Download Diet synergies and mortality—a population

Published by Oxford University Press on behalf of the International Epidemiological Association
Ó The Author 2006; all rights reserved. Advance Access publication 4 January 2006
International Journal of Epidemiology 2005;35:418–426
doi:10.1093/ije/dyi296
Diet synergies and mortality—a
population-based case–control study
of 32 462 Hong Kong Chinese older adults
C Mary Schooling,1 Sai Yin Ho,1 Gabriel M Leung,1* G Neil Thomas,1 Sarah M McGhee,1
Kwok Hang Mak2 and Tai Hing Lam1
Accepted
29 November 2005
Background Food and drink are not consumed in isolation and can have complimentary effects
enhancing or blocking the overall uptake of nutrients. We investigated how
combinations of foods, drinks, and smoking affected mortality.
Method
Adjusted logistic regression was used to assess the joint effect of healthy foods, less
healthy foods, smoking, and alcohol use on mortality in a case–control study of
all Chinese adults aged 60 or over who died in 1998; 21 494 dead cases (81% of
all registered deaths) and 10 968 live controls were included.
Results
There was a significant trend of increasing all-cause mortality risk with decreasing
healthy food consumption (P , 0.001), and the increase in risk was significantly
steeper for people with high intakes of less healthy food (P for interaction ,0.001).
There was a steeper risk from increasing less healthy food intake in ever-smokers
and people not drinking tea regularly (P , 0.001), while the J-shaped relationship
between alcohol and mortality differed in shape with level of less healthy food
intake.
Conclusion
Intake of some dietary items may modify the effect of others. An analysis
framework explicitly recognizing complementary and potentially synergistic
effects of food, drinks, and smoking could enhance our understanding of dietary
epidemiology.
Keywords
Diet, synergy, mortality, smoking, older adults, case–control
Introduction
A reductionist approach has been very successful in determining the relationship between diet and disease. However,
specific nutritional items are not consumed in isolation, and the
absorption, bioavailability, and action of dietary constituents
may depend on what else is ingested. These potential synergies
have mostly been investigated from the perspective of providing adequate nutrition to identify foods that should be eaten
together, such as the protein complementation from bean and
cereal combinations.1 Others have examined how food combinations may block or promote the uptake of nutrients, such
as iron or calcium.2–4 Potential synergies have less often been
1
2
Department of Community Medicine, The University of Hong Kong,
Pokfulam, Hong Kong, China.
Department of Health, Kowloon, Hong Kong, China.
* Corresponding author. Department of Community Medicine, 5/F Academic
and Administration Block, University of Hong Kong, 21 Sassoon Road,
Pokfulam, Hong Kong SAR, China. E-mail: [email protected]
investigated from the perspective of over-nutrition or chronic
disease prevention to investigate whether there are any foods
that affect the uptake of nutrients, which may be harmful in
excess, or if there are foods which eaten in combination
may prevent disease. Nevertheless, there is some evidence for
such synergies. Tea may moderate lipid metabolism.5–8 Certain
nutrient combinations, such as tea and soy, are particularly
effective in suppressing some types of tumour growth9–11
or lipid oxidation.12 The role of diet may also vary for
smokers.13,14 Thus, there is increasing recognition of the need
for a synergistic approach15–17 although no consensus on how to
investigate synergies in epidemiological studies.
In assessing the overall effects of diet, studies have generally
focused on data driven dietary patterns or theoretically derived
diet scores.18 Data derived dietary patterns are typically obtained
using factor analysis or cluster analysis. Factor analysis is
premised on exposing underlying dimensionality, implicitly
assuming the existence of independent orthogonal factors.
Cluster analysis is designed to categorize subjects discretely.
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DIET SYNERGIES AND MORTALITY
Although both approaches have uncovered elements of a
healthy diet, they are predicated on linear combinations of
dietary items, which will not specifically reveal their joint or
synergistic effects nor provide insight into whether particular
patterns are healthy because of the constituent items or
combinations thereof. Empirically in this study, we explored
the factor structure of dietary components using principal
components analysis with varimax rotation but the resulting
2-factor solution grouped fish and meat consumption together
(fruits, vegetables, soy, and dairy was the other factor grouping)
and thus would have precluded the examination of our main
research question of dietary synergy between a priori healthy
and unhealthy food items. On the other hand, diet scores
are typically uni-dimensional giving a ranking according to
overall healthiness of the diet. However, diet scores have been
operationalized in terms of two dimensions,19 giving the
possibility of examining joint effects. As we aimed to investigate
potentially synergistic effects of different dietary components,
we followed the two-dimensional food score components of
Michels et al.19 Each person receives a recommended food score
(RFS), based on consumption of pre-defined healthy foods,
such as fruits and vegetables, and a not recommended food
score (NRFS) based on consumption of pre-defined less healthy
foods, such as red meat. We examined the effect of RFS and
NRFS in relation to all-cause mortality, using data from a large
population-based case–control study in a homogeneous Asian
population. Additionally, we considered their joint effect and
whether the effect of less healthy food (NFRS group) or
healthy food (RFS group) varied with tea consumption, alcohol
use, or tobacco consumption.
Subjects and methods
The ethnic Chinese in Hong Kong, comprising 95% of the total
population, are a culturally homogeneous group with a common
cooking style. The staple food is white rice, accounting for ~80%
of cereal consumption,20 with a correspondingly low consumption of wheat and potatoes. The main cooking oils are corn oil,
peanut oil, and safflower oil.20 Typically, the main meals (i.e.
lunch and dinner) consist of rice or noodles, vegetables and meat,
fish or tofu, possibly followed by some dessert, such as fruit.
Chinese meals are usually served as a selection of separate dishes
to be shared around the table. Although eating habits are
changing with increasing Westernization, this is less likely to be
the case for older people.
The LIMOR (LIfestyle and MORtality) study is a populationbased case–control study of adult deaths among ethnic Chinese
residents in Hong Kong between mid-December 1997 and
mid-January 1999. The study was originally designed to examine
the effect of smoking as a risk factor for mortality.21 It captured
81% of all deaths registered during the study period. Information on demographic characteristics (age, sex, education, and
type of job) and health behaviours (smoking, use of alcohol,
leisure exercise, and dietary habits) 10 years before the death of
the case was collected from the person registering the death,
who was usually one of the more educated members of the
deceased’s family. The same information was also obtained for a
living person, other than the informant, either the decedents’
spouse or a person, preferably aged 60 or above, whose habits
10 years ago the informant was most familiar with. When
419
the informant was the spouse an eligible control was seldom
obtained, therefore there were more cases than controls,
especially in the older age-groups, and our method tended
to select female controls (since wives generally survive their
husbands). Information about both cases and controls was
obtained from a third-party, who was usually an adult child of
the subject, often living in close, multi-generational Chinese
families. Proxy reports were chosen first for expediency,
second because of the collectivist values of Chinese society,
and third because proxies have been shown to be capable of
providing reliable answers to simple questions,22,23 particularly
in Hong Kong.24 In addition proxy reports of food intake are
potentially preferable because self-report dietary data validates
poorly against associated bio-markers25 probably because of
socio-cultural pressures to present oneself as eating a ‘correct’
diet.26,27 Issues of self-presentation are unlikely to be so
strong when reporting on another person. Proxy reports of
food intake in a similar setting, i.e. adult reports on other
family members, such as children, validated better against biomarkers.28 Thus, simple food frequency questions were asked
about the consumption of nutritionally significant foods groups,
which are commonly eaten in Hong Kong, vary between
individuals,20 and were thought to be related to disease risk at
the time of the study. Simple food frequency questions on
food groups, such as fruits and vegetables, have been found to
relate to relevant bio-markers29 and have been validated in
developed Chinese societies.30 The food groups chosen were
fresh fruits, fresh vegetables, soy products, fish (excluding salted
fish), Chinese tea, dairy products (specified as milk, cheese, and
ice cream), and meat. Potatoes, bread, and wheat products
were not included because of relatively low consumption, while
assessing oil use would have been unlikely to produce useful
data and would have been potentially less relevant when the
main oils used are unsaturated. Consumption was specified as
‘not at all’, ‘monthly less than once’, ‘monthly 1–3 times’,
‘weekly 1–3 times’, and ‘weekly at least 4 times’. These data are
suitable for ranking the intake of certain foods, but do not
enable us to assess overall dietary content, the intake of specific
nutrients, or total dietary intake; as such they are useful for
the assessment of food groups or dietary patterns, but not of
food constituents. As a reliability check, repeat interviews were
conducted by telephone on a random sample of 235 cases and
106 proxy controls, 3 weeks after initial interview on average.
The percentage agreement to within one category for case and
controls, respectively, were 92 and 97% for fruit, 98 and 100%
for vegetables, 90 and 93% for soy products, 98 and 99% for fish,
90 and 91% for tea, 70 and 66% for dairy products, and 99 and
98% for meat.
Statistics
The outcome considered is all-cause mortality. Based on the
RFS/NRFS scoring system19 and the likely constituents of food
groups in Hong Kong in 1998,20 the food groups were classed as
RFS (fresh fruits, fresh vegetables, soy, and fish) or NRFS (meat
and dairy products). In Hong Kong in 1995, most meat consumed
was red meat (pork, beef, lamb, organ meat).31 Dairy products
include ice cream, cheese, and milk; most milk consumed at the
time was full fat.20 Vegetables are mainly green vegetables, such
as Chinese cabbage, Chinese kale, lettuce, and broccoli.20 Fruits
include apples, oranges, bananas, and a wide range of tropical
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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
fruits, such as melon, papaya, and mango.20 Subjects were given
a score according to how often they ate foods in these groups.
The score for each item was weighted according to the proportion of days on which it was consumed, e.g. 2/7 for 1–3 times a
week. The weighted scores were summed for RFS and NRFS
foods, and divided into groups representing a ranking of food
scores for the controls, such that there were at least three groups
for each dimension (to investigate dose–response relationships)
and we avoided having .40% of controls in the highest or
lowest group.
Chinese tea is tea without milk often drunk with meals. In
Hong Kong 90% of the tea imported at the time of exposure
ascertainment in 1988 was black tea.32 This is in contrast to
Japan, where tea is mainly green. Tea was categorized on
frequency into less than weekly, 1–3/week, and 4 or more times
per week.
On the basis of the potential benefits of light drinking, alcohol
was categorized as never, fewer than 8 drinks per week, 8 or
more drinks per week, and ex-drinkers, based on the frequency
of alcohol use, typical consumption per occasion, and type of
alcohol. A drink was defined as equivalent to 12 g of ethanol.
Smokers were classified into current smokers, ex-smokers, and
never-smokers. Ex-smokers and ex-drinkers are a heterogeneous group who might have changed their habits as a health
protection measure or as a response to ill health. We excluded
ex-smokers and ex-drinkers when investigating the potential
moderating effect of smoking or use of alcohol, thus in a mainly
older sample we are comparing surviving and possibly healthier
smokers and alcohol consumers with lifelong abstainers, which
will make our findings conservative.
Unconditional logistic regression adjusted for potential confounding factors was used to assess the effect of NRFS and RFS
group independently on all-cause mortality. The joint association of NRFS and RFS with mortality was examined from (i) the
significance of interaction terms within a multiplicative model
and (ii) the effect of variables representing the combination of
RFS and NRFS groups. Additionally, we considered in a similar
way whether the effect of NRFS or RFS group varied with tea,
smoking, and use of alcohol. Men and women, and all ages were
considered together as there is little evidence that diet has a
different effect on men and women or by age-group. Potential
confounding factors considered were age (in 5 year age-bands),
sex, leisure-time exercise (,1/month or >1/month), educational level (none, primary, secondary/tertiary), type of longest
held job (none, sedentary, light/medium, or heavy manual),
type of housing (public/hut/shared, self-owned, or other), and
where appropriate, other food score, tea, smoking (current
smoker, ex-smoker, or never-smoker), and alcohol history
(never, ,4/weeks, 41/week or ex-drinker). The nature of the
data collection made it impossible to collect body mass index
but that should be correlated with diet and physical activity.
To minimize the potential for reverse causality and to obtain
more conservative results, the cases who had been in very poor
health or unable to go outdoors alone more than 10 years
previously were excluded, as these people might have changed
their diet in response to their health problems. Consistent
with the control selection and to ensure an adequate number
of controls representative of the population in terms of socioeconomic status, we restricted our analysis to cases and controls
aged 60 or over. The project received ethics approval from the
Ethics Committee of the Faculty of Medicine, University of
Hong Kong.
Results
After excluding all the under-60s (n 5 6386) and cases in very
poor health or housebound 10 years previously (n 5 1395) there
were 21 494 cases and 10 968 proxy controls. Consistent with
Hong Kong mortality patterns, cancer was the leading cause of
death (33.7%), followed by cardiovascular disease (27.8%),
respiratory diseases (excluding lung cancer) (21.4%), and other
(17%). The cases and controls had similar birthplaces, education,
type of housing, jobs, and ever use of alcohol (Table 1). The
mean (SD) age of the cases and controls were 73.6 (8.1) and
75.2 (8.4) years for men but 71.0 (7.3) and 79.7 (9.2) years,
respectively for women. These differences in age were controlled for automatically in the analyses. As reported previously,
a higher proportion of the cases were ever smokers and rarely
took exercise.33,34 In keeping with the traditional diet in a
wealthy, coastal Chinese city31 most subjects ate meat (90%),
fish (90%), vegetables (95%), and fruit (80%) at least 4 times
a week, but fewer ate soy products (21%) or dairy products
(16%) as frequently. Healthy food scores for the controls
created three RFS groups, with 26% in the group eating least
healthy food, 55% in the middle group and 19% in the high
group. Less healthy food scores had a bimodal distribution and
fell most evenly into four NRFS groups with 38% in the lowest
group, 19% in the next group, 29% in the next, and 14% in the
highest group. The proportion of people eating high amounts
of healthy food was greatest in the controls in both age-groups,
but there was little difference in less healthy foods between
the cases and controls (Table 1). Smokers and drinkers were
less likely to be in the high healthy or high less healthy food
groups, while regular tea drinkers were more likely to be in the
high healthy and high unhealthy food groups.
Table 2 shows the effect of NRFS and RFS on all-cause
mortality in three models, adjusting for an increasing number
of confounding factors. There was no effect of NRFS group in the
first two models, which adjusted for age, sex, and education
(Model 1) and then also for housing type, job type, and leisuretime exercise (Model 2). However, the effect of NRFS group
changed when the RFS group, tea frequency, alcohol use, and
smoking status were entered into the model (Model 3). In the
fully adjusted model there was a linear dose–response relationship between higher NRFS group and increased risk of mortality.
In contrast, a higher RFS group was associated with lower
mortality in all three models, with consistent odds ratios and
clear dose–response relationships in each model. Deaths from
injury and poisoning did not show the same pattern of relationships with NRFS or RFS groups. In the fully adjusted model
(Model 3) there was no evidence that the effect of NRFS or RFS
group varied with sex (P for interaction .0.5 in both cases—data
not shown). The relationships in the fully adjusted model were
essentially unchanged when we restricted the sample to the
potentially most reliable informants, i.e. those living with the
case or control 10 years ago during the time of exposure
ascertainment (Model 4), or those reported on by their adult
children (data not shown). Subsequent analyses (as presented in
the following tables) gave similar results for all the subjects, or
the subjects living with the informant at the time of exposure
DIET SYNERGIES AND MORTALITY
421
Table 1 Demographic characteristics (%) of cases and controls
Male
Female
60–74
n
751
All
60–74
751
All
Case
Control
Case
Control
Case
Control
Case
Control
Case
Control
Case
Control
5742
1975
6040
1577
11 782
3522
2976
5069
6736
2347
9712
7416
Education
None
24.3
23.2
33.7
29.3
29.1
25.9
54.7
53.2
72.6
63.8
67.1
56.5
Primary
51.5
51.4
44.4
47.5
47.8
49.7
34.1
36.5
20.6
25.8
24.7
33.1
Secondary
24.3
25.4
22.0
23.2
23.1
24.4
11.2
10.3
6.8
10.5
8.2
10.4
Public/hut/shared
60.2
56.9
53.3
52.5
56.7
55.0
56.2
55.1
51.6
49.7
53.0
53.4
Self owned
34.2
39.4
39.6
43.0
37.0
41.0
40.3
40.6
39.1
44.6
39.4
41.9
5.5
3.7
7.1
4.5
6.3
4.0
3.6
4.3
9.3
5.7
7.6
4.8
Housing
Quarter/other
Job type
Sedentary
14.4
15.8
18.5
17.7
16.5
16.6
7.8
5.7
4.7
5.1
5.7
5.5
Light/medium
62.4
63.0
58.8
59.2
60.5
61.4
45.7
41.9
38.6
34.2
40.8
39.4
Heavy
22.6
20.7
21.8
22.0
22.2
21.3
8.7
9.0
8.5
9.4
8.6
9.1
None
0.6
0.6
0.9
0.9
0.8
0.7
37.9
43.4
48.1
51.4
45.0
45.9
Birth place
85.8
86.8
90.0
91.7
88.1
89.0
84.6
87.0
88.8
91.2
88.7
88.3
Ever used alcohol
Immigrant
55.6
54.2
47.2
49.0
51.2
51.9
12.5
11.7
13.1
11.8
12.9
11.8
Ever smoked
73.6
60.9
67.0
60.3
70.2
60.7
21.1
12.8
24.0
17.3
23.1
14.2
72.5
59.3
64.8
53.2
68.0
56.7
63.3
55.7
64.8
53.2
64.3
54.9
87.7
86.6
86.6
87.7
86.7
87.0
74.2
77.9
72.8
79.1
73.3
78.3
Exercise
,1 per month
Tea
4 or more per week
Healthy food
Low
38.3
29.9
35.4
31.3
36.8
30.5
31.2
23.6
31.7
24.9
31.6
24.0
Medium
48.6
53.3
48.2
50.3
48.4
51.9
53.6
56.9
50.4
54.0
51.4
56.0
High
13.1
16.9
16.3
18.4
14.8
17.6
15.2
19.6
17.9
21.1
17.0
20.0
Less healthy food
Low
41.1
41.0
39.4
40.0
40.2
40.6
36.9
36.0
39.0
38.0
38.4
36.6
Lower
20.1
19.9
18.4
18.2
19.2
19.2
20.0
19.4
16.4
16.1
17.5
18.3
Higher
26.9
26.3
27.0
27.0
27.0
26.6
29.7
31.2
28.4
29.7
28.8
30.7
High
11.8
12.8
15.2
14.8
13.6
13.7
13.4
13.5
16.2
16.2
15.3
14.3
ascertainment, or for the subjects reported on by their adult
children, so results for all subjects are presented.
The effect of NRFS group varied with RFS group (Table 3),
and this effect persisted within analyses stratified by sex or
age-group (60–74 and 75 or over) (data not shown). Table 3 also
shows the joint effect of NRFS group and RFS group on allcause mortality, with high RFS group and low NRFS group as
the reference category. In the lowest RFS group, there was a
clear dose–response relationship for an increased risk of
mortality with increasing NRFS group. In the middle RFS
group there was also a clear dose–response relationship for an
increased risk of mortality with increasing NRFS group.
However, in the high RFS group there was no clear relationship between increasing NRFS group and increasing risk.
Within NRFS and RFS combinations, the highest risk was
observed for the combination of highest NRFS group and the
lowest RFS group. The effect of low RFS group was more marked
in the higher NRFS groups than in the low NRFS group. In the
highest NRFS group there was more than double the risk
between the low RFS group and the high RFS group while in
the lowest NRFS group the difference was smaller.
Similarly the effect of NRFS group also varied with tea
drinking frequency, smoking status, and alcohol use (Table 4).
Table 4 also shows the joint effect of NRFS group and tea
drinking, NRFS group and smoking, and NRFS group and
alcohol use on all-cause mortality. In the lower two tea drinking
groups, there was an increasing risk of mortality with increasing NRFS group. However, in the high tea drinking group,
although there was still a trend for increasing risk with
increasing NRFS group, the gradient was attenuated. Within
NRFS and tea combinations, the highest risk was observed for
the lowest tea frequency and highest NRFS group. The effect of
infrequent tea drinking was more marked in the higher two
NRFS groups than the lower two NRFS group.
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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Table 2 Odds ratios and associated confidence intervals for the effect of NRFS group and RFS group on all cause adjusted for an increasing number
of confounders and adjusted injury and poisoning mortality
All cause mortality
a
Model 1
OR
All cause mortality
b
Model 2
95% CI
OR
All cause mortality:
informants lived
with case/control.
c
Model 4
All cause mortality
c
Model 3
95% CI
OR
95% CI
OR
1.08*
1.00–1.17
Death from
injury and
c
poisoning
95% CI
OR
95% CI
1.09
0.99–1.20
1.02
0.77–1.36
NRFS group
Low
1
1
1
1
1
Medium-low
1.03
0.96–1.10
1.03
0.96–1.11
Medium-high
0.98
0.92–1.05
1.02
0.95–1.08
1.11**
1.04–1.19
1.08
0.99–1.18
0.83
0.63–1.09
High
0.97
0.90–1.05
1.03
0.95–1.12
1.22***
1.12–1.33
1.20*
1.07–1.36
0.74
0.51–1.07
P for trend
0.37
0.512
0.004
,0.001
0.06
RFS group
Low
1
1
1
1
1
Medium
0.74***
0.70–0.79
0.77***
0.73–0.82
0.79***
0.74–0.84
0.78***
0.72–0.85
0.81
0.63–1.04
High
0.63***
0.59–0.68
0.67***
0.62–0.72
0.66***
0.60–0.72
0.72***
0.64–0.82
1.05
0.75–1.46
P for trend
,0.001
,0.001
,0.001
0.88
,0.001
*P , 0.05; **P , 0.005; ***P , 0.001.
a
Adjusted for age, sex, and education.
b
Adjusted for age, sex, education, housing, job type, and leisure exercise.
c
Adjusted for age, sex, education, housing, job type, leisure exercise, NFRS, or RFS group as appropriate, tea frequency, alcohol use, and smoking.
a
Table 3 Adjusted odds ratios and associated confidence intervals for unhealthy food group in combination with healthy food group
NRFS food group
Low
Medium low
Medium high
High
95% CI
OR
95% CI
OR
95% CI
OR
95% CI
Trend P
for strata
1.38***
1.21–1.59
1.40***
1.20–1.65
1.73***
1.46–2.05
2.13***
1.69–2.69
,0.001
1.11
0.97–1.27
1.21*
1.05–1.40
1.21**
1.06–1.38
1.50***
1.26–1.78
,0.001
1
1.21
0.97–1.49
1.04
0.86–1.27
1.01
0.87–1.18
0.93
,0.001
0.070
OR
P for
interaction
term
RFS group
Low
Medium
High
Trend P for strata
,0.001
,0.001
,0.001
*P , 0.05; **P , 0.005; ***P , 0.001.
a
Analysis adjusted for age, sex, educational level, job type, leisure exercise, housing, tea, alcohol, and smoking.
For current smokers there was a clear trend for higher
NRFS group to be associated with greater risk. However, for
never-smokers there was only a small increase in risk in the
higher NRFS, and the trend was not significant.
There was a clear trend for increased risk with higher
NRFS group for the never-drinkers, there was also a trend for
a increased risk with higher NRFS group in the light drinkers
(,8 units per week), however there was no clear trend for
drinking .8 units per week. The U-shaped relationship
between alcohol use and mortality was very clear in the lower
two NRFS group. In the medium high NRFS group the
relationship appeared more J-shaped, and in the highest NRFS
group it appears that never drinking is most risky. However
additional analysis of alcohol use within the highest NRFS
group found that there was a U-shaped relationship that
became evident at higher levels of alcohol use, such that there
was almost the same risk for never-drinkers and people
drinking 21 or more units per week (data not shown). Thus
NRFS group appeared to modify the shape of the U, rather than
change the nature of the relationship.
The effect of RFS group did not vary with smoking status
(P 5 0.94) or use of alcohol (P 5 0.36), but did vary with tea
drinking (Table 5). Table 5 also shows the joint effect of tea
drinking and RFS group on mortality. In the lowest tea-drinking
group, there was a trend for an increase in mortality with
decreasing RFS group. In the highest tea drinking group there
was a strong trend for increasing mortality with lower RFS group.
There was also a strong trend for increasing mortality in the
higher two RFS groups as tea drinking decreased. However, for
the low RFS group the effect of tea drinking was less clear.
Discussion
In common with other research showing that the consumption
of fruits and vegetables is protective against major causes of
death such as cancer35 and heart disease,36 we have shown that
consumption of healthy foods including fruits and vegetables
is associated with lower all-cause mortality. Consistent with a
previous study using similar dietary dimensions, we found that
in an Asian population higher use of healthy foods had a
DIET SYNERGIES AND MORTALITY
423
a
Table 4 Adjusted odds ratios and associated confidence intervals for NRFS group in combination with tea frequency, smoking status,
and alcohol use
NRFS group
Low
Medium Low
OR
Tea
95% CI
OR
Medium high
High
95% CI
OR
95% CI
OR
95% CI
Trend P
for strata
P for
interaction
term
b
,1/week
1.17*
1.02–1.34
1.23*
1.01–1.50
1.68***
1.37–2.06
1.90***
1.48–2.43
,0.001
1–3/week
0.91
0.79–1.05
1.05
0.84–1.30
1.21*
1.04–1.41
1.67***
1.32–2.12
,0.001
>4/week
1
1.07
0.99–1.17
1.06
0.99–1.15
1.12*
1.01–1.23
0.011
0.27
0.18
Trend P for strata
Smoking
,0.001
c
Current
Never
Alcohol
,0.001
,0.001
1.41***
1.27–1.56
1
1.80***
1.57–2.06
1.86***
1.65–2.10
2.01***
1.68–2.39
,0.001
1.00
0.91–1.10
1.04
0.95–1.13
1.13*
1.01–1.25
0.126
0.002
d
1.07
0.98–1.18
1.06
0.98–1.15
1.23***
1.11–1.36
0.003
<7/week
0.75***
0.66–0.85
0.72***
0.61–0.83
0.92
0.80–1.05
0.83
0.68–1.01
0.013
81/week
1.04
0.89–1.22
1.46*
1.11–1.92
1.38*
1.09–1.75
0.94
0.65–1.36
0.265
Never
1
0.016
*P , 0.05; **P , 0.005; ***P , 0.001.
a
All models were adjusted for age, sex, educational level, job type, leisure-time exercise, housing, and RFS group.
b
Additionally adjusted for alcohol use and smoking status.
c
Additionally adjusted for tea drinking and alcohol use.
d
Additionally adjusted for tea drinking and smoking status.
protective effect, but higher use of less healthy foods had a
smaller impact on mortality.19 In addition, we extended the
analysis to demonstrate in our population that use of less
healthy foods did not have a consistent impact on mortality,
varying with healthy food use, tea drinking, possibly alcohol use,
and smoking status. Our finding that smoking may magnify the
harm of a less healthy diet is consistent with a previous
ecological study that suggested the effect of smoking on lung
cancer might vary with fat intake.37 Smokers may also have
different diets.38 Although the best strategy for reducing the
harm of smoking is cessation, a better diet would be beneficial.
We found that the effect of high intake of less healthy food was
partially offset by a high intake of healthy food, frequent tea
drinking, and possibly moderate alcohol consumption. However
at lower intakes of less healthy foods use of healthy foods or tea
was less relevant. Finally it appeared that healthy food and tea
combined decreased the risk of mortality more than either
singly. Statistical interaction does not imply biological synergy
but there are several mechanisms by which the effect of some
foods could be moderated by the intake of other items, for
example where some items specifically neutralize less healthy
foods or where healthy foods complement other items.
Many healthy foods are plant based and so provide fibre.
Fibre alters the physical properties of the food being digested, by
increasing bulk, owing to its own volume and the resulting
increased bacterial mass, which decreases transit time. Both
the faster transit and the greater volume decrease duration of
mechanical contact between potentially harmful substances,
such as carcinogens from cooked meat, and the intestine wall,
and absorption is also decreased.39 However, faster transit
time and decreased absorption would be of little value in the
absence of harmful substances and could even lead to deficiencies. Bowel movements have been investigated in relation
to colorectal cancer with somewhat mixed results.40–43
However, bowel movements alone cannot approximate the
complex process of food digestion and passage within the body.
Hitherto, nutritional epidemiology has mainly been concerned
with intake, not length of time in the body, internal flora, or
absorption, so we know little about these potentially moderating
effects, which deserve greater consideration.
Plant matter also has chemical properties that may inhibit
the formation of harmful substances or neutralize them. Fruits
and vegetables may inhibit nitrosation44; associated fibre may
absorb hydrophobic carcinogens.45 Many plants contain antinutrients, which prevent absorption of potentially harmful
substances, for example plant sterols reduce cholesterol absorption, particularly in people with high fat intakes,46,47 while yams
reduce fat absorption in mice.48 Both tea and alcohol may share
some of the neutralizing qualities of other plant-based foods.
Black tea inhibits nitrosation49 and has a wide range of antimutagenic properties.50 Tea may enhance insulin action,51 while
moderate alcohol intake enhances insulin sensitivity.52 Tea
and alcohol may moderate lipid absorption or metabolism7,53
with particular benefit for high fat consumers.54 However, these
neutralizing effects are not always advantageous, for example
alcohol also disrupts folate uptake and metabolism, such that
alcohol users need higher folate intakes to avoid increased
mortality, while folate intakes make little difference for nonusers.55 The mechanism for synergist effects of tea and some
plant compounds are not yet clear, but could be due to a
complementation process. Building on findings of synergies
dietary items could be considered within a paradigm of potentially positive and negative qualities based on their biological
action. An item whose action is mainly neutralizing might
have a harmful effect in the absence of its partner, and a more
obviously beneficial effect in those consuming large amounts of
its partner, and possibly a U-shaped relationship overall, as for
alcohol. Recent studies that found increased mortality in those
424
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
a
Table 5 Adjusted odds ratios and associated confidence intervals for RFS group in combination with tea drinking
RFS group
Low
Medium
high
OR
95% CI
OR
95% CI
OR
95% CI
Trend P
for strata
,1/week
1.90***
1.63–2.22
1.67***
1.43–1.94
1.56***
1.23–1.97
0.015
1–3/week
1.49***
1.28–1.73
1.35***
1.18–1.56
1.41*
1.08–1.84
>4/week
1.63***
1.48–1.79
1.24***
1.14–1.34
1
P for
interaction
term
Tea drinking
Trend P for strata
0.25
,0.001
0.184
,0.001
0.016
,0.001
*P , 0.05; **P , 0.005; ***P , 0.001.
a
Analysis was adjusted for age, sex, educational level, job type, leisure-time exercise, housing, NRFS group, alcohol, and smoking.
taking large doses of vitamin E or antioxidant supplements56,57
would be consistent with this paradigm. Examining the effect of
dietary items with respect to doses and other intakes, as in this
study, may help unravel these complex relationships and
reconcile some of the conflicting findings in nutritional
epidemiology, which could be due to different levels of intakes
of other items between groups studied.
Strengths
Our study is based on an ethnically homogeneous, East Asian
population. Our study was large enough to have sufficient
power to investigate interactions. We were able to adjust for
potentially confounding factors, such as physical activity and
socioeconomic status, using several indicators, i.e. job type for
longest held occupation, education, and type of housing. Finally
our study provides a new way of analysing dietary patterns that
could be used by others to investigate the joint effect of dietary
items within a framework based on complementary and antagonistic effects of foods and drinks.
therefore there was little reason to suspect such a strategic bias
on the part of informants or research assistants conducting the
interviews. Third, we constructed crude food groups from a
limited number of food items, however our results were fairly
resilient to changing the definitions of the less healthy group,
such as collapsing down to two groups, or of excluding fish
from the healthy food score (data not shown). Fourth, the two
dietary components are a very broad categorization that may
not correspond to dietary recommendations for all groups;
however this does not detract from findings about synergies.
Fifth, we do not have total food intake. We do not know if the
effect of these dietary patterns occurred because overall people
ate less of the less healthy foods as they ate more of the healthy
foods, or because some nutrients are blocking others, or because
the response is different to some combinations, for example if a
mixture of food may reduce the insulin response with associated benefits.60 However, we found similar modifications for
tea drinking, which should not affect food intake, and the
correlation between NFRS and RFS is relatively low (0.28),
which suggests it is more simply an amount producing the
joint effect.
Weaknesses
First, we are relying on 10 year recall of dietary patterns, which
may be inaccurate, however other studies have found that
remote diet recalled from 10 years earlier may be as reliable as
recent dietary recall.58 Random recall error would bias the
result towards the null. We did not find that NFRS and RFS
had any relationship with deaths from injury and poisoning,
which should be biologically unrelated. Second, it is possible that
our informants ascribed some form of socially undesirable or
unhealthy eating pattern to the people who had died (cases)
but not to the controls, although it would have needed to be a
complex pattern ascribing varying intakes of healthy foods with
different levels of other foods. However, the dietary items chosen
are largely neutral in Chinese tradition, where war and famine
occurred within recent memory. Dietary health promotion was
not a priority in a hospital-focused health service in laissez faire
colonial Hong Kong in the 1990s. At the time of data collection
interest was focused on dietary fat and salt,31,59 rather than on
specific foods. In addition in Chinese society communitarian
values and a Confucian respect for authority may have
contributed towards informants providing quality responses to
a survey introduced as helping ‘all Hong Kong people to lead
more healthy lives’. Finally, the main focus of LIMOR was to
assess the effects of active and passive smoking on mortality,
Conclusion
Our analysis of mortality in a large case–control study in an
Asian population has found similar results to other studies on
the importance of eating healthy foods. However, our analysis
is strongly suggestive that a framework explicitly recognizing
synergistic effects of healthy foods, tea drinking, and alcohol
use on less healthy foods and of complementary effects could
enhance our understanding of nutritional epidemiology and
possibly reconcile some of the conflicting findings. We have also
demonstrated that a simple way of exploring potential dietary
synergies can produce useful results.
Acknowledgements
The LIMOR study was supported by Hong Kong Health Services
Research Committee (631012), Hong Kong Council on Smoking
and Health. We thank the Immigration Department of the
Government of the Hong Kong Special Administrative Region
for their help with data collection. All the authors jointly planned
the study. C.M.S. did the analysis and wrote the first draft,
all authors made substantial contributions to the final draft.
None of the authors had any financial interest.
DIET SYNERGIES AND MORTALITY
425
KEY MESSAGES
Consumption of healthy foods (fruit, vegetables, soy products, and fish) is associated with lower mortality.
Healthy foods may moderate the effect of less healthy foods on mortality, such that less healthy food is only a risk for
those consuming lower levels of healthy food.
Less healthy food may be a greater mortality risk to smokers than never-smokers.
An analysis framework explicitly exploring potentially synergistic effects of complementary dietary components
may be valuable for dietary epidemiology.
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