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1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 2 IARC, Lyon, France; 3 Finnish Cancer Registry, Helsinki, Finland; 4 Norwegian Cancer Registry, Oslo, Norway; 5 Department of Community Medicine, Medical Facility, Tromso University, Tromso, Norway; 6 Finnish Institute of Occupational Health, Helsinki, Finland; 7 Department of Epidemiology, School of Public Health, Harvard University, Boston, Massachusetts
Requests for reprints: Elisabete Weiderpass, IARC, 150, cours Albert Thomas, F-69008 Lyon, France. Phone: 33-4-72738049; Fax: 33-4-72738345. E-mail: elisabete.weiderpass{at}cancer.fi
| Abstract |
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| Introduction |
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We present here results from a large, population-based cohort study carried out among premenopausal women in Norway and Sweden in relation to body size in three different periods of life (age 7, age 18, and adulthood) and breast cancer risk.
| Subjects and Methods |
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A letter of invitation to participate in the study and a health survey questionnaire was sent to all women. In Norway, the questionnaire was mailed to 10 subgroups at regular intervals. In Sweden, two mailings were done: one in 1991 and one in 1992. The questions relevant to the analysis presented here were identical in the two countries. This common set of questions included a detailed assessment of body size and shape in different periods of life, oral contraceptive use, reproductive history, prevalent diseases, history of breast cancer in the mother and sister(s), and other lifestyle habits. To facilitate recall, a color brochure with pictures of almost all contraceptive pill packages ever sold in Norway and Sweden was sent to all women together with the letter of invitation.
Measurement of Exposure
Information about anthropometric measures is based on the questionnaire administered at cohort enrolment. Women were asked about their perceived body shape at age 7 as compared with other girls of the same age. They classified themselves as very thin, thin, average, fat, and very fat. They were also asked about their weight at age 18 and at study enrolment and their adult height (cm). Body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m). We calculated BMI at age 18 and at study enrolment. We created an indicator variable on the difference in body size between age 7 and adulthood with the following categories:
20 kg/m2 at age 18 or 1 year before interview or had average body size at age 7 and a BMI > 25 kg/m2 at age 18 or 1 year before interview; and We also calculated the changes in BMI between age 18 and entry to the study and classified this difference (BMI units) as decreased, increased up to 1.4 units, increased 1.5 to 4.0 units, and increased >4.0 units.
Information on menopausal status was obtained from the questionnaire. We have no information about menopausal status after start of follow-up. Only women who reported a natural menopause or a bilateral oophorectomy at cohort enrolment were considered as postmenopausal. All other women were considered as premenopausal, regardless of hysterectomy or use of hormonal replacement therapy until age 50 (mean age of natural menopause in these populations), and censored from all analyses. Only breast cancers diagnosed before age 50 were considered as relevant outcome in the present analysis.
Information on self-perceived physical activity levels at age 14, age 30, and at cohort enrolment was collected as scores from 1 (lowest level of physical activity) to 5 (highest level of physical activity). Subsequently, the variables were compiled into three levels for each age period: low (scores 1 and 2), middle (scores 3 and 4), and high (score 5).
To identify women who had possible anovulatory cycles, we asked how many years after menarche their periods became regular. The answers were classified as <1 year, 1 to 3 years, >3 years, never, and I do not know. We considered those who answered "never" as having irregular menstrual cycles. We also asked women if they had ever tried to become pregnant for a period of >1 year without succeeding and considered those who answered "yes" as probably having an infertility problem.
Follow-up
Follow-up was performed through links between the cohort data set and various population-based registries. This was possible by use of the national registration numbers present in the cohort data set and in all national registers. We obtained information on the date of death for deceased persons from the death registers and on the date of emigration from the registers of population migration. The national cancer registers, established in the 1950s in both countries, provided data on prevalent cancer cases at cohort enrolment and incident cancers diagnosed in cohort during follow-up. These registries are estimated to be almost complete (6, 7).
The start of follow-up was defined by the return of the questionnaire in 1991 or 1992. The follow-up ended on December 31, 1999, at emigration, death, or primary breast cancer diagnosis, whichever occurred first. Of the 100,000 invited women in Norway, 57,582 (57.6%) returned a completed questionnaire as did 49,259 (51.3%) of the 96,000 invited women in Sweden. Thus, the overall crude participation rate was 54.5% (106,841 of 196,000). During follow-up, 789 women emigrated and 1,360 died.
For the analysis presented here, we further excluded 15 women who were dead or had emigrated before we started follow-up, 1,663 women who had been reported as having an invasive cancer before study enrolment, 237 women without any information on weight during their lifetime, and 5,209 women reporting being postmenopausal (see criteria above) at cohort enrolment. In summary, 99,717 women were included altogether in the analysis presented here.
Statistical Methods
We calculated relative hazards using the Cox proportional hazard models (8), considering anthropometric measures as the independent variable and premenopausal breast cancer as the dependent variable. We interpreted relative hazards as estimates of relative risks (RR) with 95% confidence intervals (CI). The comparison group is specified in each table.
We kept the following covariables in the final multivariate models: age at cohort enrolment (as a continuous variable in years), a combined variable with parity (0, 1, 2, or
3 children) and age at first birth (<21, 22 to 24, or
25 years), age at menarche (as a continuous variable), use of oral contraceptives (current, former, or never used at cohort entry), history of breast cancer in the mother or sister(s), and total duration of breast-feeding (as a continuous variable in months). Because these variables were all repeatedly reported as being associated with premenopausal breast cancer (9), we kept them in the models although they did not affect the association between anthropometric measures and breast cancer in our data. In addition, we included the country of residence (Norway/Sweden) in multivariate models because breast cancer incidence varies slightly. The variables on physical activity were not kept in the final model because they did not alter risk estimates meaningfully but in fact worsened model fitting.
We analyzed BMI at cohort enrolment and breast cancer risk both with inclusion of all women and following exclusion of women who probably had long periods of anovulation as indicated by irregular menses, infertility, or both. We did so because long periods of anovulation might indicate polycystic ovarian syndrome, a possible risk factor for breast cancer, which is also associated with obesity (10, 11).
Possible interactions were evaluated by including appropriate product terms in the models. The responsible Data Inspection Boards and Ethical Committees in both countries approved the study design, and all women gave their informed consent to participate in the study.
| Results |
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25 kg/m2). The prevalence of overweight or obesity (BMI
25 kg/m2) increased from 4.9% (n = 4,531) at age 18 to 22.7% (n = 22,018) at cohort enrolment. During the same period, over 24,000 (26.1%) women increased their BMI by
4 units. Body size at age 7 was moderately correlated with BMI at age 18 (r = 0.43) and weakly correlated with BMI at cohort enrolment (r = 0.26). BMI at age 18 was moderately correlated with BMI at cohort enrolment (r = 0.48).
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25 kg/m2 at age 18 were at lower breast cancer risk (RR 0.74, 95% CI 0.59-0.91) as compared with leaner women, but the association was attenuated after adjustment for BMI at cohort enrolment (RR 0.90, 95% CI 0.66-1.24).
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2-fold gradient in risk between the lowest and the highest categories of BMI, and women with a BMI
30 kg/m2 at enrolment had a 38% lower RR compared with women with a BMI between 20 and 25 kg/m2. In a multivariate model having BMI at enrolment adjusted for measures of body size at age 7 and BMI at age 18, the risk reduction among women who were fat/very fat was slightly attenuated (RR 0.66, 95% CI 0.40-1.07; P for trend = 0.007). When BMI at cohort enrolment was modeled as a continuous variable, each one-unit increment of BMI reduced breast cancer risk by 4%. BMI at cohort enrolment was positively associated with a history of irregular menses (P < 0.001) but not with evidence of infertility (P = 0.95). We repeated the analyses on adult BMI (Table 3) after the exclusion of 3,051 women with irregular menses (of which 19 developed breast cancer during follow-up), 3,561 women with infertility (36 developed breast cancer), and 233 women with both irregular menses and infertility (2 developed breast cancer). The risk estimates were largely identical: in the full multivariate model, women who were obese had RR = 0.65 (95% CI 0.40-1.07) compared with those who had a BMI = 20 to 25 kg/m2. When BMI was modeled as a continuous variable, risk reduction per unit increase was still 4% (95% CI 0.94-0.99).
Women taller than 160 cm had a 30% increased breast cancer risk as compared with shorter women (Table 3). However, there was no evidence of a linear association between height and premenopausal breast cancer (RR 1.02, 95% CI 1.00-1.03 for increase in 1 cm height).
We did a stratified analysis to further clarify whether body shape during different periods of lifeand adult heightcould mutually confound or modify the effects on breast cancer risk (Table 4). We found no clear evidence of significant interaction between these characteristics. The most salient finding in this analysis was the consistently reduced risk among women who were overweight or obese (BMI
25 kg/m2) at cohort entry, particularly among those reporting being fat/very fat during childhood, and some indicationalthough not statistically significantof a reduced risk among women who were fat/very fat at childhood (Table 4). Among tall women (measuring
170 cm), there was no significant effect of BMI on breast cancer risk, whereas in short women (<162 cm), the risk reduction was clearest in heavy women (BMI
25 kg/m2; Table 4).
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30 kg/m2) was limited to those without any family history of breast cancer. No such reduction was seen in obese women with a family history of breast cancer in first-degree relatives, although this stratified analysis was hampered by small numbers (low statistical power; Table 6).
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| Discussion |
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Our finding of a protective effect of adult BMI on premenopausal breast cancer risk is in accordance with a growing body of evidence from numerous investigations (1). In a recent analysis of pooled data from prospective cohort studies (12), women with a BMI
31 kg/m2 had a RR of premenopausal breast cancer of 0.54 (95% CI 0.34-0.85) as compared with those with a BMI < 21 kg/m2. In our study, this association was closely similar and seemed to stand independent of body size at an early age. We had no opportunity to explore whether perinatal anthropometryrelevant because birth weight is a potential breast cancer risk factor (13, 14), which has been demonstrated to carry a U-shaped relationship with adult BMI (15)confounded the relationship. The mechanisms linking adult obesity to premenopausal breast cancer are not fully known (1). Although an increased frequency of anovulatory menstrual cycles has been repeatedly invoked, empirical support for this theory is scant (1, 16). In our data, the association with adult BMI was not influenced by the exclusion of women who reported irregular menses and/or infertility. However, even when women are ovulating regularly, obesity may be associated with luteal insufficiency as shown by decreased levels of progestins or other changes in the sex steroid profile (16). The suggested effect modifiers of the association between adult BMI and risk in our dataheight and family history of breast cancerawaits confirmation or rejection and biological interpretation in future studies.
In two previous cohort studies, BMI during adolescence and young adulthood was associated with a 25% to 40% decrease in premenopausal breast cancer risk (17, 18). In case-control studies, heavier weight during young adulthood was associated with 20% to 30% decrease in premenopausal breast cancer (19-24), increased risk in one study (25), and no association in other studies (26, 27). Data on the influence of childhood obesity on premenopausal breast cancer risk are scarce, and an association is far from established (1). In the Nurses' Health Study, recalled body fatness at age 10 was associated with a decreased risk of breast cancer (28). In contrast to our findings, this relationship remained statistically significant even after adjustment for body fatness at age 30.
In a recent case-control study in twins, the risk of premenopausal breast cancer was increased for women who were less obese than their twin at age 10 among dizygotic twins but not among monozygotic twins (29). This indicates that the potential impact of childhood obesity operates through environmental rather than genetic pathways. Excessive prepubertal body fat might slow adolescent physical growth (28), allowing more time for repair of DNA damage occurring in the highly proliferating adolescent breast epithelium (30). Data from a study by Li et al. (31) suggest that risk is lower among women who reach their maximum adult height at a later age independently of age at menarche, supporting the notion that slow physical maturation might be beneficial with regard to breast cancer development.
In the limited number of studies of premenopausal breast cancer risk, adult weight gain was associated with a 30% reduction in risk (21-23). The apparent inverse association in our data was, however, completely explained by BMI at enrolment.
As reported previously by others (32, 33), we also found height to be positively associated with increased breast cancer risk, albeit in a nonlinear fashion. Possible mechanisms suggested to explain this association include energy intake (because in rodents, energy restriction has been associated with decreased mammary tumor rates) and circulating insulin-like growth factor-I levels during childhood, both associated with adult height (9).
Our study presents strengths and limitations. The strengths include those inherent to a prospective cohort study such as lack of recall and selection bias (34).
Furthermore, we collected detailed information on anthropometric measure during different periods of life and on potential confounders. Because we used self-administered questionnaires to assess body size in different periods of life, exposure measurement error or recall error could be substantial especially concerning childhood body build. However, if exposure measurement error occurred, it should be nondifferential with regard to case status.
In summary, body size at childhood and early adulthood as well as weight gain are related to premenopausal breast cancer risk chiefly as predictors of adult body size (18). In contrast, BMI in adulthood is one of the strongest predictors of premenopausal breast cancer thus far established and one of the few that are potentially modifiable. Nevertheless, no public heath message can be meaningfully founded on this association, because overweight and obesity increase overall mortality even before menopause and also increase the risk of breast cancer as women become postmenopausal. Hence, the main scientific challenge now is to understand the biological mechanisms by which obesity prevents malignant transformation of breast tissue in younger women. Our data do not support the hypothesis that anovulation is likely to play an important role in this process.
| Footnotes |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 11/25/03; revised 2/11/04; accepted 3/ 1/04.
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