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American Journal of Epidemiology Advance Access originally published online on June 4, 2007
American Journal of Epidemiology 2007 166(4):456-464; doi:10.1093/aje/kwm112
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

ORIGINAL CONTRIBUTIONS

Association of Childhood and Adolescent Anthropometric Factors, Physical Activity, and Diet with Adult Mammographic Breast Density

TA Sellers1, CM Vachon2, VS Pankratz2, CA Janney2, Z Fredericksen2, KR Brandt3, Y Huang1, FJ Couch4, LH Kushi5 and JR Cerhan2

1 Division of Cancer Prevention and Control, H. Lee Moffitt Cancer and Research Institute, Tampa, FL
2 Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN
3 Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN
4 Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN
5 Division of Research, Kaiser Permanente Northern California, Oakland, CA

Correspondence to Dr. Thomas A. Sellers, Division of Cancer Prevention and Control, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612 (e-mail: thomas.sellers{at}moffitt.org).

Received for publication August 22, 2006. Accepted for publication February 28, 2007.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Early-life exposures may influence the development of breast cancer. The authors examined the association of childhood and adolescent anthropometric factors, physical activity levels, and diet with adult mammographic breast density, a strong risk factor for breast cancer. Women in the Minnesota Breast Cancer Family Study cohort who had undergone mammograms but had not had breast cancer (n = 1,893) formed the sample. Information on adolescent exposures, including relative height, weight, and physical activity at ages 7, 12, and 18 years and diet at age 12–13 years, was self-reported during two follow-up studies (1990–2003). Mammographic percent density was estimated using a computer-assisted thresholding program. Statistical analyses were performed using linear mixed-effects models with two-sided tests. Positive associations with height at ages 7 (p < 0.001), 12 (p < 0.001), and 18 (p < 0.001) years and percent density were evident overall and within menopausal status categories. The minimum difference in percent density between the tallest and shortest girls was 3 percent, with a maximum of 7 percent. Weight at age 12 years (p = 0.005) and adiposity at age 12 years (p = 0.005) were both inversely associated with adult percent density. Adolescent physical activity and diet were unrelated to percent density. These results suggest that adolescent height, a known risk factor for breast cancer, is also associated with mammographic percent density.

adiposity; anthropometry; breast; breast neoplasms; diet; exercise; mammography

Abbreviations: IGF-I, insulin-like growth factor I


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Mammographic breast density is an important risk factor for breast cancer, with magnitudes of risk second only to age and inherited mutations in the breast cancer genes BRCA1 and BRCA2 (1). The association appears to be independent of other measured risk factors (2) and remains strong 10 years prior to cancer diagnosis, illustrating that it is not a result of breast tumors' being masked by regions of dense tissue during the course of screening mammography (e.g., see Kerlikowske et al. (3)). Despite the importance of the trait, the biologic basis for a possible causal effect on risk is largely unknown.

Several early studies based on small samples provided suggestive evidence that mammographic density has a heritable component (4, 5). Larger, more recent studies of Australian and American twins demonstrated 60 percent heritability for breast density after adjustment for age and other risk factors (6) and approximately 65 percent heritability for both dense and nondense regions (7). Pankow et al. (8) reported results from a segregation analysis of families that provided statistical evidence for the existence of a major gene that could account for up to 30 percent of the variability in the trait. Careful inspection of the model parameters suggested that this genetic influence could be exerted through an effect on the rate of dense tissue involution. It also raised the question of the potential importance of mammographic density levels earlier in life and the factors that potentially influence those levels.

There is emerging evidence that early-life factors may portend increased risk for breast cancer (9). Given the importance of mammographic density as a risk factor, there is a need to identify modifiable exposures, especially those present during childhood and adolescence. Our previous analyses of data from the Minnesota Breast Cancer Family Study provided evidence that birth weight was positively associated with adult mammographic density (10). In addition, heavy and frequent use of alcohol during adolescence that continues through adulthood may also influence breast density (11). Thus, early-life exposures could affect breast cancer risk through their effect on breast density. Here we present findings on childhood and adolescent anthropometric factors, physical activity, and dietary factors.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Study population
Details on the baseline phase of the Minnesota Breast Cancer Family Study (12) and the first follow-up (13) have been previously published. Briefly, a family study of breast cancer was initiated in 1944 at the University of Minnesota (Minneapolis, Minnesota). Breast cancer probands were women whose cancers were ascertained at the tumor clinic of the University of Minnesota Hospital between 1944 and 1952 (n = 544). Data on probands and history of cancer among relatives were obtained through interviews, medical history questionnaires, and death certificates. The first follow-up study of this cohort was initiated in 1990 and completed in 1996. Family members eligible for the follow-up were sisters, daughters, nieces, and granddaughters of the breast cancer probands and women identified as the spouses of male first- and second-degree relatives of probands. Information on 426 (78 percent) families was successfully updated, and extensive risk factor data on 6,194 women were collected.

A second follow-up was initiated in 2001 and completed in 2003; questionnaires were mailed to all blood relatives and spouses of blood relatives included in the 426 pedigrees that completed the first follow-up survey. Of the 6,194 eligible women from follow-up 1, 604 were deceased (9.8 percent), 654 were lost to follow-up (10.6 percent), 1,109 refused (17.9 percent), and 84 were unable to complete the questionnaire (1.4 percent). This left 3,743 women who completed the 2001 questionnaire, producing a response rate of 77.1 percent of those contacted and competent to complete the survey and an overall participation rate of 60.4 percent of those who participated in the first follow-up.

For the present analyses, we excluded women with a reported history of breast cancer at follow-up 1 or follow-up 2 (n = 227) and women under age 40 years (n = 789), the age at which initiation of regular screening mammograms is recommended. Of the remaining 2,727 women who provided authorization for mammogram retrieval, mammograms from 295 (10.8 percent) women were unavailable and mammograms from 539 (19.8 percent) women were not requested within the time period allocated for mammogram collection; this left 1,893 women (69.4 percent) whose mammogram films were retrieved and digitized.

Risk factor data collection
The follow-up 2 questionnaire collected information on early childhood and adolescence exposures, health and lifestyle history, female health history, current diet and preparation of food, and new cancers diagnosed since the first follow-up (1990). Participants were asked to retrospectively compare themselves with other girls their age regarding their height (short, average, tall), weight (thin, average, heavy), and physical activity level (less than average, average, more than average) at ages 7 years (about the first grade), 12 years (about the sixth grade), and 18 years (about the 12th grade). They were also asked to recall the age at which they stopped getting taller and their weight at that time. Body somatotype was reported for ages 12 and 18 years using a nine-level pictogram (1 represented extremely lean, while 9 represented extremely obese) (14). This measure was found to have reasonable Pearson crude correlations for somatotype at ages 10 years (r = 0.65), 15 years (r = 0.75), and 20 years (r = 0.66) as recalled in 1988 against actual measurements taken from 1922–1935 (14). Finally, women were asked to estimate the average number of hours per week in which they had been involved in vigorous physical activity at ages 13–18 years. The response categories (hours/week) were none, 2, 3–6, 7–13, 14–20, 21–27, and 28 or more.

To assess diet at age 12–13 years, we used a semiquantitative food frequency questionnaire developed by Potischman et al. (15), which was designed to identify key foods related to fat intake and intake of fruits and vegetables during adolescence but was not intended to provide estimates of nutrient or caloric intake. Set portion sizes were specified for each of 29 food items, and respondents were asked to identify their usual frequency of consumption, with options including never or less than once per month, once per month, 2–3 times per month, once per week, twice per week, 3–4 times per week, 5–6 times per week, and every day. These 29 items were found to have more than 50 percent concordance (within one frequency category) with maternal reports on the basis of 1,133 mother-daughter pairs (15). Specific food groupings were based on the report by Potischman et al. (15) and are specified in table 3.


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  		TABLE 3. Association of diet at age 12–13 years with mammographic percent density, for all women with complete dietary data (n = 1,552) and by menopausal status at the time of mammography, Minnesota Breast Cancer Family Study, 1990–2003

 
Estimation of mammographic density
Details on mammography collection have been previously published (10). Briefly, for the 1,893 women included in this report, an average of 5.6 mammograms per woman were available for analysis (range, 1–16 mammograms). We utilized the mammogram with the date closest to the date of first follow-up (follow-up 1), from which most of the data on confounding variables (including weight and menopausal status) were ascertained. Over 90 percent of the mammograms occurred within 5 years of the follow-up 1 survey; 39 percent occurred within 1 year or less. Mammograms were digitized on a Lumiscan 75 scanner with 12-bit gray-scale depth. The pixel size was 0.130 x 0.130 mm2 for both the 18- x 24-cm2 and 24- x 30-cm2 films. Mammographic percent density (number of dense pixels divided by the total number of pixels x 100) was estimated from the left mediolateral oblique view of the mammogram using a computer-assisted thresholding program (Cumulus) developed at the University of Toronto (16). All images were read by a single trained technician who has consistently demonstrated high reliability (r > 0.90) while reading more than 500 duplicate images across varying time frames.

Statistical analysis
Distributions and mean values (when appropriate) for all variables were calculated for all women and within strata of menopausal status, since weight and obesity are known to have differential effects on risk by menopause. Linear mixed-effects models were used for statistical modeling of the association between adolescent exposures and percent density to account for the correlated nature of our family data. We fitted linear mixed-effects models that estimated a variance component for a polygenetic effect using a correlation matrix (twice the kinship matrix) that described the average amount of genetic sharing between pairs of relatives (17). We used the S-Plus kinship library developed at the Mayo Clinic (www.mayo.edu/hsr/Sfunc.html) for the linear mixed-effects modeling. Selected models were validated by repeating the fits using SOLAR (18).

There were three stages to our model-building approach. We first fitted a simple model consisting of variables consistently reported in the literature to be associated with mammographic breast density (19): age at menarche, age at mammography, body mass index (weight (kg)/height (m)2) at first follow-up, use of hormone replacement therapy, and menopausal status. We then fitted a full model which included the variables in the simple model plus parity and age at first birth, oral contraceptive use, usual adult frequency of alcohol consumption, education, and adolescent and adult smoking status; these are variables that were univariately associated with percent density in our sample. Finally, to further adjust for familial breast cancer history, we added a correlated frailty score derived from a mixed-effects Cox proportional hazards model (20). Briefly, we obtained a specific frailty score based on the degree of relationship (kinship) among the women in the family and the pattern of breast cancer in the pedigree. The median frailty score was obtained and was used as the cutpoint to classify the woman's frailty score as either high or low. This dichotomous frailty score was used in the linear mixed-effects models to adjust for the impact of a family history of breast cancer on the results.

The appropriate functional form for each linear covariate was determined. Adjusted mean values were computed for each of the three models using a smearing estimate (21). Mean values for the full model were adjusted for the following reference distribution: postmenopausal, 60-year-old woman whose body mass index was 27.1, no use of hormone replacement therapy, 1–2 births over the age of 20 years, menarche at age 13 years, completion of high school, never smoking in adolescence or adulthood, nondrinking in adulthood, and never use of oral contraceptives. Separate models were fitted for pre- and postmenopausal women. In the premenopausal models, mean values were adjusted to a reference distribution similar to that shown above, but an age of 45 years rather than 60 years was used as the reference age. Since similar results were observed for the three model-building approaches, we report results from the full model without the correlated frailty score. All statistical tests were two-sided.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Summary characteristics of the study sample are presented in table 1. The mean mammographic density was 22.8 percent, with density being higher among premenopausal women than among postmenopausal women. As expected, ever use of hormone replacement therapy was higher for the postmenopausal women than the premenopausal women, but there were no differences in weight at follow-up 1, age at menarche, or relationship to the proband. Premenopausal women tended to have higher levels of formal education.


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  		TABLE 1. Characteristics of the study sample according to menopausal status at the time of mammography, Minnesota Breast Cancer Family Study, 1990–2003

 
The first set of analyses examined the association of adolescent anthropometric factors and physical activity with adult mammographic density (table 2). Significant positive associations with height at ages 7, 12, and 18 years and mammographic density were evident, overall and within menopausal status categories. There was no association between age at which the participant stopped getting taller or weight at the time the participant stopped getting taller and mammographic density. Neither weight nor physical activity level relative to peers was consistently associated with adult mammographic density. The notable exceptions were weight at age 12 years and adiposity at age 12 years, which were both inversely associated with adult mammographic density, particularly among postmenopausal women.


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  		TABLE 2. Association of childhood and adolescent anthropometric factors and physical activity with mammographic percent density, for all women (n = 1,893) and by menopausal status at the time of mammography, Minnesota Breast Cancer Family Study, 1990–2003

 
Simultaneous inclusion of relative height at age 7 years, weight at age 7 years, and physical activity at age 7 years in the same model (adjusted for all potential confounders) did not alter the associations reported in table 2; this was also true for simultaneous adjustment for these variables at ages 12 and 18 years (data not shown).

Data suggest that low birth weight is associated with growth velocity and may also be a risk factor for breast cancer (22). Moreover, we have shown birth weight to be positively associated with percent density (10). Therefore, we performed post-hoc analyses on a subset of 940 participants for whom self-reported birth weight was available. Adjustment for birth weight did not alter the associations of height or weight at age 7, 12, or 18 years with mammographic density overall or by menopausal status (data not shown).

The next set of analyses explored whether aspects of reported dietary intake at age 12–13 years were associated with adult mammographic density (table 3). None of the dietary variables were significantly associated with percent density, and there were no patterns suggestive of an association.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
The present report examined the associations of recalled anthropometric measures, diet, and physical activity during childhood and adolescence with mammographic density in adulthood. Diet and physical activity levels in adolescence appeared to be unrelated to mammographic density. However, women who described themselves as being heavier on average than their peers at age 12 years or having a more obese somatotype had lower mean mammographic densities as adults, primarily after the menopause. In addition, women who reported being taller on average than their peers at age 7, 12, or 18 years had higher mean mammographic densities as adults. These latter associations were evident for pre- and postmenopausal women and were generally monotonic across gradations of height at each recalled age.

The association between adult height and breast cancer risk has been the subject of investigation for over 35 years (23), with the association having been examined in more than 70 case-control studies and 25 cohort studies (9, 24), as well as a pooled analysis of findings from prospective cohort studies (25). The observation of increased risk with increased height is remarkably consistent and is evident for both premenopausal and postmenopausal disease. Since maximal adult height is reached in young adulthood, several investigators have studied height at various periods of development (26, 27). The largest and most detailed study was based on 117,415 Danish women with data on birth weight and measured height and weight from school records and over 3.3 million person-years of follow-up (28). Increased risk of breast cancer was evident for taller adolescents across ages 8–14 years, but later ages of peak growth were associated with lower risks than early onset of peak growth. Conversely, far less is known about the association between height and mammographic density, other than the early report by Saftlas et al. (29), which noted a positive correlation with adult height. Thus, the current findings that adolescent height is associated with adult mammographic density are consistent with expectations based on the literature.

If the observed associations between adolescent height and adult mammographic density were merely a reflection of overnutrition during early life, one might have expected to see some association with adolescent diet in our population. Since the approach to adolescent dietary assessment in this study was not designed to capture total energy intake, this lack of association may be expected, even if an association with overnutrition exists. Adolescent diet (particularly a diet high in fat or meat or low in fruits and vegetables), as measured with the food frequency questionnaire used here, was not found to be associated with breast cancer among young women (15), and to date there have been few consistent links of adolescent diet with breast cancer risk (30–32). Another explanation may be greater nonsystematic misclassification of remote dietary habits than of the other exposures we measured, such as relative height and weight.

If overnutrition were an important explanation for the impact of adolescent height on mammographic density, we might also expect associations with the various measures of weight or adiposity. Aside from an inverse association of weight and somatotype at age 12 years with postmenopausal density levels, no consistent trend was apparent. Although one must acknowledge that these findings may be a chance observation, fatter somatotype has been inversely associated with both pre- and postmenopausal breast cancer risk (33). It will be important to examine adolescent weight history and adiposity in relation to adult mammographic density in other study populations.

The biologic plausibility of the current findings may be informed by consideration of the factors that influence growth velocity. The regulation of pubertal growth is complex, and it appears to include endocrine hormones and growth factors, such as gonadotropins, growth hormone, and sex steroids (34, 35). Several of these factors, such as estrogens, progesterone, and insulin-like growth factors, are associated with breast cancer risk (36, 37). Many of these factors associated with height also coordinate mammary gland development, exerting direct (e.g., estrogen and progesterone) or indirect (e.g., growth hormone and insulin) influences on mammary-cell differentiation and proliferation (38, 39). Levels of insulin-like growth factor I (IGF-I) in early adulthood appear to be associated with childhood growth and adult stature (40). In addition, estrogen stimulates ductal growth and increases cell proliferation rates, which can increase the likelihood of a random genetic error (41). Therefore, the intrinsic connection of adolescent height and breast cancer risk could be explained in part by endocrine hormones and growth factors.

There are also data to support direct linkage of circulating levels of height-related hormones and growth factors with mammographic density. An early study of 110 premenopausal women suggested that women in the Wolfe P2 and DY categories of breast density (the highest density) tended to have higher levels of progesterone than women in the N or P1 categories (42). This has been confirmed in premenopausal women (43) but not in postmenopausal women (44, 45). A single nucleotide polymorphism in the regulatory region of GH1 (–75G > A) has been associated with density and levels of both growth hormone and IGF-I (46). Diorio et al. (47) reported a correlation of 0.083 (p = 0.021) between IGF-I and mammographic density, but only among premenopausal women, a finding similar to that seen in other studies (48–50). In another small study of 88 overweight postmenopausal women who were not using hormone therapy, Aiello et al. (51) examined associations of endogenous sex hormones, IGF-I, and lipids with mammographic density. Although several statistically significant associations were observed, they pointed in a direction opposite that hypothesized, and the mean mammographic density was less than 10 percent. More research is clearly needed in this area, but growth hormones and growth factors appear to be promising areas for further investigation (52).

There is now a fairly substantial body of literature supporting the notion that habitual physical activity reduces risk of breast cancer (53). A review of 19 case-control studies and four cohort studies of physical activity in adolescence and young adulthood found consistent evidence of a lower risk of subsequent breast cancer among the most active youth (54). However, far fewer studies have examined physical activity in relation to mammographic density, and the results have been inconsistent. Vachon et al. (55) observed no association between adult level of physical activity and mammographic percent density. Suijkerbuijk et al. (56) obtained similar results in their cross-sectional study of 620 Dutch participants in the EPIC (European Prospective Investigation into Cancer and Nutrition) Study. Although a different categorization scheme for mammographic patterns was used by Gram et al. (57), no statistically significant association with moderate physical activity was observed. Conversely, Lopez et al. (58) found that sedentary Hispanic women had a slightly higher mean percent density than less sedentary women. The basis for the differences in results among studies is not obvious.

Strengths of our study include the relatively large sample size, the inclusion of pre- and postmenopausal women, and the collection of data referring to different time periods during childhood and adolescence. This permitted the ability to place less credence in statistically significant findings that were not consistent across ages. The method used to quantify mammographic density is largely objective and is highly reproducible, and density was determined by a single rater. Although we had to rely on recall for the exposures of interest, the subjects were not ascertained on the basis of their mammographic density, which reduced the potential for recall bias.

Despite these strengths, there are limitations that should be considered when interpreting the results. It may have been instructive to have actual heights, rather than height relative to the height of one's peers, as the exposure of interest. However, we were concerned about the accuracy of such recalled data versus mere recall of height relative to peers. The study population was based on families originally ascertained among breast cancer patients who were residents of Minnesota between 1944 and 1952, and ethnically they were almost entirely non-Hispanic Whites. Although this had no effect on internal validity, the generalizability of the findings is unknown, raising the need to examine these findings in more diverse populations.

In summary, the current study provides evidence that height in adolescence, and perhaps growth velocity, is associated with adult mammographic density. Weight at age 12 years was also inversely associated with mammographic density, but not weight at other ages. We found no apparent association with aspects of diet or physical activity. The supporting literature for a physiologic basis of the observed association with height merits further exploration, as it may provide insight into possible mechanisms relating height and mammographic density to risk of breast cancer, as well as the importance of early-life developmental factors in breast cancer risk.


   ACKNOWLEDGMENTS
 
This study was supported by National Cancer Institute grant CA39741.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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H. K. Neilson, C. M. Friedenreich, N. T. Brockton, and R. C. Millikan
Physical Activity and Postmenopausal Breast Cancer: Proposed Biologic Mechanisms and Areas for Future Research
Cancer Epidemiol. Biomarkers Prev., January 1, 2009; 18(1): 11 - 27.
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