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Dietary patterns and risk of breast cancer

Article (PDF Available) inBritish Journal of Cancer 104(3):524-31 · February 2011with93 Reads
DOI: 10.1038/sj.bjc.6606044 · Source: PubMed
Abstract
Evidence is emerging that prudent/healthy dietary patterns might be associated with a reduced risk of breast cancer. Using data from the prospective Melbourne Collaborative Cohort Study, we applied principal factor analysis to 124 foods and beverages to identify dietary patterns and estimated their association with breast cancer risk overall and by tumour characteristics using Cox regression. During an average of 14.1 years of follow-up of 20 967 women participants, 815 invasive breast cancers were diagnosed. Among the four dietary factors that we identified, only that characterised by high consumption of fruit and salad was associated with a reduced risk, with stronger associations observed for tumours not expressing oestrogen (ER) and progesterone receptors (PR). Compared with women in the lowest quintile of the factor score, the hazard ratio for women in the highest quintile was 0.92 (95% confidence interval (CI)=0.70-1.21; test for trend, P=0.5) for ER-positive or PR-positive tumours and 0.48 (95% CI=0.26-0.86; test for trend, P=0.002) for ER-negative and PR-negative tumours (test for homogeneity, P=0.01). Our study provides additional support for the hypothesis that a dietary pattern rich in fruit and salad might protect against invasive breast cancer and that the effect might be stronger for ER- and PR-negative tumours.
Figures
Dietary patterns and risk of breast cancer
L Baglietto*
,1,2
, K Krishnan
1
, G Severi
1,2
, A Hodge
3
, M Brinkman
1
, DR English
1,2
, C McLean
4
, JL Hopper
2
and GG Giles
1,2,5
1
Cancer Epidemiology Centre, The Cancer Council Victoria, 100 Drummond Street, Carlton, Melbourne, Victoria 3053, Australia;
2
Centre for Molecular,
Environmental, Genetic and Analytical Epidemiology, University of Melbourne, Melbourne, Australia;
3
Department of Medicine, St Vincent’s Hospital,
University of Melbourne, Melbourne, Australia;
4
The Alfred Hospital, Melbourne, Australia;
5
Department of Epidemiology and Preventive Medicine,
Monash University, Melbourne, Australia
BACKGROUND: Evidence is emerging that prudent/healthy dietary patterns might be associated with a reduced risk of breast cancer.
METHODS: Using data from the prospective Melbourne Collaborative Cohort Study, we applied principal factor analysis to 124 foods
and beverages to identify dietary patterns and estimated their association with breast cancer risk overall and by tumour characteristics
using Cox regression.
RESULTS: During an average of 14.1 years of follow-up of 20 967 women participants, 815 invasive breast cancers were diagnosed.
Among the four dietary factors that we identified, only that characterised by high consumption of fruit and salad was associated
with a reduced risk, with stronger associations observed for tumours not expressing oestrogen (ER) and progesterone
receptors (PR). Compared with women in the lowest quintile of the factor score, the hazard ratio for women in the highest quintile
was 0.92 (95% confidence interval (CI) ¼0.70 –1.21; test for trend, P¼0.5) for ER-positive or PR-positive tumours and 0.48
(95% CI ¼0.26–0.86; test for trend, P¼0.002) for ER-negative and PR-negative tumours (test for homogeneity, P¼0.01).
CONCLUSION: Our study provides additional support for the hypothesis that a dietary pattern rich in fruit and salad might protect
against invasive breast cancer and that the effect might be stronger for ER- and PR-negative tumours.
British Journal of Cancer (2011) 104, 524 531. doi:10.1038/sj.bjc.6606044 www.bjcancer.com
Published online 14 December 2010
&2011 Cancer Research UK
Keywords: breast cancer; dietary patterns; factor analysis; prospective study
Most of the studies on the risk of breast cancer in relation to
individual foods and nutrients have had inconsistent results
(Michels et al, 2007). Dietary pattern analysis is an increasingly
common alternative approach for examining the relationship
between diet and the risk of disease or biomarkers of disease
(Newby and Tucker, 2004). Factor analysis applied to dietary data
produces a few patterns of dietary intake that represent underlying
independent dimensions of food and nutrient consumption. The
assessment of dietary patterns in this way overcomes limitations of
the single food or nutrient approach (Newby and Tucker, 2004).
Two recent systematic literature reviews (Edefonti et al, 2009;
Brennan et al, 2010) found consistent evidence that a ‘prudent’/
‘healthy’ dietary pattern was associated with a reduced risk of
breast cancer.
Breast cancer, similar to most cancers, is heterogeneous in its
pathology, natural history and response to treatment. Breast
cancer differ, for example, with respect to oestrogen (ER) and
progesterone receptor (PR) status; hormone receptor-positive
cancers exhibiting better differentiated morphological appearance
and stronger clinical response to hormonal treatment (Althuis
et al, 2004). There is evidence to suggest that risk factors (Colditz
et al, 2004), including food items (Olsen et al, 2003; Stripp et al,
2003; Suzuki et al, 2005; Zhang et al, 2005; Cho et al, 2006) and
dietary patterns (Fung et al, 2005; Velie et al, 2005; Agurs-Collins
et al, 2009) differ in their association by tumour receptor status.
Using data from the Melbourne Collaborative Cohort Study
(MCCS), we analyze the effect of dietary patterns on breast cancer
risk overall, by attained age during follow-up and by tumour
characteristics, including tumour grade and ER and PR status.
MATERIALS AND METHODS
The MCCS is a prospective cohort study of 41 514 people (24 469
women) aged 27–76 years at baseline (99.3% of whom were aged
40–69), recruited in 1990 1994 in the Melbourne metropolitan
area. Subjects were recruited via the Electoral Rolls (registration to
vote is compulsory in Australia), advertisements and community
announcements in local media (e.g. television, radio and news-
papers). The Cancer Council Victoria’s Human Research Ethics
Committee approved the study protocol. Subjects gave written
consent to participate and for the investigators to obtain access to
their medical records.
At baseline interview, questions were asked about conventional
risk factors, such as reproductive history, hormone replacement
therapy and oral contraceptive use, country of birth, alcohol
consumption, physical activity, smoking habits and highest level
of education. Subjects also completed a dietary questionnaire
that included questions relating to dietary habits and a 121-item
food frequency questionnaire (FFQ), specifically developed for the
Received 31 August 2010; revised 4 November 2010; accepted 17
November 2010; published online 14 December 2010
*Correspondence: Dr L Baglietto;
E-mail: laura.baglietto@cancervic.org.au
British Journal of Cancer (2011) 104, 524 – 531
&
2011 Cancer Research UK All rights reserved 0007 – 0920/11
www.bjcancer.com
Epidemiology
MCCS (Ireland et al, 1994). Intake of energy was computed using
Australian food composition tables (Lewis et al, 1995), and
included energy from the FFQ and energy from alcohol. Standard
procedures were used to measure height and weight at baseline
attendance for each participant (Lohman et al, 1988; MacInnis
et al, 2004a) from which body mass index (BMI) was calculated.
Women were excluded from the study if they did not complete
the dietary questionnaire (N¼11); reported extreme values of
total energy intake (o1st percentile or 499th percentile)
(N¼488) or a history of angina, diabetes or heart disease before
baseline as their diet might have changed in consequence
(N¼1421); had a confirmed diagnosis of invasive breast cancer
before baseline (N¼415); or had missing values in any of the
potential confounders (N¼1167). These exclusions left 20 967
women eligible for these analyses.
Cases included women with a first diagnosis of adenocarcinoma
of the breast (International Classification of Diseases for Oncology,
3rd edition, 10th revision rubric C50.0 –C50.9) during follow-up
until 31 December 2007. Cases were ascertained by record
linkage to the population-based Victorian Cancer Registry
(VCR), which covers the state in which the cohort resides, and
to the National Cancer Statistics Clearing House, which holds
cancer incidence data from all Australian states. Women with
in situ breast cancer were not counted as cases. Addresses and vital
status of all subjects were determined by record linkage to
Electoral Rolls, Victorian death records, the National Death Index,
from electronic phone books and from responses to mailed
questionnaires and newsletters.
The medical records of women with breast cancer were reviewed
and their cancers classified according to tumour grade and ER and
PR status as recorded in histopathology reports held at the VCR. We
repeated the measurement of ER and PR status for a subset of cases
with archival tissue available (67% of all cases in the sample). The
original diagnostic tumour slides for these cases were retrieved from
pathology laboratories and reviewed by a single pathologist (CM)
who assessed ER and PR status using immunohistochemistry
techniques. The archival material was sectioned at four micron
and placed on superfrost plus slides. A routine dewaxing procedure
was followed by heat-induced epitope retrieval with either citrate
buffer pH 6 or TRIS EDTA pH 8 using a DAKO (Carpinteria, CA,
USA) Pascal pressure chamber. The following antibodies were used:
ER (Labvision rabbit monoclonal SP1) at 1/250, PR (DAKO PGR636)
at 1/1200. Immunoreaction was performed using a Lab Vision
(Fremont, CA, USA) autostainer using Lab Vision HRP polymer
detection system and DAKO DAB þ. The agreements between the
ERandPRstatusassessedbyimmunohistochemistryandthevalues
held by the VCR were 89 and 81%, respectively (for ER, k¼0.71,
Po0.001; for PR, k¼0.62, Po0.001). Because of the good agreement
between the ER and PR data, when archival tumour tissue was not
available, ER and PR status was assigned according to the
histopathology reports held at the VCR.
Statistical analysis
Factor analysis was performed on the 121 items from the FFQ, plus
olive and vegetable oil and alcohol from wine (other alcoholic
beverages were rarely consumed by women). For the 121 items,
intake was measured as daily equivalent frequency, intake of oils
was measured as ml per week, and alcohol as g per day. The
principal factor method was used to extract factors, followed by
orthogonal (varimax) rotation to assist in interpretation of the
factors and to ensure that the factors were uncorrelated. Factors
with Eigenvalues of 2 or greater were retained. Variables with
factor loadings having absolute values of 0.2 or greater were used
in interpreting the factors. For each rotated factor, scores were
computed as the sum of products of the standardized observed
variables multiplied by weights proportional to the factor loadings.
Scores were categorized in quintiles.
Multivariate regression methods were used to calculate how
much of the variance in factor scores was associated with country
of birth, total energy intake and potential confounders.
Follow-up began at baseline and continued until diagnosis of
breast cancer (N¼815), diagnosis of unknown primary cases
(N¼49), death (N¼1216), date left the area covered by the cancer
registries (N¼117) or 31 December 2007, whichever came first.
Hazard ratios (HRs) were estimated using Cox regression with
age as the time metric. Analyses were adjusted for potential
confounders, including country of birth, total energy intake, age at
menarche, duration of lactation, parity (parous vs nulliparous),
oral contraceptive use (ever vs never), hormone replacement
therapy (never, past and current users), menopausal status at
baseline, level of education (some primary education, some
secondary education and completed secondary education, degree
or diploma), level of physical activity (none, low, medium and
high; see MacInnis et al, 2004b for further detail), total alcohol
intake (lifetime abstainers, ex-drinkers, 1 –19 g per day, 20 g per
day or more), smoking and BMI. In order to account for the
heterogeneity of the association with BMI by menopausal status,
we treated BMI as an age-dependent variable, by splitting the data
into two age groups (p55 and 455) and including an interaction
between BMI (continuous) and age groups. We estimated separate
HRs for the two follow-up age groups (p55 and 455), by fitting
Cox models with the inclusion of a term for the interaction
between factor scores and groups, and for each dietary factor score
overall and by grade and hormone receptor status of the tumours.
To test for heterogeneity in the HRs by grade (well vs moderately
vs poorly differentiated), and ER and PR status (ERvs ER þ;
PRvs PR þ; and ER þor PR þvs ERand PR), Cox’s
proportional regression models were fitted using a data duplica-
tion method (Lunn and McNeil, 1995). For sensitivity analysis
purposes, we repeated all analyses (i) including only women born
in Australia/New Zealand/the United Kingdom, (ii) excluding the
first two years of follow-up, (iii) censoring after 10 years of follow-
up, (iv) using ER and PR measures from immunohistochemistry
only or the VCR only. To address the possibility that differential
reporting of alcohol intake according to dietary patterns biased the
associations, the analysis of risk overall was stratified by alcohol
drinking after excluding ex-drinkers.
Tests for linear trend were based on pseudo-continuous
variables under the assumption that all subjects within each
quintile of factor score had the same score, equal to the within-
quintile median. Statistical analyses were performed using Stata
11.0 (Stata Corporation, College Station, TX, USA).
RESULTS
Table 1 summarizes the baseline characteristics. In total, 79% of
the women were born in Australia, New Zealand or in the United
Kingdom and 21% in Italy or Greece. The mean age at baseline
was 55 (range: 31–76 years) years; 34% of the women were aged
under 50 years, 17% between 50 and 55 years and 49% over
55 years. We identified 815 incident invasive breast cancer cases
(813 were histopathologically verified) over an average of 14.1
years of follow-up between baseline attendance and 31 December
2007 for the eligible 20 967 women.
The mean age at diagnosis of cases was 63 years (range: 41 85
years), 22% were diagnosed before age 55 years, 12% within the
first 2 years of follow-up and 30% after 10 years. The status of
ER or PR was known for 95% of the cases, of which 575 (74%) were
ER þand 202 (26%) ERand 426 (55%) were PR þand 349
(45%) PR. There were 605 (78%) cases expressing ER or PR and
173 (22%) cases expressing neither ER nor PR. Information on
grade was available for 741 (91%) cases, including 162 (22%) well-
differentiated, 333 (45%) moderately differentiated and 246 (33%)
poorly differentiated tumors. Tumours not expressing hormone
Diet and breast cancer risk
L Baglietto et al
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British Journal of Cancer (2011) 104(3), 524 – 531&2011 Cancer Research UK
Epidemiology
receptors had a higher grade than those expressing ER or PR (76%
of the ERand PRtumours and 22% of the ER þor PR þ
tumours were poorly differentiated).
Four factors with eigenvalues greater than 2 were identified that
explained 66% of the variance in all the food and beverage items.
Rotated factor loadings for food items with an absolute value of 0.2
or greater for any factor are reported in Table 2. The four factors
were characterized as follows; (1) high intakes of vegetables, boiled
rice, wholemeal bread, yoghurt, chicken, fish (not fried), potato
cooked without fat, fruit salad, banana and pineapple, with low
intakes of white bread; (2) high intakes of salad greens, cucumber
and fruit; (3) high intakes of desserts, cheddar cheese, margarine,
lamb, sausages, bacon, potato cooked without fat, green beans and
peas, pumpkin, tea, chocolate, other confectionary, jam, honey and
vegemite, with low intakes of olive oil, pasta or noodles, ricotta and
feta cheese, beef or veal schnitzel, steamed fish, legume soup,
tomato, salad vegetables, legumes, olives and figs; (4) high intakes
of fried rice, white bread, pizza, savoury pastries, feta cheese, fried
eggs and egg dishes, meats (fresh and processed), fried fish,
pickled vegetables, potatoes cooked in fat and olives.
Correlations with energy intake were 0.36, 0.27, 0.35 and 0.44 for
factors 1–4, respectively (Table 3). Factor 1 had a strong positive
correlation with vegetable consumption (r¼0.87), which indicates
that higher intake of the diet pattern characterized by factor 1 is
associated with higher intake of vegetables, and is labelled
‘vegetable’. Factor 2 had a strong positive correlation with fruit
(r¼0.90); because it was also characterized by high consumption
of salad green, it is labelled ‘fruit and salad’. Factor 3 was positively
correlated with cereals (r¼0.36), dairy products (r¼0.30),
margarine (r¼0.43) and potatoes (r¼0.37) and negatively
correlated with olive oil (r¼0.43) and leafy green vegetables
(r¼0.37); because women born in Australia or New Zealand
scored higher on this factor (Table 4) it is labelled ‘traditional
Australian’. Factor 4 was mainly positively correlated with red
meat (r¼0.75) and is labelled ‘meat’.
Of all the variables listed in Table 1, only country of birth, level
of education and total energy intake explained more than 5% of the
variance in any factor, with country of birth explaining between
5 and 34% of the total variance of the factors (Table 4). Women
born in Italy or Greece had higher scores for the ‘fruit and salad’
and ‘meat’ patterns and women born in Australia or New Zealand
had higher scores for the traditional Australian pattern; level of
education was positively associated with the ‘vegetable’ pattern
and negatively associated with the ‘meat’ pattern.
Scores for the ‘fruit and salad’ pattern were inversely associated
with breast cancer risk (HR for the highest vs the lowest quintile of
the factor, 0.81; 95% confidence interval (CI): 0.63 1.03; test for
trend, P¼0.03; Table 5). There was a marginal statistically
significant association between the ‘traditional Australian’ pattern
and breast cancer risk before the age of 55 years (Table 5). No
other dietary pattern was significantly associated with breast
cancer risk (Table 5). There was no evidence of heterogeneity by
attained age for the association between risk and any of the dietary
patterns (Table 5).
Table 6 shows the association between the four dietary pattern
scores and breast cancer risk by tumour ER and PR status. The ‘fruit
and salad’ pattern score was inverselyassociatedwiththeriskofER-
negative (HR for the highest vs the lowest quintile of the factor, 0.55;
95% CI: 0.32–0.93; test for trend, P¼0.004; test for homogeneity
P¼0.03) and PR-negative breast cancer (HR for the highest vs the
lowest quintile of the factor, 0.67; 95% CI: 0.46 0.98; test for trend,
P¼0.01; test for homogeneity P¼0.08), whereas no association was
observed for ER-positive or PR-positive cancers (Table 6). When cases
were classified according to their ER and PR status combined, the HR
for the highest vs lowest quintile of the ‘fruit and salad’ pattern were
0.92 (95% CI: 0.70–1.21; test for trend, P¼0.5) for ER þor PR þ
tumours and 0.48 (95% CI: 0.26 –0.86; test for trend, P¼0.002) for
ERand PRtumours (test for homogeneity by ER/PR, P¼0.01).
Table 1 Characteristics of the study population
All women
(N¼20 967)
BC cases
(N¼815)
Characteristic N(%) N(%)
Age, mean (s.d.) 55 (9) 56 (8)
Country of birth
Australia/New Zealand/Other 15 143 (72) 647 (79)
The United Kingdom 1391 (7) 40 (5)
Italy 2476 (12) 77 (9)
Greece 1957 (9) 51 (6)
Age at menarche (years)
o12 3411 (16) 136 (17)
12 4113 (20) 149 (18)
13 5547 (26) 216 (27)
X14 7896 (38) 314 (39)
Parity
Nulliparous 3085 (15) 147 (18)
Parous 17 882 (85) 668 (82)
Duration of lactation (months)
Never 6177 (29) 274 (34)
Up to 6 4581 (22) 148 (18)
7 – 12 3321 (16) 114 (14)
13 – 24 4044 (19) 179 (22)
424 2844 (14) 100 (12)
Oral contraceptive
Never user 8378 (40) 329 (40)
Past user 12 187 (58) 472 (58)
Current user 402 (2) 14 (2)
HRT
Never user 15 566 (74) 564 (69)
Past user 1774 (8) 67 (8)
Current user 3627 (17) 184 (23)
Menopausal status
Having periods 7566 (36) 285 (35)
Periods stopped for natural reasons 8970 (43) 381 (47)
Periods stopped for other reasons
a
4431 (21) 149 (18)
Level of physical activity
None 4503 (21) 139 (17)
Low 4461 (21) 185 (23)
Medium 7599 (36) 317 (39)
High 4404 (21) 174 (21)
Alcohol consumption
Lifetime abstainers 7866 (38) 298 (37)
Ex-drinkers 647 (3) 29 (4)
1 –19 g per day 10 062 (48) 397 (49)
20 – 39 g per day 1850 (9) 66 (8)
40 g per day or more 542 (3) 25 (3)
Smoking
Never 14 547 (69) 574 (70)
Current 1831 (9) 63 (8)
Former 4589 (22) 178 (22)
Level of education
pPrimary school 3902 (19) 114 (14)
Some high/technical school 9029 (43) 362 (44)
Completed high/technical school 3761 (18) 153 (19)
Degree/diploma 4275 (20) 186 (23)
BMI (kg m
–2
), mean (s.d.) 26.6 (4.8) 26.9 (4.9)
Energy from diet (Mj per day), mean (s.d.) 8.5 (2.8) 8.5 (2.8)
Abbreviations: BC ¼breast cancer; BMI ¼body mass index; HRT ¼hormone
replacement therapy.
a
Hysterectomy, ovariectomy or other reasons.
Diet and breast cancer risk
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Epidemiology
Scores for the ‘fruit and salad’ pattern were inversely associated
with high-grade tumours, (HR for the highest vs the lowest
quintile, 0.58; 95% CI: 0.36–0.92; test for trend, P¼0.001), whereas
no association was observed for moderate or low-grade tumours
(test for homogeneity by grade, P¼0.02). None of the other three
patterns showed heterogeneity by tumour grade (results not
shown).
Similar results were obtained when the analyses were restricted
to women born in Australia/New Zealand/the United Kingdom or
when excluding the first 2 years of follow-up, or when censoring
after 10 years of follow-up or when the analyses by tumour ER and
PR status used the data from our own immunohistochemistry only
or the data from histopathology reports held by the VCR only (not
shown).
There was no evidence of heterogeneity of the association
between dietary pattern and breast cancer risk by level of alcohol
consumption (not shown).
DISCUSSION
Using data from a prospective cohort study of 20 967 women with
an average follow up of 14 years we identified four dietary patterns
Table 2 Rotated factor loadings for food and beverage items with
loadings having absolute values of 0.2 or greater for any factor
Rotated factor loading
Food item Factor 1 Factor 2 Factor 3 Factor 4
Olive oil 0.39
Boiled rice 0.20
Fried rice 0.28
White bread 0.20 0.28
Wholemeal bread 0.25
Sweet biscuits 0.33
Cakes/sweet pastries 0.34
Puddings 0.34
Pasta/noodle dish 0.25
Pizza 0.23
Savoury pastries 0.32
Ricotta cheese 0.27
Fetta cheese 0.26 0.25
Cheddar cheese 0.24
Ice cream 0.25
Custard 0.22
Cream/sour cream 0.26
Yoghurt 0.25
Fried egg 0.27
Egg dish 0.24
Margarine 0.39
Beef/veal schnitzel 0.21 0.41
Beef/veal roast 0.44
Beef steak 0.32
Beef rissole 0.43
Beef dish 0.29
Roast/fried chicken 0.30
Boiled chicken 0.20
Chicken dish 0.22
Lamb roast/chops 0.28 0.23
Lamb dish 0.27
Pork roast/chops 0.25
Salami 0.21
Sausage/frankfurter 0.33
Bacon 0.20 0.26
Steamed fish 0.24 0.22
Fried fish 0.34
Canned fish 0.22
Legume soup 0.25
Pickled vegetables 0.21
Tomato 0.27 0.23
Capsicum 0.35 0.28
Salad greens 0.37 0.21 0.32
Cucumber 0.37 0.22 0.35
Celery/fennel 0.46 0.21
Beetroot 0.42
Coleslaw 0.34
Potato cooked in fat 0.36
Potato cooked without fat 0.35 0.33
Carrot 0.56
Cabbage/brussels sprouts 0.54
Cauliflower 0.58
Broccoli 0.58
Leafy greens 0.45
Green beans/peas 0.44 0.26
Cooked dried legumes 0.29 0.26
Pumpkin 0.50 0.27
Onion/leek 0.29
Mushroom 0.27
Sweet corn 0.23
Zucchini/squash/eggplant 0.36
Vegetable dish 0.25
Fruit salad 0.21 0.21
Orange/mandarin 0.43
Apple 0.40
Banana 0.25 0.25
Peach/nectarine 0.66
Pear 0.51
Table 2 (Continued )
Rotated factor loading
Food item Factor 1 Factor 2 Factor 3 Factor 4
Cantaloupe/honeydew 0.55
Watermelon 0.54
Strawberry 0.48
Plum 0.65
Apricot 0.67
Pineapple 0.22 0.29
Olives 0.28 0.26 0.20
Fig 0.40 0.20
Grape 0.56
Tea 0.39
Chocolate 0.27
Other confectionery 0.24
Jam/honey 0.26
Vegemite 0.22
Table 3 Correlation between factors and energy intake and food groups
Vegetable
Fruit and
salad
Traditional
Australian Meat
Energy intake 0.36 0.27 0.35 0.44
Food group
Olive oil 0.10 0.12 0.43 0.13
Vegetable oil 0.06 0.02 0.07 0.13
Cereals 0.19 0.07 0.36 0.11
Dairy 0.18 0.01 0.30 0.04
Eggs 0.12 0.01 0.10 0.34
Butter 0.02 0.01 0.19 0.08
Margarine 0.12 0.09 0.43 0.04
Red meat 0.05 0.03 0.17 0.75
Chicken 0.24 0.02 0.13 0.35
Fish 0.31 0.04 0.20 0.15
Vegetables 0.87 0.24 0.26 0.14
Leafy green vegetables 0.54 0.24 0.37 0.12
Potatoes 0.36 0.02 0.37 0.14
Fruit 0.26 0.90 0.02 0.01
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Epidemiology
that we described as ‘vegetable’, ‘fruit and salad’, ‘traditional
Australian’ and ‘meat’. Overall, we observed an inverse association
between breast cancer risk and the ‘fruit and salad’ dietary pattern.
This inverse association was more pronounced for hormone
receptor-negative tumours and for high-grade tumours. No other
dietary pattern was associated with risk.
The MCCS is a multicultural cohort that includes a high
proportion of Southern European migrants in order to extend the
range of lifestyle exposure, including diet. It has long and virtually
complete follow-up of participants and has extensive data on
potential confounding variables. Baseline diet was measured using
an FFQ specifically designed to capture the variety of food
consumption. To avoid making assumptions about food con-
sumption patterns, we chose not to group the food items for the
dietary factor analysis (Hodge et al, 2007). This allowed us to
identify four dietary patterns that together reflected the usual
dietary intake of our population. The factors were correlated with,
but not synonymous with country of birth, as shown by the
relatively low proportion of the variance in factors’ scores
explained by country of birth, and by the fact that all associations
with dietary patterns were confirmed when restricting the analysis
to women born in Australian, New Zealand and the United
Kingdom. Ascertainment of cases through linkage with the VCR
provided us with accurate information about grade and hormone
receptor status for a high proportion of the tumours. We found
good agreement between the ER and PR status of the tumours
recorded on histopathology reports held by the VCR and those
obtained from immunohistochemical anlaysis of archival tissue
using a standardised protocol.
Excluding the first 2 years of follow-up did not change the
results, suggesting that changes in diet due to preclinical lesions
are unlikely to have contributed to the observed associations.
A limitation of this study is the single measure of diet. Diet
was recorded only once at baseline using a FFQ that only showed
a fair to moderate agreement for foods when administered on
two occasions 12 months apart (Hodge et al, 2007). We cannot
exclude that random error in measuring dietary intake and dietary
change during follow-up might have attenuated any association
between dietary patterns and breast cancer risk. Our data do not
provide evidence of heterogeneity of the associations by alcohol
intake. This suggests that it is unlikely that the observed
associations are due to differential reporting of alcohol consump-
tion in the different categories of dietary pattern. However, we
cannot exclude that the positive finding are due to residual
confounding or to the chance given the number of comparisons we
performed.
The association between fruit and vegetables and breast cancer
is biologically plausible because of their high contents of
potentially anticarcinogenic compounds (Collins, 2005), but
epidemiological studies of individual foods or of food groups
indicate that high intakes of fruit or vegetables have a limited
or no major effect on breast cancer incidence (Smith-Warner
et al, 2001b; IARC, 2003; Willett, 2005; van Gils et al, 2005; AICR,
2007). On the other hand, the literature on dietary patterns and
breast cancer risk suggests that a diet characterized by food with
high fat and high sugar content (western dietary pattern) is
associated with an increased risk of breast caner, whereas a diet
characterized by vegetables, fruit, fish and white meat (prudent
dietary pattern) is associated with a reduced risk (Edefonti et al,
2009; Brennan et al, 2010). Between the two ‘prudent’ patterns we
identified, one characterized by high intake of vegetables and the
other by high intake of fruits and salad, only the latter was
associated with reduced breast cancer risk. Fruit and vegetables
both contain high levels of nutrients with antioxidant properties,
such as carotenoids and vitamins (Steinmetz and Potter, 1991).
Some of these nutrients are destroyed by cooking, such as
oxygenated carotenoids (e.g., lutein), the predominant carotenoid
of green leafy vegetables (spinach, broccoli, Brussels sprouts and
cabbage), water-soluble vitamin C, and folate (Steinmetz and
Potter, 1991) and this might explain why we found a protective
effect against aggressive breast cancers for the fruit and salad
pattern, characterised by high intake of fruit and salad, but not for
the vegetable pattern, characterised by high intake of vegetables
that are usually consumed cooked. The evidence on the role of
single vitamins in the risk of breast cancer is inconsistent (Zhang,
2004), but there is some evidence suggesting a role of multivitamin
intake in decreasing the risk of ER-negative cancers, which might
be due to folate (Ishitani et al, 2008). The Nurses’ Health Study
found an inverse association between total folate intake and
ER-negative tumours, that could be explained by the role of folate
in maintaining normal DNA methylation; aberrant DNA methyl-
ation might be associated with the loss of ER expression (Zhang
et al, 2005). In our study, we found that the inverse association
between with the ‘fruit and salad’ pattern was stronger for ER- or
PR-negative tumours. The literature reporting associations of risk
with fruits and vegetables by hormonal receptor status is sparse
and inconsistent (Olsen et al, 2003; Gaudet et al, 2004; Fung et al,
2005; Sant et al, 2007; Cui et al, 2008; Agurs-Collins et al, 2009).
Similar to our finding, results from the Danish Diet, Cancer and
Health cohort (Olsen et al, 2003), the Nurse’s Health study (Fung
et al, 2005) and the Black Women’s Health Study (Agurs-Collins
Table 4 Means (s.d.) of factor scores and R
2
by categories of women’s
characteristics
Factors
Vegetable
Fruit and
salad
Traditional
Australian Meat
Country of birth
Australia/New Zealand 0.12 (0.88) 0.11 (0.81) 0.29 (0.77) 0.14 (0.75)
The United Kingdom 0.14 (0.89) 0.05 (0.87) 0.07 (0.71) 0.09 (0.77)
Italy 0.66 (0.75) 0.35 (1.16) 0.91 (0.57) 0.17 (0.88)
Greece 0.19 (1.15) 0.43 (1.14) 1.12 (0.67) 0.94 (1.25)
R
2
0.08 0.05 0.34 0.13
HRT
Never user 0.04 (0.94) 0.01 (0.93) 0.03 (0.92) 0.04 (0.91)
Past user 0.11 (0.91) 0.03 (0.83) 0.11 (0.88) 0.08 (0.80)
Current user 0.13 (0.87) 0.03 (0.91) 0.09 (0.80) 0.15 (0.77)
R
2
0.01 o0.01 o0.01 0.01
Alcohol consumption
Lifetime abstainers 0.05 (1.01) 0.08 (0.99) 0.04 (1.03) 0.10 (0.99)
Ex-drinkers 0.04 (0.96) 0.01 (0.98) 0.02 (0.93) 0.09 (0.90)
1 – 19 g per day 0.03 (0.90) 0.02 (0.88) 0.06 (0.82) 0.07 (0.80)
20 g per day or more 0.03 (0.77) 0.17 (0.78) 0.09 (0.73) 0.00 (0.81)
R
2
o0.01 0.01 o0.01 0.01
Level of education
pPrimary school 0.41 (0.98) 0.27 (1.06) 0.82 (0.84) 0.48 (1.15)
Some high/technical school 0.05 (0.93) 0.12 (0.86) 0.30 (0.82) 0.06 (0.80)
Completed
high/technical school
0.11 (0.85) 0.05 (0.88) 0.13 (0.81) 0.10 (0.77)
Degree/diploma 0.18 (0.84) 0.04 (0.90) 0.01 (0.72) 0.22 (0.69)
R
2
0.05 0.02 0.21 0.07
BMI
o25 kg m
–2
0.09 (0.88) 0.04 (0.87) 0.13 (0.81) 0.17 (0.77)
25 – 29 kg m
–2
0.03 (0.93) 0.01 (0.95) 0.03 (0.91) 0.05 (0.89)
30 kg m
–2
or more 0.13 (1.01) 0.07 (0.97) 0.22 (1.00) 0.27 (1.00)
R
2
0.01 o0.01 0.02 0.04
Total energy intake
I tertile 0.37 (0.69) 0.27 (0.62) 0.34 (0.66) 0.37 (0.55)
II tertile 0.00 (0.78) 0.02 (0.83) 0.02 (0.81) 0.06 (0.69)
III tertile 0.37 (1.11) 0.29 (1.15) 0.36 (1.05) 0.43 (1.11)
R
2
0.13 0.07 0.12 0.19
Abbreviations: BMI ¼body mass index; HRT ¼hormone replacement therapy.
R
2
measures how much of the variation in factor score is explained by each variable.
Diet and breast cancer risk
L Baglietto et al
528
British Journal of Cancer (2011) 104(3), 524 – 531 &2011 Cancer Research UK
Epidemiology
Table 5 Association between quintile of dietary pattern scores and breast cancer risk overall and by attained age during follow-up
Overall By attained age during follow-up
p55 years 455 years
Dietary pattern Quintiles No. of cases HR (95% CI) No. of cases HR (95% CI) No. of cases HR (95% CI) P-value
a
Vegetable 1 147 Reference 30 Reference 117 Reference 0.66
2 154 0.97 (0.77, 1.22) 42 1.11 (0.69, 1.79) 112 0.93 (0.71, 1.21)
3 169 1.02 (0.81, 1.30) 39 1.07 (0.66, 1.74) 130 1.01 (0.77, 1.32)
4 185 1.10 (0.87, 1.40) 33 0.97 (0.58, 1.62) 152 1.13 (0.87, 1.48)
5 160 0.98 (0.76, 1.28) 36 1.23 (0.74, 2.05) 124 0.93 (0.69, 1.24)
P-trend 0.97 0.72 0.84
Fruit and salad 1 174 Reference 47 Reference 127 Reference 0.47
2 189 1.04 (0.84, 1.28) 41 0.88 (0.58, 1.34) 148 1.10 (0.86, 1.40)
3 158 0.87 (0.69, 1.08) 32 0.73 (0.46, 1.14) 126 0.91 (0.71, 1.18)
4 156 0.88 (0.70, 1.10) 33 0.73 (0.46, 1.15) 123 0.93 (0.72, 1.21)
5 138 0.81 (0.63, 1.03) 27 0.66 (0.40, 1.07) 111 0.86 (0.65, 1.13)
P-trend 0.03 0.10 0.11
Traditional Australian 1 116 Reference 21 Reference 95 Reference 0.10
2 163 1.34 (1.03, 1.76) 37 1.13 (0.65, 1.97) 126 1.41 (1.05, 1.91)
3 162 1.27 (0.94, 1.71) 41 1.28 (0.73, 2.22) 121 1.24 (0.89, 1.73)
4 202 1.54 (1.14, 2.09) 48 1.81 (1.05, 3.13) 154 1.44 (1.03, 2.01)
5 172 1.25 (0.90, 1.74) 33 1.58 (0.87, 2.85) 139 1.17 (0.82, 1.66)
P-trend 0.24 0.04 0.64
Meat 1 162 Reference 25 Reference 137 Reference 0.16
2 161 1.00 (0.80, 1.25) 30 1.08 (0.63, 1.85) 131 1.00 (0.78, 1.27)
3 168 1.07 (0.85, 1.34) 43 1.43 (0.86, 2.37) 125 1.00 (0.78, 1.29)
4 170 1.12 (0.88, 1.41) 38 1.23 (0.73, 2.07) 132 1.11 (0.86, 1.44)
5 154 1.12 (0.85, 1.46) 44 1.50 (0.88, 2.55) 110 1.03 (0.77, 1.38)
P-trend 0.45 0.11 0.93
Abbreviations: BMI ¼body mass index; 95% CI ¼confidence interval; HR ¼hazard ratio; HRT ¼hormone replacement therapy. Estimates from the model including four factor
scores, adjusted for country of birth, age at menarche, parity, duration of lactation, oral contraceptive use, HRT use, menopausal status at baseline, physical activity, alcohol,
smoking, level of education, total energy intake and BMI.
a
Test for homogeneity by attained age at follow-up.
Table 6 Association between quintile of dietary pattern scores and breast cancer risk by ER and PR status of the tumor
By ER status By PR status
Positive Negative Positive Negative
Dietary
patterns Quintiles
No. of
cases
HR
(95% CI)
No. of
cases
HR
(95% CI) P
a
No. of
cases
HR
(95% CI)
No. of
cases
HR
(95% CI) P
a
Vegetable 1 101 Reference 35 Reference 0.41 82 Reference 53 Reference 0.39
2 102 0.94 (0.70, 1.25) 43 1.02 (0.64, 1.63) 73 0.85 (0.61, 1.18) 72 1.14 (0.79, 1.64)
3 119 1.04 (0.78, 1.38) 43 0.99 (0.63, 1.58) 91 1.03 (0.74, 1.42) 71 1.05 (0.72, 1.52)
4 136 1.17 (0.88, 1.56) 42 0.94 (0.58, 1.51) 93 1.06 (0.76, 1.48) 84 1.19 (0.82, 1.71)
5 117 1.04 (0.75, 1.43) 39 0.92 (0.55, 1.55) 87 1.06 (0.74, 1.53) 69 0.98 (0.64, 1.48)
P-trend 0.6 0.53 0.47 0.61
Fruit and salad 1 120 Reference 45 Reference 0.03 86 Reference 80 Reference 0.08
2 127 1.01 (0.78, 1.30) 53 1.14 (0.76, 1.69) 89 0.99 (0.73, 1.34) 91 1.08 (0.80, 1.46)
3 111 0.87 (0.67, 1.14) 44 0.94 (0.61, 1.44) 88 0.98 (0.72, 1.33) 67 0.78 (0.56, 1.09)
4 111 0.91 (0.69, 1.19) 34 0.73 (0.46, 1.14) 86 0.99 (0.72, 1.35) 58 0.70 (0.49, 0.99)
5 106 0.92 (0.69, 1.22) 26 0.55 (0.32, 0.93) 77 0.92 (0.66, 1.28) 53 0.67 (0.46, 0.98)
P-trend 0.47 0.004 0.58 0.01
Traditional Australian 1 84 Reference 24 Reference 0.66 69 Reference 36 Reference 0.41
2 111 1.24 (0.90, 1.71) 42 1.63 (0.85, 3.12) 83 1.22 (0.85, 1.76) 70 1.62 (0.98, 2.66)
3 115 1.21 (0.86, 1.70) 39 1.41 (0.71, 2.81) 85 1.28 (0.86, 1.89) 70 1.38 (0.83, 2.32)
4 139 1.42 (0.99, 2.03) 55 1.97 (0.98, 3.96) 99 1.52 (1.00, 2.32) 95 1.73 (1.03, 2.92)
5 126 1.19 (0.81, 1.74) 42 1.51 (0.69, 3.33) 90 1.40 (0.89, 2.19) 78 1.25 (0.71, 2.21)
P-trend 0.39 0.34 0.11 0.73
Meat 1 116 Reference 39 Reference 0.20 86 Reference 69 Reference 0.96
2 114 1.03 (0.79, 1.34) 38 0.91 (0.58, 1.43) 89 1.07 (0.79, 1.45) 63 0.91 (0.64, 1.28)
3 112 1.05 (0.80, 1.37) 47 1.09 (0.70, 1.71) 89 1.09 (0.80, 1.48) 70 1.03 (0.73, 1.45)
4 125 1.23 (0.93, 1.62) 40 0.91 (0.56, 1.48) 85 1.10 (0.79, 1.53) 81 1.20 (0.84, 1.71)
5 108 1.21 (0.88, 1.66) 38 0.86 (0.50, 1.47) 77 1.13 (0.78, 1.64) 66 1.04 (0.69, 1.55)
P-trend 0.19 0.48 0.60 0.69
Abbreviations: ER ¼oestrogen receptor; PR ¼progestero ne receptor; HRT ¼hormone replacement therapy. Estimates from the model including four factor scores, adjusted for
country of birth, age at menarche, parity, duration of lactation, oral contraceptive use, HRT use, menopausal status at baseline, physical activity, alcohol, smoking, level of
education, total energy intake and BMI.
a
Test for homogeneity by ER or PR status.
Diet and breast cancer risk
L Baglietto et al
529
British Journal of Cancer (2011) 104(3), 524 – 531&2011 Cancer Research UK
Epidemiology
et al, 2009) suggest that a prudent diet and higher intakes of fruits
and vegetables might protect against ER-negative tumours, but not
against ER-positive tumours. In the ORDET study, the salad and
oil pattern was particularly associated with HER-2-positive cancers
(Sant et al, 2007) and HER-2 over-expression is associated with
more aggressive and ER-negative cancers. It has been suggested
that for breast cancers that are less dependent on hormones, such
as ERand PRbreast cancers, the protective effect of
phytochemicals present in fruit and vegetables on many cellular
functions may be fully expressed, whereas for ER þand PR þ
cancers it can be overridden by hormonal factors (Olsen et al,
2003; Fung et al, 2005). More aggressive breast cancers, and
tumours not expressing ER or PR receptors only represent a small
proportion of all breast tumours and epidemiological studies
considering breast cancer as a single homogeneous disease might
have failed to detect any effect due to the inclusion of a small
number of aggressive tumours not expressing hormone receptors.
We did not find significant evidence of an association with
the other two dietary patterns that we identified, the meat
pattern, correlated with high consumption of meat, and the
traditional Australian pattern, correlated with high consump-
tion of dairy products and margarine. Studies of dietary fat intake
and risk have been inconsistent (Howe et al, 1990; Hunter et al,
1996; Smith-Warner et al, 2001a; Boyd et al, 2003; Cho et al, 2003;
Thiebaut et al, 2007). Evidence from the Nurse’s Health Study II
also suggest that higher red meat intake may be a risk factor for
premenopausal ER þ/PR þbreast cancer (Cho et al, 2006). We
had too few data to perform the analysis by age and hormone
receptor status.
Our analysis supports the hypothesis that a dietary pattern
characterized by high intake of fruit and salad is associated
with a slightly lower risk of breast cancer and for hormone
receptor-negative breast cancer in particular. Breast cancer is a
heterogeneous disease and we have provided additional evidence
that risk factors might differ in their relationship to cancer
subtypes.
ACKNOWLEDGEMENTS
This study was made possible by the contribution of many people,
including the original investigators, the Program Manager, and the
diligent team who recruited the participants and who continue
working on follow-up. We would like to express our gratitude to
the many thousands of Melbourne residents who continue to
participate in the study. Cohort recruitment was funded by
VicHealth and The Cancer Council Victoria. This study was funded
by grants from the National Health and Medical Research Council
(251533, 209057 and 504711) and The National Breast Cancer
Foundation and was further supported by infrastructure provided
by The Cancer Council Victoria. JLH is a NHMRC Australia Fellow.
LB conceived the study objectives, designed and performed the
statistical analyses and prepared the first draft of the article; KK
collaborated on the statistical analyses and prepared of the first
draft of the article; GS conceived the study objectives, contributed
in interpreting the study results and in writing the first draft of the
article; AH was responsible for the nutritional program of MCCS,
contributed to identify the dietary patterns and to interpret the
study results; MB contributed to interpret the study results; CM
reviewed the original tumour slides and assessed ER and PR status;
DRE, JLH and GGG are the principal investigators of MCCS. All
authors contributed to the writing and editing of the final version
of the manuscript.
Conflict of interest
The authors declare no conflict of interest.
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