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Outcomes: Heeding Familiar Advice May Add Years to Your Life
http://www.nytimes.com/2008/01/22/health/research/22outc.html

Vital Signs
By NICHOLAS BAKALAR

The advice is as sound as it is familiar: avoid smoking, exercise,
eat lots of fruits and vegetables, drink alcohol if you want (but
not too much). Now researchers have figured out exactly how many
years these habits will add to your life.

An 11-year study, published Jan. 8 in PLoS Medicine, began with
interviews of more than 25,000 men and women ages 45 to 79 in the
English county of Norfolk. The researchers gathered information on
health and illness, smoking, alcohol consumption and physical
activity both in manual work and at leisure. The participants also
had physical exams and blood tests to determine vitamin C levels as
evidence of fruit and vegetable consumption.

Using this data, the researchers built a simple 0-to-4 scale that
indicated how many of the four behaviors each person habitually
engaged in -- one point each for not smoking, exercising, drinking
moderately and eating the proper amounts of fruits and vegetables.

The trend was unmistakable: with each added positive behavior,
people lived longer. Those who scored 4 had about one-quarter the
risk of dying of those who received a 0 -- equivalent to living an
additional 14 years. The trend was strongest for cardiovascular
disease and cancer, but also significant for other causes.

"We're not talking about extremes of behavior," said Dr. Kay-Tee
Khaw, the lead author and a professor of gerontology at the
University of Cambridge, "but easy behaviors that most people can
achieve."

PLoS Medicine: Combined Impact of Health Behaviours and Mortality in Men 
and Women: The EPIC-Norfolk Prospective Population Study
http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pmed.0050012&ct=1

Kay-Tee Khaw^1^*, Nicholas Wareham^2, Sheila Bingham^3, Ailsa
Welch^1, Robert Luben^1, Nicholas Day^1

1 Department of Public Health and Primary Care, Institute of Public
Health, University of Cambridge School of Clinical Medicine,
Cambridge, United Kingdom, 2 Medical Research Council, Epidemiology
Unit, Cambridge, United Kingdom, 3 Medical Research Council, Dunn
Nutrition Unit, Cambridge, United Kingdom

Background

There is overwhelming evidence that behavioural factors influence
health, but their combined impact on the general population is less
well documented. We aimed to quantify the potential combined impact
of four health behaviours on mortality in men and women living in
the general community.

Methods and Findings

We examined the prospective relationship between lifestyle and
mortality in a prospective population study of 20,244 men and women
aged 45-79 y with no known cardiovascular disease or cancer at
baseline survey in 1993-1997, living in the general community in
the United Kingdom, and followed up to 2006. Participants scored
one point for each health behaviour: current non-smoking, not
physically inactive, moderate alcohol intake (1-14 units a week)
and plasma vitamin C >50 mmol/l indicating fruit and vegetable
intake of at least five servings a day, for a total score ranging
from zero to four. After an average 11 y follow-up, the age-, sex-,
body mass-, and social class-adjusted relative risks (95%
confidence intervals) for all-cause mortality(1,987 deaths) for men
and women who had three, two, one, and zero compared to four health
behaviours were respectively, 1.39 (1.21-1.60), 1.95 (1.70--2.25),
2.52 (2.13-3.00), and 4.04 (2.95-5.54) p < 0.001 trend. The
relationships were consistent in subgroups stratified by sex, age,
body mass index, and social class, and after excluding deaths
within 2 y. The trends were strongest for cardiovascular causes.
The mortality risk for those with four compared to zero health
behaviours was equivalent to being 14 y younger in chronological
age.

Conclusions

Four health behaviours combined predict a 4-fold difference in
total mortality in men and women, with an estimated impact
equivalent to 14 y in chronological age.

Funding: EPIC-Norfolk is supported by programme grants from Medical
Research Council and Cancer Research United Kingdom with additional
support from the Stroke Association, British Heart Foundation,
Research Into Ageing, and the Academy of Medical Science. The
sponsors had no role in the design and conduct of the study,
collection, management, analysis and interpretation of the data,
and preparation, review, or approval of the manuscript.

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

Academic Editor: Alan Lopez, The University of Queensland,
Australia

Citation: Khaw KT, Wareham N, Bingham S, Welch A, Luben R, et al.
(2008) Combined Impact of Health Behaviours and Mortality in Men
and Women: The EPIC-Norfolk Prospective Population Study . PLoS Med
5(1): e12 doi:10.1371/journal.pmed.0050012

Received: July 18, 2007; Accepted: October 26, 2007; Published:
January 8, 2008

Abbreviations: CI, confidence interval; ICD, International
Classification of Disease; RR, relative risk

* To whom correspondence should be addressed. E-mail:
kk101 at medschl.cam.ac.uk

Editors' Summary

Background.

Every day, or so it seems, new research shows that some aspect of
lifestyle--physical activity, diet, alcohol consumption, and so
on--affects health and longevity. For the person in the street, all
this information is confusing. What is a healthy diet, for example?
Although there are some common themes such as the benefit of eating
plenty of fruit and vegetables, the details often differ between
studies. And exactly how much physical activity is needed to
improve health? Is a gentle daily walk sufficient or simply a
stepping stone to doing enough exercise to make a real difference?
The situation with alcohol consumption is equally confusing. Small
amounts of alcohol apparently improve health but large amounts are
harmful. As a result, it can be hard for public-health officials to
find effective ways to encourage the behavioral changes that the
scientific evidence suggests might influence the health of
populations.

Why Was This Study Done?

There is another factor that is hindering official attempts to
provide healthy lifestyle advice to the public. Although there is
overwhelming evidence that individual behavioral factors influence
health, there is very little information about their combined
impact. If the combination of several small differences in
lifestyle could be shown to have a marked effect on the health of
populations, it might be easier to persuade people to make
behavioral changes to improve their health, particularly if those
changes were simple and relatively easy to achieve. In this study,
which forms part of the European Prospective Investigation into
Cancer and Nutrition (EPIC), the researchers have examined the
relationship between lifestyle and the risk of dying using a health
behavior score based on four simply defined behaviors--smoking,
physical activity, alcohol drinking, and fruit and vegetable
intake.

What Did the Researchers Do and Find?

Between 1993 and 1997, about 20,000 men and women aged 45-79 living
in Norfolk UK, none of whom had cancer or cardiovascular disease
(heart or circulation problems), completed a health and lifestyle
questionnaire, had a health examination, and had their blood
vitamin C level measured as part of the EPIC-Norfolk study. A
health behavior score of between 0 and 4 was calculated for each
participant by giving one point for each of the following healthy
behaviors: current non-smoking, not physically inactive (physical
inactivity was defined as having a sedentary job and doing no
recreational exercise), moderate alcohol intake (1-14 units a week;
a unit of alcohol is half a pint of beer, a glass of wine, or a
shot of spirit), and a blood vitamin C level consistent with a
fruit and vegetable intake of at least five servings a day. Deaths
among the participants were then recorded until 2006. After
allowing for other factors that might have affected their
likelihood of dying (for example, age), people with a health
behavior score of 0 were four times as likely to have died (in
particular, from cardiovascular disease) than those with a score of
4. People with a score of 2 were twice as likely to have died.

What Do These Findings Mean?

These findings indicate that the combination of four simply defined
health behaviors predicts a 4-fold difference in the risk of dying
over an average period of 11 years for middle-aged and older
people. They also show that the risk of death (particularly from
cardiovascular disease) decreases as the number of positive health
behaviors increase. Finally, they can be used to calculate that a
person with a health score of 0 has the same risk of dying as a
person with a health score of 4 who is 14 years older. These
findings need to be confirmed in other populations and extended to
an analysis of how these combined health behaviors affect the
quality of life as well as the risk of death. Nevertheless, they
strongly suggest that modest and achievable lifestyle changes could
have a marked effect on the health of populations. Armed with this
information, public-health officials should now be in a better
position to encourage behavior changes likely to improve the health
of middle-aged and older people.

Additional Information.

Please access these Web sites via the online version of this
summary at http://dx.doi.org/10.1371/journal.pmed.0050012.
*The MedlinePlus encyclopedia contains a page on healthy
  living (in English and Spanish)
http://www.nlm.nih.gov/medlineplus/ency/article/002393.htm

*The MedlinePlus page on seniors' health contains links to
  many sites dealing with healthy lifestyles and longevity (in
  English and Spanish)
http://www.nlm.nih.gov/medlineplus/seniorshealth.html

*The European Prospective Investigation into Cancer and
  Nutrition (EPIC) study is investigating the relationship
  between nutrition and lifestyle and the development of cancer
  and other chronic diseases; information about the
  EPIC-Norfolk study is also available
http://www.iarc.fr/epic/
http://www.srl.cam.ac.uk/epic/

*The US Centers for Disease Control and Prevention provides
  information on healthy aging for older adults, including
  information on health-related behaviors (in English and
  Spanish)
http://www.cdc.gov/aging/
http://www.cdc.gov/aging/info.htm

*The UK charity Age Concerns provides a fact sheet about
  staying healthy in later life
http://www.ageconcern.org.uk/AgeConcern/fs45.asp

*The London Health Observatory, which provides information for
  policy makers and practitioners about improving health and
  health care, has a section on how lifestyle and behavior
  affect health
http://www.lho.org.uk/

Introduction

A huge body of evidence indicates that lifestyles such as smoking,
diet, and physical activity have a major influence on health
[1-16]. However, achievable behavioural changes are often believed
to have limited impact at an individual level. Nevertheless, a
recent report from 2,339 men and women aged 70-90 y in 11 European
countries indicated that adherence to a Mediterranean diet,
nonsmoking, any alcohol use, and moderate physical activity were
associated with more than 50% lower rate of all-cause and
cause-specific mortality [6]. An advantage of an Europe-wide study
is the great diversity in diet and other lifestyles [17,18], but
one issue is whether such mortality differences can be observed in
a single, relatively homogenous population within the usual range
of lifestyle variations that may be more realistically achievable
and directly relevant to immediate public health.

Additionally, assessment of diet and physical activity in most
studies usually involves complex methodological analyses [6,16],
and simpler indicators might be more feasible to use in estimating
the potential combined impact of behavioural changes.

We have previously reported that high fruit and vegetable intake,
as indicated by plasma vitamin C concentrations, predicts lower
all-cause mortality in men and women [19]. We have also previously
shown that low work and leisure-time physical activity predicts
all-cause mortality and cardiovascular disease incidence [20]. Many
health behaviours such as smoking habit, diet, and physical
activity are highly correlated and, in aetiologically focused
papers, treated as covariates. In the current analysis, we wished
to explore the potential magnitude of their combined impact.

We examined the relationship between lifestyle using a simple
health behaviour score based on smoking, physical activity, alcohol
drinking, and fruit and vegetable intake, and total mortality by
cause in men and women aged 45-79 y living in the general
community.

Methods

The participants were part of a prospective population study of
25,639 men and women aged 45-79 y, 99.5% white (as self-defined on
questionnaire), resident in Norfolk, UK, first surveyed in
1993-1997. (Norfolk is a county in the UK encompassing a wide
socioeconomic and urban-rural distribution.) They were recruited
from age-sex registers of general practices as part of a
ten-country collaborative study, the European Prospective
Investigation into Cancer and Nutrition (EPIC). As virtually 100%
of people in the UK are registered with general practitioners
through the National Health Service, the age-sex registers form a
population-based sampling frame. From the inception of the
EPIC-Norfolk cohort, data collection was broadened to enable the
examination of a wider range of determinants of chronic diseases.
The Norfolk cohort was comparable to national population samples
with respect to characteristics including anthropometry, blood
pressure, and lipids, but with a lower prevalence of current
smokers [21].

At the 1993-1997 baseline survey, participants completed a detailed
health and lifestyle questionnaire. They were asked about medical
history with the question "Has a doctor ever told you that you have
any of the following?" followed by a list of conditions that
included heart attack, stroke, and cancer. Smoking history was
derived from yes/no responses to the questions "Have you ever
smoked as much as one cigarette a day for as long as a year?" and
"Do you smoke cigarettes now?" Alcohol consumption derived from the
question "How many alcoholic drinks do you have each week?" with
four separate categories of drinks. A unit of alcohol
(approximately 8 g) was defined as a half pint of beer, cider, or
lager; a glass of wine; a single unit of spirits (whisky, gin,
brandy, or vodka); or a glass of sherry, port, vermouth, or
liqueurs. Total alcohol consumption was estimated as the total
units of drinks consumed in a week. For these analyses, a moderate
drinker was defined as someone who drank one or more units a week
(that is, not a nondrinker), but not more than 14 units a week.

Habitual physical activity was assessed using two questions
referring to activity during the past year. The first question
asked about usual physical activity at work, classified as four
categories: sedentary, standing (e.g., hairdresser or guard),
physical work (e.g., plumber or nurse), and heavy manual work
(e.g., construction worker). The second question asked about the
amount of time spent, in hours per week, in winter and summer in
other physical activity. The average time spent daily in
recreational activity was estimated as the total hours spent per
week (average of winter and summer) in cycling and other physical
activity such as swimming or jogging, divided by seven. A simple
index allocated individuals to four ordered categories: inactive
(sedentary job and no recreational activity); moderately inactive
(sedentary job with <0.5 h recreational activity per day, or
standing job with no recreational activity); moderately active
(sedentary job with 0.5-1 h recreational activity per day, or
standing job with <0.5 h recreational activity per day, or physical
job with no recreational activity); and active (sedentary job with
>1 h recreational activity per day, or standing job with >1 h
recreational activity per day, or physical job with at least some
recreational activity, or heavy manual job). This index was
validated against heart rate monitoring with individual calibration
in two independent studies [22,23]. We have also previously
reported that this four-point index is inversely related to
all-cause mortality and cardiovascular disease incidence in the
EPIC-Norfolk population in men and women across a wide age and
social class range [20]. For the purposes of the current study, we
dichotomised the population into physically inactive (sedentary job
and no recreational activity) and not physically inactive (any
category with activity levels above the latter).

Social class was classified according to the Registrar General's
occupation-based classification scheme into five main categories,
with social class I representing professionals, social class II
managerial and technical occupations, social class III subdivided
into nonmanual and manual skilled workers, social class IV partly
skilled workers, and social class V unskilled manual workers. We
also recategorized social class into manual and nonmanual social
classes. Social classes I, II, and III nonmanual were classified as
nonmanual, whereas social classes III manual, IV, and V were
classified as manual.[24].

Trained nurses carried out a health examination at a clinic. Height
and weight were measured with subjects in light clothing without
shoes. Body mass index was estimated as weight in kilograms divided
by height in meters squared. Blood was taken by venepuncture into
plain and citrate bottles. After overnight storage in a dark box in
a refrigerator at 4-7 °C, they were spun at 2,100g for 15 min at 4
°C, and plasma and serum samples obtained. Six months after the
start of the study, when funding became available, samples from
participants were additionally taken for vitamin C assays. Plasma
vitamin C was measured from blood drawn into citrate bottles.
Plasma for vitamin C was stabilized in a standardized volume of
metaphosphoric acid stored at -70 °C. Plasma vitamin C
concentration was estimated using a fluorometric assay within 1 wk
of sampling [25]. The coefficient of variation was 5.6% at the
lower end of the range (mean, 33.2 µmol/l) and 4.6% at the upper
end (mean, 102.3 µmol/l). We have previously reported that high
plasma vitamin C level is inversely associated with mortality from
all causes. Because humans do not manufacture vitamin C and have to
rely on exogenous sources, plasma vitamin C is a good biomarker of
plant food intake; previous studies have reported that a blood
value of 50 mmol/l or more indicates an intake of at least five
servings of fruit and vegetables daily [19;26].

We constructed a simple pragmatic health behaviour score.
Participants scored one point for each of the following health
behaviours: current nonsmoking, not physically inactive, moderate
alcohol intake (1 to 14 units a week), and plasma vitamin C level
>50 mmol/l, indicating fruit and vegetable intake of at least five
servings a day. Participants could therefore have a total health
behaviour score ranging from zero to four (Table 1). These
particular health behaviours and their categorization were chosen
based on extensive previous evidence on the relationship between
these lifestyle factors and health endpoints.
thumbnail

Table 1.

Health Behaviour Score: Score One Point for Each of the Health
Behaviours Below for a Total Score of Zero to Four

All participants are followed up for health events. We report
results for follow-up to July 2006, an average of 11 y. All
participants are flagged for death certification at the Office of
National Statistics, United Kingdom which is virtually complete.
Death certificates for decedents are coded by trained nosologists
according to the International Classification of Disease (ICD).
Cardiovascular death was defined as those who had ICD 400-438
(ICD9) or ICD I10-I79 (ICD 10) as underlying cause of death and
encompasses stroke and coronary heart disease as well as other
vascular causes. Cancer death was defined as those who had ICD
140-208 (ICD9) or ICD C00-C97 (ICD 10) as underlying cause of
death. Deaths not due to cardiovascular or cancer were classified
as deaths from other causes. The study was approved by the Norwich
District Health Authority Ethics Committee, and all participants
gave signed informed consent.

The present analysis included all men and women aged 45-79 y who
completed the health and lifestyle questionnaire and attended the
health examination, who had complete data for physical activity,
alcohol intake, and plasma vitamin C. Of the 22,301 with available
data, 2,057 had a history of heart disease, stroke, or cancer at
the baseline visit and were excluded from the main analyses,
leaving 20,244 individuals.

We examined risk factor distributions in men and women. The Cox
proportional hazards model was used to determine the relative risks
of all-cause and cause-specific mortality by each of the individual
health behaviours: current smoking, physical activity, moderate
alcohol intake, and plasma vitamin C category after adjusting for
age, sex, body mass index, and social class. We then examined
mortality rates and relative risks of all-cause and cause-specific
mortality by health score, adjusted for age, sex, body mass index,
and social class. We estimated the difference in survival between
those with health behaviour score of four compared to zero in
age-equivalent terms by comparing the beta coefficient for
mortality associated with each year of age with the beta
coefficient difference in mortality for those with a score of four
compared to zero [27]. We also examined relative risks in
subgroups, stratified by sex, age group (<65 y and >=65 y), body
mass index category (<27 kg/m^2 and >=27/kg^2), and manual and
nonmanual social class, and also after excluding those who died
within 2 y of follow-up. We additionally examined the relationship
between health behaviour score and mortality in the 2,057
individuals with prevalent disease excluded from the main analyses.

Results

Table 2 shows characteristics of the participants at baseline
survey and mortality rates by cause after follow-up to 2006.
thumbnail

Table 2.

Distribution of Variables in 20,244 Men and Women Aged 45-79 y
without Known Cardiovascular Disease or Cancer in EPIC-Norfolk at
Baseline 1993-1997 and Mortality after Follow-Up to 2006 (Average
11 y)

Table 3 shows the relative risks for individual health behaviours
by cause, adjusted for sex, body mass index, and social class. Each
of the health behaviours: smoking, being physically inactive, not
having a moderate alcohol intake, and a low fruit and vegetable
intake as indicated by plasma vitamin C level <50 mmol/l. were
associated with significantly higher risks of mortality from all
causes. As might be expected, there were some differentials in the
observed risk reductions observed for different health behaviours
and cause-specific mortality in men and women; current smoking was
the most consistent and strongest risk factor.
thumbnail

Table 3.

Independent Relative Risk (RR) of Mortality for Individual Health
Behaviours by Cause, Adjusted for Age, Sex, Body Mass Index, and
Social Class in 20,244 Men and Women Aged 45-79 y without Known
Cardiovascular Disease or Cancer in EPIC-Norfolk 1993-2006, Cox
Regression Model

Table 4 shows the relative risks for cause-specific mortality by
number of health behaviours, adjusted for age, sex, body mass
index, and social class. Risk of total mortality significantly
increased with decreasing number of health behaviours, with a
strong trend observed. Those who scored zero for the health
behaviours had a relative risk of 4.04 (95% confidence interval
[CI] 2.95-5.54) compared to those with a score of four. The
greatest risk differences were observed for deaths attributed to
cardiovascular diseases (relative risk [RR] 5.02; 95% CI 2.93-8.61)
for score 0 versus score 4. Table 3 also shows that the trends were
significant and consistent for all-cause mortality stratified by
sex, age group <65 and >=65 y, body mass index <27 and >27 kg/m^2,
manual and nonmanual social class, and after excluding deaths in
the first 2 y. None of the interaction terms for health score with
sex, age, body mass index, and social class were significant in
multivariate analyses. In this cohort, vitamin supplement use was
not associated with mortality, and results were similar after
adjusting for vitamin supplement use or excluding vitamin users
from the analyses (unpublished data and [19]). Table 5 shows the
relative risks for cause-specific mortality by number of health
behaviours in the 2,057 individuals with prevalent chronic disease
not included in the main analyses. Results were very similar to
those observed in individuals without known prevalent disease.
thumbnail

Table 4.

Mortality Rates and Relative Risk of All-Cause Mortality by Number
of Health Behaviours, Adjusted by Age, Sex, and Body Mass Index,
and Stratified by Cause, Sex, Age, Body Mass Index, and Social
Class in 20,244 Men and Women Aged 45-79 y without Known
Cardiovascular Disease or Cancer in EPIC-Norfolk 1993-2006, Cox
Regression Model
thumbnail

Table 5.

Mortality Rates and Relative Risk of All-Cause Mortality by Number
of Health Behaviours, Adjusted by Age, Sex, and Body Mass Index,
and Stratified by Cause, Sex, Age, Body Mass Index, and Social
Class in 2,057 Men and Women Aged 45-79 y with Self-Reported
Cardiovascular Disease or Cancer in EPIC-Norfolk 1993-2006, Cox
Regression Model

Figure 1 shows survival curves over the average 11 y of follow-up,
adjusted for age, sex, and body mass index by health score. As with
the relative risks of mortality, the adjusted cumulative survival
was about 75% for those scoring zero and 95% for those scoring
four, respectively, for health behaviours. From the Cox model, the
beta coefficient for mortality associated with each year increase
in chronological age was 0.10 (± standard error 0.004). The
difference in beta coefficients between a health score of zero
versus four was 1.43, that is, equivalent to approximately 14 y in
chronological age for mortality risk.
thumbnail

Figure 1. Survival Function According to Number of Health Behaviours in
Men and Women Aged 45-79 Years without Known Cardiovascular Disease or
Cancer, Adjusted for Age, Sex, Body Mass Index and Social Class,
EPIC-Norfolk 1993-2006

Discussion

In these middle-aged and older men and women, four health
behaviours--not smoking, not being physically inactive, having a
moderate alcohol intake (1-14 units a week), and having a high
fruit and vegetable intake (as indicated by plasma vitamin C level
>50 mmol/l)--were combined into a simple pragmatic four-item health
behaviour score that was inversely related with mortality over an
average 11 y of follow-up. There was a strong trend of decreasing
mortality risk with increasing number of positive health
behaviours, with those who scored four having approximately one
quarter the mortality risk of those who scored zero, equivalent to
about 14 y difference in chronological age. Although the trends
were strongest for deaths from cardiovascular causes, they were
also apparent for deaths from cancer and from other causes. They
were also consistent after stratifying by sex, age group, body mass
index, and social class, and after exclusion of deaths in the first
2 y. In the individuals with prevalent disease who were not
included in the main analyses, we also found similar trends in
mortality with the health behaviour score.

The evidence that behavioural factors such as diet, smoking, and
physical activity influence health is overwhelming. However, these
health behaviours are usually highly correlated, and only recently
have these factors been examined in combination. Chiuve et al.
reported that in men in the US Health Professionals Study, men with
five low-risk health behaviours, that is nonsmokers, with a body
mass index <25 kg/m^2, moderate to vigorous activity, moderate
alcohol consumption, and the top 40%of a healthy diet score had a
0.13 risk of coronary heart disease compared to men who did not
adhere to any of these behaviours [2]. Our estimates with
comparable measures for smoking, alcohol, and physical activity,
but with a simpler diet measure, are comparable for deaths from
cardiovascular causes. Whether combined lifestyle factors are also
related to other diseases or all-cause mortality has been less well
documented till recently. Knoops et al. reported that in 2,339 men
and women aged 70-90 y in 11 European countries, the combination of
four factors--adherence to a Mediterranean diet, moderate alcohol
use, being physically active, and nonsmoking--was associated with a
mortality rate one third of those who did not have these behaviours
[6]. As Rimm and Stampfer have pointed out, these results are
consistent with studies suggesting similar substantial reductions
in risk of chronic diseases such as coronary heart disease,
diabetes, and cancer associated with lifestyle behaviours [28].
However, as Rimm and Stampfer and others have also highlighted, the
Knoops study was conducted on a highly selected older group of
individuals in 11 different European countries with very different
mortality rates, and the generalisability of these results to
younger populations is uncertain [17,18]. It also did not have the
power to examine the consistency of findings within subgroups, for
example, stratifying by sex or obesity. Findings from the current
study support those from previous reports in more diverse
populations: even within the range of usual lifestyle in a
free-living, relatively homogenous population living in one region
of UK, there were substantial differences in mortality associated
with the four health behaviours combined, and these differences
were consistent in several population subgroups stratified by sex,
age, social class, and obesity.

Additionally, many studies that have reported on diet and physical
activity have used detailed complex instruments for assessment of
these lifestyles, to obtain for example, a Mediterranean diet score
or a physical activity score [6,16]. These instruments are useful
for research purposes, but a simpler, more pragmatic health
behaviour score may be more easily used for clinical or public
health practice. We also wished to examine the relationship with
mortality and consistency over a wide range of different groups in
the population stratified by sex, age, body mass index, and social
class. The score, though simple, was based on instruments that have
been extensively previously validated. We used plasma vitamin C as
that has been previously shown to be a good biomarker of fruit and
vegetable intake, and the association between blood biomarker and
dietary intake well quantified. In this cohort, vitamin supplement
use was not associated with mortality, and results were similar
after excluding those using vitamin supplements. Since many dietary
practices are highly correlated, it may also be a surrogate marker
for particular dietary patterns such as high fibre intake, or low
fat intake that may have additional health effects. Although the
recent Women's Health Initiative reported that women in the dietary
intervention arm did not have significantly lower cardiovascular
endpoints and nonsignificant differences for breast cancer,
explanations for the lack of effect have been extensively discussed
elsewhere, including smaller dietary differences between control
and intervention arms than originally planned [29-31].
Nevertheless, there is a large body of experimental and
epidemiologic evidence indicating a high intake of fruit and
vegetables is beneficially associated with health [5,7,11,32]
Similarly, the simple physical activity score used here has been
extensively validated as a measure of total energy expenditure and
also predicts total mortality and cardiovascular disease incidence.

There is also a large body of evidence relating alcohol intake to
mortality. There is some debate about the nature of the
relationship, with the general consensus of a U-shaped
relationship; with nondrinkers and heavy drinkers being at
increased risk. Internationally, upper-limit recommendations for
alcohol intake range from maximum of five drinks daily for men and
three drinks daily for women in France to two drinks daily for men
and one for women in the United States. In the UK, the
recommendations are up to 21 drinks weekly for men and 14 drinks
weekly for women [33]. We used a generally accepted definition of
moderate drinking as at least one drink a week, but not more that
14 drinks a week, with the upper end well within the generally
recommended upper range.

It is possible that people who are already ill may be more likely
to be physically inactive and change their diet as a result of
prevalent disease. However, individuals with known serious chronic
disease, namely cancer, heart disease, and stroke, were excluded
from the main analyses. Nevertheless, even in those individuals
with known diseases, subsequent survival was also strongly related
to health behaviour score. Additionally, the relationships were
consistent after excluding all those who died within 2 y of the
baseline and after stratification for major potential confounders
such as age, obesity, and social class. Though we cannot exclude
residual confounding, our results are consistent with the existing
evidence indicating these behavioural factors are beneficial for
health. Any potential unknown confounders would have to explain
plausibly the substantial differences in mortality risk. In these
particular analyses, we did not examine how far, if at all, the
behavioural associations were mediated through classical
cardiovascular risk factors, though previous analyses have
suggested these are independent. Nevertheless, the magnitude of the
behavioural associations are substantially greater than those
reported for many individual physiological risk factors such as
blood pressure, lipids, or C-reactive protein, such that they are
likely to act synergistically on several different biological
pathways.

This study has several limitations. There are potential large
measurement errors in the assessment of exposures. We used only a
measure at one point in time to characterize individuals and did
not take into account likely changes in lifestyles over the
follow-up period. Nevertheless, random measurement error is likely
to attenuate any associations observed, so the estimated
differences in risk are likely to be larger than those observed.
Secondly, though clearly different health behaviours differ
somewhat in their association with different endpoints, we did not
weight them because the aim of the current approach was to examine
the use of a simple score that could be conceptually easy to
understand and use in clinical practice, rather than complicated
algorithms. Nevertheless, the simple score was strongly related
with mortality; imprecision is likely again only to attenuate any
relationships. Thirdly, the proportions of the population with some
or all positive health behaviours were relatively high since the
definitions for health behaviours were not necessarily optimal, for
example, for physical activity [20], and dichotomizing behaviours
between inactive and not inactive may have obscured the gradient in
mortality between those who were moderately inactive and those who
were active. Nevertheless, this demonstrates that the behaviours
associated with substantial differences in mortality risk are
entirely feasible and achievable by most of the population.

Implications

Our data examined only mortality. With ageing populations, a major
challenge is not just premature mortality, but functional health,
which relates to quality of life. Nevertheless, we have also
previously reported that these lifestyle factors are also
associated with similar substantial differences, with subjective
functional health of comparable magnitude [34;35], and subjective
functional health is also predictive of mortality [36]. The four
health behaviours were within the usual range found in a
free-living population. Though relatively modest and achievable,
their combined impact was associated with an estimated 4-fold
difference in mortality risk, equivalent to 14 y in chronological
age. Notably, the differences in survival were also observed in
people with existing chronic disease. These results may provide
further support for the idea that even small differences in
lifestyle may make a big difference to health in the population and
encourage behaviour change.

Acknowledgments

Author contributions. KTK, NW, SB, and ND are principal
investigators in the EPIC-Norfolk population study. NW developed
and validated the physical activity measures and scales. AW was
responsible for nutritional data involved in the physical activity
validation and calibration studies. RL is responsible for data
management, record linkage, and computing overall. KTK conducted
the data analyses, wrote the paper with coauthors, had full access
to all of the data in the study, and takes responsibility for the
integrity of the data and the accuracy of the data analysis.

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