 years fewer
than a 30 year-old male with a BMI of 24. The corresponding
figure for a 30 year old female (BMI 40 compared to BMI 26) is
5.3 years. These examples reflect the overall results which suggest
that obesity is more of a risk for males than females.
Our research also supports the premise that WHtR is a better
predictive risk measure of mortality than BMI [7,9]. We have been
able to quantify the YLL for different values of WHtR at three
representative ages. For example, we find that a 30 year-old male
non-smoker with a WHtR of 0.7 is expected to live 7.2 years fewer
than a 30 year-old male with a WHtR of 0.5. The corresponding
figure for a 30 year old female (WHtR 0.7 compared to WHtR
0.46) is 4.6 years.
The evidence presented here suggests that government policy
and future research should therefore place more emphasis on
WHtR as a screening tool. Current UK policy tends to be
restricted to BMI and waist circumference [35,36]. We argue that
focusing on WHtR will identify those with central obesity and will
focus resources on those most at risk.
Although waist circumference is a good proxy for central
obesity, there are problems with setting cut-off values that can be
used for all ethnic groups [25] and for children. WHtR has the
advantage that, by making allowance for height, the same cut-off,
or boundary value, (0.5), can be used for everyone [9].
Other authors have suggested the use of A Body Shape Index
(ABSI) as a way to quantify abdominal obesity [34]. Interestingly,
changes in ABSI have also been calculated between two HALS
examinations seven years apart and shown that greater mortality
risk was shown in those people with initial high ABSI who had a
rising ABSI between examinations [37]. However the calculation
of ABSI is based on waist circumference relative to height and
BMI. We believe it would be much too complicated to calculate
ABSI for practical purposes. Further, a comparison of various
surrogate obesity indicators as predictors of CVD mortality in four
European populations found the prediction with WHtR to be
stronger than that with ABSI [38].
Other anthropometric indices, such as saggital abdominal
diameter [39], could be even more accurate than WHtR in
predicting mortality risk, but we believe that the logistics of
measurement are of great importance and a simple index such as
waist-to-height ratio has great practical advantages:
NOur YLL data support the pragmatically determined bound-
ary values for WHtR, which can be used to promote very
simple public health messages. Thus, WHtR between 0.4 and
0.5 is OK; WHtR between 0.5 and 0.6 signifies ‘Consider
Action’; and WHtR above 0.6 indicates ‘Take Action’ [25].
NThis first boundary value of WHtR 0.5 gives rise to the simple
message ‘‘Keep your waist circumference to less than half your
height’’. Our research now quantifies the effects of not doing
so. The promotion of this simple message could be powerful in
persuading people to consider, or take, action if their WHtR is
inappropriate. [9]. If a tape measure is not available, a piece of
string could suffice!
This paper is a response to the concern that, worldwide, the
prevalence of obesity has been increasing over the last few
decades. It is known that obesity can lead to several diseases, in
particular cardiovascular diseases, diabetes, and various cancers. It
has been predicted that there will be 11 million more obese adults
in the UK and 65 million more obese adults in the US by 2030. By
then, it is estimated that in the UK and US combined, there will
be an additional 6–8.5 million cases of diabetes, 5.7–7.3 million
cases of heart disease and stroke and 0.5 to 0.7 million additional
cases of cancer. In total, taking UK and US together, 26–55
million quality-adjusted life years will have been lost. The total
medical costs associated with these chronic diseases are estimated
to increase by
£
1.9–2.0 billion per year in the UK and by $48–66
billion per year in the US by 2030. Therefore, there are
considerable economic benefits to reducing the levels of obesity
worldwide [10].
Increased Waist-to-Height Ratio Reduces Life Expectancy
PLOS ONE | www.plosone.org 10 September 2014 | Volume 9 | Issue 9 | e103483
On present trends, health care providers will find themselves
treating more people for diseases caused by excess body fat. Our
study has focused on the relationship between obesity and
mortality. Many of the diseases associated with obesity, such as
diabetes, are also associated with factors such as smoking habits or
genetic pre-disposition. Thus, it is difficult to separate out the
influences of each with any certainty in order to estimate
morbidity as well as mortality reductions; however, death is likely
to have been preceded by a period of higher health care
consumption especially where the cause of death is from chronic
disease. In this regard, further work is needed around rates of
obesity-associated diseases and their relationship to age, gender
and other risk factors. Nevertheless, the research presented here
emphasizes how important it is for the government to promote
healthy lifestyles in order to avoid premature death (i.e. YLL).
Author Contributions
Conceived and designed the experiments: MA LM JR BR. Performed the
experiments: MA LM JR BR. Analyzed the data: MA LM JR BR.
Contributed reagents/materials/analysis tools: MA LM JR BR. Wrote the
paper: MA LM JR BR.
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