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Vitamin D and Breast Cancer Risk
by
Laura Nicole Anderson
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Dalla Lana School of Public Health University of Toronto
© Copyright by Laura Nicole Anderson 2010
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Vitamin D and Breast Cancer Risk
Laura Nicole Anderson
Doctor of Philosophy
Dalla Lana School of Public Health University of Toronto
2010
Abstract
It has long been known that vitamin D is important for calcium absorption and bone health. More
recently, vitamin D has been found to modulate breast cancer cell growth and increasingly
epidemiologic studies suggest vitamin D may be associated with reduced breast cancer risk. The
primary objective of this thesis was to evaluate the associations between vitamin D from all
sources (food, supplements and sunlight exposure) and breast cancer. Secondary objectives were
focused on methodological issues including the development of a solar vitamin D score and
adapting the measurement of vitamin D from foods for use among Canadians. The data source
for this study was the “Ontario Women’s Diet and Health Study”, a population-based case-
control study of women in Ontario. Cases (n = 3,101) diagnosed between 2002 and 2003 were
identified through the Ontario Cancer Registry and controls (n = 3,471) were identified through
random digit dialing of Ontario households. Study participants completed mailed risk factor and
food frequency questionnaires. Vitamin D intake from supplements (>400 IU/day compared to
none) was found to be associated with reduced breast cancer risk (OR = 0.76; 95% CI: 0.59,
0.98). However, total vitamin D intake (from food and supplements) and intake from food alone
were not associated with breast cancer risk. Time spent outdoors during 4 periods of life
(including adolescence) was associated with reduced breast cancer (e.g., highest versus lowest
categories of exposure at age 40 to 59: OR = 0.74; 95% CI: 0.61, 0.88). The novel solar vitamin
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D score, derived from time spent outdoors, skin color, sun protection practices, and ultraviolet
radiation of residence, was also associated with reduced breast cancer risk. In summary, there is
some evidence to suggest that vitamin D intake from supplements and determinants of cutaneous
vitamin D production are associated with reduced breast cancer risk.
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Acknowledgments
I am grateful to numerous people who contributed to this thesis. I extend my sincerest
appreciation to my supervisor, Dr. Michelle Cotterchio, for her guidance and support and for
making this process an enriching and valuable experience. I would like to acknowledge my
thesis committee members Drs. Vicki Kirsh, Julia Knight, and Reinhold Vieth for contributing
substantial time and effort to my training and this resultant thesis. I would also like to thank Dr.
Julia Knight for providing additional mentorship and direction.
It is a pleasure to thank many people at Cancer Care Ontario including Nancy Deming, Noori
Chowdhury, Todd Norwood, Patrick Brown, Lucia Mirea, and Beatrice Boucher. I am
particularly grateful to Beatrice Boucher for her constant encouragement and nutritional
expertise. I would also like to recognize Torin Block at NutritionQuest and Dr. Vitali Fioletov at
Environment Canada for sharing their expertise. Many thanks also to the vitamin D journal club
members for provoking interesting discussion and the valuable exchange of relevant information.
I would also like to express many thanks to my friends, including my fellow PhD students, for
their encouragement over the past 4 years. Last of all, I would like to extend my heartfelt
gratitude to my family, and to Sean McIntyre and his family for their enduring support.
This research would not have been possible without the generosity of all women who
participated in this study. The study was supported by grants from the Canadian Breast Cancer
Research Alliance and Canadian Breast Cancer Foundation - Ontario Chapter. I received
financial support through a Doctoral Award in Public Health Research from the Canadian
Institutes for Health Research.
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Table of Contents
Acknowledgments........................................................................................................................iv
Table of Contents .........................................................................................................................v
List of Tables ...............................................................................................................................viii
List of Figures ..............................................................................................................................xi
List of Appendices .......................................................................................................................xii
List of Abbreviations ...................................................................................................................xiii
Chapter 1 Introduction and Objectives ........................................................................................1
1.1 Introduction ......................................................................................................................1
1.2 Study Objectives ..............................................................................................................2
Chapter 2 Background and Literature Review.............................................................................3
2.1 Breast Cancer ...................................................................................................................3
2.1.1 Breast Cancer Biology, Screening and Treatment ...............................................3
2.1.2 Burden of Breast Cancer in Canada .....................................................................4
2.1.3 Breast Cancer Risk Factors ..................................................................................5
2.2 Vitamin D Background ....................................................................................................11
2.2.1 Sources of Vitamin D...........................................................................................11
2.2.2 Vitamin D Biologic Action ..................................................................................14
2.2.3 Recommended Vitamin D Intake from Diet ........................................................16
2.2.4 Determinants and Optimal Level of 25(OH)D ....................................................17
2.3 Epidemiologic Studies of Vitamin D and Breast Cancer.................................................18
2.3.1 Reviews and Meta-analyses .................................................................................18
2.3.2 Trials ....................................................................................................................19
2.3.3 Biomarker Studies ................................................................................................20
2.3.4 Cohort Studies ......................................................................................................23
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2.3.5 Case-Control Studies ...........................................................................................27
2.3.6 Ecologic and Sun Exposure Proxy Studies ..........................................................30
2.4 Factors that May Influence or Modify the Vitamin D and Breast Cancer Association ...31
2.4.1 Calcium ................................................................................................................31
2.4.2 Timing of Exposure to Vitamin D .......................................................................32
2.4.3 Hormone Receptor Status (ER/PR) .....................................................................33
2.4.4 Genetic Variants...................................................................................................33
2.5 Vitamin D and Breast Cancer Mortality, Prognosis or Precursors ..................................34
2.6 Appraisal of the Vitamin D and Breast Cancer Literature ...............................................35
2.7 Summary and Rationale for the Current Study ................................................................38
Chapter 3 Study Methods.............................................................................................................40
3.1 Data Source and Study Design ........................................................................................40
3.2 Identification of Cases and Controls ................................................................................40
3.2.1 Cases ....................................................................................................................40
3.2.2 Controls ................................................................................................................41
3.3 Data Collection ................................................................................................................41
3.4 Variable Definitions .........................................................................................................43
3.4.1 Vitamin D and Calcium from Food and Supplements .........................................43
3.4.2 Individual Variables Related to Cutaneous Vitamin D Production .....................44
3.4.3 Derivation of a Solar Vitamin D Score ................................................................48
3.4.4 Potential Confounders ..........................................................................................49
3.5 Statistical Analysis ...........................................................................................................54
3.6 Ethics................................................................................................................................57
Chapter 4 Study Results ...............................................................................................................58
4.1 Overview of Results .........................................................................................................58
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4.2 Paper 1: Vitamin D Intake from Food and Supplements among Ontario Women Based
on the US Block Food Frequency Questionnaire with and without Modification for
Canadian Food Values ....................................................................................................59
4.3 Paper 2: Vitamin D and Calcium Intakes and Breast Cancer Risk in Pre- and
Postmenopausal Women ..................................................................................................72
4.4 Paper 3: Ultraviolet Sunlight Exposure and Breast Cancer Risk: A Population Based
Case-Control Study in Ontario ........................................................................................97
Chapter 5 Discussion and Conclusions ........................................................................................123
5.1 Summary of Findings and Comparison to the Literature.................................................123
5.1.1 Objectives 1 and 5 ................................................................................................123
5.1.2 Objectives 2 and 4 ................................................................................................125
5.1.3 Objective 3 ...........................................................................................................126
5.2 Limitations and Methodological Issues ...........................................................................127
5.2.1 Selection Bias.......................................................................................................127
5.2.2 Information Bias ..................................................................................................129
5.2.3 Confounding and Effect Modification .................................................................132
5.2.4 Analytical Issues ..................................................................................................133
5.2.5 External Validity ..................................................................................................134
5.2.6 General Limitations .............................................................................................135
5.3 Study Strengths ................................................................................................................135
5.4 Causation and Future Studies...........................................................................................136
5.5 Conclusions and Public Health Importance .....................................................................137
References ....................................................................................................................................140
Appendices ...................................................................................................................................162
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List of Tables
Chapters 1-3
Table 1. Amount of vitamin D in selected foods and supplements (values obtained from Health
Canada, Canadian Nutrient File, 2007)......................................................................................... 14
Table 2. Summary of previous studies of serum 25(OH)D and breast cancer risk ..................... .22
Table 3. Summary of previous cohort studies of vitamin D (from diet or sunlight) and breast
cancer risk ..................................................................................................................................... 25
Table 4. Summary of previous case-control studies of vitamin D (from diet or sunlight) and
breast cancer risk........................................................................................................................... 29
Table 5. Distribution of breast cancer cases and controls and age-group adjusted odds ratio
(AOR) estimates for selected known and suspected breast cancer risk factors ............................ 51
Table 6. Spearman rank correlations (rs) between physical activity and time outdoors per week at
4 age periods of exposure ............................................................................................................. 55
Table 7. Spearman rank correlations (rs) between vitamin D and calcium from food, supplements
and total combined (food and supplements) intake ...................................................................... 56
Chapter 4
Paper 1
Table 1. Vitamin D food values assigned to the standard (US) nutrient analysis and the modified
Canadian analysis.......................................................................................................................... 69
Table 2. Distribution of subject characteristics among all participating Ontario women ............ 70
Table 3. Distribution of vitamin D intake among Ontario women (total and stratified by age
group) ............................................................................................................................................ 71
Paper 2
Table 1. Distribution of selected characteristics and age-group adjusted odds ratio (OR) estimates
among 3,101 breast cancer cases and 3,471 controls in the Ontario Women’s Diet and Health
study .............................................................................................................................................. 87
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Table 2. Distribution of breast cancer cases (n = 3101) and controls (n = 3471) and odds ratio
(OR) estimates for intake of selected foods and supplements (frequency and duration) known to
contain vitamin D or calcium among Ontario women ...................................................................89
Table 3. Distribution of breast cancer cases (n = 3101) and controls (n = 3471) and odds ratio
(OR) estimates for derived vitamin D and calcium nutrient intake from food, supplements and
total combined among Ontario women ......................................................................................... 92
Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
vitamin D intake variables stratified by total calcium intake .......................................................94
Table 5. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
vitamin D and calcium variables stratified by menopausal status among Ontario women ...........95
Paper 3
Table 1. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for sun
exposure related variables during 4 age periods ..........................................................................114
Table 2. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
derived proxy measures of vitamin D from sunlight during 4 age periods, recent exposure only,
and cumulative life exposure ......................................................................................................117
Table 3. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
derived proxy measures of vitamin D from sunlight during 4 age periods stratified by vitamin D
supplement use .............................................................................................................................119
Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
combined solar vitamin D score and vitamin D from supplements created by cross-classification
......................................................................................................................................................120
Table 5. Application of the predicted 25(OH)D model from the Health Professionals Follow-Up
Study to our Ontario Women’s Diet and Health study data ........................................................121
Table 6. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
predicted 25(OH)D using the Health Professionals Study algorithm ..........................................122
Chapter 5
Table 1. Corrected ‘true’ risk estimates for selected measures of validity (γ) and observed risk
estimates of 0.76 and 0.85............................................................................................................130
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Appendix 3
Table 1. Missing data for each main variable examined .............................................................166
Appendix 4
Table 1. The association between ethnicity and sun protection practices at each age group of
exposure .......................................................................................................................................170
Table 2. Spearman rank correlations (rs) between time spent outdoors and sun protection
practices, erythemal UV and latitude during each age period of exposure .................................171
Table 3. Spearman rank correlations (rs) between time spent outdoors and parity, income and
education during each age period of exposure …………………………………………………172
Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates of
variables associated with cutaneous vitamin D production created by cross-classification during
4 age periods ................................................................................................................................173
Table 5. Sensitivity analyses for solar vitamin D score ...............................................................174
Table 6. Odds ratio (OR) estimates for derived solar vitamin D score during 4 age periods and
breast cancer risk overall and among lifelong residents of Canada only……………………… 175
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List of Figures
Chapter 3
Figure 1. Map of erythemal UV radiation (mW/m2) worldwide and locations where cases and
controls resided in their teenage years ......................................................................................... 47
Figure 2. Hypothesized model of vitamin D and breast cancer with details of the proposed
algorithm for the measurement of UV radiation conditional on factors that affect vitamin D
production……………………………………………………………………………………… 49
Appendix 4
Figure 1. Distributions of vitamin D intake from a) foods, b) supplements (multivitamins and
vitamin D or cod liver oil), and c) total vitamin D intake (food and supplements)…………… 169
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List of Appendices
Appendix 1. Questionnaires ......................................................................................................163
Appendix 2. Ethics approval .....................................................................................................165
Appendix 3. Missing data. ........................................................................................................166
Appendix 4. Supplementary analyses .......................................................................................168
Appendix 5. Power calculations ...............................................................................................176
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List of Abbreviations
25(OH)D 25-hydroxyvitamin D
1,25(OH)2D 1,25-dihydroxyvitamin D
AI adequate intake
BBD benign breast disease
BDDS Block dietary data system
BMI body mass index
CI confidence interval
CNF Canadian Nutrient File
DRI dietary reference intake
IARC International Agency for Research on Cancer
ER estrogen receptor
FFQ food frequency questionnaire
HRT hormone replacement therapy
NHANES National Health and Nutrition Examination Survey
OCR Ontario Cancer Registry
OR odds ratio
PR progesterone receptor
RDA recommended dietary allowance
RR relative risk
SZA solar zenith angle
VDR vitamin D receptor
WHI Women’s Health Initiative
UV ultraviolet
USDA United States Department of Agriculture
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Chapter 1 Introduction and Objectives
1.1 Introduction
A growing body of literature emerging from epidemiologic and laboratory studies has led to the
hypothesis that vitamin D may reduce breast cancer risk (as reviewed in Bertone-Johnson, 2007;
Bertone-Johnson, 2009; Colston, 2008; Cui & Rohan, 2006; Lipkin & Newmark, 1999; Perez-
Lopez, Chedraui, & Haya, 2009; Rohan, 2007); however, the epidemiologic study findings are
inconclusive and many gaps exist in the literature. The International Agency for Research in
Cancer (IARC) recently concluded there is evidence of an inverse association between vitamin D
and breast cancer risk but not sufficient evidence to conclude a causal effect exists (IARC,
2008). Sources of vitamin D include supplements and a few foods and, unlike any other vitamin,
vitamin D is produced in the skin following sunlight exposure. Thus, in addition to the usual
challenges of measurement in nutritional epidemiology, there are unique challenges in the
measurement of vitamin D for observational epidemiologic studies (Millen & Bodnar, 2008).
Furthermore, the association between vitamin D and breast cancer risk may be modified by other
factors (e.g., menopausal status, body fatness) or depend upon timing of exposure (Bertone-
Johnson, 2007; Bertone-Johnson, 2009; Rohan, 2007), but few previous studies have explored
these differences. Vitamin D, from both sunlight and diet, is important for calcium absorption
(Heaney, 2008) and both vitamin D and calcium have some common dietary sources (e.g.,
vitamin D fortified milk) (McCullough et al., 2005). Calcium may also have anti-cancer
properties but has less consistently been associated with reduced breast cancer risk (as reviewed
in Al Sarakbi, Salhab, & Mokbel, 2005; Cui & Rohan, 2006). Despite the biologic relationship
between calcium and vitamin D, few studies have included calcium in their investigations of
vitamin D and breast cancer.
The overall aim of this thesis was to investigate the associations between vitamin D from all
sources (food, supplements, and sunlight exposure) and breast cancer risk in a population-based
case-control study of over 6,000 Ontario women aged 25-74 years. This study improves upon the
aforementioned limitations and addresses some of the current gaps in knowledge. An emphasis
was placed on the measurement of vitamin D, including adaptation of a measure of dietary
vitamin D specific to our Canadian population, and the development of a unique algorithm
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(incorporating time outdoors, ultraviolet radiation of residence, sun protection practices and skin
color) to measure vitamin D from sunlight. Vitamin D intake is modifiable and research
identifying modifiable breast cancer risk factors is important for prevention, as most well-
established risk factors are not modifiable (Rockhill, Weinberg, & Newman, 1998). The ultimate
aim of this study was to develop a better understanding of the association between vitamin D and
breast cancer among Ontario women.
A detailed literature review and rationale for the study is included in chapter 2, followed by
methods and related methodological results in chapter 3. This thesis was written in manuscript
format with 3 manuscripts in chapter 4. Chapter 5 contains a detailed discussion of study results,
methodological issues and overall conclusions.
1.2 Study Objectives
Primary Objectives
Objective 1. To evaluate the associations between breast cancer risk and vitamin D intakes from
food and supplements (and potential effect modification by calcium, menopausal
status or body mass index).
Objective 2. To evaluate the association between breast cancer risk and sunlight exposure
variables from adolescence through adulthood – as reported and using the solar
vitamin D score from objective 4 (and potential effect modification by calcium,
menopausal status or body mass index).
Objective 3. To evaluate the association between breast cancer risk and total vitamin D from all
sources (food, supplements and sunlight exposure).
Secondary Objectives
Objective 4. To develop and apply an algorithm to derive a solar vitamin D score (for objective
2).
Objective 5. To modify and compare vitamin D intake from a standard US nutrient analysis of a
food frequency questionnaire (FFQ) versus a modified nutrient analysis that reflects
additional vitamin D sources and Canadian food fortification (for objective 1).
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Chapter 2 Background and Literature Review
2.1 Breast Cancer
2.1.1 Breast Cancer Biology, Screening and Treatment
Cancer of the breast results from unregulated cell growth and the new growth of undifferentiated
tissue (Medline Plus Merriam-Webster, 2009). Breast tissue consists of mammary glands and
ducts, made up of epithelial tissue, in addition to adipose, connective tissue and vessels of the
lymphatic and blood system. Development begins during puberty, is generally quite advanced by
menarche and final differentiation only occurs during pregnancy and lactation (Colditz, Baer, &
Tamimi, 2006). After menopause, hormone levels decline and breast cells do not continue to
divide. Breast cells are potentially more susceptible to exposures during the period from
menarche to first birth when breast tissue is undifferentiated (Colditz & Frazier, 1995; Okasha,
McCarron, Gunnell, & Smith, 2003; Russo, Moral, Balogh, Mailo, & Russo, 2005) or during
pregnancy when breast tissue is growing (Kelsey & Berkowitz, 1988). There is also some
evidence that earlier life exposures may be important (Kelsey & Berkowitz, 1988; Okasha et al.,
2003). Thus, exposures during critical periods of breast development may be more likely to
influence breast cancer risk.
Breast cancers are classified as in situ (non-invasive) or invasive (invading the breast stroma). In
situ breast cancers occur in the duct or lobule whereas the majority (>95%) of invasive breast
cancers are infiltrating ductal adenocarcinomas, cancers of the glandular epithelium (Colditz et
al., 2006; Kelsey & Bernstein, 1996). Breast cancers are further classified by stage and grade,
based on histopathology and differentiation of the tumor cells. Stage is determined based on
tumor size, regional lymph node involvement and distant metastasis (Singletary & Connolly,
2006). Treatment and prognosis are determined based on breast cancer stage (Bland et al., 1998).
Breast cancers are often further classified by time of diagnosis (pre- versus post- menopause),
and by their expression of human epidermal growth factor receptor 2 (HER2) and positive or
negative expression of hormone receptors (estrogen-receptors (ER) and progesterone-receptors
(PR)) (Colditz et al., 2006).
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Women in Ontario between the ages of 50-69 years are recommended to undergo mammography
and clinical breast exams every 2 years; guidelines vary for women at high risk (e.g., family
history of breast cancer) (Lipskie, 1998). Mammography rates among these women have
increased from 40% in 1990 to 73% in 2008 (Shields & Wilkins, 2009). Breast cancer screening
is important for the early detection of breast cancer and has been associated with a 30%
reduction in mortality among women 50-69 years of age (Fletcher, Black, Harris, Rimer, &
Shapiro, 1993). Breast cancer diagnosis is confirmed by histology from tissue biopsy. Treatment
options depend on stage, hormone receptor status and other characteristics of the primary tumor.
Treatment of breast cancer often involves surgery, radiation therapy, and adjuvant systematic
therapies (e.g., chemotherapy, tamoxifen, aromatase inhibitors) (National Cancer Institute, 2009;
Steering Committee on Clinical Practice Guidelines for the Care and Treatment of Breast
Cancer, 1998; Veronesi, Boyle, Goldhirsch, Orecchia, & Viale, 2005).
2.1.2 Burden of Breast Cancer in Canada
The Ontario Cancer Registry (OCR) collects data on all cancer cases in Ontario (excluding non-
melanoma skin cancers) and data from the OCR are combined with other provincial and
territorial registries or data sources to create the Canadian Cancer Registry. Canadian Cancer
Statistics are published annually using these data and provide detailed information on the burden
of cancer in Canada (Canadian Cancer Society/National Cancer Institute of Canada, 2009).
Breast cancer is the most common cancer among Canadian women and it is estimated that
22,700 Canadian women were diagnosed with breast cancer in 2009. The lifetime probability of
developing breast cancer among females is 11%; this statistic takes into account the risk at each
stage of life using life tables to estimate average life expectancy. Age-standardized annual breast
cancer incidence rates remained relatively stable from 1991 to 2002 with about 100 cases per
100,000. A slight decrease was observed from 2003 to 2005 with approximately 97 cases per
100,000 (actual data not yet available for more recent years). In contrast, age-standardized breast
cancer mortality rates have decreased steadily from about 30 deaths per 100,000 in 1980 to an
estimated 22 deaths per 100,000 in 2009. This decrease is likely due to improved treatment and
early detection. Despite this decrease, breast cancer mortality remains a leading cause of cancer
mortality among females in Canada, second only to lung cancer. The 5-year relative survival
ratio for breast cancer is 87%. Breast cancer incidence rates in the US are highest among White
women, lower among Black women and the lowest among Asian women (Altekruse, Kosary,
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Krapcho, Neyman et al., 2009); Canadian statistics are not available by race/ethnicity. Breast
cancer in men is much less common (<1% of all breast cancer cases) and not the focus of this
thesis.
The burden of breast cancer has significant public health implications in Canada. The average
lifetime treatment cost per case in Canada was $25,661 in 1995 corresponding to a total cost of
$454 million for all cases diagnosed in 1995 (Will et al., 2000). To the best of our knowledge
more recent data on costs in Canada are not available, however, we would suspect that costs have
continued to rise with increased survival. Canadian women with breast cancer also report
significant personal financial impact. In the year following breast cancer diagnosis working
women reported an average wage loss of 27% (Lauzier et al., 2008). Beyond the economic costs
and physical health effects, breast cancer has a significant impact on the psychological wellbeing
of patients, caregivers and ‘healthy’ women with a family history of breast cancer (e.g., Badger,
Segrin, Dorros, Meek, & Lopez, 2007; Lauzier et al., 2009; Maheu, 2009).
2.1.3 Breast Cancer Risk Factors
Although breast cancer is the most common cancer among women in North America, relatively
few breast cancer risk factors are well-established and most that are established are non-
modifiable. Multiple lines of evidence suggest that genetic and reproductive factors influence
breast cancer risk; however these factors are for the most part not readily modifiable for cancer
prevention. The subsequent section briefly reviews the known and suspected breast cancer risk
factors.
Genetic factors
Studies of breast cancer risk among twins suggest some breast cancer is attributable to hereditary
factors (Lichtenstein et al., 2000). Mutations in BRCA1 and BRCA2 genes lead to high-risk
genotypes associated with up to 80% lifetime risk of developing breast cancer; however, there is
much variation in penetrance which is likely due to non-genetic factors (as reviewed by Narod,
2006). Mutations in BRCA1 and BRCA2 are relatively rare and account for only 5-10% of all
breast cancers (Claus, Schildkraut, Thompson, & Risch, 1996; Martin & Weber, 2000). Other
more common genetic variants have also been associated with breast cancer risk, but these
explain only a small amount of the variation in breast cancer risk (as reviewed in Martin &
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Weber, 2000). Approximately 10% of breast cancer cases have at least one first degree relative
with a history of breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer,
2001). The risk of breast cancer increases substantially with number of affected relatives; the
relative risks of having 1, 2 or 3 first degree relative versus none are, respectively, 1.80 (99% CI:
1.69–1.91), 2.93 (99% CI: 2.36–3.64), and 3.90 (99% CI: 2.03–7.49) (Collaborative Group on
Hormonal Factors in Breast Cancer, 2001).
Breast cancer rates also differ substantially by country: the age-adjusted standardized incidence
rates in North American and Northern European countries are two to four times greater than in
Asian and Latin American countries (Althuis, Dozier, Anderson, Devesa, & Brinton, 2005).
Some of this variation may be attributable to differences in screening/diagnosis or reporting, but
lifestyle and genetic factors are also possible contributors (Althuis et al., 2005). Migrant studies
have shown that women moving from countries of low to high cancer risk develop the higher
rates of breast cancer of the new population (Andreeva, Unger, & Pentz, 2007; Kliewer & Smith,
1995). These findings suggest that although genetic factors may be important, environmental
factors also influence breast cancer risk.
Reproductive factors
As early as the 1700’s there was evidence suggesting that reproductive factors may be important
for breast cancer risk (Mustacchi, 1961). It is now well-established that nulliparity or lower
parity, older age at first full-term pregnancy, younger age at menarche and older age at
menopause are all associated with increased breast cancer risk (as reviewed in Colditz et al.,
2000; Kelsey, Gammon, & John, 1993; Veronesi et al., 2005). Nulliparous women have a 20-
70% increased risk of breast cancer compared to parous women and a comparable increase in
risk is observed among women greater than 30 years of age at first full-term pregnancy (Kelsey
et al., 1993). Among parous women, breast cancer risk decreases 7% on average for each
additional birth (Veronesi et al., 2005). However, breast cancer risk temporarily increases for a
few years following a birth, particularly the first one (Kelsey et al., 1993; Pathak, 2002). Breast
tissue becomes further differentiated during pregnancy and it is through these changes to breast
tissue, that parity and age at first birth are suspected to influence breast cancer risk (Kelsey et al.,
1993).
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Breastfeeding has also been associated with breast tissue differentiation and several reviews have
concluded that lactation, or ever breastfeeding, is associated with reduced overall breast cancer
risk (Colditz et al., 2000; World Cancer Research Fund, 2007), premenopausal breast cancer
only (Lipworth et al., 2000) or only among premenopausal women with a family history of
breast cancer (Stuebe, Willett, Xue, & Michels, 2009). The association between being breastfed
as an infant and later breast cancer risk is less well-established, findings from individual studies
are inconclusive (e.g., Freudenheim et al., 1994; Nichols et al., 2008; Wise et al., 2009).
Whereas, results from a meta-analysis suggest having been breastfed may be protective against
pre- but not postmenopausal breast cancer (Martin, Middleton, Gunnell, Owen, & Smith, 2005).
Younger age at menarche and older age at menopause are two additional well-established breast
cancer risk factors (as reviewed in Colditz et al., 2000; Kelsey, Gammon, & John, 1993;
Veronesi et al., 2005), these reproductive-related variables are associated with increased estrogen
exposure. Consistent with the hypothesis that lifetime estrogen exposure increases breast cancer
risk (Colditz et al., 2006; Martin & Weber, 2000), circulating levels of estrogen (Bernstein &
Ross, 1993; Hankinson & Eliassen, 2007) and testosterone (Hankinson & Eliassen, 2007) have
also been associated with increased breast cancer risk, particularly among postmenopausal
women. Use of exogenous hormones including hormone replacement therapy, oral
contraceptives, and diethylstilbestrol (DES) (during pregnancy) also increases breast cancer risk
(Colditz et al., 2000; Veronesi et al., 2005). The Women’s Health Initiative (WHI) trial, where
postmenopausal women were randomized to hormone replacement therapy (estrogen plus
progestin) (n = 8506) or placebo (n = 8102), found HRT use increased breast cancer risk
(HR=1.26; 95% CI: 1.00, 1.59) (Rossouw et al., 2002). Immediately following publication of the
WHI trial (between 2002 and 2003), breast cancer incidence decreased by nearly 6% in Canada
and HRT prescription rates decreased by 27% (Kliewer, Demers, & Nugent, 2007), similar
decreases were observed in the US over this one year period (Ravdin et al., 2007). Between 2000
and 2005, HRT use has decreased by more than 50% in the US and it has been suggested that
this may explain the corresponding 9% decrease in breast cancer incidence observed over this
same time period (Coombs, Cronin, Taylor, Freedman, & Boyages, 2010).
Well-established non-reproductive risk factors
8
Most well-established non-reproductive breast cancer risk factors not directly related to genetics
are not highly modifiable and thus not very influential for cancer prevention. These include age,
height, higher socioeconomic status, benign breast disease (BBD), mammographic density,
ionizing radiation exposure (Colditz et al., 2000; Kelsey & Bernstein, 1996; Veronesi et al.,
2005). The risk of breast cancer increases markedly with increasing age; with the rate of increase
more pronounced under 50 years of age (Pike, Krailo, Henderson, Casagrande, & Hoel, 1983).
Risk of breast cancer is also positively associated with height (Colditz et al., 2000; Kelsey &
Bernstein, 1996); this may be due to increased energy intake during childhood or confounding by
body size or mammographic density (Hunter & Willett, 1993). Unlike many chronic diseases,
higher socioeconomic status is associated with increased breast cancer risk (Kelsey & Bernstein,
1996; Veronesi et al., 2005). This association may be due to confounding by other factors such
as delayed childbirth, increased screening, differences in body weight or exogenous hormone use
(Kelsey & Bernstein, 1996; Mettlin, 1992).
Benign breast disease (BBD) and mammographic density are considered well-established breast
cancer risk factors. BBD is a non-cancerous condition characterized by atypical hyperplasia or
cysts. Breast cancer risk among women with a history of BBD is 4 to 5 times greater (dependent
upon histological type) than women with no history of BBD (Hartmann et al., 2005; Kabat et al.,
2010). Mammographic density is determined based on the appearance of fat versus
epithelial/stromal breast tissue from mammogram images and is also strongly associated with
breast cancer risk (Boyd et al., 2005; Veronesi et al., 2005). High density (> 75% fibro-glandular
tissue) is associated with 5 times increased risk of breast cancer (Boyd et al., 2005; Veronesi et
al., 2005). Exposure to ionizing radiation, from x-rays or other sources of radiation, is also
associated with increased breast cancer risk (Colditz et al., 2000; Kelsey & Bernstein, 1996;
Veronesi et al., 2005), particularly high exposure during puberty (Ronckers et al., 2005; Veronesi
et al., 2005).
There are few well-established lifestyle breast cancer risk factors that are potentially modifiable:
alcohol intake, body fat and low physical activity. Alcohol consumption has been consistently
associated with increased breast cancer risk (e.g., Key, Hodgson, Omar, Jensen, Thompson, et
al., 2006; Colditz et al., 2000; Giovannucci et al., 1993; Hamajima et al., 2002; Singletary &
Gapstur, 2001; Terry et al., 2006; Tjonneland et al., 2007). A positive linear relationship has
been observed between daily alcohol intake and breast cancer risk and suggesting a 9 -10%
9
increased risk with each drink consumed (Smith-Warner et al., 1998) or per 10g ethanol (Key et
al., 2006). The association between alcohol intake and breast cancer risk is modified by common
genetic polymorphisms (e.g., Boffetta & Hashibe, 2006; Platek, Shields, Marian, McCann et al.,
2009) and limited evidence suggests the association may be attenuated by folic acid intake (as
reviewed in Linos, Holmes, & Willett, 2007).
The association between BMI or body fat and breast cancer risk varies substantially by
menopausal status. Increased BMI is inversely associated with premenopausal breast cancer risk
and positively associated with postmenopausal breast cancer risk (Colditz et al., 2000; Linos et
al., 2007; Stephenson & Rose, 2003; Veronesi et al., 2005). Increased body fatness is associated
with higher estrogen levels in postmenopausal women which may explain the increased breast
cancer risk observed among postmenopausal women (Cleary & Grossmann, 2009). In contrast,
body fatness may decrease risk in premenopausal women through anovulation (Linos et al.,
2007). Body fatness is also commonly associated with reduced physical activity, and higher
levels of physical activity have been associated with a 20 to 30% reduction in breast cancer risk
(as reviewed in Friedenreich & Cust, 2008; Warburton, Katzmarzyk, Rhodes, & Shephard,
2007). This association is strongest among postmenopausal women and, limited evidence
suggests, among hormone receptor negative breast cancers, women with normal BMI, and no
family history of breast cancer (Friedenreich & Cust, 2008). Physical activity may exert a
protective effect on breast cancer risk through decreased levels of BMI, hormones (e.g.,
estrogens), insulin resistance, and/or inflammation (e.g., c-reactive protein) (Neilson,
Friedenreich, Brockton, & Millikan, 2009). Changes in hormone levels, insulin resistance and
inflammation may also be influenced by BMI and perhaps contribute to the observed
associations between BMI and breast cancer risk.
Possible dietary risk factors
Only a few of the aforementioned established risk factors are readily modifiable: exogenous
hormone use, alcohol intake, physical activity, and obesity. There is conflicting evidence
regarding the association between other more common modifiable factors and breast cancer risk.
For example, a recent report by the World Cancer Research Fund (WCRF) and IARC (World
Cancer Research Fund, 2007) reviewed more than 40 specific dietary factors or groups of foods
(including milk, dairy products, vitamin D, and calcium) and concluded there was limited or no
10
evidence for nearly all dietary factors. Only the evidence for total dietary fat intake was judged
to be suggestive with respect to increasing breast cancer risk among postmenopausal women
only. However, a low-fat dietary intervention was not found to reduce breast cancer risk among
postmenopausal women in the WHI (Prentice et al., 2006). Other comprehensive reviews of
breast cancer risk factors have also concluded there are no well-established dietary breast cancer
risk factors (with the exception of alcohol and weight gain) (Colditz et al., 2000; Colditz, 2005;
Kelsey & Bernstein, 1996; Linos et al., 2007; Lof & Weiderpass, 2009; Michels, Mohllajee,
Roset-Bahmanyar, Beehler, & Moysich, 2007; Veronesi et al., 2005).
Several reviews have identified dietary factors for which there is limited evidence suggesting a
possible association with reduced breast cancer risk. These include phytoestrogens or soy foods
(Bissonauth, Shatenstein, & Ghadirian, 2008; Kelsey & Bernstein, 1996; Linos et al., 2007),
fruits (Bissonauth et al., 2008), vegetables (Bissonauth et al., 2008; Colditz et al., 2000), fish
(Bissonauth et al., 2008), monounsaturated fat (Bissonauth et al., 2008; Colditz et al., 2000),
dairy products (Bissonauth et al., 2008; Linos et al., 2007), calcium (Bissonauth et al., 2008; Cui
& Rohan, 2006; Linos et al., 2007) and vitamin D (Bissonauth et al., 2008; Cui & Rohan, 2006;
Linos et al., 2007). Possible dietary risk factors associated with increased risk include saturated
or total dietary fat (Bissonauth et al., 2008; Colditz et al., 2000; Linos et al., 2007), total energy
(Bissonauth et al., 2008), high glycemic index foods (Linos et al., 2007) and meat or specifically
red meat intake (Bissonauth et al., 2008; Linos et al., 2007; Taylor, Misra, & Mukherjee, 2009).
However, the literature on all of these factors is inconclusive. Emerging interest in dietary
patterns provides some evidence that a high fat or ‘Western’ pattern may be associated with
increased breast cancer risk (Lof & Weiderpass, 2009). Future studies are needed on all of these
dietary factors that should take into consideration timing of exposure, gene-environment
interactions and breast cancer characteristics (Bissonauth et al., 2008; Linos et al., 2007; Lof &
Weiderpass, 2009). Relatively few studies have evaluated childhood or adolescent diet and risk
of adult breast cancer and the results are generally inconsistent with few statistically significant
results (e.g., Frazier et al., 2004; Frazier et al., 2003; Linos et al., 2010)
Smoking and other possible environmental risk factors
Tobacco smoking, a well-established modifiable risk factor for other cancers (e.g., lung,
pancreas) (Colditz et al., 2000), has not been consistently associated with breast cancer risk
11
(Morabia, 2002; Palmer & Rosenberg, 1993; Terry & Rohan, 2002). Smoking and alcohol intake
are highly correlated and after adjustment for smoking, alcohol remains associated with breast
cancer risk but no effect of smoking is observed (Hamajima et al., 2002). There is somewhat
more consistent evidence, although not complete agreement, that environmental tobacco smoke,
also known as passive or secondhand smoke, is associated with increased breast cancer
especially in premenopausal women (Lee & Hamling, 2006; Miller et al., 2007, Johnson &
Glantz, 2008). Furthermore, the associations between smoking (active or passive) and breast
cancer risk may be modified by genotype (Terry & Goodman, 2006). Overall, the associations
between smoking and breast cancer are not well understood. There is great interest and public
perception that environmental contaminants and consumer products are associated with breast
cancer risk but there is currently insufficient evidence to support these associations (Brody et al.,
2007).
Risk factors by hormone receptor status
Increasingly studies are suggesting that risk factors may vary by breast cancer subtype. Triple
negative breast cancers (ER-, PR- , and HER2 -) are associated with a poorer prognosis than
hormone receptor positive or luminal type cancers and occur more frequency in African
Americans women than white women, are often diagnosed at younger ages and frequently
associated with BRCA1 mutations (as reviewed in Ray & Polite, 2010). Reproductive factors
traditionally associated with reduced overall breast cancer risk (e.g., later age at menarche, parity
and younger age at first birth) are associated with hormone receptor positive but not receptor
negative tumors (as reviewed in Althuis et al., 2004).
2.2 Vitamin D Background
2.2.1 Sources of Vitamin D
A unique property of vitamin D is that it can be produced endogenously in the skin following
sufficient sunlight exposure; specifically exposure to ultraviolet (UV) B radiation is required.
UVB wavelengths range from 280 nm to 315 nm; although shorter than the UVA range, UVB
wavelengths are more damaging. UVB radiation drives the conversion of provitamin D3 (7-
dehydrocholesterol) in the skin to previtamin D3 through isomerization. Previtamin D3 is then
12
spontaneously converted to vitamin D31 (Holick et al., 1980; MacLaughlin, Anderson, & Holick,
1982). The body strictly regulates the amount of vitamin D that can be produced in the skin at
any given time and additional sun exposure is not beneficial once maximal vitamin D production
is reached (Wolpowitz & Gilchrest, 2006). Within 24 to 48 hours of sufficient exposure to UVB
radiation, levels of the circulating form of vitamin D, 25-hydroxyvitamin D increase 5 to 10 fold
(Holick, 1987). The amount of time required for maximal vitamin D production depends on
several factors (both ecologic and person-specific); this may be as short as 5 minutes for
individuals with highly exposed lightly pigmented skin and is usually reached at suberythemal
(before skin reddening) doses (Wolpowitz & Gilchrest, 2006).
The strength of UVB that reaches the skin depends upon a number of factors. Sun exposure
varies depending upon the solar zenith angle (SZA) – the distance between the sun and zenith (a
point directly overhead) – which changes with time of year, time of day and geographic location.
Sun exposure is correlated with latitude; it is greatest near the equator and decreases with
increasing or decreasing latitude. At higher latitudes there is insufficient sunlight during the
winter months for the skin to produce vitamin D (Webb, Kline, & Holick, 1988). For example, in
Boston (42.2°N) no vitamin D is produced by skin from November to February, and in
Edmonton (52°N) this period extends from October to March. Other ambient factors that affect
UVB exposure, and hence vitamin D synthesis, include altitude, ozone, and other aerosols.
In addition to geographic factors and ambient UVB radiation there are person-specific variables
that affect endogenous vitamin D production: time spent outdoors, sun protection practices and
skin colour (Webb, 2006). Time spent outdoors is generally positively associated with increased
vitamin D, although maximal vitamin D production can be reached within short time periods
(Wolpowitz & Gilchrest, 2006) and frequent short periods of exposure may maximize vitamin D
intake. Clothing and sunscreen block the ability of UVB radiation to penetrate the skin, hence
decreasing vitamin D production (Matsuoka, Ide, Wortsman, MacLaughlin, & Holick, 1987).
Skin pigmentation (i.e., more melanin) acts as a natural sunscreen and people with darker skin
require more UVB exposure (e.g., longer time outdoors) to produce vitamin D. Adults with
whole body exposure to either one minimal erythemal dose (amount of time to produce minimal
1 Vitamin D3 is also known as cholecalciferol
13
skin reddening) or daily sun exposure (time unknown) have serum 25(OH)D increases equivalent
to oral doses of >10,000 IU vitamin D supplements (Stamp, Haddad, & Twigg, 1977; Holick,
1995). This value is regularly reported in the literature but is supported by limited data from
small studies.
Exogenous sources of vitamin D include foods and supplements. Very few foods naturally
contain vitamin D. Fatty fish, such as salmon or mackerel, contain relatively high amounts,
whereas other foods, such as meats, eggs, and shellfish, contain low quantities (Calvo, Whiting,
& Barton, 2004; Health Canada, 2007). Additionally, there has been mandatory fortification of
all fluid cows’ milk and margarine with vitamin D in Canada since the 1970’s and voluntary
fortification occurred much earlier (Health Canada, 2005). Some milk products (e.g., cheese,
yogurt) are made with fortified milk and discretionary fortification of some other items, such as
orange juice and milk beverage substitutes (e.g., soy milk), has been permitted since 2005 (as
reviewed in Sacco & Tarasuk, 2009) but these items are not universally enriched. Food
fortification practices differ between countries; using US nutrient values has been found to
underestimate Canadian vitamin D intakes (Csizmadi et al., 2007). Multivitamins, single product
vitamin D supplements and cod liver oil are all supplemental sources of vitamin D available to
Canadians. Table 1 summarizes the current vitamin D content of some foods in Canada.
There are two types of vitamin D from diet and supplements: D2 and D3. Vitamin D22 is derived
from plant sources, yeast and fungi whereas D3 is found in animal sources such as fish (and is
identical to vitamin D3 produced in skin). Current nutrient databases do not distinguish between
the two forms (Health Canada, 2007; United States Department of Agriculture, 2009). Either
form of vitamin D can be used in supplements and food fortification but most supplements now
contain vitamin D3. Historically vitamin D2 was thought to be equivalent to D3, however, some
studies (Armas, Hollis, & Heaney, 2004; Trang et al., 1998), but not all (Holick et al., 2008),
suggest that vitamin D2 may be less biologically active than D3 and may not be as effective as
vitamin D3 at maintaining serum 25(OH)D levels.
2 Vitamin D2 is also known as ergocalciferol
14
Table 1. Amount of vitamin D in selected foods and supplements (values obtained from Health
Canada, Canadian Nutrient File, 2007)
Source Amount of vitamin D
IU (µg) per serving 3
Serving size
Cod liver oil 1280 (32) 15 ml (14g)
Salmon 200 - 640 (5 - 16 ) 75g
Milk 100 (2.6 ) 260g (250mL)
Enriched orange juice 100 (2.6 ) 263g (250mL)
White fish (e.g., sole or halibut) 60 - 144 (1.5 – 3.6) 75g
Mushroom, shiitake 76 (1.9 ) 77g (125mL)
Margarine 76 (1.9 ) 14g (15ml)
Canned Tuna 60 (1.5) 165g (1 can)
Egg 28 (0.7) 50g (1 large egg)
2.2.2 Vitamin D Biologic Action
Vitamin D4 from all sources is not biologically active and must undergo hydroxylation in the
liver by 25-hydroxylase (CYP27A1) to produce 25-hydroxyvitamin D (25(OH)D) – the
circulating form of vitamin D (Holick, 2003; Schwartz & Blot, 2006). 25(OH)D circulates bound
to vitamin D binding protein (DBP). Limited evidence suggests that 25(OH)D3 has a higher
binding affinity for DBP than vitamin 25(OH)D2 (Houghton & Vieth, 2006). A second
hydroxylation is necessary to produce the biologically active form of vitamin D: 1,25-
dihydroxyvitamin D (1,25(OH)2D)5. It has been long known that 1,25(OH)2D is produced in the
kidney by 1-α-hydroxylase and it is through this well-established pathway that vitamin D
regulates calcium metabolism, important for the maintenance of healthy bones and the
prevention of rickets in children. More recently it has been identified that many other cells in the
body can also express the 1-α-hydroxylase enzyme and locally produce 1,25(OH)2D from
3 40 IU = 1 µg
4 The term vitamin D without a subscript is used to refer to either D2 or D3
5 Also known as Calcitriol
15
25(OH)D (Hewison et al., 2000; Hewison, Zehnder, Bland, & Stewart, 2000; Hewison et al.,
2007).
1,25(OH)2D is a fat soluble hormone that can be stored in adipose tissue (Holick, 2002) and has
both endocrine (regulation of calcium metabolism) and non-endocrine functions. The vitamin D
receptor, through which 1,25(OH)2D interacts, has been found to be present in most cells in the
body including intestine, bone, kidney, brain, breast, prostate, colon and some immune cells
(Buras et al., 1994; Holick, 2002; Norman, 2008; Stumpf, Sar, Reid, Tanaka, & DeLuca, 1979).
The biologically active form of vitamin D (1,25(OH)2D) has a short half life (~5 hours) and is
very tightly regulated, with circulating 1,25(OH)2D levels up to 1000 times less than 25(OH)D
(Holick, 2009). The enzyme 24-hydroxylase (CYP24A1) breaks down 1,25(OH) 2D to
1,24,25(OH)D. Low vitamin D levels, and thus decreased intestinal calcium absorption, cause an
increase in parathyroid hormone (PTH) levels promoting the re-absorption of calcium from
bones and the conversion of 25(OH)D to 1,25(OH)D (as reviewed in Holick, 2009).
Paradoxically, 1,25(OH)2D serum levels that are in the high or normal range can be observed
when 25(OH)D levels are in the low range. Thus, 25(OH)D is the preferred biomarker for
determining vitamin D status (Holick, 2009).
The finding that vitamin D receptors (VDR) are present in cells throughout the body, and not
restricted to bone, intestine and kidney, has led to the rapidly expanding body of research
suggesting that vitamin D may be associated with reduced risk of all-cause mortality (Autier &
Gandini, 2007), some autoimmune diseases (in particular multiple sclerosis), certain cancers, and
other chronic diseases (Giovannucci, 2008; Holick, 2007; Holick, 2008). The vitamin D receptor
is present in both normal and cancerous breast cells, enabling them to respond to 1,25(OH)2D
(Buras et al., 1994; Colston & Hansen, 2002; Holick, 2003; Welsh, 2004). In vitro and animal
studies have shown 1,25(OH)2D inhibits cell proliferation and angiogenesis, and promotes cell
differentiation and apoptosis (Buras et al., 1994; Colston & Hansen, 2002; Deeb, Trump, &
Johnson, 2007; Giovannucci, 2005; Holick, 2003; Holick, 2006; Welsh, 2004). These anti-cancer
properties of vitamin D occur through both genomic (mediated by VDR) and non-genomic
(direct) pathways (Norman, 2008). A recent review (Krishnan, Swami, & Feldman, 2010)
identified 3 mechanisms by which 1,25(OH)2D inhibits the growth of breast cancer cells: 1) cell
cycle arrest and differentiation, 2) apoptosis and 3) inhibition of invasion and metastasis.
Additionally, these researchers propose 6 newly discovered mechanisms which may further
16
explain the beneficial effects of 1,25(OH)2D on breast cancer cell growth involving anti-
inflammatory effects, inhibition of estrogen synthesis and signaling or down-regulation of the
estrogen receptor and aromatase inhibition (Krishnan et al., 2010).
2.2.3 Recommended Vitamin D Intake from Diet
Dietary Reference Intakes (DRI), used by Canada and the US, are established by the Institute of
Medicine in partnership with Health Canada. Currently, there is no Recommended Dietary
Allowance (RDA) for vitamin D as there was not considered sufficient evidence to calculate an
Estimated Average Requirement of vitamin D for based on adequacy for health or disease
reduction when the DRIs were released in 1995. In its absence, the Adequate Intake (AI) is used
as the reference level. An AI is the average daily nutrient intake level recommended based on
healthy people who are assumed to be in an adequate nutritional state. The AIs for vitamin D are
200 IU/day, 400 IU/day and 600 IU/day (5, 10 and 15 µg/day)6 for adults ≤ 50, 51-70 and >70
years of age, respectively (Health Canada, 2006; Office of Dietary Supplements National
Institutes of Health, 2006). The tolerable upper limit (the highest level at which no adverse
effects are expected if consumed daily) is 2,000 IU/day (Institute of Medicine National Academy
of Sciences, 2009) .
Some in the scientific community have proposed that a higher intake of vitamin D should be
recommended (Vieth et al., 2007). Recent reviews have indicated that vitamin D intake above
the current AIs is not associated with adverse effects (Cranney et al., 2007; Hathcock, Shao,
Vieth, & Heaney, 2007) and a safe upper limit for adults may be as high as 10,000 IU/day
(Hathcock et al., 2007). DRIs for vitamin D are currently under review by the Institutes of
Medicine (Office of Dietary Supplements National Institutes of Health, 2006; Yetley et al.,
2009). Previous studies of vitamin D intake have reported a high proportion of the Canadian
population did not meet the current AIs (Gozdzik et al., 2008; Poliquin, Joseph, & Gray-Donald,
2009; Statistics Canada, 2004; Whiting, Green, & Calvo, 2007). The Canadian Cancer Society
has recommended that Canadians take 1,000 IU vitamin D supplements to reduce cancer risk
“based on the growing body of evidence about the link between vitamin D and reducing risk for
colorectal, breast and prostate cancers” (Canadian Cancer Society, 2007); this recommendatation
6 To convert vitamin D from micrograms (µg) to international units (IU) multiply by 40 (25 µg = 1 IU)
17
was made shortly after publication of the vitamin D and calcium trial by Lappe et al. (Lappe,
Travers-Gustafson, Davies, Recker, & Heaney, 2007) (described in detail in section 2.3.2).
2.2.4 Determinants and Optimal Level of 25(OH)D
It is regularly reported that more than 90% of vitamin D intake is from sun exposure (Holick,
2004; Holick, 2003; John et al., 2007). There is, however, little evidence to support this
statement and it is not appropriate to assume sufficient year round sun exposure in all
populations. The true contribution of diet versus sunlight to 25(OH)D is not well understood.
Even in Hawaii, a population with high year-round sun exposure, low 25(OH)D levels have been
observed (Binkley et al., 2007); which may be due to genetics, or other differences that limit
cutaneous production of vitamin D. Studies of 25(OH)D predictors have generally found
variables related to sun exposure and dietary vitamin D are associated with serum levels
(Brustad, Alsaker, Engelsen, Aksnes, & Lund, 2004; Burgaz, Akesson, Oster, Michaelsson, &
Wolk, 2007; Gozdzik et al., 2008; Sahota et al., 2008; van der Meer et al., 2008).
Additional well-established predictors include age and body fat, which are inversely associated
with 25(OH)D levels (Vieth, Ladak, & Walfish, 2003). Age may affect the ability for cutaneous
synthesis of vitamin D through a decline in either skin thickness (Need, Morris, Horowitz, &
Nordin, 1993) or 7-dehydrocholesterol in the skin (MacLaughlin & Holick, 1985). It has been
suggested that since vitamin D is fat soluble, body fat may sequester vitamin D resulting in lower
serum 25(OH)D levels (Harris & Dawson-Hughes, 2007; Need et al., 1993). However, the levels
of vitamin D found in adipose tissue are not especially high and people experiencing weight loss
do not become vitamin D intoxicated (as reviewed in Heaney et al., 2009). An alternative
hypothesis is that low vitamin D levels may be associated with body fat due to increased surface
area or confounding by diet and time spent outdoors, although the latter was not found in one
study (Harris & Dawson-Hughes, 2007). Other less well-established predictors associated with
higher 25(OH)D levels include oral contraceptives (Harris & Dawson-Hughes, 1998) and
hormone replacement therapy (Heikkinen et al., 1998). In contrast, smoking has been found to be
associated with lower 25(OH)D levels (Brot, Jorgensen, & Sorensen, 1999).
18
There are no well-established standard reference values for 25(OH)D. In 1997 the Institute of
Medicine considered vitamin D deficiency as 25(OH)D levels <27.5 nmol/L7 (Institute of
Medicine National Academy of Sciences, 2009). More recently vitamin D deficiency has been
defined as serum 25(OH)D levels <50 nmol/L and insufficiency is considered 50 to 74 nmol/L
(Holick, 2009). Optimal vitamin D is often defined as serum 25(OH)D levels >75 nmol/L
(Bischoff-Ferrari, 2008; Holick, 2008; Holick, 2009; Vieth, 2006). This optimal level has been
proposed to meet the needs of the non-endocrine (non-calcitropic) pathway. In populations with
high sun exposure (e.g., lifeguards, farm workers) 25(OH)D levels are often greater than 130
nmol/L (Hollis, 2005; Vieth, 1999). It has been shown that vitamin D intakes of at least 1600
IU/day are required to maintain optimal wintertime 25(OH)D levels in Northern populations,
such as Canada (Whiting et al., 2007; Barake, Weiler, Payette, & Gray-Donald, 2010; Cashman
et al., 2008; Hall et al., 2010). The Canadian Health Measures Survey found that 25(OH)D levels
were above 75 nmol/L in only 37.8% (95% CI: 27.6, 38.9) of women age 6 to 79 (Langlois,
Greene-Finestone, Little, Hidiroglou, & Whiting, 2010).
2.3 Epidemiologic Studies of Vitamin D and Breast Cancer
There is now a relatively large body of literature from epidemiologic studies of vitamin D and
breast cancer. Most of it has been published within the past 4 years, since the start of this thesis.
The literature is reviewed here by study design: reviews/meta-analyses, randomized controlled
trials, observational studies (cohort and case control), and ecologic studies. Previous reviews and
meta-analyses are described first.
2.3.1 Reviews and Meta-analyses
In the past ten years at least seven review papers specific to vitamin D and breast cancer risk
have generally concluded there is some evidence from epidemiologic studies to support the
hypothesis that intake of vitamin D from diet or supplements or sunlight exposure may reduce
breast cancer risk (Bertone-Johnson, 2007; Bertone-Johnson, 2009; Colston, 2008; Cui & Rohan,
2006; Lipkin & Newmark, 1999; Perez-Lopez et al., 2009; Rohan, 2007). All of these reviews,
however, have highlighted inconsistencies in the literature and identified areas that still require
investigation. A meta-analysis of 6 studies concluded there was no overall association between
7 2.5 nanomoles per litre (nmol/l) = 1 nanograms/millilitre (ng/ml)
19
vitamin D from diet and supplements and breast cancer risk (pooled RR = 0.98, 95% CI: 0.93-
1.03), but a significant association was observed when intakes ≥400 IU/day were compared to
<150 IU/day (pooled RR = 0.92, 95% CI: 0.87-0.97) (Gissel, Rejnmark, Mosekilde, &
Vestergaard, 2008). An IARC working group review concluded there was not sufficient evidence
of a causal association between 25(OH)D and breast cancer risk but results from their meta-
analyses of 5 studies suggested an inverse association although not statistically significant; for
every 25 nmol/L increase in 25(OH)D (as a continuous variable) the pooled RR was 0.85 (95%
CI: 0.71-1.02) and when comparing highest versus lowest categories (cutpoints not provided) the
RR was 0.46 (95% CI: 0.21-1.03) (IARC, 2008). However, significant heterogeneity was
observed (p<0.001) and both prospective and post-diagnosis studies were combined.
2.3.2 Trials
Two trials of vitamin D plus calcium have reported on cancer risk as the outcome (Chlebowski et
al., 2008; Lappe, et al., 2007). The trial by Lappe et al. evaluated cancer at all sites (not specific
to breast cancer) and was a four-year trial designed to investigate bone fracture as a primary
outcome. Postmenopausal women (n = 1179) from rural Nebraska ( 41° N) were randomized to
a placebo, 1500 mg calcium, or 1500 mg calcium plus 1100 IU vitamin D3. A significant
reduction in all cancer risk was observed for the vitamin D and calcium group (RR = 0.40; 95%
CI, 0.20-0.82) and a similar, although non-statistically significant, association was observed for
calcium only (RR = 0.53; 95% CI, 0.27-1.03). Only 19 breast cancer cases developed throughout
the study: 5 (1.1%) in the vitamin D and calcium arm, 6 (1.4%) in the calcium only arm, and 8
(2.8%) among the placebo group. Specific to breast cancer, results have also been published
from the Women’s Health Initiative (WHI) (Chlebowski et al., 2008). The WHI trial evaluated
calcium and vitamin D intervention with the a priori objectives of evaluating fractures and
colorectal cancer; among the WHI there was also a low fat diet and HRT intervention and
observational study (The Women's Health Initiative Study Group, 1998). Postmenopausal
women from across the US were randomized to placebo (n = 18,106) or 400 IU vitamin D3 with
1000 mg calcium (n = 18,176). After a mean follow-up time of 7 years no significant difference
in breast cancer incidence was observed (HR, 0.96; 95% CI, 0.85, 1.09), although, tumor size
was significantly smaller in the group receiving calcium and vitamin D (Chlebowski et al.,
2008).
20
This trial by Lappe et al., has been criticized for limitations in statistical analysis, and insufficient
information on randomization, loss to follow-up and compliance (Ojha, Felini, & Fischbach,
2007). Additional concerns have been raised regarding lack of statistical power or discussion of
adverse effects (Sood & Sood, 2007). Furthermore, high rates of cancer were observed among
the placebo group but cancer rates among the intervention group were similar to the Nebraska
population rates, suggesting a failure of randomization (Bolland & Reid, 2008; Schabas, 2008).
One strength of the Lappe trial is that a relatively high dose of vitamin D (1100 IU/day) was
administered. It is possible that the findings by Lappe et al. reflect a true effect, but in
consideration of the many limitations raised and the very few breast cancer cases, the results
from this study are largely uninformative with respect to vitamin D and breast cancer. In
contrast, a major limitation of the WHI trial was the low dose of vitamin D used (400 IU/day)
and there was a high potential for contamination as controls were allowed to take vitamin D
supplements and likely also received vitamin D from diet or sunlight (Speers, 2008). There were
also issues of compliance in the intervention group and measures of 25(OH)D serum levels
following randomization were not provided, thus, it is unknown if this trial was successful in
increasing vitamin D in the intervention group. Similar to the trial by Lappe et al., the WHI was
restricted to postmenopausal women only with a relatively short follow-up and did not include a
vitamin D-only trial arm. Neither of these trials was designed specifically to assess vitamin D
and breast cancer risk.
2.3.3 Biomarker Studies
Many studies of 25(OH)D and breast cancer risk (summarized in Table 2) have reported
statistically significant inverse associations (Abbas et al., 2008; Abbas et al., 2009; Crew et al.,
2009; Lowe et al., 2005; Rejnmark et al., 2009;), or non-significant inverse associations
(Bertone-Johnson et al., 2005; Chlebowski et al., 2007; McCullough et al., 2009), few have
reported null associations (Freedman et al., 2008; Janowsky et al., 1999). There was considerable
range in the cutpoints used for comparison of serum 25(OH)D levels. Cutpoints for the highest
categories ranged from greater than 60-150 nmol/L versus less than 30-60 nmol/L for the low
categories; some significant results were observed at both the low (Abbas et al., 2009) and high
(Crew et al., 2009) upper cutoffs.
21
One limitation of these biomarker studies is that only 5 of these studies measured 25(OH)D prior
to cancer diagnosis (Bertone-Johnson et al., 2005; Chlebowski et al., 2008; Freedman et al.,
2008; Rejnmark et al., 2009; McCullough et al., 2009) and although inverse associations were
observed, only one was statistically significant (OR = 0.52; 95% CI, 0.32-0.85) (Rejnmark et al.,
2009). In contrast, four of the five post-diagnosis case-control studies reported statistically
significant inverse associations, however, post-diagnosis 25(OH)D levels may not reflect pre-
diagnostic levels. Breast cancer patients are often recommended to take vitamin D supplements
to prevent treatment-associated bone loss (Wang-Gillam, 2008) thus post-diagnosis serum
25(OH)D might over-estimate usual pre-diagnosis levels potentially biasing study results
towards the null. It is also plausible that women with cancer spend less time outdoors or make
dietary changes that limit their intake of vitamin D which may explain why post-diagnosis case-
control studies have more consistently found 25(OH)D is associated with reduced breast cancer
risk.
Serum 25(OH)D is the established biomarker of vitamin D, reflecting vitamin D from all
sources, but it may not be the best measure of long-term or usual vitamin D status (as reviewed
in Millen & Bodnar, 2008). Moderate correlation was found between two measures of 25(OH)D
measured 14 years apart (correlation between 0.42-0.52 depending on seasonal adjustment
method); stronger correlations have been observed for one to 5-year periods (correlation from
0.53 to 0.80 depending on study population and length of time) (Hofmann, Yu, Horst, Hayes, &
Purdue, 2010; Jorde et al., 2010). None of the studies in Table 2 had more than one measure of
25(OH)D, ideally a long-term average 25(OH)D level would be the best measure. Seasonal
variations in 25(OH)D levels are expected and bias can be introduced by not adequately
adjusting for seasonal variation (Wang et al., 2009). Furthermore, it is unknown how well
circulating 25(OH)D-levels reflect breast cell specific levels.
22
Table 2. Summary of previous studies of serum 25(OH)D and breast cancer risk
Study details
1st author, yr
N cases/ N controls 25 (OH)D cutpoints
for comparison in
nmol/L1
OR (95% CI)
Case-control studies
(post-diagnosis
25(OH)D)
Abbas 2008
1394/1365 Postmenopausal only
>75 vs <30 0.31 (0.24-0.42)
Abbas 2009 289/595 Premenopausal only
≥60 vs <30 0.45 (0.29-0.70)
Crew 2009
1026/1075 >100 vs. <50 0.56 (0.41-0.78) Premenopausal: 0.83 (0.36-1.30) Postmenopausal: 0.46 (0.09-0.83)
Janowsky 1999
156/184 Not provided No association (data not provided)
Lowe 2005
179/179
<50 vs >150 5.83 (2.31-14.7) (reciprocal = 0.17)2
Nested case-control
(pre-diagnosis
25(OH)D)
Bertone-Johnson 2005 Nurses’ Health Study
701/ 724 ≥100 vs. ≤50 0.73 (0.49-1.07) Age <60: 0.92 (0.57-1.48) Age >60: 0.57 (0.31-1.04)
Chlebowski 2007 Women’s Health Initiative
1067/1067 Postmenopausal only
<32.4 vs ≥67.6 1.22 (0.89-1.67) (reciprocal = 0.82)2
Freedman 2008 PLCO
1005/1005 Postmenopausal only
≥84.25 vs <45.75 1.04 (0.75-1.45)
McCullough 2009
516/516 Postmenopausal only
≥75 vs <50 0.86 (0.59-1.26)
Rejnmark 2009
142/420 >84 vs <60 0.52 (0.32-0.85) Premenopausal: 0.38 (0.15-0.97) Postmenopausal: 0.71 (0.38-1.30)
1 Converted from ng/ml to nmol/L (multiplied by 2.5) for the studies by Crew, Bertone-Johnson and Freedman
2 Results were reported comparing lowest to highest categories – opposite direction of other studies
23
2.3.4 Cohort Studies
Table 3 summarizes the six large US (John, Schwartz, Dreon, & Koo, 1999; Lin et al., 2007;
McCullough et al., 2005; Millen et al., 2009; Robien, Cutler, & Lazovich, 2007; Shin et al.,
2002), and one Swedish (Kuper et al., 2009), cohort studies that have evaluated the association
between vitamin D and breast cancer risk. Despite variable measures of vitamin D and diverse
populations, all of these studies, except one (Kuper et al., 2009), reported some inverse
associations between vitamin D and breast cancer risk. However, none of the studies reported
significant inverse associations consistently for all sources of vitamin D intake measured or
among all women. Some studies reported overall risk estimates suggestive of an inverse
association (e.g., HR<1.0) but not statistically significant for all measures of vitamin D (John et
al., 1999; Robien et al., 2007). Other studies reported statistically significant associations
consistent with the vitamin D hypothesis for time spent outdoors (Millen et al., 2009) or for
dietary measures among only premenopausal women (Lin et al., 2007; Shin et al., 2002) or
women living in states with low UV (McCullough et al., 2005). Only two studies reported any
effect estimates that were opposite of the expected direction; one suggested dietary vitamin D
may be associated with increased breast cancer risk (not statistically significant) among post- but
not pre-menopausal women (Lin et al., 2007); the other reported low solar radiation was
significantly associated with reduced breast cancer risk in postmenopausal women but reported
effect estimates that were consistent with the vitamin D hypothesis for other sun exposure
measures (Millen et al., 2009).
A major strength of cohort studies is that temporality can be established; the exposure is
measured before the outcome occurs. The average follow-up length ranged from around 10 years
(Lin et al., 2007; McCullough et al., 2005; Millen et al., 2009) to 18 years or greater (John et al.,
1999; Robien et al., 2007). In one study, the association between total vitamin D and breast
cancer risk was stronger and statistically significant when the analysis was restricted to within 5
years of baseline (RR = 0.66; 95% CI: 0.46-0.94) (Robien et al., 2007). Despite the advantage of
cohort studies – in terms of reduced recall bias and establishing temporality – none of these were
designed specifically for vitamin D; hence, comprehensive measures of vitamin D from all
sources were not used, introducing the potential for nondifferential measurement error which
often biases results towards null although not always.
24
Among the studies of dietary vitamin D, only three studies had measures of vitamin D from food,
multivitamins and single product vitamin D supplements (John et al., 1999; Robien et al., 2007;
Shin et al., 2002), the remaining studies included food and multivitamins only (Kuper et al.,
2009; Lin et al., 2007; McCullough et al., 2005). None of the studies specify the inclusion of cod
liver oil. The highest categories of vitamin D intake varied from approximately 200 IU/day from
food only (John et al., 1999; Kuper et al., 2009) to greater than 700 or 800 IU/day for total intake
(Robien et al., 2007; McCullough et al., 2005). The studies with upper cutoffs at only ≥200
IU/day were, unsurprisingly, not significantly associated with breast cancer risk with risk
estimates were only slightly less than 1.0 (John et al., 1999; Kuper et al., 2009). Although the
study with the highest category of intake (≥800 IU/day) was not significant, the risk estimate for
food was 0.55 with a 95% CI from 0.24 to 1.22 (Robien et al., 2007), suggesting a possible
reduced risk; however, despite the large confidence interval the sample size was quite large with
34,321 women and 2,440 cases. Significant inverse associations (or borderline significant where
upper 95% CI limit equals 1.00) were observed at intakes >500 IU/day among premenopausal
women only (Shin et al., 2002; Lin et al., 2007) and >300 IU/day among women living in states
with low UV (McCullough et al., 2005).
Only three studies included any measures of sunlight exposure as proxy measures for vitamin D
exposure and a range of variables were assessed: ecologic-level measures of solar or ultraviolet
radiation (John et al., 1999; Millen et al., 2009), variables associated with sunburn/skin damage
(John et al., 1999; Kuper et al., 2009) and time spent outdoors (John et al., 1999; Millen et al.,
2009). Only one study investigated the combined effect of diet and sunlight by comparing a
measure of high versus low sun and diet (John et al., 1999). There is some evidence of an
interaction between diet and sunlight (McCullough et al., 2005) but this was not found elsewhere
(Millen et al., 2009).
25
Table 3. Summary of previous cohort studies of vitamin D (from diet or sunlight) and breast cancer
risk
Study details
1st author, yr
N cases/ N total
cohort
Dietary vitamin D measures
RR or HR1 (95% CI)
Sun exposure measures
RR or HR (95% CI)
NHANES I
(John et al. 1999)
190/ 5009 Food (≥200 vs <100 IU/day): 0.85 (0.59-1.24) Supplements (daily vs never): 0.89 (0.60-1.32) Food or supplements (≥200 or daily vs <100 IU/day or never): 0.86 (0.61-1.20) High sun & diet (vs low sun & diet): 0.71 (0.44-1.14)
Occupational & recreational sun exposure: 0.67 (0.42-1.06) Sun-induced skin damage: 0.80 (0.48-1.29) Region of residence (south vs northeast): 0.71 (0.47-1.09) Solar radiation of birth place (high vs low): 0.73 (0.49-1.09)2
Swedish
Women’s
Lifestyle and
Health Cohort
(Kuper et al, 2009)
840/41,889 Dietary vitamin D3 (>204 vs 116): 0.9 (0.8-1.1) Multivitamin use yes vs no: 1.0 (0.8-1.2)
Exposure during age period 40-49 years4: Annual number of sunburns (≥2 vs never): 0.9 (0.7-1.3) Sunbathing vacations( ≥4wks/y vs never): 1.2 (0.9-1.6) Solarium use (≥1 per month vs never): 0.9 (0.8-1.2)
Women’s
Health Study
(Lin et al. 2007)
Premenopausal: 276/10,578 Postmenopausal: 743/20,909
Premenopausal: Food (≥319 vs <142 IU/day): 1.02 (0.69, 1.53) Multivitamins (≥400 vs 0 IU/day): 0.76 (0.50-1.17) Total (≥548 vs <162 IU/day): 0.65 (0.42-1.00) Postmenopausal: Food ( ≥319 vs <142 IU/day): 1.22 (0.95, 1.55) Multivitamins (≥400 vs 0 IU/day): 0.87 (0.68-1.12) Total (≥548 vs <162 IU/day): 1.30 (0.97-1.73)
Cancer
Prevention
Study II
(McCullough,2005)
2,855/68.567 postmenopausal only
Food (>300 vs ≤100 IU/day): 0.89(0.76-1.03) Total (>700 vs ≤100 IU/day): 0.95 (0.81-1.13) Low UV state of residence5: Food (>300 IU/day vs ≤100): 0.81 (0.67-0.97) High UV state of residence Food (>300 IU/day vs ≤100): 1.05 (0.82-1.35)
Not reported but food results stratified by UV.
Continued…
26
WHI
observational
study (Millen et al, 2009)
2,535 / 71,662 postmenopausal only
Effect measures not reported6 State of residence 7 (north vs. south): 0.97 (0.89-1.07) Clinic centre Watts per m2 (≤0.5 vs ≥1.5): 0.85 (0.74-0.98)8 Time outdoors summer (<30min vs >2 hrs): 1.18 (1.05-1.34)
Iowa Women’s
Health Study
(Robien et al, 2007)
2440 / 34,321 Postmenopausal only
Total (≥800 vs <400 IU/day): 0.89 (0.77-1.03) Supplement (≥800 vs 0 IU/day): 0.89 (0.74-1.08) Diet (≥800 vs <500 IU/day): 0.55 (0.24-1.22)9 Restricted to within 5 years of baseline: Total (≥800 vs <400 IU/day): 0.66 (0.46-0.94)
Nurses’ Health
Study
(Shin, 2002)
3172 / 88,691
Premenopausal: Total (>500 vs ≤150 IU/day): 0.72 (0.55-0.94) Food (>300 vs ≤75 IU/day): 0.66 (0.43-1.00) Postmenopausal: Total (>500 vs ≤150 IU/day): 0.94 (0.80-1.10) Food (>300 vs ≤75 IU/day): 1.06 (0.85-1.34)10
1 Effect estimates from fully adjusted multivariate models are reported. Each study controlled for different variables. 2 Additional measures of sun exposure not reported here 3 Quartile cutpoints were obtained from personal communication with the authors 4 Three additional age periods of exposure were measured (10-19, 20-29, and 30-39 years) and were not associated with
breast cancer risk. Additional measures of sun sensitivity (e.g., skin color, use of sun block) were also not associated
with breast cancer risk 5 No significant associations between dietary vitamin D and breast cancer risk overall. Significant reduced risks were
also observed among ER+ only(not in ER-). 6 Vitamin D intake from foods and supplements did not confound or modify the associations observed for the measures
of sunlight. 7 Also measured at birth, 15 and 35 years of age and results were similar. Additional sunlight measures were provided. 8 This association is not in the expected direction given the vitamin D hypothesis. 9 Data also provided stratified by years of follow-up. With 0-5 years follow-up total vitamin D: RR=0.66 (0.46-0.94). 10 Cumulative average diet model is reported here. Effect estimates are also provided for earlier diet (collected at baseline
in 1980). Estimates were adjusted for outdoor sun exposure and residential area.
27
2.3.5 Case-Control Studies
Table 4 summarizes the case-control studies of vitamin D and breast cancer risk. Similar to the
cohort studies, a range of dietary and sunlight measures of vitamin D have been assessed and
most case-control study results suggest an inverse association between vitamin D and breast
cancer risk but not all effect estimates were statistically significant (Abbas, Linseisen, & Chang-
Claude, 2007; John, Schwartz, Koo, Wang, & Ingles, 2007; Knight, Lesosky, Barnett, Raboud,
& Vieth, 2007; Rossi et al., 2009). Results from two European studies found strong significant
reductions in breast cancer risk at relatively low intakes of vitamin D from food alone. Vitamin
D intake from food alone ≥400 IU/day was significantly associated with a 50% reduced breast
cancer risk among a German population (Abbas et al., 2007) and intake >190 IU/day was
associated with a 64% reduced risk among women living in Southern Italy (Rossi et al., 2009);
the association was attenuated and not significant among women living in Northern Italy.
The study by Knight et al., (Knight et al., 2007) is the most comprehensive case-control study as
it included a wide range of vitamin D measures (including vitamin D rich foods, multivitamins,
supplements and cod liver oil and measures of sunlight exposure) at three specific periods of life
and is the only previous Canadian study. Although overall vitamin D dose from food or
supplements was not measured, specific foods (e.g., milk, and salmon) known to contain vitamin
D and supplements, including cod liver oil, were significantly associated with reduced breast
cancer risk. A range of sun exposure measures that may be important for vitamin D production
were also measured and most were associated with reduced breast cancer risk consistent with the
vitamin D hypothesis. Overall the associations between supplements, foods and sunlight
exposures were strongest for exposure during the ages 10-19 (Knight et al., 2007). The fourth
case-control study was conducted in California and included a sun exposure index (derived from
facultative (sun exposed) and constitutive (usual) skin pigmentation measured by reflectometry),
measures of lifetime outdoor activity and race/ethnicity (John et al., 2007). These sun measures
may not be specific to vitamin D generating potential, however, a high sun exposure index was
associated with up to 50% reduced risk of advanced (but not localized) breast cancer among light
skinned women (John et al., 2007).
In addition to the studies included in Table 4 there are two other small (<300 cases) hospital
(Levi, Pasche, Lucchini, & La Vecchia, 2001) or screening-based studies (Simard, Vobecky, &
28
Vobecky, 1991) that both reported positive associations between vitamin D from food only and
breast cancer risk, however, these studies do not provide sufficient data for interpretation. The
study by Levi et al., assessed a range of micronutrients, not specific to vitamin D, and the values
of vitamin D intake are improbable – the median values for highest and lowest tertiles were 2.7
and 1.4 milligrams/day1 (OR = 1.39; 95% CI: 1.01, 1.92) (the usual units for vitamin D are
micrograms). Simard et al., provided descriptive data only (no measure of effect or statistical
significance – from the crude data the unadjusted OR could be calculated comparing >200 vs
<50 IU/day: OR = 2.78).
One threat to the validity of case-control studies is the potential for recall bias. For this reason,
case-control studies are often considered inferior to cohort studies. However, with respect to
vitamin D intake there is no obvious reason to suspect that cases would differentially recall their
dietary intake of foods containing vitamin D or sun exposure particularly for these studies in
Table 4 which began before the recent publicity regarding the vitamin D hypothesis. All except
for one of the case-control studies (Rossi et al., 2008) were population-based, improving the
likelihood that controls would be similar to cases with respect to everything except disease
outcome. As with cohort studies there is the potential for measurement error in case-control
studies and only one study evaluated all sources of vitamin D (food, supplements, and sunlight)
(Knight et al., 2006), the others evaluated food (Abbas et al., 2007; Rossi et al 2008) or sun
exposures only (John et al., 2007). Although there are fewer case-control studies than cohort
studies of vitamin D and breast cancer risk, more of the results observed were statistically
significant, potentially due to timing of exposure; both North American studies evaluated
lifetime exposures from adolescence through adulthood (Knight et al., 2006; John et al, 2007).
1 2.7 milligram = >100,000 IU/day. If we assume this was a typo and should have been 2.7 µg this would yield a
highest intake category of only 108 IU/day.
29
Table 4. Summary of previous case-control studies of vitamin D (from diet or sunlight) and breast
cancer risk
Study details
1st author, yr
N cases/ N
controls
Dietary vitamin D measures
OR (95% CI)
Sun exposure measures
OR (95% CI)
Abbas, 2007 Population-based Germany
278 / 666 Premenopausal only
Food (≥400 vs <80 IU/day): 0.50 (0.26-0.96)
John, 2007 Population-based California
1786 / 2127 Among women with light skin
pigmentation only1
Advanced breast cancer: Lifetime outdoor activity (4 vs 1 hrs/week): 0.86 (0.51-1.45) Sun exposure index (high vs low): 0.53 (0.31-0.91) Localized breast cancer Lifetime outdoor activity (4 vs 1 hrs/week): 1.05 (0.72-1.54) Sun exposure index (high vs low): 1.10 (0.74-1.63)
Knight, 2006 Population-based Ontario
972 / 1135 Exposure from age 10-19:2 Milk (≥10 vs 0 glasses/week): 0.62 (0.45-0.86) Salmon or tuna (>1 vs 0 per week): 0.86 (0.64-1.14) Supplement/multivitamin (yes vs no):0.53 (0.39-0.73) Cod liver oil (yes vs no): 0.76 (0.62-0.92)
Exposure from age 10-19:2 Days outside (<3 vs 7): 1.49 (1.00-2.22) Outdoor activity episodes (high vs low): 0.65 (0.50-0.85) Outdoor job (≥1yr vs never): 0.61 (0.46-0.80) Limbs covered (yes vs no): 1.68 (1.14-2.50) Skin burned (no vs yes): 1.55 (1.08-2.24) Sunscreen use (yes vs no): 1.04 (0.72-1.51) Winter sun trip (yes vs no): 1.00 (0.77-1.30) Sunlamp use (yes vs no): 0.81 (0.57-1.14)
Rossi, 2008 Hospital-based Italy
2560 / 2588 Food (>190 vs <60 IU/day):0.76 (0.58-1.00) Stratified by geographical area: Northern: 0.86 (0.63-1.16) Southern: 0.36 (0.20-0.68)
1 Effect measures are also provided for medium and dark skin pigmentation – no significant associations were observed
among these groups. 2 Exposures at ages 20-29 and 45-54 were also assessed. Effect estimates were attenuated at age 20-29 and non-
significant at age 45-54.
30
2.3.6 Ecologic and Sun Exposure Proxy Studies
Maps showing higher mortality from cancer among people living in northern latitudes in the US
first led to the hypothesis that vitamin D may be associated with reduced breast cancer risk
(Garland, Garland, Gorham, & Young, 1990). Since then, ecologic studies have shown higher
latitude (a proxy for sun exposure) and lower UVB irradiance are positively associated with
breast cancer rates (Mohr, Garland, Gorham, Grant, & Garland, 2008) or mortality (Grant,
2002a; Grant, 2002b; Porojnicu et al., 2006), respectively; consistent with the vitamin D
hypothesis. The interpretation of these studies is limited since all observations were made only at
a population level; it is not possible to determine if this association remains at an individual level
and there is a high potential for residual confounding.
Other studies that have evaluated the association between skin cancer, as a proxy for UV
exposure, and breast cancer risk have been inconsistent (Cantwell et al., 2009; Levi,
Randimbison, Te, Conconi, & La Vecchia, 2008; Chen et al., 2008; Grant, 2007; Soerjomataram,
Louwman, Lemmens, Coebergh, & de Vries, 2008; Tuohimaa et al., 2007). Contradicting the
vitamin D hypothesis, women with a previous skin cancer diagnosis (squamous cell carcinoma
(SCC), basal cell carcinoma (BCC) or cutaneous malignant melanoma (CMM)) have been
observed to have a higher rate of breast cancer (standardized incidence ratio (SIR) = 1.18; 95%
CI: 1.08-1.30) (Levi et al., 2008). Soerjomataram et al. also observed a positive association
between breast cancer risk and CMM but an inverse (not statistically significant) association
with SCC (Soerjomataram et al., 2008); Soerjomataram et al., suggest the positive association
may be due to confounding by SES or other variables known to be related to both skin and breast
cancer risk. Elsewhere an inverse association was observed between breast cancer risk and SCC
(SIR = 0.58; 95% CI: 0.30-0.86) but not BCC (Cantwell et al., 2009). A meta-analysis of 10
studies of non-melanoma skin cancers, SCC and BCC combined (SCC, BCC or CMM) and
breast cancer risk reported a pooled OR of 1.13 (95% CI: 1.09-1.17) (Soerjomataram et al.,
2008). Paradoxically, 25(OH)D has been found to be inversely associated with non-melanoma
skin cancer among men and thus Tang et al. propose that BCC and SCC are not good proxy
measures of UV exposure or vitamin D status (Tang et al., 2010). Overall these studies provide
little evidence in support of an association between vitamin D and breast cancer risk.
31
2.4 Factors that May Influence or Modify the Vitamin D and Breast Cancer Association
2.4.1 Calcium
It is well established that the active form of vitamin D regulates calcium absorption and that
calcium also has a role in vitamin D metabolism (Heaney, 2008). Therefore the interaction
between calcium and vitamin D may be important for breast cancer risk. Furthermore, vitamin D
and calcium are found in some of the same foods (e.g., vitamin D fortified milk and dairy
products made with fortified milk) and thus it is difficult to tease out the independent effects in
dietary studies. Evidence from animal and in vitro studies suggests that calcium may also have
anticarcinogenic properties that include regulation of cell differentiation, proliferation and
apoptosis (Carroll, Jacobson, Eckel, & Newmark, 1991; Khan et al., 1994; McGrath & Soule,
1984; Sergeev, 2004; Whitfield, Boynton, MacManus, Sikorska, & Tsang, 1979; Xue, Lipkin,
Newmark, & Wang, 1999). Hence, it is important to elucidate the independent roles of both
calcium and vitamin D (as reviewed by Cui & Rohan, 2006; Heaney, 2008).
Results from epidemiologic studies do not strongly support an inverse association between
calcium, or more generally dairy products, and breast cancer risk (as reviewed by Al Sarakbi et
al., 2005; Bissonauth et al., 2008; Cui & Rohan, 2006; Larsson, Bergkvist, & Wolk, 2009;
Moorman & Terry, 2004; Parodi, 2005). As discussed earlier, the trial by Lappe et al. of all
cancers included a calcium only arm and found inverse associations of similar magnitude, but
not significant, to calcium plus vitamin D (Lappe et al., 2007). The WHI trial included only a
calcium and vitamin D group and no protective effect of the combined nutrients was observed
(Chlebowski et al., 2008). Recent prospective cohort studies have found an inverse association
between breast cancer incidence and serum calcium among pre- and post-menopausal women
with BMI ≥ 25 (Almquist, Manjer, Bondeson, & Bondeson, 2007), and dietary calcium intake
among both pre- and post-menopausal women (stronger among premenopausal women) (Kesse-
Guyot et al., 2007).
Few observational studies of vitamin D and breast cancer risk have investigated the interaction
between calcium and vitamin D (Abbas et al., 2007; Lin et al., 2007; Shin et al., 2002). Results
suggest either no interaction (Abbas et al., 2007; Shin et al., 2002) or an interaction among
postmenopausal women only (Lin et al., 2007), such that an inverse association between calcium
32
and breast cancer risk was observed only among the highest category of vitamin D intake. Three
other studies have included both measures of vitamin D and calcium but did not report on the
interaction (John et al., 1999; McCullough et al., 2005; Robien et al., 2007). The combined and
independent associations between calcium and vitamin D and breast cancer risk require further
investigation.
2.4.2 Timing of Exposure to Vitamin D
Most of the case-control and cohort studies described above have focused on recent exposure
during adulthood; few studies have looked at earlier life vitamin D intakes. The most
comprehensive of these studies (Knight et al., 2007) observed the strongest associations for
measures of vitamin D at ages 10-19 (e.g. sun exposure (OR = 0.65; 95% CI: 0.50-0.85), cod
liver oil use (OR = 0.76; 95% CI: 0.62-0.92), and milk consumption (OR = 0.62; 95% CI, 0.45-
0.86)). Weaker associations were observed for vitamin D during ages 20-29 and no associations
were observed during ages 45-54. In contrast, sunburns, sunbathing vacations and solarium use
exposure during the age period 10-19 years, or any later age period, were not associated with
breast cancer risk (Kuper et al., 2009). As for dietary vitamin D, two previous studies
investigating a range of micronutrients reported no associations between adult breast cancer risk
and vitamin D intake from food during adolescence (Frazier, Ryan, Rockett, Willett, & Colditz,
2003; Frazier et al., 2004). Elsewhere, an inverse, but not statistically significant, association was
reported between milk consumption during high school and premenopausal breast cancer risk
(RR = 0.76; 95% CI: 0.48-1.21) (Shin et al., 2002).
In addition to early life exposures and adult breast cancer risk, the association between dietary
vitamin D and breast cancer risk may (Lin et al., 2007; Shin et al., 2002) or may not (Knight et
al., 2007; Rossi et al., 2009) differ by menopausal status. Many studies have been conducted
among postmenopausal women only and unable to evaluate if menopausal status modifies the
association between vitamin D and breast cancer risk (Robien et al., 2007; Millen et al., 2009;
McCullough et al., Chlebowski et al., 2007; Freedman, et al., 2008; McCullough, et al., 2009).
One of these cohort studies among postmenopausal women only, found significant inverse
associations only when the analyses were restricted to within 5-years of baseline (Robien et al.,
2007) and the authors suggest exposure misclassification may increase with time since baseline;
alternatively, recent vitamin D intake may influence cancer development.
33
2.4.3 Hormone Receptor Status (ER/PR)
Some studies of vitamin D and breast cancer risk (Lin et al., 2007; McCullough et al., 2005;
Robien et al., 2007), but not all (Blackmore et al., 2008), have reported differences by hormone
receptor status (ER/PR). Among the studies that observed differences, the results were
inconsistent. Among premenopausal women significant inverse associations were observed
between total dietary vitamin D and breast cancer risk for both ER and PR positive breast cancer
cases; no differences were found among postmenopausal women (Lin et al., 2007). McCullough
et al. observed significant inverse associations among ER+ cases and no association among ER-
cases in their study of postmenopausal women only (McCullough et al., 2005). In contrast
Robien et al. observed stronger associations between vitamin D intake and ER- or PR- cases
(also among a study of postmenopausal women) (Robien et al., 2007). Blackmore et al. found no
differences by ER or PR status and this was not modified by menopausal status (Blackmore et
al., 2008). The representativeness of these findings is limited since receptor status was only
known for a portion of cases in each study: 53% (McCullough, Bostick, & Mayo, 2009), 68%
(Robien et al., 2007), 68% (Blackmore et al., 2008) and was not reported for the fourth study
(Lin et al., 2007).
2.4.4 Genetic Variants
Variants in genes on the vitamin D pathway may result in functional changes that may affect
endogenous vitamin D levels resulting in modification of breast cancer risk. A comprehensive
review of the literature identified three genes in the vitamin D pathway that have been studied in
relation to BC risk: vitamin D receptor (VDR), vitamin D binding protein (Gc), and CYP24A1
(involved in degradation of 1,25-dihydroxyvitamin D) (McCullough et al., 2009). There is some
support suggesting that VDR polymorphisms may be directly associated with breast cancer risk
or may modify the association between vitamin D exposure and breast cancer risk (as reviewed
in McCullough et al., 2009; Slattery, 2007). A pooled analysis of 6 prospective studies of the two
most commonly studied VDR polymorphisms (FokI (rs2228570) and BsmI (rs1544410)) found
that FokI was associated with increased breast cancer risk (ff versus FF: OR = 1.16; 95% CI:
1.04-1.28) (McKay et al., 2009). Only 3 studies have investigated variants in the Gc or
CYP24A1 genes (as reviewed by McCullough et al., 2009) and the results of these studies are
inconclusive. Few studies have evaluated gene-environment interactions and the current
34
evidence on variants in vitamin D related genes and breast cancer risk is minimal and requires
further investigation.
2.5 Vitamin D and Breast Cancer Mortality, Prognosis or Precursors
In addition to breast cancer risk, vitamin D has also been shown to be inversely associated with
breast cancer stage, recurrence and mortality. Among a cohort of women with early breast
cancer, vitamin D deficiency (defined as 25(OH)D levels <50 nmol/L versus >72 nmol/L) was
associated with increased risk of both distant recurrence (HR = 1.71; 95% CI: 1.02-2.86) and
death (HR = 1.60; 95% CI: 0.96-2.64) (Goodwin, Ennis, Pritchard, Koo, & Hood, 2009). A
significant inverse association between serum 25(OH)D and breast cancer mortality was also
reported in a prospective cohort study (n = 16,818) with only 28 breast cancer cases (comparing
≥62.5 versus < 62.5 nmol/L: RR = 0.28; 95% CI: 0.08-0.93) (Freedman, Looker, Chang, &
Graubard, 2007). Similarly, a death certificate based case-control study found significant inverse
associations between residential and occupational sun exposure and breast cancer mortality
(Freedman, Dosemeci, & McGlynn, 2002). In regards to breast cancer stage, tumor size was
significantly smaller among women in the vitamin D and calcium arm of the WHI than the
intervention group (Chlebowski, et al., 2008). Elsewhere, higher levels of serum 25(OH)D have
been observed among women with early versus advanced stage breast cancer (Palmieri,
Macgregor, Girgis, & Vigushin, 2006). In a large study among women in Norway, season of
diagnosis was found to be associated with breast cancer prognosis; women diagnosed in the
summer or fall – when higher vitamin D levels are expected – had a lower risk of breast cancer
death (Robsahm, Tretli, Dahlback, & Moan, 2004).
Mammographic density and benign breast disease (BBD) are known breast cancer risk factors
and may also be intermediate markers. In addition to having a direct effect on breast cancer risk,
the effects of vitamin D may also be mediated by changes to mammographic density or BBD.
However, studies of vitamin D and mammographic density are conflicting. Only one study has
found an overall inverse association between dietary vitamin D and calcium intake and
mammographic density (Berube, Diorio, Verhoek-Oftedahl, & Brisson, 2004). Other studies of
dietary vitamin D and calcium have variously reported inverse associations among pre- but not
postmenopausal women (Berube et al., 2005), or no associations with vitamin D but inverse
35
associations with calcium among postmenopausal women only (Mishra et al., 2008). One
biomarker study of 25(OH)D and calcium intake found no associations of either with
mammographic density (Knight et al., 2006). Elsewhere, serum 25(OH)D was found to be
inversely associated with mammographic density when a four month lag time, reflecting
seasonal changes, was applied (Brisson et al., 2007). In the WHI trial, the effect of calcium plus
vitamin D on benign proliferative breast disease has also been investigated and no significant
association was observed (Rohan et al., 2009). However, this study of BBD is affected by the
same limitations of the WHI trial described earlier in terms of breast cancer risk, namely a low
dose of vitamin D.
2.6 Appraisal of the Vitamin D and Breast Cancer Literature
The results of all epidemiologic studies of vitamin D and breast cancer risk were described above
by study design. The purpose of this section is to provide an appraisal of the vitamin D and
breast cancer literature in consideration of causation (using the guidelines provided by Elwood,
2007) and to propose a minimal ideal vitamin D dose.
Intervention trials are often considered the most rigorous study design and often demonstrate
causation since randomization and blinding reduce the potential for confounding and observer
bias (Elwood, 2007). The WHI trial is currently the only trial to evaluate vitamin D and breast
cancer (Chlebowski et al., 2008). Unfortunately, as discussed previously, the low vitamin D dose
(400 IU/day) in the WHI and lack of information on compliance and contamination among
controls limits our ability to draw any meaningful overall conclusions regarding the association
between vitamin D and breast cancer from this study.
One of the main threats to the validity of observational studies is confounding. However, the vast
majority of the observational studies described in tables 2, 3 and 4 had information available on
many potential confounders (e.g., known breast cancer risk factors including reproductive factors
and physical activity) and adjusted for confounding through matching or analysis; only a few
studies reported limited data on potential confounders (Rossi et al, 2008; Lowe et al., 2005;
Janowsky et al.,1999). Measurement error and bias are also of concern in observational studies.
The primary objectives of most of these observational studies were not specific to vitamin D and
breast cancer and many began before the vitamin D and cancer hypothesis was well-known, thus
reducing the potential for recall or observer bias. However, this may have introduced additional
36
measurement error since vitamin D from all sources was rarely measured; this measurement
error would likely be non-differential which often biases results towards the null. The
measurement of dietary vitamin D was most often through validated FFQ (e.g., Abbas et al.,
2007; Lin et al., 2007; Robien et al., 2007), 24-hour recall (John et al., 1999) or established
serum 25(OH)D assays (e.g., McCullough et al., 2009; Freedman et al., 2008; Bertone-Johnson
et al., 2005; Lowe et al., 2005; Crew et al., 2009); the validity of the various sun exposure
measures are not well-established. The sample sizes for all studies were quite large and only a
few studies had fewer than 500 cases (Abbas et al., 2009; Janowsky et al., 1999; Lowe et al.,
2005; Rejnmark et al., 2009; John et al., 1999; Abbas et al., 2007). Despite the large sample
sizes, many studies reported risk estimates less than 1.0 (suggestive of an inverse association
between vitamin D and breast cancer) but were not statistically significant at p<0.05 thus the
probability that these findings are due to chance is greater than the conventionally accepted 5%
among many studies. The few studies that may be considered of less rigorous design (smaller
sample size, fewer confounders or less established measures of vitamin D) (Rossi et al, 2008;
Lowe et al., 2005; Janowsky et al.,1999; Rejnmark et al., 2009) did not consistently find
different findings than the more rigorous studies.
Despite the aforementioned potential threats to internal study validity, other features support a
causal association between vitamin D and reduced breast cancer risk. The evidence for a biologic
mechanism is strong (as reviewed in section 2.2.2), accordingly the plausibility is high. Study
results have also been relatively highly consistent; of the 21 studies described in tables 2, 3 and 4
all except for 3 (Janowsky et al., 1999; Freedman et al., 2008; Kuper et al., 2009) provide at least
some evidence of an inverse association between vitamin D and breast cancer risk. Consistency
across subgroups has been less dependable such that some differences have been observed by
menopausal status, hormone receptor status, geographic location or timing of exposure.
Furthermore, the specificity of the vitamin D and breast cancer association is not well established
and other potential explanations still need to be ruled out. A dose-response relationship is also
not well-established; most studies have been conducted within a relatively small range of vitamin
D intake or serum 25(OH)D levels. Although most of the observational studies on vitamin D and
breast cancer risk (as described in tables 2, 3 and 4) have been rigorously conducted, we cannot
rule out all potential threats to study validity or conclude that all characteristics of a causal
association have been met therefore ability to determine causality is limited.
37
In terms of external validity, many of the observational studies were population-based and thus
the findings should be generalizeable to at least the source population (e.g., Knight et al., 2006;
John et al., 2007; Kuper et al., 2009; Crew et al., 2009; Abbas et al., 2007, 2008 & 2009). Other
studies were clinic or screening based (Janowsky et al., 1999; Lowe et al., 2005; Freedman et al.,
2008; Rejnmark et al., 2009; Rossi et al., 2008) or conducted among specific populations (e.g.,
the Nurses’ Health Study papers by Bertone-Johnson et al., 2005, and Shin et al., 2002) and may
not be generalizeable to the general population. The sampling methods and response/follow-up
rates were not described in sufficient detail for many of the cohort studies, but the reader was
often referred elsewhere. All studies except for one Canadian (Knight et al., 2006) and 6
European (Kuper et al., 2009; Rejnmark et al., 2009; Rossi et al., 2008; Abbas et al., 2007, 2008
& 2009) were conducted throughout the US. Although the biologic mechanism may be
independent of geographic differences, it is highly plausible that differences in food consumption
patterns (e.g., fatty fish), country-specific food fortification policies, skin color, genetics and
exposure to UVB radiation may influence population 25(OH)D levels and thus vitamin D dosage
information may not be generalizeable.
Vitamin D dosage
Optimal vitamin D is often defined as serum 25(OH)D levels >75 nmol/L (Bischoff-Ferrari,
2008; Holick, 2008; Holick, 2009; Vieth, 2006). Previous studies of serum 25(OH)D and breast
cancer risk have evaluated levels >75 nmol/L and most have reported significant inverse
associations (as shown in Table 2). There is some evidence to suggest that vitamin D intakes of
at least 1600 IU/day are required to maintain 25(OH)D levels in Northern populations, such as
Canada (Whiting et al., 2007; Barake, Weiler, Payette, & Gray-Donald, 2010; Cashman et al.,
2008; Hall et al., 2010). No studies of dietary vitamin D intake have assessed intakes as high as
1600 IU/day; the highest cutpoint assessed was >800 IU/day (compared to <400 IU/day) and was
significantly associated with a 34% reduced risk of breast cancer when analyses were restricted
to within 5 years of baseline (Robien et al., 2007). Albeit, a meta-analysis of dietary vitamin D
intake found a small but significant 8% reduction in breast cancer risk only after restricting the
analyses to the 3 studies (Robien et al., 2007; Shin et al., 2002; McCullough et al., 2005) with the
highest vitamin D intakes, which was only ≥400 IU/day (Gissel, Rejnmark, Mosekilde, &
Vestergaard, 2008).
38
Based on the current literature and biologic mechanism the ideal highest category of exposure for
studies of dietary vitamin D intake is likely much higher than those previously reported. The
maximal ideal dose should likely be at least 1000 IU/day or higher. Given the relatively low
intakes observed in study populations this may not currently be feasible but including all sources
of dietary vitamin D (including all foods and supplements) would help to improve measurement
of vitamin D intake. Furthermore, increasingly vitamin D supplements are available on pharmacy
shelves at doses of 1000 IU and the importance of vitamin D supplement use has garnered
attention in recent years. The Canadian Cancer Society now recommends all adult Canadians
take a 1000 IU vitamin D supplement. The minimal ideal dose of vitamin D that future studies
should measure is likely at least 1000 IU/day.
2.7 Summary and Rationale for the Current Study
Despite the emerging body of literature suggesting that vitamin D may reduce breast cancer risk,
study results have been inconsistent and many gaps still exist in our understanding of these
associations thus limiting our ability to conclude a causal association exists. Most previous
studies of vitamin D have included only measures of dietary intake (from food and/or
supplement sources) , few studies have measured vitamin D from sunlight. Ideally a combination
of measures should be used taking into consideration vitamin D from all sources (Millen et al.,
2008). A major focus of this thesis is on the measurement of vitamin D. To improve upon
previous studies, a food frequency questionnaire was modified for Canadian-specific vitamin D
values and used to measure vitamin D from food and supplements. Furthermore, an algorithm
was developed to derive a composite measure of vitamin D from sunlight using individual level
variables associated with endogenous vitamin D production (e.g., time spent outdoors, location
resided, skin color, sun protection practices) and was applied in this study.
In some studies, but not all, the association between vitamin D and breast cancer has been shown
to be stronger in premenopausal women and there is strong biologic evidence that obese women
are more likely to have lower levels of vitamin D suggesting a possible interaction between
vitamin D and obesity. Despite the direct relationship between calcium and vitamin D, few
studies have included calcium in their investigations of vitamin D and breast cancer. As well,
most studies have evaluated only adulthood vitamin D exposure and not the potentially critical
period during breast development. This thesis explored the association between variables related
39
to the production of vitamin D from sunlight at four age periods of exposure (from adolescence
through adulthood). Furthermore, this thesis contributes to our knowledge of the association
between vitamin D and breast cancer risk by incorporating vitamin D exposure at different stages
of life, and testing for effect modification by calcium, menopausal status, and BMI.
This population-based case-control study of over 6,000 women in Ontario improves upon these
limitations and addresses some of the gaps in knowledge. Vitamin D intake from diet or
exposure to UV light is modifiable and research identifying modifiable breast cancer risk factors
is essential since to-date, most well-established risk factors are not modifiable. The results of this
study provide a better understanding of the association between vitamin D and breast cancer in
Ontario women with the ultimate aim of primary prevention.
40
Chapter 3 Study Methods
3.1 Data Source and Study Design
The data source for this study was the “Ontario Women’s Diet and Health Study”, a population-
based case-control study conducted among Ontario women. Data were collected from 2003 to
2004 as part of a Canadian Breast Cancer Research Alliance (CBCRA) – Canadian Breast
Cancer Foundation (CBCF) funded research project entitled “Total phytoestrogen intake during
selected periods of life and breast cancer risk” (primary investigator: M. Cotterchio; co-
investigators: N. Kreiger, L. Thompson, B. Boucher). The primary objectives of the “Ontario
Women’s Diet and Health Study” were to evaluate phytoestrogen intake during various periods
of life and breast cancer risk (Cotterchio, Boucher, Kreiger, Mills, & Thompson, 2008; Thanos,
Cotterchio, Boucher, Kreiger, & Thompson, 2006) and to develop a novel Canadian database for
phytoestrogens (Thompson, Boucher, Liu, Cotterchio, & Kreiger, 2006; Thompson, Boucher,
Cotterchio, Kreiger, & Liu, 2007).
Data collection occurred prior to my involvement in this study. All decisions regarding study
design and measurement of variables were made by the study investigators. My role included
conducting the literature review, deriving the specific hypotheses, developing the specific study
objectives, evaluating methodological issues related to the measurement of vitamin D from diet
and sunlight and conducting all statistical analyses and variable derivation, including
development of the solar vitamin D score. In addition, I wrote the first draft of each manuscript
and revised these papers based on suggested revisions by my thesis committee and co-authors.
3.2 Identification of Cases and Controls
3.2.1 Cases
Cases were women aged 25 – 74 years with a first pathologically confirmed cancer of the breast
diagnosed between June 2002 and April 2003. Cases were identified through the Ontario Cancer
Registry (OCR). The OCR is a population-based registry that obtains information from nearly all
breast cancer cases in the province in Ontario (Hall, Schulze, Groome, Mackillop, & Holowaty,
41
2006). The OCR identifies new cancer cases and deaths in Ontario by computerized probabilistic
record linkage of data from the following sources: 1) pathology reports, 2) Regional Cancer
Centres, 3) hospital discharge and ambulatory care records, and 4) Ontario death certificates.
Consent and contact information was sought from the physicians identified in the registry
pathology reports, and was obtained for 4,109 eligible cases (96%). Cases were eligible for
inclusion in the study if they were women between 25 and 74 years of age with a first primary,
pathology confirmed breast cancer diagnosed between September, 2002 and August, 2003.
Physician cooperation was required to contact patients, obtain contact information and vital
status. The average time between diagnosis and interview was 11 months with a range (5th to 95th
percentile) from 7 to 18 months. In total, 3,101 of the 4,109 cases with physician consent
completed the study (75% response rate or 72% of all cases regardless of physician consent).
3.2.2 Controls
Controls were identified using a modified random digit dialing procedure of households in
Ontario and frequency matched (1:1) within 5-year age groups to the identified cases.
Recruitment of controls was conducted by the survey research unit at the Institute for Social
Research at York University (Toronto, Ontario). A sampling frame of phone numbers was
derived from Ontario telephone directories and other commercially available lists. In addition to
these listed numbers, other numbers on either side of these were added to the sampling frame to
capture unlisted numbers.
In total, 25,250 households were telephoned; approximately, 17,000 of these households were
ineligible, 2,000 did not answer the phone and 2,000 refused (eligibility of these households is
unknown). Of the 4,352 households where an eligible woman was identified, 250 (6%) women
refused, and 4,102 (94%) women agreed to participate. One woman from each household was
randomly selected for inclusion in the study. Overall, 3,471 controls completed the study out of
the 4,352 households with known eligibility (80% response rate). The true response rate
denominator for all eligible Ontario women is unknown.
3.3 Data Collection
Eligible cases and controls were mailed a risk factor (epidemiologic) questionnaire and food
frequency questionnaire (FFQ). The risk factor questionnaire consisted of 20 pages with 79
42
questions and collected information on lifestyle, reproductive and medical history factors. This
risk factor questionnaire was pre-tested among a convenience sample of women before it was
finalized. Food and supplement intake was measured using a modified 178-item version of the
1998 Block Food Frequency Questionnaire (FFQ). This FFQ has been validated several times for
many nutrients (e.g., (Block, Woods, Potosky, & Clifford, 1990; Boucher et al., 2006)). Cases
and controls were asked about their consumption of food and supplements two years prior to the
time of questionnaire, to reduce any bias due to changes in diet in cases after cancer diagnosis.
Questions related to the measurement of vitamin D from both questionnaires are included in
appendix 1 and are described in section 3.4.
To improve response rates a structured mailing timeline with a series of 5 contacts was followed.
After the initial telephone contact, study participants were mailed the questionnaire packages
with a signed letter of information, and return Canada Post business reply envelope. To improve
response rates, a $5 incentive was included in the control questionnaire packages and a magnet
was included in the case questionnaire packages. Two weeks after the initial mailing all subjects
were mailed a thank-you postcard, which served as a reminder for non-respondents. Non-
respondents were phoned after 4 weeks and new questionnaire packages were mailed after 8
weeks. A final telephone call was made 12 weeks after the initial mailing to encourage response
among non-respondents. Trained interviewers conducted scripted phone calls and 8 attempts
were made to contact subjects. If phone numbers were no longer in service or questionnaires
were returned undelivered every attempt was made to find the subjects’ new address, through
online directories for controls and by contact with physicians for cases.
Questionnaire response data were entered into an Access dataset by trained research clerks.
Study participants were contacted by telephone to clarify responses for any missing or
unexpected responses. All participants were assigned a unique 6 digit study identification
number and confidentiality was maintained by using the study id number only in the study
database. Additional logic checks, data cleaning and derivation of all variables was conducted by
the PhD candidate. For the continuous variables (e.g., height, weight, age at menopause and
menarche) all extreme responses, defined as those in the top or bottom percentile, were double
checked with the original hard copy surveys and any obvious errors were changed to missing.
43
3.4 Variable Definitions
3.4.1 Vitamin D and Calcium from Food and Supplements
A modified Block FFQ measured 178 foods and supplements and requested data on both
frequency of consumption and portion size for all foods and frequency and duration of use for
supplements. The validity and reliability of the Block FFQ have been assessed in a random
sample of Ontario women (Boucher et al., 2006). The reliability for vitamin D and calcium were
high with non-deattenuated Pearson correlation coefficients of 0.76 (95% CI: 0.66-0.83) for
vitamin D and 0.80 (95% CI: 0.71-0.86) for calcium. Validity of the FFQ, compared to two 24-
hour recalls, was also moderately high; the deattenuated Pearson correlation coefficient vitamin
D was 0.54 (95% CI: 0.29-0.79) and for calcium was 0.71 (95% CI: 0.35-1.00). Using the
recently proposed classification for correlation coefficients from FFQ validation studies (Willett,
2009; Serra-Majem, Andersen, Henrique-Sanchez, Doreste-Alonso, et al., 2009) the reliability of
vitamin D is considered very good (≥0.7) and the validity is good (0.5-0.69).
Nutrient analysis was conducted by Block Dietary Data Systems (BDDS) using nutrient values
from the USDA Nutrient Database for Standard Reference and national data on food
consumption (NHANES III and CSFII). In our modified version of the Block 1998 FFQ, two
questions were added to better capture vitamin D intake: type of fish most often consumed (fatty
or white fish) and use of vitamin D supplements or cod liver oil. Objective 5 of this thesis was to
modify the nutrient analysis specific to vitamin D to account for these additional items and
differences in fortification of foods between Canada and the U.S. The results of this objective
and more details regarding the methodology are described in paper 1 (section 4.2 of thesis
results).
Daily intakes of vitamin D (IU/day) and calcium (mg/day) were derived individually for foods
and supplements (alone, in a multivitamin or as cod liver oil for vitamin D) from the Block
nutrient analysis and a combined total intake measure (food plus supplements) was also created.
Histograms showing the distributions of these variables are included in appendix 4 (figures 1-3).
Since the variables were not normally distributed, particularly for supplement intake, and for
consistency with the current literature all vitamin D variables were categorized. Variables were
categorized using cut points that correspond to the established Dietary Reference Intakes (DRI)
when the distribution of the data was sufficient for at least 10% of controls in each category.
44
When DRIs were not available or the distribution was not amenable to such cut points variables
were categorized into quartiles or quintiles based on the distribution among controls. In addition
to the derived nutrient values for vitamin D and calcium (from foods, supplements and total),
individual foods rich in vitamin D and/or calcium and vitamin supplement sources were
individually examined. These foods included milk, fish, margarine, and the frequency and
duration of supplement use for vitamin D or cod liver oil, and calcium (alone or combined with
something else) and regular one-a-day multiple vitamins. Food intakes were calculated as
servings per day, week or month (depending on frequency of consumption) by combining
portion size and frequency of consumption data using guidelines provided by BDDS.
3.4.2 Individual Variables Related to Cutaneous Vitamin D Production
The risk factor questionnaire included a sun exposure questionnaire (appendix 1) which collected
information on the following variables related to vitamin D production: time spent outdoors, sun
protection practices, and location of residence. Self-reported ethnicity was also collected and
used as a proxy for skin color. The overwhelming majority (90%) of study participants was
Caucasian ethnicity (proxy for lighter skin color) and thus skin color was categorized as
Caucasian versus non-Caucasian. Other variables related to sun exposure (weekday time
outdoors, weekend time outdoors, sun protection and location of residence) were measured at
four periods of life: teenage years, 20-30s, 40s-50s and 60s-74. All women were at least 25 years
of age, thus, n = 6,572 for exposure during teenage years and 20s-30s, whereas, n = 6,075 for
exposure during 40s-50s, and n = 2438 for exposure during 60s-74.
Time spent outdoors was measured as the number of hours of sun exposure from April to
October during a typical day for weekends and weekdays separately. Data were collected for
these 6 months only since wintertime sun exposure in Ontario is not sufficient for the production
of vitamin D (Webb et al., 1988). Response options were: less than one hour, 1 to 2 hours, 3 to 4
hours, more than 4 hours. Study participants were instructed to include both exposures at work
and during leisure time. The variable “Hours outdoors per week” was created by weighting and
summing weekday and weekend exposures. During each period women were asked how often
they wore sunscreen or protective clothing when in the sun (never, sometimes, and always) and
where they lived during each of the 4 periods of life.
45
The validity and reliability of other similar sun exposure questionnaires have been measured.
Previous sun exposure questionnaires, focusing on time spent outdoors, have been shown to have
fair to moderate reliability (intraclass correlation coefficients range from 0.25-0.77) for both
recent adult measures (English, Armstrong, & Kricker, 1998; Kricker, Vajdic, & Armstrong,
2005; van der Mei, Blizzard, Ponsonby, & Dwyer, 2006; Yu et al., 2009) and recall of adolescent
exposures (van der Mei et al., 2006). In terms of validity, time spent outdoors measured from sun
exposure questionnaires has been significantly associated with skin measures of solar exposure
(Karagas et al., 2007; van der Mei et al., 2006; Weiler, Knight, Vieth, Barnett, & Wong, 2007)
and 25(OH)D (r = 0.17 to 0.58) (Brot et al., 2001; Kim & Moon, 2000; Need et al., 1993; Sahota
et al., 2008; van der Mei et al., 2006; Hanwell et al., 2010). Moderate agreement has been found
between questionnaire measures of sun exposure and calendar methods (Kappa = 0.54 to 0.71)
(van der Mei et al., 2006), detailed face-to-face measures (ICC = 0.54; 95% CI: 0.21-0.76)
(Kricker et al., 2005) or personal UV dosimetry (r = 0.32 to 0.69) (O’Riordan et al., 2008;
Chodick et al., 2008)
Geographic location was collected by city and province of residence for each of the 4 periods of
exposure. All women resided in Ontario when they participated in the study, but many lived
outside the province earlier in life. Women were asked to report the location where they lived
during the 4 specific periods of life but a full residential history was not collected and no
information was obtained on how long participants resided at each location reported.
Approximately 2% of women reported multiple locations lived during a given age period of
exposure and only the first location was used. There were 1628 (25%) study participants who
reported only country or province/state of residence during at least one period of life; these
participants were assigned the most populated city in their country or region (e.g., Shanghai was
assigned to those who reported China). There were 86 (1%) participants who lived in the
southern hemisphere during at least one life period; for these women the reporting period would
have corresponded to wintertime sun exposure and the analysis was performed with and without
these women. Since the results were essentially unchanged with these women included they were
kept in the analysis. Location of residence was converted from place name to latitude and
longitude through the website www.Geocoder.ca for US and Canadian cities (Geocoder, 2007),
and for international cities using http://worldkit.org/geocoder. Latitude and longitude were
46
obtained for 6,119 (93%), 5858 (89%), 5637 (93%) and 2438 (95%) women during their teens,
20-30s, 40s-50s and 60-74, respectively.
Measures of UV radiation were then obtained for each geographic coordinate (latitude and
longitude). UV radiation from sunlight, specifically UVB radiation, is the necessary precursor to
cutaneous vitamin D production. UV radiation data were obtained from National Aeronautics
and Space Administration’s (NASA) Total Ozone Mapping Spectrometer (TOMS) (NASA,
2007). Ground level UV irradiance data are calculated from TOMS onboard spacecraft
instrument measures of atmospheric UV, total ozone, surface reflectivity and cloud cover.
Monthly average noon-time erythemal UV for June 2003 was selected for use in this study. It is
estimated that the change in summertime UV over the last forty years is minimal, i.e., less than
5% (personal conversation Dr. Fioletov, Environment Canada). Wintertime UV would be
expected to be more affected by the changes to the ozone layer that has occurred over time.
These data are weighted using the McKinlay-Diffey erythemal action spectrum (McKinley,
1987) which weights radiation in the UVA (315-400 nm) and UVB (280-315 nm) wavelengths
based on the time required to induce erythema (skin reddening); shorter rays are more likely to
induce erythema. Cutaneous vitamin D is dependent on only UVB exposure and there is a
vitamin D-specific action spectrum based on human skin’s ability to produce previtamin D3
(MacLaughlin, 1982), but vitamin D weighted UV data are not currently available from TOMS.
Although vitamin D production does not always directly correspond with erythemal UV
estimates (Kimlin, 2003), the erythemal action spectra closely approximates the vitamin D action
spectra in summer north of 42⁰ (Ontario, Canada) (Fioletov, 2009; Pope, Holick, Mackin, &
Godar, 2008).
Figure 1 shows the distribution of erythemal UV radiation worldwide and the locations where all
study participants resided in their teenage years (maps for other age periods of exposure not
shown). Maps were created using ArcGIS version 9.2. Latitude was strongly correlated with
erythemal UV during teens, 20-30s, 40-50s, and 60s-74; the Pearson correlation coefficients
were -0.85, -0.84, -0.76, -0.72, respectively (all p-values <0.0001) after excluding women living
in the Southern hemisphere.
47
Fig
ure
1.
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iati
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(m
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an
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48
3.4.3 Derivation of a Solar Vitamin D Score
To derive a measure of vitamin D from sunlight, and create one variable that takes into
consideration known factors that affect vitamin D synthesis, an algorithm was created based on
the available literature on determinants of cutaneous production of vitamin D. Weighted
proportions were used to create this solar vitamin D score which included our measures of time
spent outdoors, sun protection practices, ethnicity (proxy for skin colour) and UV radiation of
location lived. The same algorithm was applied to each of the 4 periods of life (teenage
years/adolescence, 20s-30s, 40s-50s and 60s-74). A cumulative lifetime measure was also
derived by adding the solar vitamin D scores from all relevant age periods and a measure of
recent exposure was also be created by using the solar vitamin D score that pertains to the
current age of all study participants.
Figure 2 presents the derived algorithm. In regards to skin color an accommodation factor of one
third was chosen to weight UV production for non-Caucasian individuals. It has been observed
that people with highly pigmented (darker) skin colors in comparison to lighter require at least 3
times the amount of sunlight to produce equivalent vitamin D (Webb & Engelsen, 2006;
Holick,1987), although estimates range upward to 5 to 10 times (Chen et al., 2007). Thus, using
the most conservative estimate, an accommodation factor of one third was chosen to weight UV
production for non-Caucasian individuals. In regards to sun protection practices, sunscreen and
clothing both have the potential to block all vitamin D production. However, it is unlikely that
women apply a complete application of sunscreen (i.e., a thorough application to all locations of
the body prior to going outdoors with frequent reapplication) (as reviewed by Norval & Wulf,
2009) or fully cover-up with clothing. Sunscreen use does not predict 25(OH)D levels (Sahota et
al., 2008; Thieden, Philipsen, Heydenreich, & Wulf, 2009), but coverage of arms and legs does
significantly predict lower 25(OH)D levels (Sahota et al., 2008). Therefore, within this
population it was assumed that the available vitamin D generating UV light was reduced by two
thirds in participants who report “always” using sun protection (i.e., they would have one third
the potential UV in comparison to participants who reported “never” using sunscreen or
protective clothing). Correspondingly, a decrease of one third was estimated for participants
“sometimes protected”, hence a weighting factor of two thirds was assigned to these respondents.
49
Before applying the algorithm, the associations between each of the sun exposure related
variables were evaluated. Although ethnicity was significantly associated with sun protection
practices at each age of exposure (Chi-Square tests p<0.001), there were no consistent patterns
with respect to the differences in sun protection practices between Caucasians and non-
Caucasians (appendix 4, table 1). Time spent outdoors was not strongly correlated with
erythemal UV, latitude, or sun protection practices (Spearman’s r < 0.10) at any period of
exposure (appendix 4, table 2). Furthermore, self-reported time spent outdoors in the summer
did not differ by season of questionnaire completion.
Figure 2. Hypothesized model of vitamin D and breast cancer with details of the proposed algorithm for the measurement of UV radiation conditional on factors that affect vitamin D production
3.4.4 Potential Confounders
Potential confounders were identified as any variable that may be associated with either breast
cancer risk or vitamin D status based on the literature. Few variables were identified from the
literature that were known be associated with both breast cancer risk and vitamin D
50
exposure/intake, and thus, meet the true definition of a confounder (e.g., physical activity, BMI,
HRT use, and phytoestrogen intake). The following 39 variables were considered potential
confounders: marital status, education, ethnicity, BMI, smoking status, pack years smoked,
breastfed, lactation, age at menarche, oral contraceptive use, oral contraceptive duration, parity,
age at first live birth, age at last menstruation, hormone replacement therapy (HRT) use
(postmenopausal women only), duration of HRT use, history of benign breast disease, family
history of breast cancer, screening mammogram, alcoholic drinks, dietary fat intake, energy
intake, phytoestrogen intake, physical activity (strenuous, moderate and daily activity) at selected
periods of life (teenage years, 20-30s, 40-50s and 60s-74). Calcium variables were evaluated as
potential confounders of the vitamin D and breast cancer associations, and vice versa.
These variables were derived using previously established cutpoints from the literature (e.g.,
BMI) or were categorized into quartiles or quintiles based on the distribution among controls.
The distribution for 24 of these 39 variables among cases and controls and the age-group
adjusted ORs are shown in Table 5 and some have been described previously (Cotterchio et al.,
2008). Age was calculated as age at diagnosis for breast cancer cases and age at midpoint of
recruitment for controls. Variables were lagged where possible to ensure the exposure occurred
at least two years prior to interview (e.g., age at menopause, start and stop dates for HRT use and
smoking status). Daily, moderate and strenuous physical activity was measured for 4 age periods
of exposure and the category ‘age not yet reached’ was assigned for women who were not yet in
their 40s-50s and/or 60s-74.
51
Table 5. Distribution of breast cancer cases and controls and age-group adjusted odds ratio
(AOR) estimates for selected known and suspected breast cancer risk factors
Variable Cases
n = 3101
No. (%)
Controls
n = 3471
No. (%)
AOR
Age1 25 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74
181 (6) 278 (9) 380 (12) 504 (16) 511 (16) 450 (15) 439 (14) 358 (12)
316 (9) 367 (11) 482 (14) 512 (15) 470 (14) 471 (14) 508 (15) 345 (10)
N/A
Ethnic or racial background Caucasian (White) Black Aboriginal (e.g., Indian, Metis) South East Asian (e.g., Japanese, Chinese) South Asian (e.g., East Indian, Pakistani) Other
2749 (89) 48 (2) 33 (1) 155 (5) 63 (2) 42 (1)
3121 (90) 55 (2) 28 (1) 96 (3) 83 (2) 68 (2)
1.00 1.03 (0.70-1.53) 1.31 (0.79-2.18) 1.96 (1.50-2.54) 0.91 (0.65-1.27) 0.75 (0.51-1.11)
Highest level of Education Elementary High school Postsecondary
275 (9) 1384 (45) 1423 (46)
214 (6) 1558 (45) 1684 (49)
1.00 0.71 (0.58-0.86) 0.71 (0.58-0.86)
BMI2 (kg/m2) in premenopausal women < 24.9 25.0 - 29.9 ≥ 30
566 (59) 241 (25) 146 (15)
663 (54) 338 (27) 235 (19)
1.00 0.81 (0.66-0.99) 0.70 (0.54-0.87)
BMI (kg/m2) in postmenopausal women < 24.9 25.0 - 29.9 ≥ 30
780 (37) 771 (36) 578 (27)
871 (40) 834 (38) 506 (23)
1.00 1.03 (0.90-1.19) 1.28 (1.10-1.50)
Smoking status never smoker ex-smoker current smoker
1572 (51) 1176 (38) 331 (11)
1791 (52) 1122 (32) 535 (15)
1.00 1.18 (1.06-1.31) 0.71 (0.61-0.83)
Pack-years smoked Never smoker Q1 Q2 Q3 Q4
1572 (52) 360 (13) 336 (12) 388 (14) 393 (14)
1791 (52) 409 (12) 365 (11) 424 (13) 432 (14)
1.00 1.04 (0.88-1.21) 1.09 (0.92-1.28) 1.03 (0.88-1.20) 0.97 (0.83-1.13)
Ever Breastfeed your infant No Yes
1470 (48) 1622 (52)
1357 (39) 2093 (61)
1.00 0.73 (0.66-0.81)
Age at menarche (years) ≤ 11 12 13
594 (20) 753 (25) 846 (28)
615 (18) 823 (25) 973 (29)
1.00 0.95 (0.81-1.10) 0.90 (0.80-1.04)
52
≥ 14 782 (26) 939 (28) 0.86 (0.74-0.99) Age at menopause3 ≤ 45 45 - 49 ≥50 premenopausal
572 (19) 511 (17) 987 (33) 957 (32)
727 (21) 550 (16) 888 (26) 1237 (36)
1.00 1.15 (0.98-1.36) 1.37 (1.19-1.59) 1.15 (0.95-1.38)
Parity Nulliparous 1 2 - 3 > 4
543 (18) 421 (14) 1677 (55) 415 (14)
404 (12) 413 (12) 2068 (61) 524 (15)
1.00 0.75 (0.62-0.91) 0.57 (0.49-0.66) 0.53 (0.44-0.64)
Age at first live birth Nulliparous 12 - 19 20 - 24 25 - 29 30 - 34 > 35
543 (18) 408 (13) 951 (31) 743 (24) 318 (10) 93 (3)
404 (12) 507 (15) 1207 (35) 861 (25) 335 (10) 95 (3)
1.00 0.55 (0.45-0.66) 0.54 (0.46-0.63) 0.62 (0.53-0.73) 0.70 (0.58-0.86) 0.71 (0.52-0.97)
Duration of HRT use (yrs) Never use ≤ 3 3.1 – 6.0 6.1 – 10.0 >10 premenopausal
1070 (43) 58 (2) 121 (5) 135 (5) 173 (7) 957 (38)
1183 (42) 84 (3) 114 (4) 89 (3) 115 (4) 1237 (44)
1.00 0.74 (0.52-1.04) 1.13 (0.86-1.49) 1.64 (1.23-2.18) 1.67 (1.29-2.15) 1.05 (0.88-1.27)
Breast cancer in a 1st degree relative No Yes
2389 (77) 635 (21)
2973 (86) 415 (12)
1.00 1.86 (1.63-2.13)
Benign breast disease4 No Yes
2014 (66) 1015 (34)
2631 (77) 785 (23)
1.00 1.65 (1.47-1.84)
Mammogram5 No Yes
623 (20) 2473 (80)
940 (27) 2518 (73)
1.00 1.32 (1.15-1.51)
Strenuous physical activity in teenage years Never 1– 3 times per month 1– 2 times per week 3– 5 times per week >5 times per week
233 (8) 373 (13) 638 (22) 906 (31) 744 (26)
217 (6) 388 (12) 669 (21) 1034 (32) 923 (29)
1.00 0.95 (0.75-1.20) 0.94 (0.75-1.16) 0.86 (0.70-1.06) 0.78 (0.63-0.96)
Strenuous physical activity in 20s-30s Never 1– 3 times per month 1– 2 times per week 3– 5 times per week >5 times per week
231 (8) 409 (14) 828 (28) 907 (30) 616 (21)
188 (6) 443 (13) 930 (28) 1057 (32) 700 (21)
1.00 0.80 (0.63-1.02) 0.76 (0.62-0.95) 0.73 (0.59-0.91) 0.72 (0.58-0.90)
Strenuous physical activity in 40s-50s Never 1– 3 times per month 1– 2 times per week 3– 5 times per week >5 times per week
288 (10) 528 (17) 786 (26) 786 (26) 450 (15)
240 (7) 554 (16) 863 (26) 905 (27) 491 (15)
1.00 0.81 (0.66-1.00) 0.76 (0.63-0.93) 0.73 (0.60-0.89) 0.76 (0.61-0.94)
53
Age not yet reached 181 (6) 316 (9) NA Strenuous physical activity in 60s-74 Never 1– 3 times per month 1– 2 times per week 3– 5 times per week >5 times per week Age not yet reached
209 (7) 262 (9) 304 (10) 254 (8) 156 (5) 1854 (61)
175 (5) 249 (7) 339 (10) 353 (10) 144 (4) 2147 (63)
1.00 0.88 (0.67-1.15) 0.75 (0.58-0.97) 0.60 (0.47-0.78) 0.90 (0.67-1.22) n/a
Daily activity at work in 20s-30s Sitting Light Moderate Strenuous
928 (31) 769 (25) 1210 (40) 116 (4)
893 (27) 880 (26) 1428 (43) 158 (5)
1.00 0.83 (0.72-0.95) 0.79 (0.69-0.89) 0.70 (0.54-0.90)
Daily activity at work in 40s-50s Sitting Light Moderate Strenuous Age not yet reached
933 (31) 757 (25) 1080 (36) 77 (3) 181 (6)
874 (26) 886 (26) 1209 (36) 111 (3) 316 (9)
1.00 0.78 (0.68-0.90) 0.81 (0.71-0.92) 0.63 (0.47-0.86) NA
Daily activity at work in 60s-74 Sitting Light Moderate Strenuous Age not yet reached
314 (10) 478 (16) 335 (11) 12 (0) 1854 (62)
275 (8) 546 (16) 388 (12) 12 (0) 2147 (64)
1.00 0.76 (0.62-0.93) 0.75 (0.60-0.93) 0.88 (0.39-2.00) NA
Alcohol intake (drinks/week)6 Never 1 - 6 7 - 35
1558 (51) 894 (29) 610 (20)
1741 (51) 1052 (31) 634 (19)
1.00 0.97 (0.86-1.08) 1.07 (0.93-1.21)
Dietary fat intake (g/day) 6.7 - 47.9 47.9 – 66.1 66.2 - 88.4 88.4 – 412.8
803 (26) 769 (25) 729 (24) 761 (25)
855 (25) 856 (25) 859 (25) 857 (25)
1.00 0.97 (0.84-1.10) 0.92 (0.80-1.06) 0.97 (0.85-1.12)
Total Phytoestrogens (µg/day) 0 - 438 439 - 978 979 - 3077 3077 - 8594 8595 - 657,042
598 (20) 599 (20) 601 (20) 600 (20) 601 (20)
648 (19) 691 (21) 628 (19) 675 (20) 728 (22)
1.00 0.95 (0.82-1.12) 1.05 (0.90-1.23) 0.96 (0.82-1.12) 0.88 (0.76-1.03)
1 Age at cancer diagnosis for cases and age on 15Nov2002 for controls 2 BMI weight two years ago (kg) divided by height in metres squared 3 If menstrual periods are reported within or beyond one year of diagnosis or referent age, the participant is categorized as premenopausal 4 Benign breast disease is defined as as non-cancerous cysts or lumps in breasts 5 Self-reported history of ever having a screening mammogram (lagged by two years) 6 Amount of beer, wine and liquor usually consumed 2 years ago
54
3.5 Statistical Analysis
An overview of the statistical analyses that were used is provided in this section; each of the 3
papers in the results section of this thesis provides additional methods specific to each study
objective. All statistical analyses were conducted using SAS version 9.1. Statistical significance
was defined as P value less than 0.05 and all tests were two-sided. Descriptive data analyses
were conducted first for data cleaning purposes and to describe the study population. Table 1 in
appendix 3 shows the percent of missing data for each variable. Missing data was minimal for all
potential confounders 0 to 3% and less than 2% for the food and supplement data. The missing
data were greatest for the location of residence variables and correspondingly latitude, erythemal
UV and the solar vitamin D score (calculated using location of residence). Some assumptions
were made as described above for location of residence to preserve as much data as possible, no
other assumptions were made when data were missing. Since the study sample size is large and
the missing data were minimal, imputation methods or inverse probability weighting were not
used. Any cases or controls with missing data were allowed to fall out of the multivariable
models and analyses were conducted on subjects with complete data only.
For objectives 1-3, multivariate logistic regression analysis was used to obtain odds ratios (OR)
and 95% confidence intervals for all vitamin D exposures with breast cancer risk as the
dependent variable. Since controls were frequency age-matched to cases, all logistic regression
models were adjusted for age-group using unconditional models (Rothman & Greenland, 1998).
The likelihood-ratio test was used to test the significance of multiplicative interactions.
Interactions were evaluated between each of the vitamin D variables and calcium intake,
menopausal status, and BMI. Stratified analyses were conducted when significant interactions
were observed. If no significant interaction was observed then each of the potential effect
modifiers was investigated as potential confounders.
Confounders were defined as any variable that changed the OR of the exposure variable by more
than 10% when added to the model (Maldonado & Greenland, 1993). This was assessed by
adding each of the 39 potential confounding variables (listed in section 3.4) one at a time to the
age-adjusted model of interest. This was done for all of the dietary vitamin D and calcium
variables (food and nutrient level and supplements) and for each of the sun exposure related
variables and the derived scores for each age group. If a confounder was identified then it was
55
included in the model. Test for linear trend was calculated by treating the median intake for each
exposure category as a continuous variable in the age-adjusted and fully-adjusted logistic
regression models.
For objective 2, associations between breast cancer risk and each of the following exposure
variables were evaluated: 1) variables associated with cutaneous vitamin D production (as
reported from the epidemiologic questionnaire), 2) latitude and UV radiation of residence, and 3)
derived solar vitamin D scores. All of the exposure variables were evaluated for each of the 4
time periods of exposure (adolescence through adulthood) and the cumulative and recent
measures of the solar vitamin D score. For each age period of exposure, women who had not yet
reached that age period were removed from the models.
It was hypothesized that time spent outdoors might actually be a proxy for physical activity, thus
the correlations between time spent outdoors and physical activity were evaluated. As shown in
Table 6, the correlations between time spent outdoors and each measure of physical activity, at
all 4 periods of exposure, were statistically significant but the strength of the associations were
relatively weak (r < 0.26). Physical activity (daily, moderate or strenuous) at any age period of
exposure was not found to confound any of the sun exposure related variables or the solar
vitamin D score.
Table 6. Spearman rank correlations (rs) between physical activity and time outdoors per week at
4 age periods of exposure
Physical Activity Hrs outdoor per week
Teenage years
20-30s
40-50s
60-75
rs (p-value)
Daily 0.14 (<0.0001) 0.17 (<0.0001) 0.15 (<0.0001) 0.18 (<0.0001) Moderate 0.24 (<0.0001) 0.20 (<0.0001) 0.20 (<0.0001) 0.24 (<0.0001) Strenuous 0.26 (<0.0001) 0.20 (<0.0001) 0.18 (<0.0001) 0.17 (<0.0001)
For objective 1, associations between breast cancer risk and the following dietary measures of
daily vitamin D intake were evaluated: 1) derived intake from foods, supplements, and combined
total (as derived by the Block nutrient analysis), 2) individual foods rich in vitamin D (e.g., milk
and fish), and 3) frequency and duration of supplement use (multivitamin and single product
56
vitamin D or cod liver oil). Vitamin D and calcium intakes were highly correlated (table 7), thus,
various ways of accounting for multicollinearity were evaluated (e.g. creation of a combined
variable, or two new variables, or restriction of vitamin D to only UV exposures).
Table 7. Spearman rank correlations (rs) between vitamin D and calcium from food, supplements
and total combined (food and supplements) intake.
Vitamin D (IU/day)
Total
Supplements
Food
rs (p-value)
Total
0.64 (<0.0001) 0.43 (<0.0001) 0.54 (<0.0001)
Calcium
(mg/day) Food 0.50 (<0.0001) 0.10 (<0.0001) 0.79 (<0.0001)
Supplements 0.56 (<0.0001) 0.70 (<0.0001) 0.05 (0.0003)
To evaluate measurement error associated with the development of the solar vitamin D score for
objective 4, sensitivity analyses were conducted. A range of other plausible values were assumed
and substituted for the parameters assigned in the algorithm and the impact of these changes on
the association between solar vitamin D and breast cancer risk were evaluated. An alternative
solar vitamin D measure was also created through cross-classification. UV exposure, time
outdoors, skin colour and sun protection practices were categorized as high versus low based on
vitamin D production potential and an additive score was then created categorizing women into 4
categories.
In addition to our derived algorithms, we also evaluated the use of a previously published
algorithm for the measurement of predicted serum 25(OH)D values (Giovannucci et al., 2006).
Multiple linear regression was used to develop a predictive model among a subset of men in the
Harvard Health Professionals’ Follow-Up Study with serum 25(OH)D measures. The model was
then applied to predict 25(OH)D among other men in the Health Professionals’ Study and has
been previously applied to women in the Nurses’ Health Study (Forman et al., 2007; Ng et al.,
2009). The predictive model by Giovannucci et al was not specific to the measurement of
vitamin D from sunlight and included 6 variables: dietary vitamin D, supplemental vitamin D,
BMI, race, physical activity (included as a proxy for time spent outdoors), and region of
57
residence. This predictive model was able to explain only 28% of the variation in 25(OH)D.
More details regarding the methodology used are provided in Paper 3.
To evaluate the impact of modifying the vitamin D nutrient analysis (objective 5), descriptive
statistics were calculated to compare vitamin D intake before and after modification. The
weighted kappa statistic and 95% CI were calculated to assess the chance-corrected agreement
between the categorical vitamin D from food intake variables obtained from the Canadian and
US nutrient analyses. The paired t-test was used to determine if the mean difference between the
Canadian and US nutrient analyses using the continuous measures of vitamin D from foods was
different from zero.
3.6 Ethics
Ethics approval for the “Ontario Women’s Diet and Health Study” was initially obtained from
the University of Toronto Research Ethics Board on December 4, 2002 by M. Cotterchio. Ethics
approval for this secondary data analysis project “Vitamin D and Breast Cancer Risk” was
obtained from the University of Toronto Research Ethics Board by L. Anderson on December
20, 2007 and one-year renewals were granted in 2008 and 2009 (appendix 2). This study
involves secondary data analysis only and study participants were not contacted for additional
information, thus, the risk was classified as low and expedited review was granted.
58
Chapter 4 Study Results
4.1 Overview of Results
The results of this thesis are presented in manuscript format with 3 independent papers. Although
all papers have multiple authors, the PhD candidate developed the research questions, completed
all literature reviews, variable derivation, statistical data analysis and wrote each manuscript.
The first paper describes vitamin D intake among the controls only and evaluates the impact of
modifying the nutrient analysis applied to the FFQ for Canadian vitamin D food values and
additional vitamin D sources (thesis objective 5). The second paper evaluates the associations
between vitamin D, calcium intake from food and supplements and breast cancer risk, using the
modified Canadian vitamin-D nutrient analysis (thesis objectives 1). The third paper describes
the development of a solar vitamin D score and evaluates the association between this score and
each variable related to the cutaneous production of vitamin D and breast cancer risk (thesis
objectives 2, 3 and 4).
59
Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
4.2 Paper 1: Vitamin D Intake from Food and Supplements among Ontario Women Based on the US Block Food Frequency Questionnaire with and without Modification for Canadian Food Values
Accepted for publication by Canadian Journal of Public Health (to be published July/Aug 2010)
Laura N. Anderson1, 2 *, Michelle Cotterchio1, 2, Beatrice A. Boucher1, 2, 3, Julia A. Knight1, 4,
Torin Block5
1Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada;
2Population Studies & Surveillance, Cancer Care Ontario, Toronto, Ontario, Canada;
3Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada;
4Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai
Hospital, Toronto, Ontario, Canada; 5Block Dietary Data Systems, NutritionQuest, Berkeley,
CA, USA
RUNNING TITLE: Dietary vitamin D intake among Ontario women
Conflict of Interest: TB is an owner of NutritionQuest, which holds the copyright on the Block
FFQ. LNA, MC, BAB and JAK have none to declare.
60
Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
ABSTRACT
Objectives: To measure and compare dietary vitamin D intake among women in Ontario using a
modified Block 1998 (US) food frequency questionnaire (FFQ) before and after modification for
Canadian-specific vitamin D food fortification.
Methods: An age-stratified random sample of 3,471 women in Ontario (aged 25-74) was
identified using random digit dialing methods. Standard US food values, and a modified
Canadian-specific vitamin D nutrient analysis were applied to the FFQ.
Results: Intake of vitamin D from foods (Canadian nutrient analysis) was 5.3 ± 3.4 µg/day
(mean ± SD) and 45% of women reported vitamin D intake from supplements. Total vitamin D
intakes met the current Adequate Intakes of 5, 10 and 15 µg/day for only 62%, 47%, and 28% of
women aged ≤ 50, 51-70 and ≥71, respectively. Relatively high agreement was found between
the US and Canadian nutrient analysis methods of measuring vitamin D from food (weighted
kappa = 0.74, 95% CI 0.72, 0.76). Intake differences (US minus Canadian) ranged from -5.0
µg/day to +2.0 µg/day (1st – 99th percentile); however, the mean difference was only -0.54
µg/day (95% CI: -0.58, -0.50).
Conclusions: Lower than recommended total vitamin D intakes were observed among our study
participants which may negatively impact the health status of women. Adjustment for Canadian
food fortification and the inclusion of fatty fish had little impact on the measurement of vitamin
D from food.
MeSH subject headings: vitamin D; food, fortified; nutrition surveys; female; Canada; United
States
61
Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
Vitamin D is important for maintenance of healthy bones, and low vitamin D intake may be a
risk factor for some cancers and other chronic diseases (1-3). Vitamin D is synthesized by the
skin following sunlight exposure and is present in foods and supplements. Since few foods
contain high amounts of vitamin D (4), many countries have their own food fortification policies
to improve vitamin D levels. In Canada, fortification of fluid milk and margarine with vitamin D
is mandatory (5). Manufacturers are permitted to use fortified milk to make milk products (e.g.,
yogourt) and to fortify milk beverage substitutes, and some other foods such as orange juice, but
these items are not universally enriched. In the United States (US), where vitamin D fortification
is optional, most milk and many breakfast cereals are fortified (6).
In Canada and the US, vitamin D intakes are evaluated against Adequate Intakes (AIs). The AIs
for vitamin D are 5, 10 and 15 µg/day for adults ≤ 50, 51-70 and >70 years of age, respectively
(7). Measuring diet is important for nutritional epidemiology studies and surveillance and a
commonly used tool is the food frequency questionnaire (FFQ). American FFQs are frequently
applied in such studies, without modifying nutrient databases for population-specific food
values. Few studies (8) have investigated the impact of this practice on the measurement of
vitamin intakes.
The objectives of the current study were to 1) describe vitamin D intake among women in
Ontario, from food and supplements, and 2) compare vitamin D intakes using a US nutrient
analysis versus a modified analysis that reflects additional vitamin D sources (fatty fish), and
Canadian food fortification.
METHODS
Study description
Women aged 25-74 years were identified using random digit dialing of households in Ontario
between 2002 and 2003. These women were recruited as controls for a case-control study
evaluating various epidemiologic factors and breast cancer risk (9). This study was approved by
the University of Toronto Research Ethics Board. Of 4,352 households with eligible women,
3,471 (80%) completed a mailed self-administered risk factor questionnaire and an FFQ.
Subjects were asked about foods and supplements they “usually ate about two years ago”.
62
Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
Measurement of vitamin D (FFQ and database values for nutrient analysis)
Description of the FFQ
The quantitative Block 1998 FFQ (9) used in this study was modified to improve measurement
of specific dietary components, including vitamin D, and included 178 food items. A sub-
question querying the type of fish eaten most often (white or fatty), and a supplement item for
vitamin D or cod liver oil were added to the FFQ. Validity was assessed against two 24-hour
recalls among Ontario women using US nutrient data (10). Based on usual (current) intake, FFQ
reliability for vitamin D was relatively high (non-deattenuated r = 0.76, 95% CI 0.66, 0.83), and
its validity was moderately high (deattenuated r = 0.54, 95% CI 0.29, 0.79) (10).
Description of the standard (US) and modified (Canadian) nutrient analyses
Vitamin D intake from food was initially measured by applying standard US nutrient values from
Block Dietary Data Systems (BDDS) to the FFQ. Nutrient values were based on the US
Department of Agriculture (USDA) National Nutrient Database (11), and published literature.
BDDS uses national US consumption data to estimate and weight the proportionate use of foods
within each FFQ item (12, 13).
To modify the nutrient analysis for Canada, vitamin D values of all relevant foods in the BDDS
FFQ database were compared to corresponding foods in the Canadian Nutrient file (CNF),
Canada’s standard reference database (4). Table 1 presents vitamin D values assigned to the
primary food sources on the FFQ. The added fish question, only incorporated in the Canadian-
analysis, assigned a higher vitamin D value to women reporting they most often consumed fatty
rather than white fish based on average fish values in the CNF. Items with very low levels of
naturally occurring vitamin D were not modified despite some observed differences between the
BDDS (US) nutrient values and those listed in the CNF. BDDS' standard US-based consumption
weighting values were used in both analyses.
Supplement analysis
The analysis for vitamin D from supplements (multivitamins, and vitamin D supplements or cod
liver oil) was not modified as there are no data suggesting a consistent difference in the vitamin
D content of supplements between Canada and the US. BDDS assigned a vitamin D value of 10
63
Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
µg to multivitamins; 10 µg was also assigned to the additional vitamin D supplement or cod liver
oil question (14).
Statistical analysis
The frequency distributions of respondent characteristics and vitamin D intake were tabulated.
Four vitamin D intake variables are reported: 1) Canadian vitamin D from foods (values from the
CNF); 2) US vitamin D from foods (US values provided by BDDS); 3) vitamin D from
supplements; and 4) total vitamin D (combined vitamin D from supplements and Canadian food
values). The weighted kappa statistic and 95% CI were calculated to assess the chance-corrected
agreement between categories of vitamin D intakes obtained from the Canadian and US food
analyses. The paired t-test was used to determine if the mean difference between the Canadian
and US analyses using the continuous measures of vitamin D from foods was different from
zero. Statistical analysis was conducted using SAS version 9.1.
RESULTS
Data analysis was completed on 3,393 of the 3,471 (98%) questionnaires; 44 were considered
incomplete due to a large number of missing responses and 34 were excluded due to unlikely
energy intakes (< 500 or > 4500 kcal per day). The maximum daily vitamin D intakes from food
and supplements were 30 µg and 20 µg, respectively, and seemed plausible. Table 2 describes
the distribution of subject characteristics.
Using Canadian values, the proportion of women meeting the AI for vitamin D from food alone
decreased with age (Table 3); 44% of women age 25-50 met the AI of 5µg/day, only 10% of
women age 51-70 met the AI of 10µg/day, and no women age 71-74 met the AI of 15µg/day.
Most women (55%) did not consume any vitamin D from supplements (single product vitamin
D/cod-liver oil, or multivitamin); 38% were multivitamin users and 14% were vitamin D/cod
liver oil users (7% took both). For total vitamin D intake (supplements and Canadian food
values) only 62% of women age 25-50, 48% of women age 51-70, and 28% of women age 71-74
had total vitamin D intake that met the AI for their age range. No women had total dietary
vitamin D intakes greater than the tolerable upper intake level (50 µg/day).
Mean (± SD) vitamin D intake from supplements alone was 4.4 ± 5.7 µg/day and total combined
intake was 9.7 ± 6.9 µg/day. Mean (± SD) intake of vitamin D from food was 5.3 ± 3.4 and 4.8 ±
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Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
3.2 µg/day for the Canadian and US nutrient analyses, respectively. The mean US minus
Canadian food difference was -0.54 µg/day (95% CI: -0.50, -0.58) (p<0.0001) and the
distribution of differences (1st - 99th percentile) ranged from -5.0 µg/day to 2.0 µg/day. There
was relatively high agreement between the categories of vitamin D intake from food alone, using
US and Canadian values (weighted kappa = 0.74, 95% CI: 0.72, 0.76). However, an additional
4%, 3% and 0% of women age 25-50, 51-70 and 70-74, respectively, would be misclassified as
‘inadequate’ from food if US values were relied upon (Table 3).
DISCUSSION
Even after modification for Canadian specific values, low intake of total combined vitamin D
(foods and supplements) was observed in our study and was most pronounced among women age
71-74, despite their higher use of supplements. High agreement and limited misclassification
were observed between the two food measures, suggesting the standard US Block FFQ and
nutrient analysis may be adequate for the measurement of vitamin D foods among Canadians.
Using a standard FFQ with US food values can both over- and under-estimate Canadian vitamin
D intakes, although the magnitude of the mean difference was relatively small.
One previous study examined differences in US versus Canadian vitamin intakes using an FFQ
and also found mean Canadian vitamin D intake was slightly underestimated using US values
(8). This study, conducted in Alberta among 7,659 women age 35-69, also reported few women
meeting the AI for vitamin D from food only (30% of women age 31-50, and only 3% of women
age 51-70); supplement intake was not described (8). The Canadian Community Health Survey
(CCHS), a population-based survey of food only using 24-hour recalls (Canadian nutrient
analysis), suggests the AI are met by only 36%, 42% and 9.3% of Canadian women ages 19-30,
31-50, and 51-70, respectively (supplement data are not currently available) (15). The CCHS
(15) reports higher intakes of vitamin D from food than measured by FFQ in our study or
Csizmadi et al (8) but still suggests a large proportion of women are not meeting the current AIs.
The inclusion of supplements in our study increased the proportion of women meeting the AIs
but still indicates that many women may have inadequate intakes of vitamin D. Since our data
were collected in 2002-2003 reflecting 2000-2001 intakes, it is possible that recent mass media
describing the potential benefits of vitamins D may have led to increased vitamin D intake from
food and/or supplements.
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A potential limitation of any dietary study is measurement error. There are concerns regarding
the accuracy of the analysis of vitamin D content of foods (16, 17) and the amount (as reported
in nutrient databases) has been found to vary greatly in both fish (18) and fortified milk (19, 20).
FFQs are also a source of measurement error in nutritional epidemiology and are best used to
capture relative rather than absolute individual intake (21, 22). However, the measurement of
usual vitamin D using this FFQ was shown to have moderately high validity and mean intake
was not significantly different when compared to two 24-hour recalls (10). Although, our study
measured usual diet ‘two years ago’, diet is expected to be stable over time (23). An additional
limitation is that nutrient databases change over time and we applied the most recent version of
the CNF to earlier intake data. The lack of modification of nutrient databases for country specific
vitamin D fortification regulations likely introduces error that may bias disease association study
findings, although, we found high agreement and little misclassification between the two
measures of vitamin D intake. Measurement error due to respondent memory is always a concern
in epidemiologic studies and we were unable to evaluate this.
Sun exposure is another important source of vitamin D, yet, many studies of vitamin D and
disease risk have focused only on vitamin D from diet/supplements. Optimal vitamin D status
has been proposed at 25 hydroxyvitamin D [25(OH)D] serum levels >30 ng/mL (1, 24) and it has
been shown that intakes >12.5 µg/day (25, 26) are required to maintain optimal wintertime
25(OH)D levels in Northern populations, such as Canada. The mean total intake among all
women in our study was only 9.7 µg/day suggesting the potential for sub-optimal serum
25(OH)D levels. There are two forms of vitamin D: vitamin D3 (from fatty fish, most
supplements and fortified foods) and vitamin D2 (from plants). These forms of vitamin D may
differ in biologic activity (27) and were not measured in this study, but we would suspect that
most would be vitamin D3.
Many researchers have concluded that the current AIs are not sufficient to maintain optimal
serum levels of 25(OH)D (25, 28) and the dietary reference intakes for vitamin D are under
review (29, 30). Considering the low proportion of women who met current AIs in this study,
more work is needed to ensure that women are consuming sufficient vitamin D. Despite
fortification differences between Canada and the US there is relatively high agreement in vitamin
D intake from food using a US FFQ before and after modification for Canadian values (and
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Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
including fatty fish). Lower than recommended total vitamin D intakes were observed among our
study participants which may negatively impact health (1-3).
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1. Holick MF. Vitamin D: A D-lightful health perspective. Nutr Rev. 2008;66(10 Suppl 2):S182-94.
2. Holick MF. Vitamin D deficiency. N Engl J Med. 2007;357(3):266-81.
3. Giovannucci E. Vitamin D status and cancer incidence and mortality. Adv Exp Med Biol. 2008;624:31-42.
4. Health Canada. Canadian Nutrient File, 2007b version. 2007 [cited 2009 Mar 30]. Available from: www.healthcanada.ca/cnf
5. A Health Canada. Addition of Vitamins and Minerals to Foods. 2005 [cited 2009 Mar 30]. Available from: http://www.hc-sc.gc.ca/fn-an/nutrition/vitamin/fortification_final_doc_1-eng.php#c6
6. Calvo MS, Whiting SJ, Barton CN. Vitamin D fortification in the united states and canada: Current status and data needs. Am J Clin Nutr. 2004;80(6 Suppl):1710S-6S.
7. Standing Committee on the Scientific Evaluation of Dietary Reference Intakes, Food and Nutrition Board, Institute of Medicine. Dietary Reference Intakes for calcium, phosphorus, magnesium, vitamin D and flouride. Washington, DC: National Academy Press; 1997.
8. Csizmadi I, Kahle L, Ullman R, Dawe U, Zimmerman TP, Friedenreich CM, Bryant H, Subar AF. Adaptation and evaluation of the national cancer institute's diet history questionnaire and nutrient database for Canadian populations. Public Health Nutr. 2007;10(1):88-96.
9. Cotterchio M, Boucher BA, Kreiger N, Mills CA, Thompson LU. Dietary phytoestrogen intake--lignans and isoflavones--and breast cancer risk (Canada). Cancer Causes Control. 2008;19(3):259-72.
10. Boucher B, Cotterchio M, Kreiger N, Nadalin V, Block T, Block G. Validity and reliability of the Block98 food-frequency questionnaire in a sample of Canadian women. Public Health Nutr. 2006;9(1):84-93.
11. United States Department of Agriculture, Agricultural Research Service. USDA National Nutrient Database for Standard Reference. 2009 [cited 2009 Mar 30]. Available from: http://www.ars.usda.gov/ba/bhnrc/ndl
12. Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J Epidemiol. 1986;124(3):453-69.
13. Block G. Invited commentary: Another perspective on food frequency questionnaires. Am J Epidemiol. 2001;154(12):1103,4; discussion 1105-6.
14. Moore C, Murphy MM, Keast DR, Holick MF. Vitamin D intake in the United States. J Am Diet Assoc. 2004;104(6):980-3.
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15. Health Canada, Statistics Canada. Canadian Community Health Survey (CCHS) Cycle 2.2, Nutrition (2004). Nutrient intakes from food. Provincial, regional and national summary data tables. 2nd vol. Ottawa: Health Canada; 2008. Report No.: H164–45/2–2008E-PDF.
16. Holden JM, Lemar LE, Exler J. Vitamin D in foods: Development of the US department of agriculture database. Am J Clin Nutr. 2008;87(4):1092S-6S.
17. Byrdwell WC, Devries J, Exler J, Harnly JM, Holden JM, Holick MF, Hollis BW, Horst RL, Lada M, et al. Analyzing vitamin D in foods and supplements: Methodologic challenges. Am J Clin Nutr. 2008;88(2):554S-7S.
18. Lu Z, Chen TC, Zhang A, Persons KS, Kohn N, Berkowitz R, Martinello S, Holick MF. An evaluation of the vitamin D3 content in fish: Is the vitamin D content adequate to satisfy the dietary requirement for vitamin D? J Steroid Biochem Mol Biol. 2007;103(3-5):642-4.
19. Faulkner H, Hussein A, Foran M, Szijarto L. A survey of vitamin A and D contents of fortified fluid milk in Ontario. J Dairy Sci. 2000;83(6):1210-6.
20. Chen TC, Shao A, Heath H,3rd, Holick MF. An update on the vitamin D content of fortified milk from the United States and Canada. N Engl J Med. 1993 Nov 11;329(20):1507.
21. Kristal AR, Peters U, Potter JD. Is it time to abandon the food frequency questionnaire? Cancer Epidemiol Biomarkers Prev. 2005;14(12):2826-8.
22. Schatzkin A, Subar AF, Moore S, Park Y, Potischman N, Thompson FE, Leitzmann M, Hollenbeck A, Morrissey KG, Kipnis V. Observational epidemiologic studies of nutrition and cancer: The next generation (with better observation). Cancer Epidemiol Biomarkers Prev. 2009;18(4):1026-32.
23. Goldbohm RA, van 't Veer P, van den Brandt PA, van 't Hof MA, Brants HA, Sturmans F, Hermus RJ. Reproducibility of a food frequency questionnaire and stability of dietary habits determined from five annually repeated measurements. Eur J Clin Nutr. 1995 Jun;49(6):420-9.
24.Vieth R. What is the optimal vitamin D status for health? Prog Biophys Mol Biol. 2006;92(1):26-32.
25. Whiting SJ, Green TJ, Calvo MS. Vitamin D intakes in North America and Asia-Pacific countries are not sufficient to prevent vitamin D insufficiency. J Steroid Biochem Mol Biol. 2007;103(3-5):626-30.
26. Cashman KD, Hill TR, Lucey AJ, Taylor N, Seamans KM, Muldowney S, Fitzgerald AP, Flynn A, Barnes MS, et al. Estimation of the dietary requirement for vitamin D in healthy adults. Am J Clin Nutr. 2008;88(6):1535-42.
27. Houghton LA, Vieth R. The case against ergocalciferol (vitamin D2) as a vitamin supplement. Am J Clin Nutr. 2006;84(4):694-7.
28. Vieth R, Bischoff-Ferrari H, Boucher BJ, Dawson-Hughes B, Garland CF, Heaney RP, Holick MF, Hollis BW, Lamberg-Allardt C, et al. The urgent need to recommend an intake of vitamin D that is effective. Am J Clin Nutr. 2007;85(3):649-50.
29. US Department of Agriculture, Human Nutrition Information Service. Provisional table on the vitamin D content of foods. Washington, DC: US Government Printing Office, 1980. Revised 1999.
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30. Yetley EA, Brule D, Cheney MC, Davis CD, Esslinger KA, Fischer PW, Friedl KE, Greene-Finestone LS, Guenther PM, et al. Dietary reference intakes for vitamin D: Justification for a review of the 1997 values. Am J Clin Nutr. 2009;89(3):719-27.
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Paper 1: Dietary Vitamin D Intake among Ontario Women (Anderson et al, in press)
Table 1. Vitamin D food values assigned to the standard (US) nutrient analysis and the modified
Canadian analysis
Vitamin D values
Foods US* Canada
†
µg per 100g
Breakfast cereals 3.5 0
Margarine 1.5 13.3
Fish (not fried)‡ 1.5 -
Fatty fish - 10.0 White fish - 1.5
Milk 1.0 1.0
* Values from Block Dietary Data Systems
† Values from Canadian Nutrient File
‡ Fish type was added to the modified Canadian analysis and a higher value was assigned to fatty fish. The type of
fish question was not applied to the US analysis.
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Table 2. Distribution of subject characteristics among all participating Ontario women (n =
3,393)
Variable n (%)*
Age (years) 25 - 50 51 - 70 71 - 74
1251 (37) 1902 (56) 240 (7)
Highest level of Education Elementary or High school Postsecondary
1716 (51) 1664 (49)
BMI† (kg/m2)
< 24.9 25.0 - 29.9 ≥ 30
1506 (45) 1145 (34) 719 (21)
Smoking status Never smoker Ex-smoker Current smoker
1741 (51) 1107 (33) 523 (15)
Ethnicity Caucasian
South East Asian‡
Black Other
3062 (91) 95 (3) 50 (2) 169 (5)
* Numbers may not add to totals because of missing values and/or rounding † BMI calculated as weight two years ago in kilograms divided by height in meters squared ‡ e.g., Japanese, Chinese, Korean, Vietnamese
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Table 3. Distribution of vitamin D intake among Ontario women (total and stratified by age
group)
Age group (years)
Vitamin D intake (µg/day)
Total
(n = 3,393)
n (%)
25-50
(n = 1,251)
n (%)
51-70
(n = 1,902)
n (%)
71-74
(n = 240)
n (%)
Foods - Cdn values <5 5 – 9.9 10 – 14.9 ≥15
1877 (55) 1193 (35) 280 (8) 43 (1)
699 (56) 435 (35) 102 (8) 15 (1)
1043 (55) 670 (35) 162 (8) 27 (2)
135 (56) 88 (37) 16 (7) 1 (0)
Foods - US values <5 5 – 9.9 10 – 14.9 ≥15
2096 (62) 1049 (31) 223 (7) 25 (1)
751 (60) 394 (31) 94 (8) 12 (1)
1195 (63) 573 (30) 121 (6) 13 (1)
150 (63) 82 (34) 8 (3) 0 (0)
Supplements* 0 1 - 4.9 5 – 9.9 10 – 14.9 ≥15
1875 (55) 230 (7) 178 (5) 940 (28) 170 (5)
787 (63) 113 (9) 74 (6) 246 (20) 31 (2)
973 (51) 104 (5) 99 (5) 606 (32) 120 (6)
115 (48) 13 (6) 5 (2) 88 (37) 19 (8)
Total† <5 5 – 9.9 10 – 14.9 ≥15
1132 (33) 825 (24) 729 (21) 707 (21)
475 (38)‡ 363 (29) 229 (18) 184 (15)
583 (31)‡ 413 (22)‡ 449 (24) 457 (24)
74 (31)‡ 49 (20)‡ 51 (21)‡ 66 (28)
* Supplemental vitamin D includes multivitamins and vitamin D supplements or cod liver oil. † Total vitamin D from food (Canadian nutrient values) and supplements ‡ Intakes below the AI for that age group; daily AI for ages 25-50 = 5 µg; 51-70 = 10 µg; and ≥71 = 15 µg.
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4.3 Paper 2: Vitamin D and Calcium Intakes and Breast Cancer Risk in Pre- and Postmenopausal Women
American Journal of Clinical Nutrition (AJCN). Vol. 91, No. 6, 1699-1707, June 2010. First
published April 14, 2010; doi: 10.3945/ajcn.2009.28869.
Laura N. Anderson1,2, Michelle Cotterchio1,2, Reinhold Vieth3, Julia A. Knight 2,4
1Population Studies & Surveillance, Cancer Care Ontario, 620 University Ave., Toronto, ON;
2Dalla Lana School of Public Health, University of Toronto, Toronto, ON; 3Department of
Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON; 4Prosserman Centre
for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON
Short title: Vitamin D, calcium and breast cancer risk
Abstract
Background: Some evidence suggests that vitamin D may reduce breast cancer risk. Despite the
biologic interaction between vitamin D and calcium, few studies have evaluated their joint
effects on breast cancer risk.
Objective: To evaluate the associations and potential interaction between vitamin D and calcium
(from food and supplements) and breast cancer risk in a population-based case-control study.
Design: Breast cancer cases aged 25 - 74 years (diagnosed 2002-2003) were identified through
the Ontario Cancer Registry. Controls were identified using random digit dialing. 3101 cases and
3471 controls completed epidemiologic and food frequency questionnaires. Adjusted odds ratios
(OR) and 95% confidence intervals (CI) were estimated using multivariate logistic regression.
Results: Vitamin D and calcium intakes from food only and total combined intake (food and
supplements) were not associated with breast cancer risk, although mean intake of vitamin D was
low. Vitamin D supplement intake > 10µg (400 IU) per day versus no intake was associated with
a reduced risk of breast cancer (adjusted OR: 0.76; 95% CI: 0.59, 0.98). No categories of
calcium supplement intake were significantly associated with reduced breast cancer risk but a
significant inverse trend was observed (p = 0.04). There were no significant interactions
involving vitamin D, calcium or menopausal status.
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Conclusions: No associations were found between overall vitamin D or calcium intake and breast
cancer risk. Vitamin D from supplements was independently associated with reduced breast
cancer risk. Further research is needed to investigate the effects of higher doses of vitamin D and
calcium supplements.
INTRODUCTION
Breast cancer is the most common cancer among Canadian women (1) and few modifiable risk
factors have been identified (e.g., alcohol consumption, hormone replacement therapy, obesity
among postmenopausal women) (2-4). Several review papers concluded that, despite
inconsistencies in the literature and identified areas that still require investigation, low vitamin D
intake may also increase breast cancer risk (5-11). One meta-analysis found no overall
association between vitamin D, from diet and supplements, and breast cancer risk, but did
suggest an inverse association may exist at higher intakes (12). Vitamin D is synthesized in the
skin following sufficient ultraviolet B exposure from sunlight and is found in some foods (e.g.,
fortified milk and fatty fish), and vitamin supplements (13). Vitamin D (from diet and sunlight)
is hydroxylated by the liver to the circulating form 25-hydroxyvitamin D (25(OH)D) (the
preferred biomarker for vitamin D). A second hydroxylation in the kidney or in other cells,
including breast cells, produces the active hormone 1,25-dihydroxyvitamin D (1,25(OH)2D). The
vitamin D receptor is present in many cells, including normal and cancerous breast cells,
enabling these cells to respond to 1,25(OH)2D (14-16). Laboratory studies have shown
1,25(OH)2D promotes cell differentiation and inhibits cell growth (14-17). Some studies of
25(OH)D and breast cancer risk have found an inverse association (18-21), though not all (22-
24).
It is well established that 1,25(OH)2D regulates calcium metabolism and that vitamin D and
calcium are found in some of the same foods (e.g., vitamin D fortified milk). Calcium may also
have anticarcinogenic properties that include regulation of cell differentiation, proliferation and
apoptosis (25-27). However, results from epidemiologic studies do not strongly support an
inverse association between calcium, or more generally dairy products, and breast cancer risk (8,
28-32). The Women’s Health Initiative trial –in which postmenopausal women were randomized
to 10 µg (400 IU) vitamin D and 1000 mg calcium daily or a placebo - found no reduction in
breast cancer risk after a mean follow-up time of 7 years (22). Few observational studies of
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Paper 2: Vitamin D, Calcium and Breast Cancer Risk (Anderson et al, 2010)
dietary vitamin D and breast cancer risk have investigated the interaction between calcium and
vitamin D (33-35).
Currently, it is unclear whether the possible association between dietary vitamin D and reduced
breast cancer risk is confounded or modified by calcium and vice versa. Furthermore, there is
limited evidence suggesting the association between dietary vitamin D and breast cancer risk
may (34, 35) or may not (36, 37) differ by menopausal status. The objectives of this study were
to evaluate the associations and potential interaction between vitamin D and calcium (from food
and supplements) and breast cancer risk in a population-based case-control study of pre- and
post-menopausal women in Ontario.
SUBJECTS AND METHODS
Data were collected as part of the Ontario Women’s Diet and Health Study, a large population
based case-control study evaluating various epidemiologic factors and breast cancer risk (38).
The study protocols for this study were approved by the University of Toronto Research Ethics
Board.
Cases
Cases were women aged 25 - 74 with a first, pathologically confirmed cancer of the breast
identified from the Ontario Cancer Registry and diagnosed between June 2002 and April 2003.
The Ontario Cancer Registry is a population-based registry that obtains information from nearly
all breast cancer cases in the province in Ontario (39, 40). Physician cooperation was required to
contact patients, obtain contact information and vital status. Consent was obtained for 4,109
eligible cases (96%). The average time between diagnosis and interview was 11 months, with a
range (5th to 95th percentile) from 7 to 18 months.
Controls
Random digit dialing methods were used to identify eligible controls among households in
Ontario and frequency-matched (1:1) within 5-year age groups to the identified cases, described
in detail elsewhere (38). Only one woman from each household was randomly selected for
inclusion in the study. Approximately 25,250 households were telephoned; ≈17,000 of these
households were ineligible (e.g., no woman between the ages 25 to 74 with no history of breast
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cancer), 2,000 did not answer the phone and 2,000 refused (eligibility of these households is
unknown). Of the 4,352 households where an eligible woman was identified, 250 women
refused, and 4,102 women (94%) agreed to participate.
Data Collection & Response rate
Cases and controls were mailed an epidemiologic questionnaire and food frequency
questionnaire (FFQ). The epidemiologic questionnaire consisted of 79 questions and collected
information of lifestyle, reproductive and medical history factors. These questionnaires were
completed and returned by 3,101 cases (75% response rate) and 3,471 controls (80% response
rate).
Measurement of vitamin D and calcium
A modified Block FFQ measured 178 foods and supplements and requested data on both
frequency of consumption and portion size for all foods and frequency and duration of use for
supplements. The validity and reliability of the Block FFQ have been assessed in a random
sample of Ontario women (41). The reliability was high for vitamin D, the non-deattenuated
Pearson correlation coefficients was 0.76 (95% CI: 0.66, 0.83), and for calcium (r = 0.80; 95%
CI: 0.71, 0.86). Validity of the FFQ, compared to a 24-hour recall, was also moderately high for
vitamin D (deattenuated Pearson correlation coefficient was 0.54; 95% CI: 0.29, 0.79) and for
calcium (r = 0.71; 95% CI: 0.35, 1.00) (41). Cases and controls were asked about their
consumption of food and supplements two years prior to the time of questionnaire, to reduce any
bias due to changes in diet following cancer diagnosis. Nutrient analysis was conducted by Block
Dietary Data Systems using nutrient values from the USDA Nutrient Database for Standard
Reference and national data on food consumption (NHANES III and CSFII) (42, 43). In the
modified version of the FFQ used for this study two questions were added to better capture
vitamin D intake: type of fish most often consumed (fatty or white fish) and use of vitamin D
supplements or cod liver oil. The nutrient analysis specific to vitamin D was modified to account
for these additional items and food fortification differences between Canada and the US.
Calcium supplement use, alone or combined with something else, was measured but data on
combined calcium plus vitamin D supplements were not available.
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Derived variables from the Block nutrient analysis were obtained for daily intake of vitamin D
and calcium (mg/day), from all foods, and supplements (single product supplements,
multivitamins, or cod liver oil for vitamin D). A combined total intake measure (foods plus
supplements) was also created. In addition to the derived total nutrient values for vitamin D and
calcium (from foods, supplements and total), the following individual foods rich in vitamin D
and/or calcium and vitamin supplement sources were individually examined: milk, fish,
margarine, single product supplement measures of vitamin D (or cod liver oil) and calcium
(alone or combined with something else), and regular one-a-day multiple vitamins. Individual
food intakes were calculated as servings per day, week or month (depending on frequency of
consumption).
Measurement of other variables
The following variables were tested as potential confounders: marital status, education, ethnicity,
body mass index (BMI), smoking status, pack years smoked, breastfeeding history, breastfed as
an infant, age at menarche, oral contraceptive use, oral contraceptive duration, parity, age at first
live birth, age at menopause, hormone replacement therapy (HRT) use (postmenopausal women
only), duration of HRT use, history of benign breast disease, family history of breast cancer,
screening mammogram, alcoholic drinks, dietary fat intake, calorie intake, phytoestrogen intake,
physical activity (strenuous, moderate and daily activity) at selected periods of life (teenage
years, 20-30s, 40-50s and 60s-74), and sun exposure variables (time spent outdoors, location of
residence, skin color, and sun protection practices) at selected periods of life (teenage years, 20-
39, 40-59 and 60-74 years).
Statistical data analysis
Unconditional logistic regression analysis was used to obtain age-adjusted odds ratios (OR) and
95% CIs for all vitamin D and calcium variables (at both the food and nutrient level). Age was
calculated as age at diagnosis for breast cancer cases and age at midpoint of recruitment for
controls. Confounders were defined as any variable that changed the OR of the exposure variable
by >10% when added to the model (44). None of the variables met our definition of a
confounder; however, to be conservative we also constructed multivariate models that adjusted
for age, education, age at menarche, age at first live birth, parity, menopausal status, breast
cancer in first degree relative, total energy intake, BMI, pack years smoked, moderate physical
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activity during ages 20-30, moderate physical activity during ages 40-59, time spent outdoors per
week during ages 20-39, time spent outdoors per week during ages 40-59, total calcium intake
(included in the models of vitamin D variables only) and total vitamin D intake (included in the
models of calcium variables only). Test for linear trend was calculated by treating the median
intake for each exposure category as a continuous variable in the age-adjusted and fully-adjusted
logistic regression models. Tests for multiplicative interactions were calculated using the
Likelihood Ratio test. To assess the interaction between vitamin D and calcium, calcium was
categorized into two categories: high versus low intake (since calcium and vitamin D intake were
highly correlated resulting in small numbers in the extreme categories, e.g., lowest vitamin D and
highest calcium). Stratified results are presented by calcium intake and menopausal status.
Interactions between vitamin D or calcium intake and BMI and hormone replacement were also
tested. Statistical significance was defined as P value less than 0.05 and all tests were two-sided.
All statistical analyses were conducted using SAS version 9.1.
RESULTS
Overall 6,572 (3,101 cases and 3,471 controls) women completed the questionnaires. The mean
(± SD) age of study participants was 56 years (11). The majority of women in this study (90%)
were Caucasian and many had postsecondary education (46% of cases and 49% of controls).
Characteristics of the cases and controls and age-group adjusted ORs for selected factors that
may be associated with breast cancer risk or vitamin D status are shown in Table 1 and have
been described previously (38).
No significant ORs were observed between milk, margarine, dairy, or fish intake and breast
cancer risk (Table 2). However, OR point estimates increased with milk intake (p for trend =
0.04). Single product vitamin D supplements or cod liver oil were used by only 13% of cases and
14% of controls and although no categories of frequency or duration of use were significantly
associated with breast cancer risk, but a significant inverse dose-response relation was observed
between frequency of supplement use and breast cancer risk (p for trend = 0.04). Calcium
supplement use was more common (33% of cases and 35% of controls) and, similar to vitamin D
supplement use, none of the categories of intake for either frequency or duration were
significant, but OR point estimates decreased with frequency of calcium supplement use (p for
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Paper 2: Vitamin D, Calcium and Breast Cancer Risk (Anderson et al, 2010)
trend = 0.04). Neither frequency nor duration of multivitamin use was associated with breast
cancer risk.
Results for the vitamin D and calcium nutrient-level variables (intake from foods only,
supplements only and total combined) are presented in Table 3. No confounders were identified.
Although the risk of breast cancer was reduced among women with vitamin D from supplement
intakes >10 µg/day (400 IU/day) compared to none (fully adjusted OR: 0.76; 95% CI: 0.59,
0.98), no dose-response relation was observed between vitamin D supplements and breast cancer
risk. No associations were observed between total combined vitamin D intake or vitamin D
intake from foods alone and breast cancer risk. No statistically significant associations were
observed between calcium and breast cancer risk; however, the OR point estimates decreased
with increased calcium supplement dose (p for trend = 0.04).
Vitamin D and calcium were highly correlated with a strong positive correlation observed
between the continuous measures from food (Pearson’s r = 0.79, P < 0.0001), supplements (r =
0.50, p < 0.0001) and total intake (r = 0.63, p < 0.0001). The interactions between total calcium
(high versus low intake) and all categorical vitamin D variables were not significant (Table 4).
The odds ratios in the stratified analysis do not appear substantially different suggesting no effect
modification. When the interactions were assessed using supplemental calcium intake (yes
versus no), which was less highly correlated to all measures of vitamin D, there were still no
significant interactions. The relation between calcium and vitamin D was not different among
pre- and post-menopausal women (data not shown).
Similarly, no significant interactions between the vitamin D or calcium nutrient-level variables
and menopausal status were observed (Table 5). There were also no statistically significant
interactions and the ORs did not differ substantially by menopausal status for intake of milk,
margarine, and other fish or for duration/frequency of calcium, vitamin D or multivitamin use
(data not shown). However, a significant interaction was observed between tuna intake and
menopausal status (p = 0.02); no association was observed among premenopausal women,
although a significant inverse association existed among postmenopausal women (comparing
highest to lowest category OR: 0.78; 95% CI: 0.65, 0.93). Further tests for interactions revealed
no significant interactions between any of the three vitamin D nutrient-level variables and BMI
or hormone replacement therapy use (among postmenopausal women only).
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DISCUSSION
This study provides some evidence that vitamin D supplement use, but not intake from food
alone or combined intake, is independently associated with reduced breast cancer risk. Our data
suggest there may be a threshold at >10 µg/day (400 IU/day) for vitamin D supplements. No
significant ORs were observed for calcium intake from foods, supplements or total combined
intake and breast cancer risk; however, a significant inverse trend was observed across categories
of calcium supplement use. Measurement of vitamin D or calcium from foods compared to
supplements may be more susceptible to misclassification (potentially biasing results towards the
null). It is also possible that foods containing vitamin D and calcium contain other detrimental
components that counteract the potential benefits from vitamin D (e.g., dietary fat in milk) (29,
32). We cannot rule out the possibility that our observed associations were due to chance or
residual confounding; however, multivitamin use was not associated with breast cancer risk
suggesting the associations are not due to residual confounding by other unmeasured healthy
lifestyle traits among supplement users.
A recent meta-analysis suggested a trend towards reduced breast cancer risk at vitamin D
minimum intakes of 10 µg/day or greater but no association overall (RR: 0.98; 95% CI: 0.93,
1.03) (12). Several large cohort studies (34, 35, 45-47) have all reported some inverse
associations between vitamin D intake (from diet and/or supplements) and breast cancer risk, but
none have reported significant inverse associations consistently for all sources of vitamin D
intake measured or among all women (e.g., pre- and post-menopausal). The Women’s Health
Initiative trial of breast cancer risk among postmenopausal women randomized to 10 µg vitamin
D plus 1000 mg calcium daily or a placebo (HR: 0.96; 95% CI: 0.85, 1.09) (22) was consistent
with our results that vitamin D ≤10 µg/day (400 IU/day) was not associated with breast cancer
risk. A smaller trial of all cancer sites combined (few breast cancer cases) among
postmenopausal women randomized to 27.5 µg (1100 IU) vitamin D plus 1500 mg calcium per
day versus placebo observed a significant reduction in cancers (RR: 0.40; 95% CI: 0.20, 0.82); a
reduction in risk of borderline statistical significance was also observed among calcium only
(RR: 0.53; 95% CI: 0.27, 1.03) (48).
Only one (36) of the previous large population-based case-control studies of vitamin D intake
and breast cancer risk (33, 36, 37) included vitamin D from supplements and found reduced
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breast cancer risk associated with vitamin D supplement use earlier in life only (36). In contrast
to our null results for dietary vitamin D, other case-control studies of diet only (during
adulthood) have found significant inverse associations above the threshold of only 5 µg (200 IU)
daily among European populations (33, 37).
Our results do not suggest an interaction between calcium and vitamin D intake and these two
variables did not confound one another. Elsewhere, the association between dietary calcium and
breast cancer risk was attenuated and no longer statistically significant after adjustment for
vitamin D (33). Although we observed an independent dose-response trend for calcium
supplements and reduced breast cancer risk, we cannot rule out the possibility of residual
confounding from unmeasured vitamin D intake; many calcium supplement users also consume
vitamin D (usually for bone health) and 19% of calcium supplement users did not report taking
vitamin D supplements. Previous studies (33-35) found no interaction between vitamin D and
calcium (33, 34) or an interaction among postmenopausal women only (35), such that an inverse
association between calcium and breast cancer risk was observed only among the highest
category of vitamin D intake. A recent prospective study of serum calcium (with no measures of
vitamin D) and breast cancer incidence found an inverse association among premenopausal
women only (49). The independent association between calcium and breast cancer risk requires
further investigation.
Studies evaluating the vitamin D-breast cancer relationship by menopausal status have reported
inconsistent results (34-37). Consistent with previous case-control studies (36, 37), we found no
significant interactions. Unfortunately, a small proportion of premenopausal women in our study
were taking vitamin D supplements >10 µg/day (400 IU/day). In contrast, cohort studies have
reported inverse associations between dietary vitamin D intake and breast cancer risk only
among premenopausal women (34, 35) and the potential for increased risk among
postmenopausal women (35). Some studies of vitamin D and breast cancer risk (35, 46, 47), but
not all (50), have reported differences by hormone receptor status. Data on hormone receptor
status is not currently available for our study.
While it has been hypothesized that vitamin D exposure during adolescence may be most
important, we were unable to examine early life dietary vitamin D intake, and the current
evidence is inconsistent (34, 36, 51, 52). Vitamin D may also have some short term effects in
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Paper 2: Vitamin D, Calcium and Breast Cancer Risk (Anderson et al, 2010)
reducing breast cancer risk (18). Among two large cohort studies the inverse associations with
vitamin D and breast cancer were stronger when recent measures of vitamin D (34) or cases only
within 5-years of baseline measurement of vitamin D intake were considered (47), possibly
indicating a role for vitamin D in slowing disease progression.
As with all observational studies, the potential for measurement error and other biases may limit
internal validity. Sun exposure is the primary source of vitamin D in sunny populations;
however, this study population resides north of 43◦ latitude (Ontario, Canada) where sun
exposure is insufficient for vitamin D production at least 4 months of the year (53) and skin is
mostly covered up for at least half of the year. Most studies among Canadian and other northern
populations have found that dietary vitamin D intake is a significant predictor of 25(OH)D (54-
58), but not all (59). There is the potential for misclassification, likely nondifferential, resulting
from the absence of a complete measure of vitamin D (i.e., a composite measure including
diet/supplements and sun exposure). Our study did not measure the different forms of vitamin D;
however, the majority of intake would likely be vitamin D3, which is found in fatty fish and
commonly used in supplements and food fortification in Canada, versus vitamin D2 from plant
sources. Recall bias is likely minimal in this study as there is no obvious reason why cases or
controls would differentially recall their recent intake of vitamin D or calcium. Similarly,
survival bias is expected to be minimal since there is a high rate of survival among breast cancer
cases in Ontario and women were recruited, on average, within 1 year of diagnosis.
This study has several strengths, including a large sample size, population-based recruitment of
cases and controls and high response rates. Overall the study results do not support an
association between vitamin D or calcium from food or total intake and breast cancer risk.
However, vitamin D intake levels are relatively low in this study and supplemental vitamin D
intake greater than 10 µg/day (400 IU/day) was associated with reduced breast cancer risk.
Future studies are needed among populations with higher intakes, possibly carried out as a
chemoprevention/intervention trial. Additional research is also needed to determine if the
association between vitamin D supplements and reduced breast cancer risk varies by timing of
exposure, menopausal status, and tumor characteristics (including hormone receptor status, stage
of diagnosis).
Acknowledgements
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The authors thank the study coordinator, Noori Chowdhury, for her dedication to this study.
The contributions of each author to the manuscript were as follows: LNA drafted the manuscript
and conducted the statistical analysis. MC, RV, and JAK, provided significant advice on the
study design. MC is the PI and obtained funding for the initial Ontario Women’s Diet and Health
Study. All authors reviewed, revised and approved the manuscript.
LNA, MC and JAK have no conflict of interest. RV has been a consultant or speaker for Carlson
Laboratories, DiaSorin, and Yoplait, and is related to a person employed in the dietary
supplement industry.
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Table 1. Distribution of selected characteristics and age-group adjusted odds ratio (OR) estimates
among 3,101 breast cancer cases and 3,471 controls in the Ontario Women’s Diet and Health
study
Variable Cases Controls Model 1 1 OR (95% CI)
n (%) Age-group2 25 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 - 69 70 - 74
181 (6) 278 (9) 380 (12) 504 (16) 511 (16) 450 (15) 439 (14) 358 (12)
316 (9) 367 (11) 482 (14) 512 (15) 470 (14) 471 (14) 508 (15) 345 (10)
N/A
Breast cancer in a 1st degree relative No Yes
2389 (77) 635 (21)
2973 (86) 415 (12)
1.00 1.86 (1.63, 2.13)
Age at menarche ≤ 11 12 13 ≥ 14
594 (20) 753 (25) 846 (28) 782 (26)
615 (18) 823 (25) 973 (29) 939 (28)
1.00 0.95 (0.81, 1.10) 0.90 (0.80, 1.04) 0.86 (0.74, 0.99)
Age at menopause3 ≤ 45 45 - 49 ≥50 premenopausal
572 (19) 511 (17) 987 (33) 957 (32)
727 (21) 550 (16) 888 (26) 1237 (36)
1.00 1.15 (0.98, 1.36) 1.37 (1.19, 1.59) 1.15 (0.95, 1.38)
Parity Nulliparous 1 2 - 3 > 4
543 (18) 421 (14) 1677 (55) 415 (14)
404 (12) 413 (12) 2068 (61) 524 (15)
1.00 0.75 (0.62, 0.91) 0.57 (0.49, 0.66) 0.53 (0.44, 0.64)
Pack-years smoked Never smoker ≤4 5-12 13-25 ≥26
1572 (52) 360 (13) 336 (12) 388 (14) 393 (14)
1791 (52) 409 (12) 365 (11) 424 (13) 432 (14)
1.00 1.04 (0.88, 1.21) 1.09 (0.92, 1.28) 1.03 (0.88, 1.20) 0.97 (0.83, 1.13)
BMI4 kg/m2 < 24.9 25.0 - 29.9 ≥ 30
1344 (44) 1012 (33) 723 (24)
1535 (45) 1171 (34) 740 (21)
1.00 0.94 (0.84, 1.05) 1.06 (0.93, 1.20)
Moderate physical activity age 20-39 0 – 3 times per month
316 (11)
322 (10)
1.00
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1– 2 times per week 3– 5 times per week >5 times per week
698 (23) 1044 (35) 913 (31)
722 (22) 1261 (38) 1021 (31)
1.00 (0.83, 1.21) 0.85 (0.71, 1.01) 0.90 (0.75, 1.08)
Moderate physical activity age 40-59 0 – 3 times per month 1– 2 times per week 3– 5 times per week >5 times per week Age not yet reached
408 (13) 654 (22) 1115 (37) 669 (22) 181 (6)
375 (11) 711 (21) 1179 (35) 800 (24) 316 (9)
1.00 0.84 (0.70, 1.00) 0.85 (0.73, 1.00) 0.75 (0.63, 0.90) NA
1 Age-group adjusted odds ratios (95% CIs) calculated using multivariate logistic regression. 2 Age at cancer diagnosis for cases and age on 15Nov2002 for controls 3 Women were classified as premenopausal if they had a menstrual period within 12 months of their diagnosis/referent date 4 BMI weight two years ago (kg) divided by height in metres squared
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Table 2. Distribution of breast cancer cases (n = 3101) and controls (n = 3471) and odds ratio (OR)
estimates for intake of selected foods and supplements (frequency and duration) known to contain
vitamin D or calcium among Ontario women
Food/supplement intake (serving size)
Cases Controls Model 11 OR (95% CI)
Model 22 OR (95% CI)
n (%) Glasses of milk (244g (8.5 oz.)) < 1 per week 1 per week 2 - 3 per week 4 - 6 per week ≥ 1 per day P trend
780 (26) 447 (15) 549 (18) 657 (22) 596 (20)
879 (26) 573 (17) 605 (18) 697 (21) 640 (19)
1.00 0.91 (0.77, 1.06) 1.06 (0.91, 1.23) 1.10 (0.96, 1.28) 1.10 (0.95, 1.27) 0.17
1.00 0.94 (0.79, 1.12) 1.10 (0.93, 1.30) 1.13 (0.97, 1.34) 1.15 (0.97, 1.37) 0.04
Margarine (5g (one teaspoon))3 Never or few times per year < 0.5 per week 0.5 – 5 per week 6 – 13 per week ≥ 2 per day P trend
996 (33) 218 (7) 632 (21) 867 (29) 274 (9)
1097 (33) 264 (8) 691 (21) 975 (29) 303 (9)
1.00 0.95 (0.77, 1.16) 1.03 (0.89, 1.18) 0.96 (0.84, 1.09) 0.96 (0.79, 1.15) 0.67
1.00 0.88 (0.70, 1.10) 1.04 (0.89, 1.21) 0.96 (0.83, 1.10) 0.99 (0.80, 1.27) 0.79
Tuna (51g (1/4 cup)) Never or few times per year 1 per month 2 per month 1 per week ≥ 2 per week P trend
836 (28) 521 (17) 545 (18) 547 (18) 580 (19)
884 (26) 557 (16) 620 (18) 652 (19) 674 (20)
1.00 0.99 (0.85, 1.15) 0.94 (0.81, 1.09) 0.89 (0.77, 1.03) 0.91 (0.78, 1.05) 0.95
1.00 1.04 (0.88, 1.24) 0.96 (0.81, 1.14) 0.92 (0.77, 1.08) 0.93 (0.78, 1.10) 0.27
Other fish4 (43 g (1/4 cup)) Never or few times/year 1 per month 2 per month 1 per week ≥ 2 per week P trend
758 (25) 405 (13) 608 (20) 839 (28) 405 (13)
890 (26) 516 (15) 657 (19) 877 (26) 440 (13)
1.00 0.92 (0.78, 1.09) 1.07 (0.92, 1.24) 1.10 (0.96, 1.26) 1.05 (0.89, 1.24) 0.98
1.00 0.93 (0.78, 1.12) 1.09 (0.92, 1.29) 1.07 (0.91, 1.25) 0.96 (0.79, 1.16) 0.59
Fish type consumed most often Did not eat fish or missing White (e.g., haddock, cod) Fatty (e.g., salmon) P trend
523 (17) 1713 (55) 865 (28)
624 (18) 1865 (54) 982 (28)
1.00 1.08 (0.94, 1.23) 1.02 (0.88, 1.19) 0.93
1.00 1.13 (0.97, 1.33) 1.05 (0.88, 1.25) 0.88 continued…
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Frequency of vitamin D supplement use5 Did not use or <1 per month ≥1 per month to ≤6 per week Every day P trend
2701 (87) 114 (4) 286 (9)
2984 (86) 137 (4) 350 (10)
1.00 0.89 (0.69, 1.15) 0.84 (0.71, 1.00) 0.03
1.00 0.85 (0.64, 1.14) 0.84 (0.70, 1.01) 0.04
Duration of vitamin D supplement use Did not use ≤ 2 years 3 -9 years ≥ 10 years P trend
2730 (88) 123 (4) 162 (5) 86 (3)
3022 (87) 135 (4) 201 (6) 113 (3)
1.00 0.98 (0.76, 1.26) 0.83 (0.67, 1.03) 0.78 (0.59, 1.04) 0.03
1.00 0.93 (0.70, 1.23) 0.85 (0.67, 1.08) 0.83 (0.61, 1.14) 0.08
Frequency of calcium supplement use6 Did not use or <1 per month ≥1 per month to ≤6 per week Every day P trend
2064 (67) 247 (8) 790 (25)
2269 (65) 298 (9) 904 (26)
1.00 0.89 (0.74, 1.07) 0.88 (0.78, 0.99) 0.02
1.00 0.91 (0.74, 1.11) 0.88 (0.77, 1.00) 0.04
Duration of calcium supplement use Did not use ≤ 2 years 3 - 9 years ≥ 10 years P trend
2182 (70) 248 (8) 457 (15) 214 (7)
2400 (69) 332 (10) 473 (14) 266 (8)
1.00 0.80 (0.67, 0.95) 0.98 (0.85, 1.14) 0.81 (0.67, 0.98) 0.05
1.00 0.75 (0.62, 0.92) 1.00 (0.85, 1.17) 0.84 (0.68, 1.04) 0.14
Frequency of multivitamin use Did not use or <1 per month ≥1 per month to ≤6 per week Every day P trend
1930 (62) 343 (11) 828 (27)
2179 (63) 380 (11) 912 (26)
1.00 1.05 (0.90, 1.23) 1.00 (0.89, 1.11) 0.99
1.00 1.10 (0.92, 1.31) 0.99 (0.87, 1.12) 0.95
Duration of multivitamin use Did not use ≤ 2 years 3 - 9 years ≥ 10 years P trend
2023 (65) 297 (10) 444 (14) 337 (11)
2256 (65) 320 (9) 539 (16) 356 (10)
1.00 1.05 (0.89, 1.25) 0.91 (0.79, 1.05) 1.01 (0.86, 1.19) 0.57
1.00 0.99 (0.82, 1.19) 0.95 (0.81, 1.11) 0.99 (0.83, 1.18) 0.66
1 Age group adjusted odds ratios (95% CIs) calculated using multivariate logistic regression (note: 39 variables were evaluated as potential confounders and none were identified as confounders i.e., their inclusion in the model did not change the OR>10%).
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2 Adjusted for age group, education, age at menarche, age at first live birth, parity, menopausal status, breast cancer in first degree relative, total energy intake (kcal), BMI, smoking (packyears), moderate physical activity during ages 20-39, moderate physical activity during ages 40-59, time spent outdoors per week during age 20-39, and time spent outdoors per week during ages 40-59. Odds ratios (95% CIs) calculated using multivariate logistic regression. 3 Intake of margarine (not butter) on foods such as bread or vegetables 4 Not fried fish 5 Vitamin D as a single product supplement or cod liver oil 6 Calcium as a single vitamin or combined
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Table 3. Distribution of breast cancer cases (n = 3101) and controls (n = 3471) and odds ratio (OR)
estimates for derived vitamin D and calcium nutrient intake from food, supplements and total
combined among Ontario women
Vitamin D or calcium intake Cases Controls Model 11
Model 22
n (%)
Total combined vitamin D3,4 (µg/day) < 2.5 2.5 – 4.9 5.0 – 9.9 10.0 – 14.9 ≥ 15.0 P trend
384 (13) 607 (20) 731 (24) 708 (23) 632 (21)
443 (13) 702 (20) 831 (24) 734 (21) 717 (21)
1.00 1.02 (0.86, 1.21) 1.03 (0.87, 1.22) 1.10 (0.87, 1.22) 0.99 (0.83, 1.18) 0.96
1.00 1.01 (0.82, 1.25) 1.01 (0.81, 1.26) 1.10 (0.88, 1.37) 0.99 (0.78, 1.26) 0.87
Vitamin D from foods (µg/day) < 2.5 2.5 – 4.9 5.0 – 9.9 ≥ 10.0 P trend
638 (21) 1036 (34) 1066 (35) 322 (11)
717 (21) 1182 (34) 1197 (35) 331 (10)
1.00 1.00 (0.87, 1.14) 1.01 (0.88, 1.15) 1.10 (0.91, 1.33) 0.31
1.00 0.97 (0.82, 1.14) 1.01 (0.85, 1.21) 1.13 (0.88, 1.45) 0.23
Vitamin D from all supplements5 (µg/day) 0 < 10.0 10.0 > 10.0 P trend
1679 (55) 378 (12) 847 (28) 158 (5)
1893 (55) 414 (12) 912 (27) 208 (6)
1.00 1.05 (0.90, 1.22) 1.01 (0.90, 1.13) 0.80 (0.64, 0.99) 0.17
1.00 1.08 (0.90, 1.28) 0.98 (0.85, 1.13) 0.76 (0.59, 0.98) 0.11
Total combined calcium3 (mg/day) < 500 500 - 749 750 - 999 1000 - 1499 >1500 P trend
453 (15) 539 (18) 472 (15) 698 (23) 900 (29)
501 (15) 641 (19) 504 (15) 798 (23) 983 (28)
1.00 0.94 (0.80, 1.12) 1.06 (0.88, 1.26) 0.97 (0.82, 1.14) 0.97 (0.83, 1.14) 0.81
1.00 0.99 (0.80, 1.22) 1.13 (0.90, 1.43) 1.06 (0.85, 1.33) 1.03 (0.82, 1.30) 0.95
Calcium from foods (mg/day) < 500 500 - 749 750 - 999 >1000 P trend
771 (25) 803 (26) 627 (20) 861 (28)
833 (24) 972 (28) 695 (20) 927 (27)
1.00 0.90 (0.79, 1.03) 0.99 (0.86, 1.15) 1.03 (0.90, 1.18) 0.32
1.00 0.93(0.78, 1.10) 1.07 (0.88, 1.30) 1.17 (0.95, 1.45) 0.05
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1 Age group adjusted odds ratios (95% CIs) calculated using multivariate logistic regression. (note: 39 variables were evaluated as potential confounders and none were identified as confounders i.e., their inclusion in the model did not change the OR>10%) (N = 6489). 2 Adjusted for age group, education, age at menarche, age at first live birth, parity, menopausal status, breast cancer in first degree relative, total energy intake (kcal), BMI, smoking (packyears), moderate physical activity during ages 20-39, moderate physical activity during ages 40-59, time spent outdoors per week during age 20-39, time spent outdoors per week during ages 40-59, total calcium intake (included for vitamin D models only) and total vitamin D intake (included for calcium models only). Odds ratios (95% CIs) calculated using multivariate logistic regression (N = 5489). 3 From food and supplements 4 Vitamin D: 10µg = 400 IU 5 Multivitamins and vitamin D single product supplements or cod liver oil. 6 Multivitamins and calcium supplements
Calcium from all supplements6 (mg/day) 0 < 1000 1000 > 1000 P trend
1435 (47) 837 (27) 387 (13) 403 (13)
1612 (47) 911 (27) 433 (13) 471 (14)
1.00 1.03 (0.91, 1.16) 0.92 (0.78, 1.07) 0.88 (0.76, 1.03) 0.05
1.00 0.97 (0.82, 1.14) 0.86 (0.72, 1.03) 0.85 (0.68, 1.05) 0.04
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Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for vitamin D
intake variables stratified by total calcium intake
Total combined calcium <1000 (mg/day)
Total combined calcium ≥1000 (mg/day)
Vitamin D (µg/day)
Cases n = 1464
Controls n = 1646
Model 11 Cases n=1598
Controls n = 1781
Model 11 P 2
n (%) n (%) Total combined3,4 < 2.5 2.5 – 4.9 5.0 – 9.9 10.0 – 14.9 ≥ 15.0
321 (22) 473 (32) 360 (25) 229 (16) 81 (6)
354 (22) 550 (33) 421 (26) 243 (15) 78 (5)
1.00 0.98 (0.81, 1.19) 0.96 (0.78, 1.19) 1.02 (0.81, 1.30) 1.08 (0.76, 1.53)
63 (4) 134 (8) 371 (23) 479 (30) 551 (34)
89 (5) 152 (9) 410 (23) 491 (28) 639 (36)
1.00 1.25 (0.84, 1.87) 1.30 (0.91, 1.85) 1.38 (0.98, 1.96) 1.22 (0.87, 1.72)
0.49
Foods < 2.5 2.5 – 4.9 5.0 – 9.9 ≥ 10.0
461 (31) 639 (44) 348 (24) 16 (1)
481 (29) 743 (45) 407 (25) 15 (1)
1.00 0.92 (0.78, 1.08) 0.89 (0.73, 1.08) 1.11 (0.53, 2.27)
177 (11) 397 (25) 718 (45) 306 (19)
236 (13) 439 (25) 790 (44) 316 (18)
1.00 1.21 (0.95, 1.54) 1.24 (1.00, 1.55) 1.33 (1.04, 1.71)
0.13
Supplements5 0 < 10.0 10.0 > 10.0
1032(70) 178 (12) 233 (16) 21 (1)
1193(72) 190 (12) 238 (15) 25 (2)
1.00 1.14 (0.91, 1.43) 1.08 (0.88, 1.32) 0.87 (0.48, 1.57)
647 (40) 200 (13) 608 (38) 143 (9)
700 (39) 224 (13) 666 (37) 191 (11)
1.00 0.97 (0.78, 1.20) 0.97 (0.83, 1.13) 0.79 (0.62, 1.01)
0.64
1 Age group adjusted odds ratios (95% CI) calculated using multivariate logistic regression. 2 Likelihood ratio test for interactions 3 From food & supplements 4 Vitamin D: 10µg = 400 IU 5 Multivitamins and vitamin D single product supplements or cod liver oil.
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Table 5. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for vitamin D
and calcium variables stratified by menopausal status among Ontario women
Premenopausal Postmenopausal Variable Cases
n = 948 Controls n =1226
Model 11 Cases n =2111
Controls n =2196
Model 11 P2
n (%) n (%)
Vitamin D
(µg/day)
Total combined3,4 < 2.5 2.5 – 4.9 5.0 – 9.9 10.0 – 14.9 ≥ 15.0 P trend
112 (12) 217 (23) 260 (27) 208 (22) 154 (16)
166 (13) 301 (25) 351 (29) 229 (19) 184 (15)
1.00 1.09 (0.81, 1.47) 1.10 (0.83, 1.47) 1.35 (0.99, 1.84) 1.23 (0.89, 1.70) 0.07
272 (13) 390 (18) 472 (22) 502 (24) 478 (23)
277 (13) 403 (18) 482 (22) 505 (23) 534 (24)
1.00 1.00 (0.80, 1.24) 1.00 (0.81, 1.24) 1.01 (0.82, 1.24) 0.90 (0.73, 1.11) 0.26
0.41
Foods 0 < 10.0 10.0 > 10.0 P trend
188 (20) 325 (34) 332 (35) 106 (11)
240 (20) 452 (37) 421 (34) 118 (10)
1.00 0.92 (0.73, 1.17) 1.00 (0.79, 1.27) 1.16 (0.84, 1.61) 0.11
451 (21) 711 (34) 736 (35) 216 (10)
477 (22) 732 (33) 778 (35) 214 (10)
1.00 1.03 (0.88, 1.22) 1.01 (0.86, 1.19) 1.07 (0.85, 1.34) 0.91
0.66
Supplements5 0 < 10.0 10.0 > 10.0 P trend
562 (59) 158 (17) 202 (21) 29 (3)
773 (63) 178 (15) 245 (20) 35 (3)
1.00 1.24 (0.97, 1.58) 1.12 (0.90, 1.39) 1.07 (0.64, 1.78) 0.38
1117 (53) 222 (11) 640 (30) 135 (6)
1124 (51) 237 (11) 659 (30) 181 (8)
1.00 0.94 (0.77, 1.16) 0.96 (0.84, 1.10) 0.74 (0.58, 0.94) 0.04
0.24
Calcium
(mg/day)
Total combined3 < 500 500 - 749 750 - 999 1000 - 1499 >1500 P trend
133 (14) 191 (20) 180 (19) 233 (25) 214 (23)
190 (15) 277 (23) 217 (18) 318 (26) 229 (19)
1.00 1.00 (0.75, 1.34) 1.19 (0.88, 1.60) 1.05 (0.79, 1.38) 1.32 (0.99, 1.77) 0.04
320 (15) 348 (16) 293 (14) 466 (22) 687 (33)
312 (14) 366 (17) 287 (13) 480 (22) 756 (34)
1.00 0.92 (0.74, 1.14) 1.00 (0.79, 1.25) 0.94 (0.77, 1.15) 0.87 (0.72, 1.05) 0.13
0.13
Foods < 500 500 - 749 750 - 999 >1000 P trend
201 (21) 232 (24) 206 (22) 312 (33)
250 (20) 361 (29) 252 (20) 368 (30)
1.00 0.80 (0.62, 1.02) 1.01 (0.78, 1.31) 1.06 (0.84, 1.35) 0.15
570 (27) 572 (27) 422 (20) 550 (26)
585 (27) 612 (28) 443 (20) 561 (25)
1.00 0.96 (0.81, 1.12) 0.98 (0.81, 1.17) 1.01 (0.85, 1.19) 0.84
0.36
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Supplements6 0 < 1000 1000 > 1000 P trend
527 (55) 309 (32) 48 (5) 67 (7)
709 (58) 383 (31) 60 (5) 79 (6)
1.00 1.09 (0.90, 1.32) 1.03 (0.69, 1.54) 1.09 (0.77, 1.55) 0.68
908 (43) 530 (25) 340 (16) 336 (16)
906 (41) 529 (24) 373 (17) 393 (18)
1.00 0.99 (0.85, 1.15) 0.89 (0.75, 1.06) 0.83 (0.70, 0.99) 0.02
0.68
1 Age-group adjusted odds ratios (95% CI) calculated using multivariate logistic regression
2 Likelihood ratio test for interaction (age-adjusted model only)
3 From food & supplements 4 Vitamin D: 10µg = 400 IU 5 Multivitamins and vitamin D single product supplements or cod liver oil 6 Multivitamins and calcium supplements
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4.4 Paper 3: Ultraviolet Sunlight Exposure and Breast Cancer Risk: A Population Based Case-Control Study in Ontario
Not yet submitted for publication.
Laura N. Anderson, Michelle Cotterchio, Victoria A. Kirsh, and Julia A. Knight
Short title: Sunlight and breast cancer risk
ABSTRACT
Recent studies suggest vitamin D intake may be associated with reduced breast cancer risk, but
most studies have evaluated only dietary vitamin D intake. The associations between ultraviolet
(UV) radiation from sunlight, factors related to cutaneous vitamin D production and breast
cancer risk were evaluated in a population-based case-control study among Ontario women.
Exposure was assessed during 4 periods of life, including adolescence, via mailed lifestyle and
food frequency questionnaires for all cases (n=3,101) and controls (n=3,471). Multivariate
logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI).
Time spent outdoors from age 40 to 59 was associated with reduced breast cancer risk (>21
versus ≤6 hours/week: OR = 0.74, 95% CI: 0.61, 0.88), and significant inverse associations were
also observed for exposure during 3 other periods of life. Sun protection practices and UV
radiation of residence were not associated with breast cancer risk. A combined solar vitamin D
score, including all variables related to vitamin D production, was significantly associated with
reduced breast cancer risk. These associations were not confounded or modified by menopausal
status, dietary vitamin D intake or physical activity. This study suggests that factors related to
increased cutaneous production of vitamin D are associated with reduced breast cancer risk.
Keywords: sunlight; vitamin D; breast neoplasms; case-control studies
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It has been hypothesized that vitamin D, a potentially modifiable factor, is associated with
reduced risk of multiple types of cancer including breast cancer (1, 2). Vitamin D is produced in
the skin through the conversion of 7-dehydrocholesterol to previtamin D3 following sufficient
exposure to ultraviolet (UV) B radiation from sunlight. This process is dependent upon many
extrinsic factors that affect UVB radiation (e.g., geographic location, time of day and season) and
intrinsic, person-specific, factors (e.g., time spent outdoors, sun protection practices, skin color)
(3, 4). Vitamin D is also found in supplements and few foods (e.g., fatty fish, fortified milk) (5).
Vitamin D from sun, diet and supplements undergoes hydroxylation in liver to the circulating
form 25 hydroxyvitamin D [25(OH)D]. Breast cells, among other cells in the body, are capable
of locally converting 25(OH)D to the active hormone 1,25dihydroxyvitamin D [1,25(OH)2D]
which has been shown in laboratory studies to have anti-cancer properties (6-8).
Ecologic studies have shown latitude (inversely correlated with sun exposure) is positively
associated and UVB irradiance is negatively associated with breast cancer incidence (9) or
mortality (10-12). Many observational studies of diet or supplement intakes (13-20) have found
inverse associations between vitamin D intake and breast cancer risk although often among
specific subgroups only. Some studies have found serum 25(OH)D levels are associated with
reduced breast cancer risk (21-25), but not all (26-28).
Despite the ability of vitamin D to be produced in the skin following sun exposure, fewer studies
have evaluated the associations between sun exposure related variables and breast cancer risk
(13, 19, 29-31). Time spent outdoors has been inversely associated with breast cancer risk in
most (13, 19, 30) but not all studies (29). Sunscreen, sunburns/skin damage, winter sun trips or
sunlamp/solarium use have generally not been associated with breast cancer risk (13, 19, 31),
although in one study limb coverage was associated with increased risk (19). Studies of UV or
solar radiation and breast cancer risk in the US are inconsistent (13, 30). No previous breast
cancer studies have created one measure of vitamin D from sunlight that combines person-
specific factors (e.g., time outdoors, skin color, or sun protection practices) and environmental
sun exposure. Challenges to the measurement of vitamin D in population-based studies has been
reviewed (32).
The objective of this study was to evaluate the associations between breast cancer risk and
variables related to the production of vitamin D from sunlight (time spent outdoors, ultraviolet
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radiation at residence, skin color and sun protection practices) measured at different 4 periods of
life in a large, population-based case-control study of women in Ontario, Canada. In addition to
evaluating the contributions of each vitamin D related variable, a solar vitamin D score that
combines all variables was derived and evaluated in relation to breast cancer risk. Secondary
objectives of this study are to evaluate the combined effects of vitamin D exposure from sunlight
and intake from food and supplements, and to evaluate a predictive measure of serum 25(OH)D.
MATERIALS AND METHODS
Study design
A population based case-control study was conducted among women living in Ontario, Canada,
as previously described (33). Breast cancer cases were identified from the Ontario Cancer
Registry between 2002 and 2003. Cases were women between the ages of 25-74 with a first
pathologically confirmed cancer of the breast. Of the 4,109 cases with physician consent for
contact 3,101 completed the study (75% response rate). Controls were recruited by random digit
dialing of households in Ontario and frequency age-matched 1:1 to cases. Of 4,352 households
where an eligible control was identified 3,471 women completed the study (80% response rate).
Ethics approval for this study was obtained from the University of Toronto Research Ethics
Board.
Data collection and exposure variables
Study participants completed a 20-page mailed self-administered risk factor questionnaire and a
modified Block 1998 Food Frequency Questionnaire. Ethnicity was used as a proxy for skin
color. The overwhelming majority (90%) of study participants were Caucasian ethnicity (proxy
for lighter skin color) and thus skin color was categorized as Caucasian versus non-Caucasian.
Variables related to sun exposure (weekday time outdoors, weekend time outdoors, sun
protection and location of residence) were measured at four periods of life: teenage years, 20-
30s, 40s-50s and 60s-74.
To capture time spent outdoors, participants were asked “On a typical weekday in the months
April –October about how many hours per day did you spend outside?” response options
included: less than one hour, 1 to 2 hours, 3 to 4 hours, more than 4 hours. The same question
and response options were repeated for weekend (Saturday and Sunday) exposure. Only
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summertime sun exposure data were collected since wintertime sun exposure in Ontario is not
sufficient for the production of vitamin D (34). The variable “Hours outdoors per week” was
created by weighting and summing weekday and weekend exposures. To measure sun protection
practices all participants were asked “When in the sun did you wear sunscreen or protective
clothing, such as long sleeves, etc.?” response options included: never, sometimes, and always.
To obtain location of residence respondents were asked to report where they lived during 4
specific age periods (a full residential history including duration was not collected).
All women resided in Ontario when they participated in the study but many lived outside the
province at earlier periods of life. Latitude and longitude were obtained for all cities and
provinces/states of residence at the 4 time periods from www.geocoder.ca . There were 1628
(25%) study participants who reported only country or province/state of residence during at least
one period of life; these participants were assigned the coordinates of the most populated city in
their country or region. When multiple locations were reported for a given time period only the
first location was used. There were 86 (1%) participants who lived in the southern hemisphere
during at least one life period; for these women the reporting period would have corresponded to
wintertime sun exposure. The analysis was repeated with these women excluded and the results
did not change.
Latitude and longitude were used to obtain UV radiation data from National Aeronautics and
Space Administration’s (NASA) Total Ozone Mapping Spectrometer (TOMS) (35). Ground
level UV irradiance data is calculated from TOMS onboard spacecraft instrument measures of
atmospheric UV, total ozone, surface reflectivity and cloud cover. Monthly average noon-time
erythemal UV for June 2003 was selected for use in this study. These data are weighted using the
McKinlay-Diffey erythemal action spectra (36) which weights radiation in the UVA (315-400
nm) and UVB (280-315 nm) wavelengths based on the time required to induce erythema (skin
reddening); shorter rays are more likely to induce erythema. Cutaneous vitamin D is dependent
on only UVB exposure and there is a vitamin D-specific action spectra based on human skin’s
ability to produce previtamin D3 (37), but vitamin D weighted UV is not currently available from
TOMS. Although vitamin D production does not always directly correspond with erythemal UV
estimates (38), the erythemal action spectra closely approximates the vitamin D action spectra in
summer north of 42⁰ (Ontario, Canada) (39, 40).
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Derivation of solar vitamin D score
To derive a solar vitamin D score (i.e., one variable that takes into consideration known
determinants of cutaneous vitamin D production) an algorithm was created based on the
available literature on determinants of cutaneous production of vitamin D. Weighted proportions
based on the knowledge that darker skin color and use of sun protection practices limits vitamin
D production were applied to “UV hours per week” (the product of erythemal UV and weekly
time spent outdoors). This algorithm (shown below) was applied to each of the four age periods
of exposure. Sensitivity analyses were conducted to evaluate other plausible values.
Solar vitamin D score (mW/m2 • hrs) = UV hours per week (mW/m2 • hrs) x skin color weight x sun protection weight
Where:
UV hours/week = Erythemal UV radiation (mW/m2) x Time spent outdoors (hrs/ week); skin color weight = 1 if ethnicity = Caucasian; skin color weight = 1/3 if ethnicity = non-Caucasian; sun protection weight = 1 if sun protection use = never; sun protection weight = 2/3 if sun protection use = sometimes; sun protection weight = 1/3 if sun protection use= always.
It has been estimated that people with highly pigmented (darker) skin colors in comparison to
lighter require at least 3 times the amount of sunlight to produce equivalent vitamin D (41)(4),
although estimates range from upward to 5 to 10 times (42). Thus, an accommodation factor of
one third was chosen to weight UV production for non-Caucasian individuals. In regards to sun
protection practices, sunscreen and clothing both have the potential to block all vitamin D
production. However, it is unlikely that women apply a complete application of sunscreen (i.e., a
thorough application to all locations of the body prior to going outdoors with frequent
reapplication) (as reviewed by (43)) or fully cover-up with clothing. Sunscreen use does not
predict 25(OH)D levels (44, 45), but coverage of arms and legs does significantly predict lower
25(OH)D levels (44). Therefore, within this population it was assumed that even individuals who
report “always” using sun protection are still accessing one third of the available vitamin D
generating UV light, in comparison to participants who reported “never” using sunscreen or
protective clothing. Correspondingly, a lesser decrease was estimated for participants who
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responded “sometimes protected” hence a weighting factor of two thirds was assigned to these
respondents.
In addition to our proposed algorithm, another combined measure was created using cross-
classification. UV exposure, time outdoors, sun protection practices and skin color were
classified as high versus low (1 vs 0) based on vitamin D production potential and a combined
additive score was created. Also, we evaluated the use of a previously published algorithm for
the measurement of predicted serum 25(OH)D values among men in the Health Professionals’
Follow-Up Study (46). This algorithm was created using multiple linear regression analysis to
develop a predictive model among a subset of the sample with serum 25(OH)D measures and
included: dietary vitamin D, supplemental vitamin D, BMI, race, physical activity (included as a
proxy for time spent outdoors), and region of residence. The model was then applied to predict
25(OH)D levels among men with no serum data in the Health Professionals’ Study and has also
been previously applied to women in the Nurses’ Health Study (47, 48).
Statistical analysis
Age-adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated using
unconditional logistic regression. Statistical analysis was conducted using SAS 9.1. All tests
were 2-sided and statistical significance was defined as P < 0.05. Test for linear trend was
calculated by treating the median intake for each exposure category as a continuous variable in
the age-adjusted models. Each variable contributing to vitamin D from sun (i.e., skin color, time
spent outdoors, sun protection and UV) was assessed on its own in addition to the derived solar
vitamin D score. ORs were calculated for each of the four age periods of exposure and a
cumulative measure of lifetime UV exposure was developed by combining the solar vitamin D
score from all periods of life.
Potential confounders were evaluated using the change in OR >10% method. The following
variables were tested as potential confounders: marital status, education, ethnicity, body mass
index (BMI), smoking status, pack years smoked, breastfed, lactation, age at menarche, oral
contraceptive use, oral contraceptive duration, parity, age at first live birth, age at last
menstruation, duration of hormone replacement therapy use (postmenopausal women only),
history of benign breast disease, family history of breast cancer, screening mammogram,
alcoholic drinks, dietary fat intake, calorie intake, phytoestrogen intake, physical activity
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(strenuous, moderate and daily activity) at selected periods of life (teenage years, 20-30s, 40-50s
and 60s-74) and both dietary vitamin D and calcium (from food and supplements). It was
hypothesized a priori that the effect of vitamin D from sunlight may be modified by menopausal
status (pre- versus post), vitamin D or calcium intakes from supplements or total intake (from
diet, and all supplements – including multivitamins ), BMI or smoking status, thus the statistical
significance of these multiplicative interactions was tested using the likelihood ratio test.
RESULTS
The mean age of women in this study was 56 years and many had postsecondary education (46%
of cases and 49% of controls). Breast cancer cases were more likely than controls to have a
family history of breast cancer, younger age at menarche, and lower parity. Among
postmenopausal women (68% of cases and 64% of controls), age at menopause was positively
associated with breast cancer risk. Strenuous physical activity during 20s-30s and 40s-50s was
associated with reduced breast cancer risk. Measures of daily and moderate physical activity
were also associated with reduced breast cancer risk (33). As reported elsewhere, supplemental
vitamin D intake at a dose level of at least 400 IU/day was associated with decreased breast
cancer risk; vitamin D from foods was not associated with breast cancer risk (49).
Non-Caucasian ethnicity was associated with a higher risk of breast cancer than Caucasian
(OR=1.23, 95% CI, 1.05, 1.45) (Table 1). Increasing time spent outdoors (highest vs. lowest
category) was associated with a decreased risk of breast cancer during teenage years (OR = 0.71,
95% CI: 0.60, 0.81), 20s-30s (OR = 0.64, 95% CI: 0.53, 0.76), 40s-50s (OR = 0.74, 95% CI:
0.61, 0.88) and 60s-74 (OR = 0.50, 95% CI: 0.37, 0.66), all with statistically significant trends.
Time spent outdoors was not associated with parity or education (appendix 4, table 3) and there
were no significant interactions between time spent outdoors at any period of exposure and parity
or education and breast cancer risk (data not shown). Sun protection practices, latitude and
erythemal UV of location resided were not associated with breast cancer risk during any of the 4
age periods. There were 86 (1%) participants who lived in the Southern hemisphere during at
least one life period for whom the reporting period would have corresponded to wintertime sun
exposure; the analyses were repeated with these women excluded and the results did not change.
Excluding women who only reported their country of province/state of residence did not
substantially change the results.
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No confounders were identified for any of the models, thus, the age-adjusted only models are
presented. The solar vitamin D score was consistently associated with a reduced risk of breast
cancer across all four age periods of exposure (Table 2). The age adjusted ORs comparing the
highest to lowest quartile for exposure during teenage years, 20s-30s. 40s-50s, and 60s-74, were
0.79 (95% CI: 0.68-0.91), 0.76 (95% CI: 0.65-0.89), 0.75 (95% CI: 0.64-0.88), and 0.59 (95%
CI: 0.46-0.76), respectively, and all trend tests were significant (p <0.001) (Table 2). Similar
inverse associations were also observed for the cumulative and recent measures of exposure. The
solar vitamin D scores for each age period of exposure had moderate to high correlations
(Spearman correlation coefficients ranged from 0.37 to 0.61, all p-values <0.0001) suggesting
that it may not be appropriate to include all 4 in one model due to muticollinearity.
Sensitivity analyses were conducted varying the assumptions for the vitamin D score and the
results changed minimally (appendix 4, table 5). Furthermore when the models are restricted to
lifelong residence in Canada the results were essentially unchanged (appendix 4, table 6). The
measure of high versus low vitamin D production potential created using cross-classification also
yielded inverse associations at all 4 age periods of exposure (appendix 4, table 4), although the
ORs for the highest vs. lowest categories were not all statistically significant, statistically
significant trends were observed suggesting a potential inverse dose-response relationship.
Significant interactions were not found between the derived solar vitamin D score at each of the
4 age periods and total dietary vitamin D intake, menopausal status or smoking status (data not
shown). Significant interactions were observed between the solar vitamin D score at ages 60s-74
(but not for earlier age periods) and both calcium intake and BMI, such that the solar vitamin D
score was associated with a significantly reduced breast cancer risk only among women with
total calcium intake ≥ 1000 mg/day (versus < 1000 mg/day) or BMI >25 (versus <25) (data not
shown). Although no significant interactions were observed between the solar vitamin D scores
and total vitamin D intake, the interactions between vitamin D supplement use (any versus none)
and the solar vitamin D score during 20s-30s and 40s-50s were statistically significant (Table 3).
But, stratified analyses did not reveal large differences and all OR estimates remained <1.0 (i.e.,
no qualitative interactions) (Table 3). The results of a combined solar vitamin D and vitamin D
from supplement score created using cross-classification are not substantially different than the
results for the solar vitamin D score only (Table 4).
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Table 6 shows the factors and corresponding beta coefficients obtained from multiple linear
regression in the Health Professionals’ Study and the variables used and assumptions made to
apply this model to our data in the Ontario Women’s Diet and Health Study. Applying the Health
Professionals Study algorithm to predict 25(OH)D levels in our study yielded a range of
predicted values from 38.8 to 86.8 nmol/L which were very similar to the ranges reported in the
Health Professionals Study and Nurses’ Health Study. A significant inverse association was
observed between predicted 25(OH)D and breast cancer risk (comparing the highest to lowest
quintile of predicted 25(OH)D: OR= 0.84; 95% CI:0.72, 0.98) (Table 6). When we used time
spent outdoors instead of physical activity to calculate predicted 25(OH)D, the range was 39 to
87 nmol/L (5th, 95th = 53, 79). The ORs obtained for the association between predicted 25(OH)D
with time spent outdoors and breast cancer risk were also statistically significant (OR=0.78; 95%
CI: 0.67, 0.91 comparing the highest to lowest quintile of predicted 25(OH)D) (data not shown).
DISCUSSION
The results of this large population-based case control study suggest that time spent outdoors and
our derived proxy measure of vitamin D from sun are inversely associated with breast cancer
risk. Exposure during all 4 periods of life, cumulative life exposure and recent exposure were all
associated with reduced breast cancer, with the strongest inverse associations observed for
exposure during the 60s-74, among the women who had reached this age. We did not find that
erythemal UV radiation, latitude, or sun protection practices were independently associated with
breast cancer risk. However, our measures of skin color and sun protection practices were
relatively crude. And despite a large proportion of study participants’ living outside Canada
during their teenage years there was limited variation in latitude of residence and thus also
limited variation in erythemal UV. The majority of study participants resided in the Greater
Toronto Area (~ 43ºN latitude). Our study did not find that these associations were confounded
by any known breast cancer risk factors or other variables that may be associated with time spent
outdoors (e.g., physical activity or smoking status).
Previous studies evaluating vitamin D from sunlight and breast cancer risk have evaluated
various factors that affect vitamin D production independently or using multivariate analysis to
control for each factor (13, 19, 29-31) or stratified by skin pigment (29), skin type and ethnicity
(30), intensity of outdoor physical activity(19), or solar radiation (13). These studies are
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consistent with the findings for our individual components, time spent outdoors was significantly
associated with reduced breast cancer risk (13, 19, 29-31) but no associations have been
observed for variables related to sun burns, skin damage or sun protection (13), although clothing
was consistently associated with increased breast cancer risk (19). Increasing sun exposure
index, from skin reflectometry measures, was found to be inversely associated with advanced
stage breast cancer risk among light skinned women only (29); we were unable to evaluate stage
of breast cancer in this study. Few studies have included measures of environmental sunlight
exposure (UV or solar radiation), and results have been inconsistent suggesting possible inverse
but non-significant associations(13) or an unexpected positive association such that women at
lower solar radiation had lower breast cancer risk (30). This finding may be explained by the
hypothesis that fluctuating serum 25(OH)D levels may be less desirable than stable levels (50).
Studies of the relationship between skin cancer, as a proxy for UV exposure, and breast cancer
risk have been inconsistent (51-54).
To the best of our knowledge no previous study has combined multiple factors to evaluate the
association between a composite measure of vitamin D from sunlight and breast cancer risk. One
previous study of all cancer mortality among men created a predictive model using serum
25(OH)D measures that were available on a sub-sample of study participants to derive a
predicted 25(OH)D measure for all study participants (46). Giovannucci et al. included dietary
and supplemental vitamin D, BMI, race, physical activity, and residence in their predicted model
and were able to explain 28% of the variation in 25(OH)D levels. We applied the previously
published predictive model to our data and observed that it was also associated with reduced
breast cancer risk. Future studies are needed to develop a predictive vitamin D model that
explains more of the variation in 25(OH)D and can be applied to population-based studies.
Timing of sunlight exposure may be important to confer any potential benefits of vitamin D for
breast cancer risk. Vitamin D exposure during adolescence may be most important because
breast tissue is undifferentiated prior to first pregnancy; hence breast cells are potentially more
susceptible to exposures during the period from menarche to first birth (55). Our study results
and those of another study (of sunburns, sun vacations and solarium use) (31) do not support this
hypothesis, however, one other study did find stronger associations with measures of sun
exposure and dietary vitamin D and breast cancer risk during adolescence (19).
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A limitation of our study is that we were unable to validate our algorithm used to derive the
composite measure of vitamin D from sunlight. Future studies need to be conducted to determine
how well such a measure predicts vitamin D intake from sunlight or 25(OH)D. There is the
potential for misclassification error in this study and more detailed measures of skin type (e.g.
Fitzpatrick skin typing or reflectometry), sun protection practices and data on sun bed/lamp and
wintertime sun holidays may be beneficial. The categories for sun exposure (all >1 hour)
measured in this study are beyond that necessary for vitamin D synthesis. However, it is not
realistic to expect study participants to recall their sun exposure with such a high level of
accuracy and this measure provides us with a relative estimate of high versus low sun exposure.
Additionally, time of day outdoors has a great impact on strength of UV and ultimately vitamin
D production. In observational studies there is always the potential for residual confounding
although our results were independent of many potential confounders (including physical
activity).
The validity and reliability of other similar sun exposure questionnaires has been measured.
Previous sun exposure questionnaires, focusing on time spent outdoors, have been shown to have
fair to moderate reliability (intraclass correlation coefficients range from 0.25-0.77) for both
recent adult measures (56-59) and recall of adolescent exposures (59). In terms of validity, time
spent outdoors measured from sun exposure questionnaires has been significantly associated
with skin measures of solar exposure (59-61) and 25(OH)D (44, 59, 62-64). Strong agreement
has been found between questionnaire measures of sun exposure and both calendar (59) or
detailed face-to-face measures (57).
The strengths of this study include its population-based case-control study design with high
response rates and large sample size. This study included a detailed person-specific sun exposure
questionnaire, with information on exposure during multiple periods of life, and used
environmental data sources to estimate ambient UV irradiation for each participant. Survival bias
is likely minimal in this study as cases were recruited within 11 months of diagnosis (on average)
and 5-year relative survival is 87% among breast cancer cases in Ontario (65). Although
measurement error may be of concern in this study there is no reason to suspect this would be
differential or introduce bias; study participants were not aware of the study hypothesis and data
collection occurred prior to any current media attention regarding the vitamin D hypothesis.
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In conclusion, time spent outdoors during summer and our composite measures of vitamin D
from sunlight were consistently associated with reduced breast cancer risk at multiple ages of
exposure. Future studies are needed to determine if this association is due to vitamin D exposure.
The algorithm created in this study to derive a composite measure combining multiple variables
related to cutaneous production of vitamin D may be useful for other researchers. Future studies
are needed to validate such a measure.
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Table 1. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for sun
exposure related variables during 4 age periods
Variable Cases
n = 3101
No. (%)
Controls
n = 3471
No. (%)
OR (95% CI)1
Ethnicity (proxy for skin color) Caucasian Non-Caucasian
2749 (89) 341 (11)
3121(90) 330 (10)
1.00 1.23 (1.05-1.45)
Teenage years Use of sun protection2 Never Sometimes Always
1601 (54) 1234 (41) 149 (5)
1829 (55) 1354 (40) 169 (5)
1.00 1.07 (0.97-1.19) 1.02 (0.81-1.28) P trend = 0.29
Hrs outdoor per week3 ≤ 6 hours 7 - 12 hours 13 -14 hours 15 - 21 hours >21 hours
365 (12) 558 (19) 505 (17) 566 (19) 944 (32)
324 (10) 549 (17) 569 (17) 679 (20) 1198 (36)
1.00 0.90 (0.74-1.09) 0.81 (0.66-0.98) 0.76 (0.63-0.91) 0.71 (0.60-0.85) P trend <0.0001
Latitude of residence4 ≤ 42.5 ºN 42.6 - 43.5 ºN (Toronto Area) 43.6 - 45.0 ºN >45.0 ºN
531 (19) 1276 (46) 272 (10) 686 (25)
547 (18) 1414 (46) 364 (12) 768 (25)
1.11 (0.94-1.30) 1.03 (0.91-1.17) 0.85 (0.71-1.03) 1.00 P trend = 0.07
Erythemal UV of residence (mW/m2)5 10-170 170-179 180-380
316 (11) 1576 (57) 872 (32)
358 (11) 1706 (55) 1029 (33)
1.00 1.08 (0.94-1.25) 1.06 (0.91-1.23) P trend = 0.45
20s-30s Use of sun protection Never Sometimes Always
1004 (34) 1647 (56) 270 (9)
1119 (34) 1889 (57) 300 (9)
1.00 1.04 (0.93-1.16) 1.11 (0.92-1.36) P trend = 0.31
Hrs outdoor per week ≤ 6 hours 7 - 12 hours 13 -14 hours 15 - 21 hours >21 hours
528 (18) 758 (26) 640 (22) 551 (19) 452 (15)
488 (15) 858 (26) 720 (22) 576 (17) 655 (20)
1.00 0.81 (0.70-0.95) 0.84 (0.71-0.99) 0.88 (0.74-1.05) 0.64 (0.53-0.76) P trend <0.0001
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Latitude of residence ≤42.5 ºN 42.6 - 43.5 ºN (Toronto Area) 43.6 - 45.0 ºN >45.0 ºN
644 (22) 1015 (35) 320 (11) 908 (31)
662 (20) 1137 (35) 383 (12) 1050 (32)
1.15 (1.00-1.33) 1.07 (0.94-1.21) 0.98 (0.83-1.17) 1.00 P trend = 0.22
Erythemal UV of residence (mW/m2) 10-170 170-179 180-380
497 (17) 1370 (47) 1019 (35)
575 (18) 1509 (47) 1148 (36)
1.00 1.07 (0.91-1.26) 0.99 (0.83-1.18) P trend = 0.73
40s-50s6 n = 2920 n = 3155
Use of sun protection Never Sometimes Always
501 (18) 1529 (56) 687 (25)
551 (18) 1736 (58) 697 (23)
1.00 0.98 (0.86-1.13) 1.12 (0.95-1.31) P trend = 0.15
Hrs outdoor per week ≤ 6 hours 7 - 12 hours 13 -14 hours 15 - 21 hours >21 hours
736 (27) 807 (30) 482 (18) 422 (15) 286 (10)
766 (26) 835 (28) 566 (20) 417 (14) 400 (13)
1.00 1.00 (0.87-1.15) 0.89 (0.76-1.04) 1.04 (0.88-1.23) 0.74 (0.61-0.88) P trend = 0.007
Latitude of residence ≤ 42.5 ºN 42.6 – 43.5 ºN (Toronto Area) 43.6 – 45.0 ºN >45.0 ºN
365 (13) 1480 (55) 325 (12) 541 (20)
417 (14) 1516 (52) 416 (14) 577 (20)
0.94 (0.78-1.13) 1.05 (0.92-1.21) 0.83 (0.69-1.01) 1.00 P trend = 0.62
Erythemal UV of residence (mW/m2) 10 - 170 170-179 180-380
152 (6) 1742 (64) 817 (30)
168 (6) 1754 (60) 1004 (34)
1.00 1.11 (0.88-1.40) 0.91 (0.71-1.15) P trend = 0.04
60s-747 n = 1247 n = 1224
Use of sun protection Never Sometimes Always
202 (7) 531 (17) 430 (14)
206 (16) 637 (51) 410 (33)
1.00 0.85 (0.68-1.06) 1.07 (0.84-1.36) P trend = 0.24
Hrs outdoor per week ≤ 6 hours 7 - 12 hours 13 -14 hours 15 - 21 hours >21 hours
402 (35) 370 (32) 115 (10) 171 (15) 93 (8)
366 (29) 352 (28) 143 (12) 210 (17) 172 (14)
1.00 0.96 (0.78-1.17) 0.74 (0.56-0.99) 0.74 (0.58-0.95) 0.50 (0.37-0.66) P trend <0.0001
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Paper 3: Sunlight and Breast Cancer Risk
Latitude of residence ≤ 42.5 ºN 42.6 – 43.5 ºN (Toronto Area) 43.6 – 45.0 ºN >45.0 ºN
158 (13) 637 (54) 162 (14) 230 (20)
159 (13) 622 (50) 226 (18) 244 (20)
1.05 (0.79-1.40) 1.09 (0.88-1.34) 0.76 (0.58-0.99) 1.00 P trend = 0.17
Erythemal UV of residence (mW/m2) 120-179 180-380
787 (66) 400 (34)
805 (64) 445 (36)
1.00 0.92 (0.77-1.08) P trend = 0.29
1 Age-group adjusted (note: 39 variables were evaluated as potential confounders and none were identified as confounders i.e., their inclusion in the model did not change the OR>10%) 2 Protective clothing or sunscreen use 3 Time spent outdoors from May to September only 4 Geocoded based on location lived reported as: city, and province/state. When respondents indicated country only the most populated city was used. 5 Monthly average local noon erythemal UV radiation for June 2003 obtained from NASA’s Total Ozone Mapping Spectrometer (TOMS) 6 Age 40 not reached by 181 (6%) of cases and 316 (10%) of controls 7 Age 60 not reached by 1854 (61%) of cases and 1324 (63%) of controls
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Paper 3: Sunlight and Breast Cancer Risk
Table 2. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
derived proxy measures of vitamin D from sunlight during 4 age periods, recent exposure only,
and cumulative life exposure
Solar vitamin D score1 Cases
n = 3101
No. (%)
Controls
n = 3471
No. (%)
OR2 (95% CI)
By specific age period Teenage years (n = 5924)
Q1 (≤ 1425 mw/m2 · hrs)
Q2 (1426-2295) Q3 (2296-3570) Q4 (>3570)
797 (29) 743 (27) 696 (25) 557 (20)
780 (25) 804 (26) 855 (27) 692 (22)
1.00 0.90 (0.78-1.03) 0.80 (0.70-0.93) 0.79 (0.68-0.91) P trend = 0.0007
20s-30s (n = 5579)
Q1 (≤ 957 mw/m2 · hrs)
Q2 (958-1514) Q3 (1515-2430) Q4 (>2430)
718 (27) 497 (19) 862 (33) 546 (21)
739 (25) 528 (18) 979 (33) 710 (24)
1.00 0.95 (0.81-1.12) 0.89 (0.77-1.02) 0.76 (0.65-0.89) P trend = 0.0003
40s-50s (n = 5263)
Q1 ( ≤617 mw/m2 · hrs)
Q2 ( 618-1178) Q3 (1179 -1785) Q4 (>1785)
665 (26) 560 (22) 754 (30) 541 (21)
623 (23) 615 (22) 841 (31) 664 (24)
1.00 0.85 (0.72-0.99) 0.82 (0.71-0.95) 0.75 (0.64-0.88) P trend = 0.0009
60s-74 (n = 2257) Q1 (≤ 589 mw/m2
· hrs) Q2 (590-1178) Q3 (1179-1767) Q4 (>1767)
295 (27) 313 (29) 285 (26) 197 (18)
257 (22) 300 (26) 318 (27) 292 (25)
1.00 0.91 (0.72-1.14) 0.78 (0.62-0.98) 0.59 (0.46-0.76) P trend = <0.0001
Cumulative 3 (n = 6159)
Low at all age periods Low at 3 age periods Low at 2 ages & high at 2 High at 3 age periods High at all age periods
874 (30) 639 (22) 602 (21) 573 (20) 219 (8)
878 (27) 682 (21) 716 (22) 667 (21) 309 (10)
1.00 0.93 (0.81-1.08) 0.84 (0.72-0.96) 0.81 (0.70-0.94) 0.63 (0.51-0.78) P trend = <0.0001
Recent4 (n = 5805)
Q1 (≤ 589 mw/m2 · hrs)
Q2 (590-1178) Q3 (1179-1603) Q4 (>1603)
692 (25) 799 (29) 624 (23) 619 (23)
669 (22) 856 (28) 711 (23) 838 (27)
1.00 0.90 (0.78-1.04) 0.84 (0.72-0.98) 0.72 (0.62-0.84) P trend = <0.0001
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Paper 3: Sunlight and Breast Cancer Risk
1Average weekday and weekend hours spent outdoors per week multiplied by erythemal UV radiation of residence weighted for skin color and sun protection practices. Refer to methods for more details. 2 Adjusted for age group 3 Exposure at all 4 age periods was classified as high (>50%le) or low (<50%le) and added for all age periods reached 4 Exposure during age period when questionnaire was completed
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Table 3. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
derived proxy measures of vitamin D from sunlight during 4 age periods stratified by vitamin D
supplement use
Vitamin D supplement use
None
Any
Solar vitamin D score1 OR
2 (95% CI) OR
2 (95% CI) P interaction
By specific age period Teenage years (n = 5924) Q1 (≤ 1425 mw/m2
· hrs) Q2 (1426-2295) Q3 (2296-3570) Q4 (>3570)
1.00 0.96 (0.79-1.16) 0.76 (0.63-0.92) 0.79 (0.65-0.97)
1.00 0.84 (0.68-1.04) 0.88 (0.71-1.08) 0.80 (0.64-1.01)
0.3
20s-30s (n = 5579) Q1 (≤ 957 mw/m2
· hrs) Q2 (958-1514) Q3 (1515-2430) Q4 (>2430)
1.00 0.87 (0.70-1.08) 0.97 (0.80-1.17) 0.69 (0.56-0.85)
1.00 1.06 (0.83-1.34) 0.80 (0.65-0.99) 0.88 (0.71-1.11)
0.015
40s-50s (n = 5263) Q1 ( ≤617 mw/m2
· hrs) Q2 ( 618-1178) Q3 (1179 -1785) Q4 (>1785)
1.00 0.83 (0.67-1.03) 0.95 (0.78-1.16) 0.69 (0.56-0.85)
1.00 0.86 (0.68-1.09) 0.70 (0.56-0.87) 0.79 (0.71-1.11)
0.03
60s-74 (n = 2257) Q1 (≤ 589 mw/m2
· hrs) Q2 (590-1178) Q3 (1179-1767) Q4 (>1767)
1.00 0.89 (0.64-1.24) 0.94 (0.68-1.31) 0.59 (0.42-0.84)
1.00 0.94 (0.68-1.31) 0.67 (0.48-0.93) 0.60 (0.42-0.85)
0.29
1Average weekday and weekend hours spent outdoors per week multiplied by erythemal UV radiation of residence weighted for skin color and sun protection practices. Refer to methods for more details. 2 Adjusted for age group
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Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
combined solar vitamin D score and vitamin D from supplements created by cross-classification
1 Age-group adjusted
Solar vitamin D score and vitamin
D supplement use
Cases
n = 3101
No. (%)
Controls
n = 3471
No. (%)
OR (95% CI)1
Teenage years Low solar and no supplement Low solar and any supplement High solar and no supplement High solar and any supplement
833 (30) 688 (25) 676 (24) 573 (21)
841 (27) 726 (23) 878 (28) 658 (21)
1.00 0.94 (0.81-1.08) 0.79 (0.69-0.91) 0.86 (0.74-1.00) P trend = 0.005
20-39 yrs of age Low solar and no supplement Low solar and any supplement High solar and no supplement High solar and any supplement
642 (25) 562 (22) 767 (29) 630 (24)
700 (24) 554 (19) 925 (32) 752 (26)
1.00 1.09 (0.93-1.28) 0.90 (0.78-1.03) 0.87 (0.75-1.01) P trend = 0.013
40-59 years of age Low solar and no supplement Low solar and any supplement High solar and no supplement High solar and any supplement
645 (26) 566 (23) 716 (29) 572 (23)
673 (25) 551 (20) 803 (30) 690 (25)
1.00 1.05 (0.90-1.24) 0.91 (0.79-1.06) 0.83 (0.71-0.97) P trend = 0.008
60-74 years of age Low solar and no supplement Low solar and any supplement High solar and no supplement High solar and any supplement
294 (27) 304 (28) 248 (23) 232 (22)
276 (24) 272 (24) 291 (25) 315 (27)
1.00 1.05 (0.84-1.33) 0.81 (0.64-1.02) 0.69 (0.55-0.88) P trend = 0.0003
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Table 5. Application of the predicted 25(OH)D model from the Health Professionals Follow-Up
Study to our Ontario Women’s Diet and Health study data.
Health Professionals
Follow-Up Study
Ontario Women’s Diet and
Health Study
Intercept 90.8 Same value assigned Race White African American Asian
0 (ref) -12.8 -13.3
Caucasian=0 Black, Aboriginal, other= -12.8 SE Asian and Asian = -13.3
Residence South Midwest/West Northeast/Mid-Atlantic
0 (ref) -2.4 -6.4
All women were assigned the value for Northeast (since they all lived in Ontario when completing the study)
Quintile of leisure-time physical activity 5 4 3 2 1
0 (ref) -4.5 -7.7 -9.0 -13.5
Quintiles of weekly combined moderate and strenuous physical activity (during 20-30s and 40-50s) were assigned same values 1
Body mass index (kg/m2) <22 22-24.9 25-29.9 30-34.9 ≥35
0 (ref) -1.0 -4.5 -6.5 -8.6
Same values assigned
Dietary vitamin D (IU/day) ≥400 300-399 200-299 100-199 <100
0 (ref) -3.5 -2.6 -7.2 -10.4
Same values assigned
Supplementary vitamin D ≥400 200-399 1-199 <100
0 (ref) -1.8 +2.4 -2.1
Same values assigned
1 Quintiles of time spent outdoors (cumulative over all ages) was also substituted for physical activity since physical activity was considered to be a proxy for time spent outdoors.
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Table 6. Distribution of breast cancer cases and controls and odds ratio (OR) estimates for
predicted 25(OH)D using the Health Professionals Study algorithm
Predicted 25(OH)D Cases
n = 3101
Controls
n = 3471
OR1 (95% CI)
mean (SD) mean (SD) Per 1 unit increase (nmol/L) 66.2 (7.8) 66.7 (7.6) 0.99 (0.98-1.00)
25 unit increase (nmol/L) 66.2 (7.8) 66.7 (7.6) 0.75 (0.64, 0.88)
No. (%) No. (%) Q1 (39-59.9) Q2 (60-65.2) Q3 (65.3-68.8) Q4 (69-73.2) Q5 (73.3-87)
657 (21) 650 (21) 621 (20) 594 (19) 579 (19)
699 (20) 696 (20) 665 (19) 723 (21) 688 (20)
1.00 0.97 (0.83-1.13) 0.96 (0.82-1.12) 0.84 (0.72-0.98) 0.84 (0.72-0.98)
1 Age adjusted
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Chapter 5 Discussion and Conclusions
This chapter adds to the discussion section of each paper included in chapter 4 by providing a
brief summary and comparison to the literature for the key findings of each objective followed
by a detailed discussion of methodological issues, suggestions for future research and overall
conclusions. The study results have been discussed in detail and compared to the relevant
literature in the discussion section of each of the 3 papers presented in chapter 4. For
detailed discussion of findings for objective 1 (dietary vitamin D and calcium and breast cancer
risk) see paper 2 in chapter 4.3. For detailed discussion of findings for objective 5 (modification
of FFQ) see paper 1 in chapter 4.2. And for detailed discussion of objectives 2, 3 and 4 (vitamin
D from sunlight, algorithm derivation, and combined diet and sunlight) see paper 3 in chapter
4.4.
5.1 Summary of Findings and Comparison to the Literature
When this study began in 2006, there was a paucity of literature on vitamin D and breast cancer
risk. However, over the past 4 years the number of published epidemiologic studies that assessed
vitamin D and breast cancer risk has rapidly increased, with 15 additional studies published. Our
study contributes substantially to what is now a relatively large body of literature on vitamin D
and breast cancer risk; our findings are consistent with previous literature and we contributed
novel methodology.
5.1.1 Objectives 1 and 5
The results of our study suggest that intake of vitamin D from supplements (>400 IU/day
compared to none) is associated with reduced breast cancer risk (Anderson et al., 2010).
However, no significant associations were observed between breast cancer risk and vitamin D
from foods or total combined (food and supplements) dietary vitamin D. There are a few possible
explanations for this seemingly inconsistent finding. First, vitamin D intake from foods (and
correspondingly total combined intake) may be more susceptible than supplements to
misclassification (potentially biasing results towards the null). Second, foods containing vitamin
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D may also contain other detrimental components that counteract the potential vitamin D
benefits (e.g., dietary fat in milk or contaminants in fish). Third, despite the similar cut-offs used
for food and supplements, the distribution of data within the upper categories varied
(distributions shown in figures 1-3 of appendix 4). As expected, intake of vitamin D from foods
was relatively low since few foods contain vitamin D and those with the highest amounts of
vitamin D (e.g., fatty fish) were consumed infrequently in our population and the range within
the highest category was not much greater than 400 IU/day. In contrast most supplement users in
the category >400 IU/day were assigned a value of 800 IU/day (the equivalent of one supplement
and one multivitamin). These differences in distribution alone do not explain why total combined
intake was not associated with breast cancer risk. Lastly, we cannot rule out the possibility that
our findings may be due to chance.
Adaptation of the FFQ for values from Canada versus the US did not substantially change the
measured levels of vitamin D from foods or the associations with breast cancer risk; the standard
US values only slightly underestimated modified Canadian values (Anderson et al., in press).
Furthermore, total calcium intake was not associated with breast cancer risk, and was neither an
effect modifier nor confounder of the vitamin D breast cancer association. A significant trend
towards decreased breast cancer risk was observed for calcium from supplements.
Our results are relatively consistent with the growing body of literature that suggests vitamin D
is associated with reduced breast cancer risk (see chapter 2). Most previous studies of vitamin D
from food and/or supplements, but not all (Kuper et al., 2009), report some inverse associations
with breast cancer risk (Abbas et al., 2007; John et al., 1999; Knight et al., 2007; Lin et al., 2007;
McCullough et al., 2005; Robien et al., 2007; Rossi et al., 2009; Shin et al., 2002). But, in
contrast to our study and the previous case-control studies (Abbas et al., 2007; Knight et al.,
2007; Rossi et al., 2009), the results from many cohort studies have not been statistically
significant (Lin et al., 2007; McCullough et al., 2005; John et al., 1999; Robien et al., 2007),
although the effect estimates suggest a modest risk reduction. Only one study reported positive
but non-significant associations between vitamin D from food and breast cancer risk in
postmenopausal women but not premenopausal women (Lin et al., 2007).
When differences are observed between study designs, randomized controlled trials and cohort
studies are often considered superior to case-control study results which may be subject to
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additional measurement error from recall. There are, however, some limitations of the current
RCT and cohort studies of vitamin D and breast cancer risk that may have biased results. The
cohort studies were not designed with the primary objective of evaluating vitamin D and breast
cancer risk and thus included limited measures of vitamin D (e.g., no supplements/cod liver oil
or sun exposure) which may increase measurement error and bias results towards null. The only
trial specific to vitamin D and breast cancer risk (Chlebowski et al., 2008) was conducted with a
low dose that may have been insufficient to create a difference between the intervention and
control group. Furthermore, participants of long term prospective studies and who are not lost to
follow-up may be different (e.g., more health conscious or in need of health care) than those who
are willing to participate in a less time consuming case-control study which may result in
reduced generalizability and/or reduced variation in exposure). Furthermore, the identification of
cases is usually by routine diagnosis in a case-control study; whereas, it may be conducted
through active surveillance in trials and cohort studies. It is unknown how this may affect studies
of breast cancer but if breast cancer cases are more advanced at diagnosis in case-control studies
than cohort studies or trials, and if vitamin D is important for progression or stage of disease,
then this may explain the tendency towards more null findings from cohort/trials. Prospective
studies often have fewer cases and thus may be not sufficiently powered to detect a significant
association; however, this was not true for most of the vitamin D breast cancer studies to-date.
5.1.2 Objectives 2 and 4
Our study also presents the development and application of a novel solar vitamin D score that
takes into consideration a multitude of factors that affect the cutaneous production of vitamin D.
This score, and the component time spent outdoors, were both inversely associated with breast
cancer risk and there did not appear to be a critical period of exposure; inverse associations were
observed for all age periods of exposure and were only slightly stronger for exposure during age
60 to 74. Time spent outdoors was moderately correlated between all age periods of exposure
(spearman correlation coefficients ranged from 0.37 to 0.66 and all p-values <0.0001); similar
correlations were observed for the solar vitamin D score. The solar vitamin D score appeared to
be driven primarily by time spent outdoors; the correlations between time spent outdoors and the
solar vitamin D score were high (spearman correlation coefficients ranged from 0.75 to 0.80
when comparing the same age-periods and all p-values were <0.0001). Sun protection practices
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and UV radiation – other components of the solar vitamin D score – were not independently
associated with breast cancer risk. Caucasian ethnicity, our proxy for lighter skin color, was
associated with reduced breast cancer risk consistent with the vitamin D hypothesis. However,
the association between ethnicity and breast cancer risk may be explained by many other
variables (e.g., genetics) and our observed association is inconsistent with the general finding
that breast cancer rates are highest among White women (Altekruse, Kosary, Krapcho, Neyman
et al., 2009). Since the vast majority of women in our study are Caucasian (90%) we are unable
to further explore this association.
Few studies have evaluated the association between person-specific measures of sunlight
exposure (e.g., time spent outdoors) and breast cancer risk (John et al., 1999; John et al., 2007;
Knight et al., 2007; Millen et al., 2009) and, consistent with our results, all but one previous
study (Kuper et al., 2009) report inverse associations. No previous studies of breast cancer risk
have created a combined solar vitamin D score. We did not identify any confounders or effect
modifiers of the associations between vitamin D from each source and breast cancer risk. It has
been hypothesized that the association between time spent outdoors and breast cancer risk may
be explained by physical activity (Knight et al., 2007) but this was not found in our study;
however, our measures of physical activity (daily, moderate or strenuous) did not distinguish
indoor versus outdoor activity.
5.1.3 Objective 3
When we combined both vitamin D from supplements and either our solar vitamin D score or
time outdoors into one variable, the effect estimates for breast cancer risk were not substantially
stronger than our models with only the solar vitamin D score or time outdoors; however, few
women used vitamin D supplements and we may not have had sufficient power to evaluate the
combined effect. Adjusting the solar vitamin D score models for supplemental vitamin D, or vice
versa, did not weaken the associations observed associations for either variable, suggesting that
there is an independent effect of both vitamin D from sun and supplements. Lastly, the
associations between breast cancer risk and both vitamin D from supplements and our solar
vitamin D score were not modified by menopausal status, BMI, or calcium intake and no
significant interactions were observed.
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One previous study investigated the combined effect of diet and sunlight and found the inverse
associations were slightly stronger when for a measure of high sun and diet than either measure
on its own (John et al., 1999). There is some evidence of an interaction between diet and sunlight
(McCullough et al., 2005) but this was not found elsewhere (Millen et al., 2009). The literature
from biomarker studies of breast cancer risk and 25(OH)D, which reflects vitamin D intake from
both diet and sunlight, is less consistent. Of the 10 studies that we identified, 5 reported
significant inverse associations between 25(OH)D and breast cancer risk (Abbas et al., 2008;
Abbas et al., 2009; Crew et al., 2009; Lowe et al., 2005; Rejnmark et al., 2009); however, only 1
of these studies was prospective and measured pre-diagnosis 25(OH)D levels in cases (Rejnmark
et al., 2009). Other prospective studies found no significant associations between 25(OH)D and
breast cancer risk but in most the ORs were less than 1 (Bertone-Johnson et al., 2005;
Chlebowski et al., 2008; McCullough et al., 2009) and sample sizes were not large. Overall, the
results of our study tend to support the ‘vitamin D hypothesis’ and are consistent with a growing
body of epidemiologic studies, particularly observational studies, that have reported an inverse
association between vitamin D and breast cancer risk.
5.2 Limitations and Methodological Issues
Limitations and methodological issues related to this study have been briefly discussed in each of
the 3 papers included in the results section of this thesis. In this section a more detailed
discussion of issues related to bias (both selection and information) and other threats to the
internal study validity (e.g., confounding, effect modification, and statistical analysis) and
external study is presented. Lastly, a discussion of general limitations is included.
5.2.1 Selection Bias
Selection bias occurs when there are systematic differences between the exposure and disease
relationship in study participants versus non-participants (Rothman & Greenland, 1998 p. 119).
A potential limitation of case-control studies is identifying controls from the same source
population from which the cases arose; controls should be comparable to the cases in all respects
except for the disease of interest (Kopec & Esdaile, 1990; Szklo & Nieto, 2000). Specific to our
study, incident breast cancer cases were identified from the population-based Ontario Cancer
Registry; thus, the population cohort from which the cases arose can readily be identified as all
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women in Ontario. Controls were identified from the same population using a modified random
digit dialing procedure. A sampling frame of both listed (from multiple directories) and unlisted
(numbers on either side of the listed numbers) telephone numbers was used to conduct random
digit dialing. In 2006 an estimated 93% of Ontario households had a residential phone line and
thus random digit dialing captures the base population well (Statistics Canada, 2006). To
minimize response bias, every effort was made to obtain a high response rate and overall 75%
cases and 80% of identified eligible controls participated in the study.
Although high response rates were obtained for both cases and controls (of those who were
contacted and eligible), it is unknown if the association between vitamin D and breast cancer
differs among those electing to not participate. For example, if the selection of cases is not biased
(i.e., assuming breast cancer cases were equally motivated to participate independent of vitamin
D status) but the selection of controls is biased towards the inclusion of more health conscious
women (e.g., more likely to take supplements and/or spend time outdoors) then the association
between vitamin D and breast cancer risk may be overestimated. For example, if cases were
equally likely to be selected regardless of exposure but controls were more likely to be selected if
they had higher vitamin D exposure than the true risk estimate could be 1.0 or greater
(Greenland, 1996). It is reassuring, however, that vitamin D intake in our study is relatively
consistent with previous studies that have reported mean daily vitamin D intake from food only
to be around 5 µg among Canadian women (Berube et al., 2005; Csizmadi et al., 2007; Statistics
Canada, 2004; Vieth, Cole, Hawker, Trang, & Rubin, 2001). Moreover, we found other
established breast cancer risk factors (e.g., family history of breast cancer, parity) were
associated with breast cancer risk in the expected direction.
Despite the population-based approach to recruitment of cases and controls, and the high
response rate, there is the potential for sampling bias in terms of both who was invited to
participate and who did actually participate in the study. Although we expect the coverage of
cancer cases to be nearly complete in the Ontario Cancer Registry, the population-based
approach to random digit dialing used to recruit controls excludes Ontario residents without a
phone. It also exclude potential participants who were not at home at the various times
throughout the day differences when contact was attempted. Furthermore, if the cases who
survived and participated in our study had higher vitamin D intake/exposure then non-survivors
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our effect estimates may be biased towards the null; in contrast, if non-survivors had higher
vitamin D intake then our estimates may be overestimated. However, survival bias is likely
minimal since cases were recruited on average within 1 year of diagnosis and the 5 year relative
survival rate for breast cancer is 87% .
5.2.2 Information Bias
An inherent limitation of observational studies is the potential for measurement error.
Differential measurement error between cases and controls may bias study results in either
direction yielding inaccurate effect estimates. Non-differential measurement error is usually
considered less of a concern in observational studies because it often, but not always, biases
results towards the null. Recall bias, which is a type of differential measurement error, is always
a concern in case-control studies. It is possible that cases differentially recall exposures that are
linked to disease risk, particularly those that have received considerable media attention. We do
not expect this to be a concern in our study, since at the time of data collection (2002-2004)
limited information was available linking vitamin D to breast cancer risk. Furthermore,
participants were not made aware of the study hypotheses. Conducting a similar study today may
be more problematic with the considerable media attention towards vitamin D, raising public
awareness about the vitamin D-breast cancer hypothesis.
To minimize measurement error, standardized procedures were employed for data collection
with established quality control mechanisms. Validated measures were used whenever possible
for the measurement of variables. The validity of the Block FFQ was assessed prior to its use in
the Ontario Women’s Diet and Health Study and moderate to high reliability and validity were
observed for most nutrients, including vitamin D and calcium (Boucher et al., 2006). To
minimize the effect of any dietary changes following cancer diagnosis, and to capture exposure
status prior to disease, cases and controls were asked about exposures and diet two years prior to
the study start. For this thesis, we further modified the nutrient analysis of the FFQ to be specific
for vitamin D intake among Canadians. Furthermore, both food and nutrient level analysis were
conducted as recommended to obtain maximal information (Rothman & Greenland, 1998 p.630).
There is no obvious reason to suspect that cases and controls differentially recalled vitamin D
supplement use or foods containing vitamin D.
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The validity (Pearson’s deattenuated coefficients) of vitamin D and calcium from diet and
supplements combined (using the standard US nutrient analysis) was 0.54 and 0.71, respectively
(Boucher et al., 2006). Re-arranging the formula RRobserved = (RRtrue) γ provided by Willett et al.,
(Willett, 1998) we evaluated what the possible true risk estimate would be given a range of
validity levels (γ) that overlapped these values. Assuming observed risk estimates of 0.75 and
0.85 (as were observed in our study for vitamin D and calcium from supplements, respectively
(Anderson et al., 2010)), Table 1 presents the possible corrected or ‘true’ risk estimates which
range from 0.40 to 0.68 and 0.58 to 0.80, respectively.
Table 1. Corrected ‘true’ risk estimates for selected measures of validity (γ) and observed risk
estimates of 0.76 and 0.85
Observed risk estimates
γ 0.761 0.85
2
Corrected ‘true’ risk estimates3
0.3 0.40 0.58
0.5 0.58 0.72
0.7 0.68 0.80
1 Risk estimate observed in our study for extreme categories of vitamin D supplement use and breast cancer risk
2 Risk estimate observed in our study for the extreme categories of calcium supplement use
3 Calculated as
γ√RRobserved
In regards to the measurement of vitamin D from sunlight, we captured person specific measures
of sun exposure (e.g., time spent outdoors, sun protection practices and skin color) in addition to
ambient UV at location of residence. While capturing sun exposure for 4 specific periods of life
was largely a strength of this study, the long period of recall may increase measurement error. It
is, however, unlikely that this error would differ between cases and controls. An additional
131
limitation of our study is the broad exposure categories for time spent outdoors with the shortest
period being <1hr per day; it has been proposed that a better method to capture sun exposure
relevant to vitamin D production may be to ask about the frequency of exposure instead of total
time outdoors (i.e., how many days per week did you spend at least 30 minutes outdoors)
(Knight et al., 2007). Additionally, the measures of sun protection practices and skin color are
relatively crude and future studies should use more detailed questionnaires. Our study did not
include any measures of tanning bed use or wintertime sun exposure (e.g., “snowbirds” or sun
holidays) which may introduce misclassification error when developing the solar vitamin D
score. The purpose of the solar vitamin D score was to predict vitamin D from sun and validation
of our solar vitamin D score is necessary.
Evaluating the validity of our algorithm used to derive the sun exposure score was beyond the
scope of this thesis due to feasibility constraints. To conduct a validation study, blood samples
would need to be collected from 100-200 women (as estimated a reasonable sample size for
validation studies by Serra-Majem et al., 2009) and laboratory analysis for serum 25(OH)D
would need to be conducted. Due to the length of time between the initial study questionnaires
and start of this thesis project, additional questionnaires would also have to be administered
repeating the sun exposure questionnaire. The increased costs, time and uncertainty to apply for
and secure funding, obtain ethics approval, and carry out such a study was not feasible within the
restricted time range for a PhD thesis. One previous study found a score combining time spent
outdoors and amount of skin exposed was significantly correlated with summer 25(OH)D levels
(r = 0.59); however, this was only slightly better than time spent outdoors alone (r = 0.58)
(Hanwell et al., 2010). Elsewhere a sun index (the product of sun exposure hours per week and
fraction of body surface area exposed) was moderately correlated with summer 25(OH)D levels
(r = 0.49) whereas no significant correlation was observed for exposure hours alone (Barger-Lux
& Heaney, 2002).
Although there is a high likelihood of measurement error for both vitamin D from diet and sun
exposure, it is not expected to differ between cases and controls (i.e., non-differential
misclassification) and, thus, results will typically be biased towards the null. Two exceptions to
this rule are when there are multiple exposure categories (as opposed to a binary exposure) and
when a continuous variable that is not normally distributed is categorized, then bias could be in
132
either direction. A limitation of validating such observational measures is that they are usually
done at one point in time (e.g., one biomarker 25(OH)D measure, or two 24-hour recalls) and we
are interested in usual exposure over the life course. Ideally a prospective long term validation
study would be conducted evaluating stability of 25(OH)D over many years/seasons and later
recall of dietary intake and sunlight exposure.
5.2.3 Confounding and Effect Modification
Confounding is often considered a special type of bias that results in “a confusion of effects”
(Rothman & Greenland, 1998 p. 120). Efforts to minimize the confounding effect of age were
made in both the design (i.e., frequency matching on age) and analysis (i.e., statistical
adjustment) stages of the study. Many potential confounders were evaluated in the multivariable
models. Few of these variables were known a priori to meet the definition of a confounder –
most established breast cancer risk factors were not known to be associated with vitamin D
exposure/intake. Inclusion of any of the potential confounders in the overall multivariable
models did not substantially change the observed age adjusted ORs for any of the primary
exposure variables suggesting that the association between vitamin D and breast cancer risk is
not explained by a confounding factor.
The potential for residual confounding, however, cannot be ruled out. Residual confounding may
arise if possible confounders are inadequately measured (e.g., self-reported history of screening
mammogram) or are not included. For example, residual confounding may arise if healthy
lifestyle was inadequately captured in this study and if healthy lifestyle were a common cause of
both vitamin D exposure and reduced breast cancer risk. Although healthy lifestyle itself was not
measured, neither multivitamin use nor physical activity, components of a healthy lifestyle,
confounded our observed vitamin D and breast cancer associations. Furthermore, time spent
outdoors at all age periods of exposure was not associated with parity, education or income. It is
highly plausible that the observed associations between time spent outdoors and breast cancer
risk may be confounded by other variables not included in this study.
In this study we evaluated the interactions between vitamin D exposure and calcium, menopausal
status, and BMI. No significant interactions were observed and stratified analysis did not reveal
any obvious effect modification. However, few premenopausal women were taking vitamin D
133
supplements and vitamin D and calcium were highly correlated, limiting our power to draw any
conclusions.
5.2.4 Analytical Issues
The above sections considered the study validity with respect study design and systematic error
(bias) whereas this section addresses issues related to statistical analysis and random error. A
strength of this study is that there were minimal missing data. Less than 2% of women had
missing responses for either vitamin D intake from food or supplements. With respect to time
spent outdoors, less than 6% of women had missing responses for each age period of exposure
reached. Logic checks were conducted and responses were deleted if unreasonable. In general,
cases with missing data were not included in the analyses – we did not impute values or employ
other techniques for missing data. Some assumptions were made regarding location of residence
(e.g., city of residence was assumed if only province or country was provided); however, this
was among only a small proportion of participants and sensitivity analyses were conducted to
evaluate the impact of these assumptions.
Multivariable logistic regression was used to model the association between vitamin D and
breast cancer risk and to obtain adjusted ORs. Even though cases and controls were frequency
matched on age, we included age in all our adjusted models to reduce the likelihood of residual
confounding. Other variables were defined as confounders if their inclusion in the model
changed main effect estimates by at least 10% (Maldonado & Greenland, 1993). Another
strategy would be to include all suspected confounders based upon an a priori conceptual model;
however, few variables were expected to fit the definition of a confounder (i.e., associated with
both the exposure and outcome). The goal of this model is to evaluate the association between
vitamin D and breast cancer risk not to develop a full predictive model of breast cancer risk,
thus, variables that did not substantially change the vitamin D and breast cancer effect estimate
were not included in the models.
Multiple comparisons may increase the likelihood of some findings being due to chance. With
statistical significance defined as P value less than 0.05 with 20 comparisons we would expect at
least one to be significant, thus, we cannot rule out the possibility that some of our findings are
due to chance. However, our results appear to be relatively consistent and biologically plausible
134
thus we would expect that our results are reliable. Many of our results are highly significant with
p-values much less than 0.05.
Logistic regression does not account for time-varying exposure and recently new techniques
have been proposed that use weighted Cox models with time-dependent exposures that have
been manipulated for use in case-control studies (L. S. Freedman, Oberman, & Sadetzki, 2009;
Leffondre, Abrahamowicz, & Siemiatycki, 2003; Leffondre et al., 2010). However, the data in
our study are limited by the set age periods asked in our questionnaire and evaluating the models
using these novel statistical procedures was beyond the scope of this thesis.
While the sample size for this study was already determined, post hoc power was calculated to
estimate the power to detect the main study objectives (appendix 5). The study had >80% power
to detect an independent main effect assuming at least a 20% risk reduction. The power to detect
an OR of 0.60 for the joint exposures assuming a multiplicative interaction between calcium and
vitamin D is 78%. A major strength of this study is the large sample size and ability to conduct
stratified analysis.
5.2.5 External Validity
In regards to the external validity of our study, the source population for this study was women
in the Ontario population and the findings are expected to be generalizeable beyond this
population. Patterns of vitamin D exposure/intake are likely relatively consistent across Canada
and the Northern US and, it is expected that the biologic mechanism proposed for vitamin D and
breast cancer is likely generalizeable to most women, thus, our findings are likely applicable to a
much broader population. However, our findings may not be generalizeable to women living
near the equator as they likely have higher vitamin D status all year-round and it has been
proposed that the continual fluctuation of high to low 25(OH)D levels may be more detrimental
than constant year-round levels (Vieth, 2009). Determinants of 25(OH)D status and the
proportion of vitamin D from food versus supplements likely varies by country (e.g., some
populations have high fish consumption) and thus our specific findings regarding oral intake
dose may not be generalizeable.
A major concern when considering the external validity of a study is whether the characteristics
of non-respondents (or the populations with whom you wish to generalize to) are similar to the
135
study population with respect to other characteristics that may modify the vitamin D-breast
cancer association (e.g., effect modifiers). When compared to Ontario data from Statistics
Canada our population is more highly educated and a greater proportion is Caucasian. Although
education may be associated with vitamin D intake it likely does not modify the association with
breast cancer risk. In contrast, ethnicity may be associated with genetic differences that could
modify the vitamin D breast cancer association. Considering our study was 90% Caucasian,
more studies should be conducted in other ethnic groups to determine if the findings are
generalizeable.
5.2.6 General Limitations
A limitation of our study is the lack of serum 25(OH)D measures. Blood samples were not
collected in this study and may not have been feasible given the large sample size and associated
costs and burden of collecting blood samples. Serum 25(OH)D is often considered the gold
standard for the measurement of vitamin D although it may not reflect the desired period of
exposure (Millen et al., 2008). If blood samples were available these would reflect post-diagnosis
25(OH)D levels among cases and may not be generalizeable to pre-diagnostic levels (as
discussed in section 2.3.3). However, if serum had been collected on even a subset of controls
then we could have validated the algorithm used to derive the solar vitamin D score.
Alternatively, we could have employed the strategy used in the Harvard Cohorts to develop a
predictive model of 25(OH)D specific to our population that could be applied to all study
participants (Giovannucci et al, 2006).
An additional limitation of this study is that we were unable to explore differences by breast
cancer stage or subtype (e.g., hormone receptor status) or to evaluate genetic interactions. As
discussed in Chapter 2, there is some evidence that vitamin D may influence disease progression
and there is conflicting evidence suggesting the vitamin D-breast cancer association may vary by
hormone receptor status of tumor. Saliva samples (DNA) were collected in this study and future
studies evaluating variants in vitamin D related genes are planned.
5.3 Study Strengths
Some major strengths of our study are the population-based identification of both cases and
controls, the high response-rate and the large sample size. While some believe case control
136
studies offer less convincing evidence compared to cohort or RCT studies, others argue that
well-designed case-control studies are essentially more efficient cohort studies or studies nested
within a cohort (Rothman & Greenland, 1998 p. 114). We made efforts to minimize common
threats to validity that are characteristic of case-control studies such as using population-based
cases and controls and recruiting cases soon after diagnosis (see section 5.2). Other general
advantages of a case-control study are feasibility, reduced costs and shorter time to complete.
An additional strength of our study is the measurement of vitamin D from all sources. Few
previous studies have includes all foods, supplements and such comprehensive sun exposure
measures. Our use of a validated FFQ and the additional modifications that were made to apply
the nutrient analysis to our Canadian population is a major strength of our study. With respect to
sun exposure, we were able to capture many variables that may affect the cutaneous synthesis of
vitamin D and our study collected these variables for multiple ages of exposure allowing us to
evaluate exposure during adolescence and other critical periods of exposure. Furthermore, our
solar vitamin D score is a novel tool and the methodology may be useful to other researchers.
Lastly, the comprehensiveness of the Ontario Women’s Diet and Health study allowed for the
evaluation of many possible confounders (e.g., calcium, physical activity) and effect modifiers.
5.4 Causation and Future Studies
As reviewed earlier, most studies, including ours, tend to suggest that vitamin D intake/exposure
is inversely associated with breast cancer risk, yet presently we do not have sufficient evidence
to conclude vitamin D prevents breast cancer. Establishing temporality and eliminating unknown
confounding are necessary to determine if there is a causal association. Furthermore, there are
many alternate explanations that have not yet been ruled out. For example, it has been proposed
that the observed associations between sun exposure and cancer risk may be due to changes in
melatonin, seasonal affective disorder, immunological effects, or degradation of folic acid by
UVB exposure (as reviewed in IARC, 2008). Evaluating these hypotheses was not possible
within our study, thus, we cannot determine if the observed associations between our solar
vitamin D score and time spent outdoors and breast cancer are due to vitamin D.
An experimental study of vitamin D from supplements and breast cancer risk is feasible and
warranted given the increasing body of inconclusive evidence from observational studies and the
137
noted limitations of previous trials. A well-designed randomized controlled trial specific to
supplemental vitamin D and breast cancer risk could establish temporality and reduce the
possibility of confounding by unmeasured variables. Such a trial should be conducted with a
sufficiently high vitamin D dose and monitoring of vitamin D levels to ensure serum 25(OH)D
levels in the intervention group are actually greater than control group despite contamination by
sun exposure. Previous trials (Chlebowski et al., 2008; Lappe et al., 2007) have all been
conducted with vitamin D and calcium and unable to tease out the independent effect of vitamin
D. Another option would be a prospective cohort with biomarkers collected at multiple time
periods. Additional studies, likely in the form of observational studies, are also needed to
evaluate the association between time spent outdoors and breast cancer risk and to explore the
aforementioned alternative hypotheses. Any future observational studies of dietary vitamin D
should be conducted in populations with a greater range in vitamin D intake and should include
supplements in addition to food sources.
There is only one study that reported a positive association, although non-significant, between
vitamin D intake and breast cancer risk and this was among postmenopausal women only (Lin et
al., 2007). Although these results were not significant, further study into the subgroup findings is
also needed. Future studies should be designed to evaluate menopausal status, breast cancer stage
at diagnosis, hormone receptor status and genetic variants such as polymorphisms in the vitamin
D receptor gene or vitamin D binding protein gene; the true association between vitamin D and
breast cancer risk may be masked if there are stratum specific differences that are not evaluated.
There is also conflicting results regarding critical period of exposure and future studies should
continue to evaluate vitamin D exposure during multiple periods of life. More generally, there is
also an urgent need for more research on the determinants of individual variation in vitamin D
status and a need for consensus on both serum 25(OH)D levels and recommended dietary intake.
5.5 Conclusions and Public Health Importance
Our research findings that vitamin D from supplements >400 IU/day and time spent outdoors are
inversely associated with breast cancer risk contribute to the growing body of evidence that
suggest vitamin D is associated with reduced breast cancer risk. Yet, even though there is an
established biologic mechanism and increasing body of literature supporting an association
between vitamin D and breast cancer risk, a causal association is not yet established. Low
138
vitamin D levels have generally been associated with breast cancer relative risk estimates of 1.3
to 2 (see chapter 2); although these risk estimates tend to suggest a modest association, the high
prevalence of exposure would result in a relatively large population attributable risk (PAR). For
example, if a true causal effect exists, and we assume a RR of 1.67 and 55% prevalence of low
vitamin D exposure then the PAR would be 25% (i.e., a quarter of all breast cancer cases can be
attributed to low vitamin D status). Despite the current uncertainty, this is an important area of
research, particularly important since breast cancer is the leading type of cancer among women
in Canada (Canadian Cancer Society/National Cancer Institute of Canada, 2009). Furthermore,
few established breast cancer risk factors are modifiable and vitamin D may be highly modifiable
and amenable to population prevention strategies.
Public and physician awareness surrounding the potential benefits of vitamin D seems to be
increasing. A recent review of billing data from the Ontario Ministry of Health and Long-Term
Care indicates that 25(OH)D testing has steadily increased over the past 5-years – from 56,900
tests in 2005 to a projected 696,162 tests in 2009 (Medical Advisory Secretariat, 2010). This
review, conducted by the Ontario Health Technology Advisory Committee, recommended that
Canadians should be advised to increase intake and supplements of vitamin D as per Health
Canada guidelines but “routine vitamin D testing is not warranted in the average risk
population”. Although there is not yet conclusive evidence of an association between vitamin D
and breast cancer risk, increasing vitamin D intake may prevent other diseases (e.g.,
osteoporosis, cardiovascular diseases, and type II diabetes) and prevention strategies to increase
vitamin D for other diseases may be warranted.
A population-based prevention strategy to increase vitamin D intake currently already exists in
Canada for the prevention of rickets and to maintain bone health: mandatory food fortification of
milk and margarine with vitamin D, and Canada’s Food Guide recommends a vitamin D
supplement of 400 IU/day for all Canadians over 50 years of age take since food levels are not
sufficient. Given the large margin of safety for vitamin D, such a strategy is feasible. Yet, in
spite of the current fortification practices, high levels of insufficiency continue to be observed
and concerns have been raised regarding Health Canada’s proposed discretionary fortification
policy (Sacco & Tarasuk, 2009). An alternative to food fortification may be a population-based
program to increase supplement use. Although sun exposure is a good source of vitamin D, it is
139
not likely a feasible prevention strategy to encourage increased sun exposure for 2 reasons: 1)
there is insufficient sun exposure most of the year in Canada for vitamin D synthesis, and 2) sun
exposure is a known skin cancer risk factor. Although the relatively short periods of sun
exposure needed for vitamin D production may not increase skin cancer risk, a message of
moderation may be challenging.
Our study contributes to the growing body of literature that suggests vitamin D may be
associated with reduced risk of breast cancer and explores many of the methodological issues
related to the measurement of vitamin D. Future studies are needed to further elucidate the
relationship between vitamin D and breast cancer risk and to inform public health policy. The
ultimate aim of this research is the primary prevention of breast cancer and the future looks
sunny.
140
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162
Appendices
163
Appendix 1 - Questionnaires
Sun Exposure Measure from Epidemiologic Questionnaire:
164
Vitamin D Supplements Measure from Food Frequency Questionnaire:
Note. Vitamin D from foods was measured using additional items from the copyrighted 178-
item Block Food Frequency Questionnaire.
165
Appendix 2 - Ethics Approval
166
Appendix 3 – Missing data
Table 1. Amount of missing data for each variable examined (total n = 6572 for cases and controls combined).
Potential confounders Missing
n (%)
Age 0
Marital status 15 (<1%)
Highest level of Education 34 (<1%)
BMI (kg/m2) 47 (<1%)
Pack-years smoked 102 (1.6%)
Ever breastfeed your infant 27 (<1%)
Age at menarche (years) 247 (3.8%)
Age at menopause 143 (2.2%)
Parity 107 (1.7%)
Age at first live birth 107 (1.7%)
Duration of oral contraceptive use 106 (1.6%)
Duration of HRT use (yrs) 32 (0.5%)
Breast cancer in a 1st degree relative 160 (2.4%)
Benign breast disease 127 (1.9%)
Mammogram 18 (<1%)
Strenuous physical activity in teenage years 447 (6.8%)
Strenuous physical activity in 20s-30s 263 (4.0%)
Strenuous physical activity in 40s-50s (among women >40 years of age) 184 (2.8%)
Strenuous physical activity in 60s-74 (among women >60 years of age) 126 (1.9%)
Moderate physical activity at work in teens 518 (7.9%)
Moderate physical at work in 20s-30s 275 (4.2%)
Moderate physical at work in 40s-50s (among women >40 years of age) 164 (2.5%)
Moderate physical at work in 60s-74 (among women >60 years of age) 96 (1.5%)
Daily activity at work in teens 534 (8.1%)
Daily activity at work in 20s-30s 274 (4.2%)
167
Daily activity at work in 40s-50s (among women >40 years of age) 148 (2.3%)
Daily activity at work in 60s-74 (among women >60 years of age) 211 (3.2%)
Alcohol intake (drinks/week) 83 (1.3%)
Dietary fibre intake 83 (1.3%)
Dietary fat intake 83 (1.3%)
Total phytoestrogen intake 203 (3.1%)
Total calcium intake from food and supplements 83 (1.3%)
Main vitamin D exposure variables (dietary and sunlight)
Vitamin D intake from food 83 (1.3%)
Vitamin D intake from supplements 83 (1.3%)
Ethnicity 31 (<1%)
Solar vitamin D score in teens 648 (9.8%)
Solar vitamin D score in 20s-30s 993 (15.1%)
Solar vitamin D score in 40s-50s (among women >40 years of age) 812 (12.3%)
Solar vitamin D score in 60s-74 (among women >60 years of age) 314 (4.8%)
Sun protection practices in teens 238 (3.6%)
Sun protection practices in 20s-30s 343 (5.2%)
Sun protection practices in 40s-50s (among women >40 years of age) 374 (5.6%)
Sun protection practices in 60s-74 (among women >60 years of age) 155 (2.4%)
UV of residence in teens 454 (6.9%)
UV of residence in 20s-30s 715 (10.9%)
UV of residence in 40s-50s (among women >40 years of age) 438 (6.7%)
UV of residence in 60s-74 (among women >60 years of age) 133 (6.1%)
Time spent outdoors in teens 315 (14.5%)
Time spent outdoors in 20s-30s 346 (5.3%)
Time spent outdoors in 40s-50s (among women >40 years of age) 358 (5.4%)
Time spent outdoors in 60s-74 (among women >60 years of age) 177 (2.7%)
Latitude of residence in teens 453 (6.9%)
Latitude of residence in 20s-30s 714 (10.9%)
Latitude of residence in 40s-50s (among women >40 years of age) 438 (6.7%)
Latitude of residence in 60s-74 (among women >60 years of age) 133 (2.0%)
168
Appendix 4 - Supplementary analyses
169
Figure 1. Distributions of vitamin D intake from a) foods, b) supplements (multivitamins and
vitamin D or cod liver oil), and c) total vitamin D intake (food and supplements) (n = 3,427)
0 120 240 360 480 600 720 840 960 1080 1200
0
5
10
15
20
25
P
e
r
c
e
n
t
0 80 160 240 320 400 480 560 640 720 800
0
10
20
30
40
50
60
P
e
r
c
e
n
t
40 200 360 520 680 840 1000 1160 1320 1480
0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
P
e
r
c
e
n
t
Fig 1a: Vitamin D intake from foods (IU/day)
Fig 1b: Vitamin D intake from supplements (IU/day)
Fig 1c: Total vitamin D intake (IU/day)
170
Table 1. The association between ethnicity and use of sun protection at each age group of
exposure
Use of sun protection1
Caucasian N (%)
Non-Caucasian N (%)
p-value (from Chi square test)
Teen Never Sometimes Always
3065 (54) 2362 (42) 259 (5)
350 (56) 219 (35) 51 (8)
<0.0001
20s-30s Never Sometimes Always
1850 (33) 3258 (58) 494 (9)
265 (44) 269 (45) 68 (11)
<0.0001
40s-50s Never Sometimes Always
894 (17) 2970 (58) 1277 (25)
153 (29) 282 (53) 99 (19)
<0.0001
60s-74 Never Sometimes Always
354 (16) 1089 (49) 798 (36)
52 (34) 67 (44) 35 (23)
<0.0001
1 Self-reported protective clothing or sunscreen use
171
Table 2. Spearman rank correlations (rs) between time spent outdoors and sun protection
practices, erythemal UV and latitude during each age period of exposure
Time spent outdoors (hours per week)1
Teens 20s-30s 40s-50s 60s-74
rs (p-value)
Sun protection2
-0.04 (0.003) -0.02 (0.23) -0.002(0.87) 0.11 (<0.0001)
Erythemal UV of residence
(mW/m2)3
0.05 (0.0003) 0.02 (0.11) 0.04 (0.008) 0.02 (0.39)
Latitude of
residence4
-0.001 (0.64) 0.03 (0.009) 0.01 (0.28) 0.01 (0.59)
1 Typical number of hours spent outdoors from April to October during weekdays and weekends 2 Self-reported protective clothing or sunscreen use 3 Monthly average local noon erythemal UV radiation for June 2003 obtained from NASA’s Total Ozone Mapping Spectrometer (TOMS) 4 Geocoded based on location of residence reported as: city and province/state
172
Table 3. Spearman rank correlations (rs) between time spent outdoors and parity, income and
education during each age period of exposure
Time spent outdoors (hours per week)1
Teens 20s-30s 40s-50s 60s-74
rs (p-value)
Parity 0.06 (<0.001) 0.09 (<0.001) 0.07 (<0.001) 0.03 (0.19)
Income -0.04 (0.005) -0.06 (<0.001) -0.01 (0.34) 0.07 (0.02)
Education -0.07 (<0.001) -0.11 (<0.001) -0.07 (<0.001) 0.01 (0.56)
1 Typical number of hours spent outdoors from April to October during weekdays and weekends
173
Table 4. Distribution of breast cancer cases and controls and odds ratio (OR) estimates of
variables associated with cutaneous vitamin D production created by cross-classification during
4 age periods
1 Sum of the following values: if ethnicity was Caucasian then skin color = 1 and non-Caucasian = 0; if sun protection was used sometimes or always then skin color = 0, if sun protection never used then skin color = 1; if erythemal UV of residence was <50th percentile then UV = 0 and if >50th percentile then UV = 1; if time spent outdoors (calculated as typical number of hours per week during weekdays and weekends from April to October) was <50th percentile then time outdoors = 0 and if >50th percentile then time outdoors = 1. Maximum possible score = 4 for participants with highest UV generating potential. 2 Age-group adjusted
Combined skin color, sun protection, UV,
and time outdoors1
Cases n = 3101 No. (%)
Controls n = 3471 No. (%)
OR (95% CI)2
Teenage years 1 Lowest 2 3 4 Highest
363 (12) 979 (32) 1206 (39) 553 (18)
356 (10) 1009 (29) 1472 (42) 632 (18)
1.00 0.94 (0.79-1.12) 0.79 (0.67-0.93) 0.84 (0.70-1.02)
20-39 yrs of age 1 Lowest 2 3 4 Highest
378 (12) 1053 (34) 1276 (41) 393 (13)
386 (11) 1142 (33) 1472 (42) 469 (14)
1.00 0.93 (0.79-1.10) 0.87 (0.74-1.03) 0.80 (0.66-0.98)
40-59 years of age 1 Lowest 2 3 4 Highest
257 (9) 968 (33) 1463 (50) 231 (8)
245 (8) 1006 (32) 1630 (52) 273 (9)
1.00 0.90 (0.74-1.09) 0.83 (0.69-1.00) 0.77 (0.60-0.99)
60-74 years of age 1 Lowest 2 3 4 Highest
98 (8) 365 (30) 645 (52) 138 (11)
90(7) 352 (27) 714 (54) 167 (13)
1.00 0.95 (0.69-1.31) 0.83 (0.61-1.12) 0.76 (0.53-1.09)
174
Table 5. Sensitivity analyses evaluating a range of assumptions to derive the solar vitamin D score
and the corresponding odds ratio estimates for the derived variables and breast cancer risk
Values applied in algorithm1 OR2 (95% CI) comparing highest to
lowest quartile of exposure
Skin color3 Sun protection
practices 4
Erythemal UV
(mW/m2)5
Time outdoors
(hours/week)6
Exposure during ages 20-39
Exposure during ages 40-59
Caucasian=1.0
Other =0.33
Never=1.0
Sometimes=0.66
Always=0.33
As measured
(continuous)
Hours/week
(continuous)
0.76 (0.65-0.89)7 0.75 (0.64-0.88)
2
Caucasian=1.0 Other = 0.33
Never=1.0 Sometimes=0.66 Always=0.33
As measured (continuous)
Not included 0.77 (0.69-0.89) 0.84 (0.70-0.99)
Caucasians only Never=1.0 Sometimes=0.66 Always=0.33
As measured (continuous)
Hours/week (continuous)
0.79 (0.67-0.93) 0.77 (0.65-0.92)
Non-Caucasians only
Never=1.0 Sometimes=0.66 Always=0.33
As measured (continuous)
Hours/week (continuous)
0.53 (0.22-1.31) 0.50 (0.13-1.91)
Caucasian=1.0 Other=0.33
Never=1.0 Sometimes=0.66 Always=0.33
As measured (continuous)
>1 hr/day = 1.0 <1 hr/day = 0.5
0.83 (0.71-0.96) 0.79 (0.66-0.95)
Caucasian=1.0 Other=0.33
Never=1.0 Sometimes=0.66 Always=0.33
0-120 = 1.0 120-240 = 1.2 240-360 = 1.4 >360 =1.6
>1 hr/day = 1.0 <1 hr/day = 0.5
0.82 (0.71-0.96) 0.80 (0.64-0.99)
Caucasian=1.0 Other=0.33
Never=1.0 Sometimes=0.9 Always=0.7
As measured (continuous)
Hours/week (continuous)
0.75 (0.64-0.87) 0.85 (0.73-0.99)
Caucasian=1.0 Other=0.33
Never=1.0 Sometimes=0.9 Always=0.7
0-120 = 1.0 120-240 = 1.2 240-360 = 1.4 >360 =1.6
>1 hr/day= 1.0 <1 hr/day= 0.5
0.81 (0.69-0.94) 0.81(0.65-1.0)
Caucasian=1.0 Other=0.80
Never=1.0 Sometimes=0.66 Always=0.33
As measured (continuous)
Hours/week (continuous)
0.75 (0.64-0.87) 0.78 (0.66-0.90)
1 Score derived by multiplying assigned values for skin color, sun protection practices, erythemal UV, and time spent outdoors 2 Age group adjusted 3 Ethnicity used as a proxy for skin color with Caucasians assumed to have lighter skin color then non-Caucasian. 4 Self-reported protective clothing or sunscreen use 5 Monthly average local noon erythemal UV radiation for June 2003 obtained from NASA’s Total Ozone Mapping Spectrometer (TOMS) 6 Typical number of hours spent outdoors from April to October during weekdays and weekends 7 Original solar vitamin D score as proposed a priori
175
Table 6. Odds ratio (OR) estimates for derived solar vitamin D score during 4 age periods and
breast cancer risk overall and among lifelong residents of Canada only.
1 Sum of the following values: if ethnicity was Caucasian then skin color = 1 and non-Caucasian = 0; if sun protection was used sometimes or always then skin color = 0, if sun protection never used then skin color = 1; if erythemal UV of residence was <50th percentile then UV = 0 and if >50th percentile then UV = 1; if time spent outdoors (calculated as typical number of hours per week during weekdays and weekends from April to October) was <50th percentile then time outdoors = 0 and if >50th percentile then time outdoors = 1. Maximum possible score = 4 for participants with highest UV generating potential. 2 As reported previously in paper #3, page 117.
Solar vitamin D score 1 Overall
2
(n= 6571)
Lifelong Canadians (n=4447)
Not lifelong Canadian (n= 598)
OR (95% CI) OR (95% CI) OR (95% CI)
Teenage years Q1 Q2 Q3 Q4
1.00 0.90 (0.78-1.03) 0.80 (0.70-0.93) 0.79 (0.68-0.91)
1.00 0.85 (0.71-1.02) 0.78 (0.65-0.94) 0.76 (0.63-0.91)
1.00 1.07 (0.68-1.68) 0.93 (0.58-1.48) 1.02 (0.60-1.75)
20-39 yrs of age Q1 Q2 Q3 Q4
1.00 0.95 (0.81-1.12) 0.89 (0.77-1.02) 0.76 (0.65-0.89)
1.00 1.01 (0.82-1.23) 0.92 (0.77-1.10) 0.84 (0.69-1.01)
1.00 1.19 (0.69-2.07) 0.94 (0.60-1.46) 0.43 (0.25-0.74)
40-59 years of age Q1 Q2 Q3 Q4
1.00 0.85 (0.72-0.99) 0.82 (0.71-0.95) 0.75 (0.64-0.88)
1.00 0.80 (0.65-0.98) 0.85 (0.70-1.02) 0.72 (0.59-0.88)
1.00 0.87 (0.51-1.46) 0.75 (0.46-1.24) 0.82 (0.48-1.42)
60-74 years of age Q1 Q2 Q3 Q4
1.00 0.91 (0.72-1.14) 0.78 (0.62-0.98) 0.59 (0.46-0.76)
1.00 0.79 (0.59-1.05) 0.80 (0.60-1.06) 0.53 (0.39-0.72)
1.00 0.83 (0.37-1.85) 0.99 (0.43-2.28) 0.33 (0.13-0.87)
176
Appendix 5 - Power Calculations
Although the sample size for this study was predetermined, power calculations were conducted
to estimate the power to detect the main study objectives. Power calculations were performed
using Power Program v3.0.0. (Garcia-Closas & Lubin, 1999). All calculations are 2-sided with a
specified type I error (alpha level) of 0.05. An unmatched case control design with 1 control per
case was specified for all calculations. It is acknowledged that power is reduced when
confounders are added to the models but this was not taken into consideration in the power
calculations. Formulas used by Power Program are based on a binary response model.
Vitamin D was categorized into four levels of exposure, based on quartiles, with probabilities of
exposure of 0.25 in each group and a priori an expected odds ratio (comparing the highest versus
lowest quartile) of 0.7 was assumed. This estimated risk reduction of 30% is based on previous
studies results (John et al., 1999; Knight et al., 2007). Given these assumptions, the power to
detect an association between vitamin D and breast cancer was 99.9%. The power to detect an
OR of only 0.8 was 89.8%.
Using the assumptions above for vitamin D, the power to detect a multiplicative interaction (e.g.,
between vitamin D and calcium) with an estimated OR for the interaction effect (theta) of 0.6,
was 78.1%. The assumed interaction effect for the combined effect between calcium and vitamin
D are based on the trial by Lappe et al (Lappe et al., 2007). This assumes calcium intake was also
categorized into quartiles and the expected odds ratio of for the independent effect of calcium
was 0.8 (as observed previously Cui & Rohan, 2006)