Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

Longitudinal Study on the Role of Body Size in Premenopausal Breast Cancer

Longitudinal Study on the Role of Body Size in Premenopausal Breast Cancer Abstract Background A high body mass index (BMI) has been related to a reduced risk of breast cancer in premenopausal women. The mechanisms underlying this association have not been elucidated. Methods We explored whether factors affecting ovulation may explain the inverse association between BMI (calculated as weight in kilograms divided by the square of height in meters) and breast cancer in 113 130 premenopausal participants in the Nurses' Health Study II. During 1 225 520 person-years of prospective follow-up between 1989 and 2003, 1398 incident cases of invasive breast cancer were diagnosed. Weight, height, ovulatory infertility, menstrual cycle patterns, and a multitude of covariates were assessed throughout follow-up. Cox proportional hazards regression was used to compute hazard ratios and 95% confidence intervals (CIs). Results We observed a significant linear inverse trend between current BMI and breast cancer incidence (P<.001) that was not explained by menstrual cycle characteristics or infertility due to an ovulatory disorder (covariate-adjusted hazard ratio for breast cancer in women with a BMI ≥30 vs 20.0-22.4, 0.81; 95% CI, 0.68-0.96). We found BMI at age 18 years to be the strongest predictor of breast cancer incidence (covariate-adjusted hazard ratio for breast cancer in women with a BMI at age 18 years ≥27.5 vs 20.0-22.4, 0.57; 95% CI, 0.41-0.81). Conclusions Body size during the early phases of adult life seems to be particularly important in the development of premenopausal breast cancer. Factors other than anovulation are likely to mediate the protection conferred by a high BMI. The inverse association between body mass index (BMI) and the risk of breast cancer among premenopausal women has been observed in numerous studies,1-15 but the biological mechanisms underlying this perplexing link have remained largely unresolved. A high BMI can be associated with irregular or long menstrual cycles or with polycystic ovary syndrome (PCOS), and it has been suggested that anovulation, which is associated with such characteristics and with decreased estradiol and progesterone levels, may explain the lower risk of breast cancer in these women. However, few studies have explored whether these or other factors provide mechanistic insights into the unexpected protection that a high body mass confers on the premenopausal breast. We investigated whether menstrual cycle characteristics, infertility due to an ovulatory disorder, or PCOS might explain the inverse association between BMI and premenopausal breast cancer incidence in participants in the Nurses' Health Study II (NHS II). Methods Study population: nhs ii In 1989, 116 609 female registered nurses aged 25 to 42 years living in 1 of 14 US states responded to a self-administered questionnaire about their medical history and lifestyle. Participants have since been followed up by means of biennial questionnaires updating information on demographic variables, lifestyle factors, and medical events. For this analysis, women were excluded at baseline in 1989 if they were postmenopausal (n = 2813), reported cancer (n = 989), were missing the date of diagnosis of invasive breast cancer (n = 28), or were missing information on height or weight (n = 322) (not mutually exclusive). This study was approved by the institutional review boards of Brigham and Women's Hospital and Harvard School of Public Health. Assessment of exposure and covariate information Information on height, weight at age 18 years, and current weight was obtained via the NHS II baseline questionnaire in 1989. The information on current weight was updated every 2 years. The BMI was calculated (as weight in kilograms divided by the square of height in meters) for age 18 years and at all prospective follow-up questionnaire cycles. On the baseline questionnaire, participants reported characteristics of their menstrual cycle. Information was requested on cycle length (<21 days, 21-25 days, 26-31 days, 32-39 days, 40-50 days, >50 days, or too irregular to estimate) and pattern (very regular [±3 days], regular, usually irregular, always irregular, or no periods) at ages 18 to 22 years, “excluding time around pregnancies or when using oral contraceptives.” In 1993, the participants were asked to describe their current menstrual cycle length and pattern using the same categories offered in the baseline questionnaire. Infertility status was assessed at baseline and on every subsequent questionnaire. Participants were asked whether they had tried to get pregnant for 1 year without success. If they answered “yes,” they were asked to indicate the cause(s) of their infertility: tubal blockage, ovulatory disorder, endometriosis, cervical mucous factors, factors related to their spouse, no investigation done, cause not found, or other. Polycystic ovary syndrome was defined as probable if a participant had at least 3 of the following 4 characteristics: hirsutism, a BMI of 27 or greater, irregular menstrual cycles, and infertility due to an ovulatory disorder. Information on potential confounding variables was assessed at baseline and during follow-up. Participants were asked for their date of birth, age at menarche, and family history of breast cancer (in mother, sister, or grandmother) at baseline. History of benign breast disease, parity, age at first birth, alcohol consumption, oral contraceptive use, and physical activity were assessed via the baseline and subsequent questionnaires. Data from subsequent questionnaires were used to update information on confounding variables for each individual in each period. Ascertainment of breast cancer cases New cases of breast cancer were identified through the biennial questionnaires mailed between 1989 and 2003. Deaths were reported by family members or by the US Postal Service in response to the follow-up questionnaires, and the National Death Index was searched to investigate the deaths of nonresponders. When a case of breast cancer was reported, we asked the participant (or next of kin for those who had died) for confirmation of the diagnosis and for permission to obtain relevant hospital records and pathology reports. Medical records were obtained for more than 90% of the cases. Pathology reports confirmed breast cancer in more than 99% of the women whose reports were reviewed. We restricted the study end point to invasive breast cancer. Cases of carcinoma in situ were censored at the time of diagnosis. Statistical analysis Women were followed up prospectively from the time they first reported their weight and height in 1989 until the end of follow-up in 2003. Person-years of follow-up were calculated as the time from completion of the 1989 questionnaire to the date of return of the 2003 questionnaire, the date of diagnosis of invasive or in situ breast cancer, any other cancer (except nonmelanoma skin cancer), death, loss to follow-up, or reaching menopause, whichever occurred first. Women were also censored if they did not report their weight on 3 or more questionnaires. The total number of observations varied between analyses depending on the number of women missing the main exposure (ie, current BMI or BMI at age 18 years) or outcome of interest (ie, receptor-specific breast cancer). A Cox proportional hazards regression model16 was used to calculate the hazard of developing invasive breast cancer associated with a particular level of BMI. For the analysis of current BMI, weight reported on the questionnaire preceding the report of an incident breast cancer diagnosis was used. We assessed the association between current BMI or BMI at age 18 years and breast cancer incidence, adjusting for age (in months), family history of breast cancer in a first-degree relative (dichotomous), history of benign breast disease (dichotomous), age at menarche (≤10, 11, 12, 13, 14, or ≥15 years), parity (0, 1, 2, 3, or ≥4), age at first birth (≤24, 25-30, or >30 years), oral contraceptive use (never, past for <5 years, past for ≥5 years, current for <5 years, current for 5-9 years, or current for ≥10 years), alcohol intake (none, <7.5 g/d, 7.5-14 g/d, >15-29 g/d, or ≥30 g/d), physical activity (<3, 3-8, 9-17, 18-26, 27-41, or ≥42 metabolic equivalents per week), menstrual cycle characteristics (≤25 days and regular, 26-31 days and regular, ≥32 days and regular, ≤25 days and irregular, 26-31 days and irregular, or ≥32 days and irregular), infertility due to an ovulatory disorder (dichotomous), and probable PCOS (dichotomous). Covariate values were updated in the analysis whenever new information was obtained from the biennial questionnaire. Analyses were stratified by menstrual cycle length (<32 vs ≥32 days), age (<40 vs ≥40 years), and use of oral contraceptives (current, past, or never). Effect modification was assessed by creating the cross-products between BMI and each potential effect modifier. We measured the significance of potential effect modification using the likelihood ratio test, comparing a model with the cross-products representing interaction terms and the nested model without these terms. Separate analyses were performed for estrogen receptor (ER)-positive and ER-negative breast cancer and for progesterone receptor (PR)-positive and PR-negative breast cancer. We used polychotomous logistic regression with 3 outcome categories (ER-positive breast cancer, ER-negative breast cancer, and no breast cancer or PR-positive breast cancer, PR-negative breast cancer, and no breast cancer) to evaluate whether trends in BMI at age 18 years and in current BMI differed by the receptor status of the tumor. Likelihood ratio tests with 1 df were used to compare a model with different slopes for each outcome with a model with a common slope. We used χ2 tests to obtain 2-sided P values for the likelihood ratio statistics.17 Trend tests were performed using the midpoint of the intervals. All the tests of statistical significance were 2-sided. Results During 1 225 520 person-years of follow-up, 1398 incident cases of invasive breast cancer were diagnosed in this premenopausal population, which included 113 130 women. Women with a higher current BMI were older, had a higher BMI at age 18 years, had an earlier age at menarche, were less likely to have a history of benign breast disease, were more likely to report menstrual cycle irregularity in 1993 and a history of ovulatory infertility, and reported lower alcohol consumption than women with a lower BMI (Table 1). Women on both ends of the BMI distribution were more likely to be nulliparous than women in the middle categories (Table 1). We observed a significant linear inverse trend between current BMI and breast cancer incidence (P<.001) (Table 2). Women with a BMI of 30.0 or higher had an age-adjusted hazard ratio for breast cancer of 0.79 (95% confidence interval [CI], 0.67-0.94) compared with women with a BMI between 20.0 and 22.4. Further adjustment for a family history of breast cancer, a history of benign breast disease, height, age at menarche, age at first birth, parity, alcohol consumption, physical activity, and current or past use of oral contraceptives did not alter this estimate appreciably (Table 2). Additionally, adjusting for menstrual cycle characteristics, infertility due to an ovulatory disorder, or probable PCOS similarly did not affect the estimates (Table 2). When the analysis of current BMI and breast cancer was adjusted for BMI at age 18 years, the association was considerably attenuated, eliminating the previously significant trend (Table 2). Restricting the analysis to women with no reported history of infertility due to an ovulatory disorder did not appreciably change the results (data not shown). Body mass index at age 18 years was significantly inversely associated with the incidence of premenopausal breast cancer (Table 3). Women with a BMI of 27.5 or higher at age 18 years had a covariate-adjusted hazard ratio of 0.57 (95% CI, 0.41-0.81) compared with women with a BMI between 20.0 and 22.4 at age 18 years. This association did not appreciably change when adjusting for current BMI (Table 3). Similarly, adjustment for waist-hip ratio did not alter the association (data not shown). Separate analyses were also performed for ER-positive and ER-negative breast cancer cases and for PR-positive and PR-negative breast cancer cases. The association between current BMI and premenopausal breast cancer incidence was stronger for ER-positive than ER-negative cases (P for heterogeneity = .19), whereas no apparent difference was observed between PR-positive and PR-negative cases (P for heterogeneity = .94) (Table 4). The association with BMI at age 18 years was also strongest for ER-positive breast cancer (P for heterogeneity = .68 for ER and 0.78 for PR) (Table 5). When we stratified by menstrual cycle length at ages 18 to 22 years (<32 vs ≥32 days), associations were somewhat stronger in women with longer cycle durations, but there was no significant heterogeneity (P for heterogeneity = .72) (Table 6). No effect modification by age or use of oral contraceptives was apparent. Women with probable PCOS had a covariate-adjusted hazard ratio for breast cancer of 0.89 (95% CI, 0.71-1.10) compared with women who were unlikely to have PCOS. When also adjusting for BMI at age 18 years and for current BMI, the hazard ratio for breast cancer associated with PCOS was 0.98 (95% CI, 0.78-1.23), indicating that any association between PCOS and breast cancer is likely to be mediated by BMI rather than other characteristics of PCOS. Comment Among the premenopausal participants in the NHS II, a high BMI was inversely associated with the incidence of breast cancer. This association was not explained by menstrual cycle characteristics, self-reported infertility due to an ovulatory disorder, or probable PCOS. The BMI at age 18 years, however, explained part of the association between current BMI and breast cancer incidence. In several studies, an inverse association has been reported between body size and premenopausal breast cancer. Of studies in which the relation between BMI at ages 16 to 25 years and the risk of breast cancer was explored, a significant inverse association with premenopausal breast cancer risk was found in some11,18-20 but not all.1,14,21-26 High current BMI has been linked to a reduced risk of breast cancer in premenopausal women or in women younger than 50 to 55 years in numerous studies,1-12,14,18,19 but this link was not confirmed in other studies.20,21,24-38 In a pooled analysis13 of 7 prospective cohort studies including 337 819 women and 723 incident cases of invasive premenopausal breast cancer, a nonlinear inverse association between BMI and breast cancer was found; women with a BMI exceeding 31 had a relative risk of 0.54 (95% CI, 0.34-0.85) compared with women with a BMI of 21 or less. In 3 studies,22,39,40 a positive association between BMI and breast cancer diagnosed before menopause or before age 55 years was observed. Key and Pike41 suggested that the mechanism underlying the inverse association between BMI and premenopausal breast cancer is anovulation in the heavier women, resulting in decreased estradiol and progesterone levels. We cannot exclude a possible role of anovulation because we cannot measure anovulation directly. However, because adjustment for menstrual cycle patterns, infertility due to ovulatory disorder, probable PCOS, and use of oral contraceptives did not even slightly attenuate the association with BMI, anovulation does not seem to be a primary explanation for the reduced risk in heavier women. Among women with no history of infertility due to an ovulatory disorder, the inverse association between BMI and premenopausal breast cancer incidence persisted, lending further support to the role of mechanisms other than anovulation. In the same population, women with infertility due to an ovulatory disorder had a lower incidence of premenopausal breast cancer,42 whereas menstrual cycle pattern was not associated with breast cancer incidence except in women younger than 40 years.43 In this population of premenopausal women, BMI during earlier periods of adult life was more consistently associated with breast cancer incidence than BMI during adulthood. Body fatness during childhood has also been related to a lower incidence of breast cancer in the premenopausal women of the NHS II.44 A high BMI during adulthood is highly correlated with a high body mass during adolescence, which may be more important for the development of breast cancer before menopause. Although a high birth weight has been fairly consistently linked to an increase in the risk of premenopausal breast cancer,45 the BMI–breast cancer association seems to reverse at some point during the first years of life, only to revert back after menopause.46 Because BMI was more clearly related to ER-positive than ER-negative breast cancer, a role of sex steroid hormones is likely. High plasma estradiol levels have been associated with an increased risk of premenopausal breast cancer in some studies47-50 but not in others.51-53 Most studies collected samples at any time in the menstrual cycle, making interpretation more difficult given the fluctuation of estradiol levels and other sex steroid hormone levels throughout the menstrual cycle. In the NHS II, 18 506 premenopausal women provided blood samples in the early follicular and midluteal phases; higher levels of follicular total and free estradiol levels were associated with an increased incidence of breast cancer, whereas no association was apparent for estradiol levels in the luteal phase.50 Premenopausal overweight women have been found to have lower estradiol levels than lean women, but the evidence is inconsistent.54-74 In studies in which the collection of blood specimens was timed during the menstrual cycle,54-65,71 a significant inverse association between total estradiol concentration and BMI was found in 454-57 and a nonsignificant inverse association was found in another.65 In the NHS II, we also observed a significant inverse association between total estradiol level during the follicular and luteal phases and BMI at blood sampling but not between free estradiol level and BMI.75 Obese women have decreased levels of sex hormone–binding globulin.54-57,60-74 Because sex hormone–binding globulin is the main protein carrier of estradiol, free estradiol levels should increase with higher BMI, but the pituitary gland and hypothalamus regulate free estradiol in premenopausal women; hence ovarian production of estradiol may be kept low.76 Potischman et al55 suggested that more free estradiol may be cleared by the liver and other tissues in obese women. Alternatively, obese women may experience ovulatory insufficiency, resulting in compromised estradiol production capacity.75 Whether decreased estrogen blood levels in obese premenopausal women explain the inverse association with breast cancer remains to be determined. The observation that this inverse association is stronger for ER-positive breast cancer lends support to this mechanism. Finally, detection bias has to be considered as a possible explanation for the observed associations. Obese women are less likely to seek breast cancer screening than normal-weight women.77-79 It is possible that obese women with preclinical breast cancer delay their diagnosis, moving the detection of their cancer from the premenopausal to the postmenopausal phase. In conclusion, a large body size during early adulthood is inversely related to the incidence of breast cancer in premenopausal women. Factors related to ovulation, such as menstrual cycle characteristics, infertility due to an ovulatory disorder, and probable PCOS, do not seem to explain this association. Correspondence: Karin B. Michels, ScD, PhD, Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA 02115. Accepted for Publication: August 18, 2006. Author Contributions:Study concept and design: Michels. Acquisition of data: Michels and Willett. Analysis and interpretation of data: Michels and Terry. Drafting of the manuscript: Michels. Critical revision of the manuscript for important intellectual content: Michels, Terry, and Willett. Statistical analysis: Michels and Terry. Obtained funding: Michels and Willett. Study supervision: Michels. Financial Disclosure: None reported. Funding/Support: This project was supported by a grant from the Massachusetts Breast Cancer Research Grants Program of the Massachusetts Department of Public Health (Dr Michels). The NHS II is supported by Public Health Service grant CA50385 from the National Cancer Institute, National Institutes of Health, US Department of Health and Human Services. References 1. Brinton LASwanson CA Height and weight at various ages and risk of breast cancer. Ann Epidemiol 1992;2597- 609PubMedGoogle ScholarCrossref 2. Harris RENamboodiri KKWynder EL Breast cancer risk: effects of estrogen replacement therapy and body mass. J Natl Cancer Inst 1992;841575- 1582PubMedGoogle ScholarCrossref 3. Vatten LJKvinnsland S Prospective study of height, body mass index and risk of breast cancer. Acta Oncol 1992;31195- 200PubMedGoogle ScholarCrossref 4. Bruning PFBonfrer JMHart AA et al. Body measurements, estrogen availability and the risk of human breast cancer: a case-control study. Int J Cancer 1992;5114- 19PubMedGoogle ScholarCrossref 5. Petrek JAPeters MCirrincione CRhodes DBajorunas D Is body fat topography a risk factor for breast cancer? Ann Intern Med 1993;118356- 362PubMedGoogle ScholarCrossref 6. Tornberg SACarstensen JM Relationship between Quetelet's index and cancer of breast and female genital tract in 47,000 women followed for 25 years. Br J Cancer 1994;69358- 361PubMedGoogle ScholarCrossref 7. Taioli EBarone JWynder EL A case-control study on breast cancer and body mass. Eur J Cancer 1995;31A723- 728PubMedGoogle ScholarCrossref 8. Franceschi SFavero ALa Vecchia C et al. Body size indices and breast cancer risk before and after menopause. Int J Cancer 1996;67181- 186PubMedGoogle ScholarCrossref 9. Hu YHNagata CShimizu HKaneda NKashiki Y Association of body mass index, physical activity, and reproductive histories with breast cancer: a case-control study in Gifu, Japan. Breast Cancer Res Treat 1997;4365- 72PubMedGoogle ScholarCrossref 10. Tavani AGallus SLa Vecchia C et al. Risk factors for breast cancer in women under 40 years. Eur J Cancer 1999;351361- 1367PubMedGoogle ScholarCrossref 11. Peacock SLWhite EDaling JRVoigt LFMalone KE Relation between obesity and breast cancer in young women. Am J Epidemiol 1999;149339- 346PubMedGoogle ScholarCrossref 12. Hall IJNewman BMillikan RCMoorman PG Body size and breast cancer risk in black women and white women: the Carolina Breast Cancer Study. Am J Epidemiol 2000;151754- 764PubMedGoogle ScholarCrossref 13. van den Brandt PASpiegelman DYaun SS et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 2000;152514- 527PubMedGoogle ScholarCrossref 14. de Vasconcelos ABAzevedo e Silva Mendonca GSichieri R Height, weight, weight change and risk of breast cancer in Rio de Janeiro, Brazil. Sao Paulo Med J 2001;11962- 66PubMedGoogle ScholarCrossref 15. Friedenreich CM Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev 2001;1015- 32PubMedGoogle ScholarCrossref 16. Cox DR Regression models and life tables (with discussion). J R Stat Soc Ser B 1972;34187- 220Google Scholar 17. Marshall RJChisholm EM Hypothesis testing in the polychotomous logistic model with an application to detecting gastrointestinal cancer. Stat Med 1985;4337- 344PubMedGoogle ScholarCrossref 18. Huang ZHankinson SEColditz GA et al. Dual effects of weight and weight gain on breast cancer risk. JAMA 1997;2781407- 1411PubMedGoogle ScholarCrossref 19. Coates RJUhler RJHall HI et al. Risk of breast cancer in young women in relation to body size and weight gain in adolescence and early adulthood. Br J Cancer 1999;81167- 174PubMedGoogle ScholarCrossref 20. Folsom ARKaye SAPrineas RJPotter JDGapstur SMWallace RB Increased incidence of carcinoma of the breast associated with abdominal adiposity in postmenopausal women. Am J Epidemiol 1990;131794- 803PubMedGoogle Scholar 21. Lund EAdami HOBergstrom RMeirik O Anthropometric measures and breast cancer in young women. Cancer Causes Control 1990;1169- 172PubMedGoogle ScholarCrossref 22. Mayberry RM Age-specific patterns of association between breast cancer and risk factors in black women, ages 20 to 39 and 40 to 54. Ann Epidemiol 1994;4205- 213PubMedGoogle ScholarCrossref 23. Ursin GPaganini-Hill ASiemiatycki JThompson WDHaile RW Early adult body weight, body mass index, and premenopausal bilateral breast cancer: data from a case-control study. Breast Cancer Res Treat 1995;3375- 82PubMedGoogle ScholarCrossref 24. Hirose KTajima KHamajima N et al. Association of family history and other risk factors with breast cancer risk among Japanese premenopausal and postmenopausal women. Cancer Causes Control 2001;12349- 358PubMedGoogle ScholarCrossref 25. Shu XOJin FDai Q et al. Association of body size and fat distribution with risk of breast cancer among Chinese women. Int J Cancer 2001;94449- 455PubMedGoogle ScholarCrossref 26. Wenten MGilliland FDBaumgartner KSamet JM Associations of weight, weight change, and body mass with breast cancer risk in Hispanic and non-Hispanic white women. Ann Epidemiol 2002;12435- 444PubMedGoogle ScholarCrossref 27. McNee RKMason BHNeave LMKay RG Influence of height, weight, and obesity on breast cancer incidence and recurrence in Auckland, New Zealand. Breast Cancer Res Treat 1987;9145- 150PubMedGoogle ScholarCrossref 28. Schatzkin APalmer JRRosenberg L et al. Risk factors for breast cancer in black women. J Natl Cancer Inst 1987;78213- 217PubMedGoogle Scholar 29. Parazzini Fla Vecchia CNegri EBruzzi PPalli DBoyle P Anthropometric variables and risk of breast cancer. Int J Cancer 1990;45397- 402PubMedGoogle ScholarCrossref 30. Mannisto SPietinen PPyy MPalmgren JEskelinen MUusitupa M Body-size indicators and risk of breast cancer according to menopause and estrogen-receptor status. Int J Cancer 1996;688- 13PubMedGoogle ScholarCrossref 31. Chie WCLi CYHuang CSChang KJLin RS Body size as a factor in different ages and breast cancer risk in Taiwan. Anticancer Res 1998;18565- 570PubMedGoogle Scholar 32. Galanis DJKolonel LNLee JLe Marchand L Anthropometric predictors of breast cancer incidence and survival in a multi-ethnic cohort of female residents of Hawaii, United States. Cancer Causes Control 1998;9217- 224PubMedGoogle ScholarCrossref 33. Kaaks RVan Noord PADen Tonkelaar IPeeters PHRiboli EGrobbee DE Breast-cancer incidence in relation to height, weight and body-fat distribution in the Dutch “DOM” cohort. Int J Cancer 1998;76647- 651PubMedGoogle ScholarCrossref 34. Tung HTTsukuma HTanaka H et al. Risk factors for breast cancer in Japan, with special attention to anthropometric measurements and reproductive history. Jpn J Clin Oncol 1999;29137- 146PubMedGoogle ScholarCrossref 35. Enger SMRoss RKPaganini-Hill ACarpenter CLBernstein L Body size, physical activity, and breast cancer hormone receptor status: results from two case-control studies. Cancer Epidemiol Biomarkers Prev 2000;9681- 687PubMedGoogle Scholar 36. Adebamowo CAOgundiran TOAdenipekun AA et al. Waist-hip ratio and breast cancer risk in urbanized Nigerian women. Breast Cancer Res 2003;5R18- R24PubMedGoogle ScholarCrossref 37. Adebamowo CAOgundiran TOAdenipekun AA et al. Obesity and height in urban Nigerian women with breast cancer. Ann Epidemiol 2003;13455- 461PubMedGoogle ScholarCrossref 38. Lahmann PHHoffmann KAllen N et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer 2004;111762- 771PubMedGoogle ScholarCrossref 39. Chu SYLee NCWingo PASenie RTGreenberg RSPeterson HB The relationship between body mass and breast cancer among women enrolled in the Cancer and Steroid Hormone Study. J Clin Epidemiol 1991;441197- 1206PubMedGoogle ScholarCrossref 40. Chang SBuzdar AUHursting SD Inflammatory breast cancer and body mass index. J Clin Oncol 1998;163731- 3735PubMedGoogle Scholar 41. Key TJPike MC The role of oestrogens and progestagens in the epidemiology and prevention of breast cancer. Eur J Cancer Clin Oncol 1988;2429- 43PubMedGoogle ScholarCrossref 42. Terry KLWillett WCRich-Edwards JWMichels KB A prospective study of infertility due to ovulatory disorders, ovulation induction, and incidence of breast cancer. Arch Intern Med In pressGoogle Scholar 43. Terry KLWillett WCRich-Edwards JWHunter DJMichels KB Menstrual cycle characteristics and incidence of premenopausal breast cancer. Cancer Epidemiol Biomarkers Prev 2005;141509- 1513PubMedGoogle ScholarCrossref 44. Baer HJColditz GARosner BA et al. Body fatness during childhood and adolescence and incidence of breast cancer in premenopausal women. Breast Cancer Res 2005;7R314- R325PubMedGoogle ScholarCrossref 45. Michels KBXue F Role of birthweight in the etiology of breast cancer. Int J Cancer 2006;1192007- 2025PubMedGoogle ScholarCrossref 46. Michels KBWillett WC Breast cancer: early life matters. N Engl J Med 2004;3511679- 1681PubMedGoogle ScholarCrossref 47. Rosenberg CRPasternack BSShore REKoenig KLToniolo PG Premenopausal estradiol levels and risk of breast cancer: a new method of controlling for day of the menstrual cycle. Am J Epidemiol 1994;140518- 525PubMedGoogle Scholar 48. Thomas HVKey TJAllen DS et al. A prospective study of endogenous serum hormone concentrations and breast cancer risk in premenopausal women on the island of Guernsey. Br J Cancer 1997;751075- 1079PubMedGoogle ScholarCrossref 49. Kabuto MAkiba SStevens RGNeriishi KLand CE A prospective study of estradiol and breast cancer among Japanese women. Cancer Epidemiol Biomarkers Prev 2000;9575- 579PubMedGoogle Scholar 50. Eliassen AHMissmer SATworoger SS et al. Endogenous steroid hormone concentrations and risk of breast cancer among premenopausal women. J Natl Cancer Inst 2006;981406- 1415Google ScholarCrossref 51. Wysowski DKComstock GWHelsing KJLau HL Sex hormone levels in serum in relation to the development of breast cancer. Am J Epidemiol 1987;125791- 799PubMedGoogle Scholar 52. Helzlsouer KJAlberg AJBush TLLongcope CGordon GBComstock GW A prospective study of endogenous hormones and breast cancer. Cancer Detect Prev 1994;1879- 85PubMedGoogle Scholar 53. Kaaks RBerrino FKey T et al. Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2005;97755- 765PubMedGoogle ScholarCrossref 54. Grenman SRonnemaa TIrjala KKaihola HLGronroos M Sex steroid, gonadotropin, cortisol, and prolactin levels in healthy, massively obese women: correlation with abdominal fat cell size and effect of weight reduction. J Clin Endocrinol Metab 1986;631257- 1261PubMedGoogle ScholarCrossref 55. Potischman NSwanson CASiiteri PHoover RN Reversal of relation between body mass and endogenous concentrations with menopausal status. J Natl Cancer Inst 1996;88756- 758PubMedGoogle ScholarCrossref 56. Randolph JFSowers MGold EB et al. Reproductive hormones in the early menopausal transition: relationship to ethnicity, body size, and menopausal status. J Clin Endocrinol Metab 2003;881516- 1522PubMedGoogle ScholarCrossref 57. Verkasalo PKThomas HVAppleby PNDavey GKKey TJ Circulating levels of sex hormones and their relation to risk factors for breast cancer: a cross-sectional study in 1092 pre- and postmenopausal women. Cancer Causes Control 2001;1247- 59PubMedGoogle ScholarCrossref 58. Zumoff B Relationship of obesity to blood estrogens. Cancer Res 1982;42 ((8 suppl)) 3289s- 3294sPubMedGoogle Scholar 59. Trichopoulos DPolychronopoulou ABrown JMacMahon B Obesity, serum cholesterol, and estrogens in premenopausal women. Oncology 1983;40227- 231PubMedGoogle ScholarCrossref 60. Haffner SMKatz MSStern MPDunn JF Relationship of sex hormone binding globulin to overall adiposity and body fat distribution in a biethnic population. Int J Obes 1989;131- 9PubMedGoogle Scholar 61. Dorgan JFReichman MEJudd JT et al. The relation of body size to plasma levels of estrogens and androgens in premenopausal women (Maryland, United States). Cancer Causes Control 1995;63- 8PubMedGoogle ScholarCrossref 62. De Pergola GZamboni MSciaraffia M et al. Body fat accumulation is possibly responsible for lower dehydroepiandrosterone circulating levels in premenopausal obese women. Int J Obes Relat Metab Disord 1996;201105- 1110PubMedGoogle Scholar 63. Westhoff CGentile GLee JZacur HHelbig D Predictors of ovarian steroid secretion in reproductive-age women. Am J Epidemiol 1996;144381- 388PubMedGoogle ScholarCrossref 64. Nagata CKaneda NKabuto MShimizu H Factors associated with serum levels of estradiol and sex hormone-binding globulin among premenopausal Japanese women. Environ Health Perspect 1997;105994- 997PubMedGoogle ScholarCrossref 65. Thomas HVKey TJAllen DS et al. Re: Reversal of relation between body mass and endogenous estrogen concentrations with menopausal status [letter]. J Natl Cancer Inst 1997;89396- 397PubMedGoogle ScholarCrossref 66. Ivandic APrpic-Krizevac ISucic MJuric M Hyperinsulinemia and sex hormones in healthy premenopausal women: relative contribution of obesity, obesity type, and duration of obesity. Metabolism 1998;4713- 19PubMedGoogle ScholarCrossref 67. Kraemer RRSynovitz LBGimpel TKraemer GRJohnson LGCastracane VD Effect of estrogen on serum DHEA in younger and older women and the relationship of DHEA to adiposity and gender. Metabolism 2001;50488- 493PubMedGoogle ScholarCrossref 68. Allen NEAppleby PNKaaks RRinaldi SDavey GKKey TJ Lifestyle determinants of serum insulin-like growth-factor-I (IGF-I), C-peptide and hormone binding protein levels in British women. Cancer Causes Control 2003;1465- 74PubMedGoogle ScholarCrossref 69. Kok PRoelfsema FFrolich MMeinders AEPijl H Prolactin release is enhanced in proportion to excess visceral fat in obese women. J Clin Endocrinol Metab 2004;894445- 4449PubMedGoogle ScholarCrossref 70. Tufano AMarzo PEnrini RMorricone LCaviezel FAmbrosi B Anthropometric, hormonal and biochemical differences in lean and obese women before and after menopause. J Endocrinol Invest 2004;27648- 653PubMedGoogle ScholarCrossref 71. Lukanova ALundin EZeleniuch-Jacquotte A et al. Body mass index, circulating levels of sex-steroid hormones, IGF-I and IGF-binding protein-3: a cross-sectional study in healthy women. Eur J Endocrinol 2004;150161- 171PubMedGoogle ScholarCrossref 72. Bezemer IDRinaldi SDossus L et al. C-peptide, IGF-I, sex-steroid hormones and adiposity: a cross-sectional study in healthy women within the European Prospective Investigation into Cancer and Nutrition (EPIC). Cancer Causes Control 2005;16561- 572PubMedGoogle ScholarCrossref 73. Kopelman PGPilkington TRWhite NJeffcoate SL Abnormal sex steroid secretion and binding in massively obese women. Clin Endocrinol (Oxf) 1980;12363- 369PubMedGoogle ScholarCrossref 74. Pasquali RAntenucci DMelchionda N et al. Sex hormones in obese premenopausal women and their relationships to body fat mass and distribution, B cell function and diet composition. J Endocrinol Invest 1987;10345- 350PubMedGoogle ScholarCrossref 75. Tworoger SSEliassen AHMissmer SA et al. Birthweight and body size throughout life in relation to sex hormones and prolactin concentrations in premenopausal women. Cancer Epidemiol Biomarker Prev In pressGoogle Scholar 76. Yen SJaffe RBarbieri R Reproductive Endocrinology: Physiology, Pathophysiology, and Clinical Management. Philadelphia, Pa WB Saunders Co1999; 77. Wee CCMcCarthy EPDavis RBPhillips RS Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000;132697- 704PubMedGoogle ScholarCrossref 78. Wee CCMcCarthy EPDavis RBPhillips RS Obesity and breast cancer screening. J Gen Intern Med 2004;19324- 331PubMedGoogle ScholarCrossref 79. Amy NKAalborg ALyons PKeranen L Barriers to routine gynecological cancer screening for white and African-American obese women. Int J Obes 2006;30147- 155Google ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Internal Medicine American Medical Association

Longitudinal Study on the Role of Body Size in Premenopausal Breast Cancer

Loading next page...
 
/lp/american-medical-association/longitudinal-study-on-the-role-of-body-size-in-premenopausal-breast-jnBBbSYUDk
Publisher
American Medical Association
Copyright
Copyright © 2006 American Medical Association. All Rights Reserved.
ISSN
0003-9926
eISSN
1538-3679
DOI
10.1001/archinte.166.21.2395
Publisher site
See Article on Publisher Site

Abstract

Abstract Background A high body mass index (BMI) has been related to a reduced risk of breast cancer in premenopausal women. The mechanisms underlying this association have not been elucidated. Methods We explored whether factors affecting ovulation may explain the inverse association between BMI (calculated as weight in kilograms divided by the square of height in meters) and breast cancer in 113 130 premenopausal participants in the Nurses' Health Study II. During 1 225 520 person-years of prospective follow-up between 1989 and 2003, 1398 incident cases of invasive breast cancer were diagnosed. Weight, height, ovulatory infertility, menstrual cycle patterns, and a multitude of covariates were assessed throughout follow-up. Cox proportional hazards regression was used to compute hazard ratios and 95% confidence intervals (CIs). Results We observed a significant linear inverse trend between current BMI and breast cancer incidence (P<.001) that was not explained by menstrual cycle characteristics or infertility due to an ovulatory disorder (covariate-adjusted hazard ratio for breast cancer in women with a BMI ≥30 vs 20.0-22.4, 0.81; 95% CI, 0.68-0.96). We found BMI at age 18 years to be the strongest predictor of breast cancer incidence (covariate-adjusted hazard ratio for breast cancer in women with a BMI at age 18 years ≥27.5 vs 20.0-22.4, 0.57; 95% CI, 0.41-0.81). Conclusions Body size during the early phases of adult life seems to be particularly important in the development of premenopausal breast cancer. Factors other than anovulation are likely to mediate the protection conferred by a high BMI. The inverse association between body mass index (BMI) and the risk of breast cancer among premenopausal women has been observed in numerous studies,1-15 but the biological mechanisms underlying this perplexing link have remained largely unresolved. A high BMI can be associated with irregular or long menstrual cycles or with polycystic ovary syndrome (PCOS), and it has been suggested that anovulation, which is associated with such characteristics and with decreased estradiol and progesterone levels, may explain the lower risk of breast cancer in these women. However, few studies have explored whether these or other factors provide mechanistic insights into the unexpected protection that a high body mass confers on the premenopausal breast. We investigated whether menstrual cycle characteristics, infertility due to an ovulatory disorder, or PCOS might explain the inverse association between BMI and premenopausal breast cancer incidence in participants in the Nurses' Health Study II (NHS II). Methods Study population: nhs ii In 1989, 116 609 female registered nurses aged 25 to 42 years living in 1 of 14 US states responded to a self-administered questionnaire about their medical history and lifestyle. Participants have since been followed up by means of biennial questionnaires updating information on demographic variables, lifestyle factors, and medical events. For this analysis, women were excluded at baseline in 1989 if they were postmenopausal (n = 2813), reported cancer (n = 989), were missing the date of diagnosis of invasive breast cancer (n = 28), or were missing information on height or weight (n = 322) (not mutually exclusive). This study was approved by the institutional review boards of Brigham and Women's Hospital and Harvard School of Public Health. Assessment of exposure and covariate information Information on height, weight at age 18 years, and current weight was obtained via the NHS II baseline questionnaire in 1989. The information on current weight was updated every 2 years. The BMI was calculated (as weight in kilograms divided by the square of height in meters) for age 18 years and at all prospective follow-up questionnaire cycles. On the baseline questionnaire, participants reported characteristics of their menstrual cycle. Information was requested on cycle length (<21 days, 21-25 days, 26-31 days, 32-39 days, 40-50 days, >50 days, or too irregular to estimate) and pattern (very regular [±3 days], regular, usually irregular, always irregular, or no periods) at ages 18 to 22 years, “excluding time around pregnancies or when using oral contraceptives.” In 1993, the participants were asked to describe their current menstrual cycle length and pattern using the same categories offered in the baseline questionnaire. Infertility status was assessed at baseline and on every subsequent questionnaire. Participants were asked whether they had tried to get pregnant for 1 year without success. If they answered “yes,” they were asked to indicate the cause(s) of their infertility: tubal blockage, ovulatory disorder, endometriosis, cervical mucous factors, factors related to their spouse, no investigation done, cause not found, or other. Polycystic ovary syndrome was defined as probable if a participant had at least 3 of the following 4 characteristics: hirsutism, a BMI of 27 or greater, irregular menstrual cycles, and infertility due to an ovulatory disorder. Information on potential confounding variables was assessed at baseline and during follow-up. Participants were asked for their date of birth, age at menarche, and family history of breast cancer (in mother, sister, or grandmother) at baseline. History of benign breast disease, parity, age at first birth, alcohol consumption, oral contraceptive use, and physical activity were assessed via the baseline and subsequent questionnaires. Data from subsequent questionnaires were used to update information on confounding variables for each individual in each period. Ascertainment of breast cancer cases New cases of breast cancer were identified through the biennial questionnaires mailed between 1989 and 2003. Deaths were reported by family members or by the US Postal Service in response to the follow-up questionnaires, and the National Death Index was searched to investigate the deaths of nonresponders. When a case of breast cancer was reported, we asked the participant (or next of kin for those who had died) for confirmation of the diagnosis and for permission to obtain relevant hospital records and pathology reports. Medical records were obtained for more than 90% of the cases. Pathology reports confirmed breast cancer in more than 99% of the women whose reports were reviewed. We restricted the study end point to invasive breast cancer. Cases of carcinoma in situ were censored at the time of diagnosis. Statistical analysis Women were followed up prospectively from the time they first reported their weight and height in 1989 until the end of follow-up in 2003. Person-years of follow-up were calculated as the time from completion of the 1989 questionnaire to the date of return of the 2003 questionnaire, the date of diagnosis of invasive or in situ breast cancer, any other cancer (except nonmelanoma skin cancer), death, loss to follow-up, or reaching menopause, whichever occurred first. Women were also censored if they did not report their weight on 3 or more questionnaires. The total number of observations varied between analyses depending on the number of women missing the main exposure (ie, current BMI or BMI at age 18 years) or outcome of interest (ie, receptor-specific breast cancer). A Cox proportional hazards regression model16 was used to calculate the hazard of developing invasive breast cancer associated with a particular level of BMI. For the analysis of current BMI, weight reported on the questionnaire preceding the report of an incident breast cancer diagnosis was used. We assessed the association between current BMI or BMI at age 18 years and breast cancer incidence, adjusting for age (in months), family history of breast cancer in a first-degree relative (dichotomous), history of benign breast disease (dichotomous), age at menarche (≤10, 11, 12, 13, 14, or ≥15 years), parity (0, 1, 2, 3, or ≥4), age at first birth (≤24, 25-30, or >30 years), oral contraceptive use (never, past for <5 years, past for ≥5 years, current for <5 years, current for 5-9 years, or current for ≥10 years), alcohol intake (none, <7.5 g/d, 7.5-14 g/d, >15-29 g/d, or ≥30 g/d), physical activity (<3, 3-8, 9-17, 18-26, 27-41, or ≥42 metabolic equivalents per week), menstrual cycle characteristics (≤25 days and regular, 26-31 days and regular, ≥32 days and regular, ≤25 days and irregular, 26-31 days and irregular, or ≥32 days and irregular), infertility due to an ovulatory disorder (dichotomous), and probable PCOS (dichotomous). Covariate values were updated in the analysis whenever new information was obtained from the biennial questionnaire. Analyses were stratified by menstrual cycle length (<32 vs ≥32 days), age (<40 vs ≥40 years), and use of oral contraceptives (current, past, or never). Effect modification was assessed by creating the cross-products between BMI and each potential effect modifier. We measured the significance of potential effect modification using the likelihood ratio test, comparing a model with the cross-products representing interaction terms and the nested model without these terms. Separate analyses were performed for estrogen receptor (ER)-positive and ER-negative breast cancer and for progesterone receptor (PR)-positive and PR-negative breast cancer. We used polychotomous logistic regression with 3 outcome categories (ER-positive breast cancer, ER-negative breast cancer, and no breast cancer or PR-positive breast cancer, PR-negative breast cancer, and no breast cancer) to evaluate whether trends in BMI at age 18 years and in current BMI differed by the receptor status of the tumor. Likelihood ratio tests with 1 df were used to compare a model with different slopes for each outcome with a model with a common slope. We used χ2 tests to obtain 2-sided P values for the likelihood ratio statistics.17 Trend tests were performed using the midpoint of the intervals. All the tests of statistical significance were 2-sided. Results During 1 225 520 person-years of follow-up, 1398 incident cases of invasive breast cancer were diagnosed in this premenopausal population, which included 113 130 women. Women with a higher current BMI were older, had a higher BMI at age 18 years, had an earlier age at menarche, were less likely to have a history of benign breast disease, were more likely to report menstrual cycle irregularity in 1993 and a history of ovulatory infertility, and reported lower alcohol consumption than women with a lower BMI (Table 1). Women on both ends of the BMI distribution were more likely to be nulliparous than women in the middle categories (Table 1). We observed a significant linear inverse trend between current BMI and breast cancer incidence (P<.001) (Table 2). Women with a BMI of 30.0 or higher had an age-adjusted hazard ratio for breast cancer of 0.79 (95% confidence interval [CI], 0.67-0.94) compared with women with a BMI between 20.0 and 22.4. Further adjustment for a family history of breast cancer, a history of benign breast disease, height, age at menarche, age at first birth, parity, alcohol consumption, physical activity, and current or past use of oral contraceptives did not alter this estimate appreciably (Table 2). Additionally, adjusting for menstrual cycle characteristics, infertility due to an ovulatory disorder, or probable PCOS similarly did not affect the estimates (Table 2). When the analysis of current BMI and breast cancer was adjusted for BMI at age 18 years, the association was considerably attenuated, eliminating the previously significant trend (Table 2). Restricting the analysis to women with no reported history of infertility due to an ovulatory disorder did not appreciably change the results (data not shown). Body mass index at age 18 years was significantly inversely associated with the incidence of premenopausal breast cancer (Table 3). Women with a BMI of 27.5 or higher at age 18 years had a covariate-adjusted hazard ratio of 0.57 (95% CI, 0.41-0.81) compared with women with a BMI between 20.0 and 22.4 at age 18 years. This association did not appreciably change when adjusting for current BMI (Table 3). Similarly, adjustment for waist-hip ratio did not alter the association (data not shown). Separate analyses were also performed for ER-positive and ER-negative breast cancer cases and for PR-positive and PR-negative breast cancer cases. The association between current BMI and premenopausal breast cancer incidence was stronger for ER-positive than ER-negative cases (P for heterogeneity = .19), whereas no apparent difference was observed between PR-positive and PR-negative cases (P for heterogeneity = .94) (Table 4). The association with BMI at age 18 years was also strongest for ER-positive breast cancer (P for heterogeneity = .68 for ER and 0.78 for PR) (Table 5). When we stratified by menstrual cycle length at ages 18 to 22 years (<32 vs ≥32 days), associations were somewhat stronger in women with longer cycle durations, but there was no significant heterogeneity (P for heterogeneity = .72) (Table 6). No effect modification by age or use of oral contraceptives was apparent. Women with probable PCOS had a covariate-adjusted hazard ratio for breast cancer of 0.89 (95% CI, 0.71-1.10) compared with women who were unlikely to have PCOS. When also adjusting for BMI at age 18 years and for current BMI, the hazard ratio for breast cancer associated with PCOS was 0.98 (95% CI, 0.78-1.23), indicating that any association between PCOS and breast cancer is likely to be mediated by BMI rather than other characteristics of PCOS. Comment Among the premenopausal participants in the NHS II, a high BMI was inversely associated with the incidence of breast cancer. This association was not explained by menstrual cycle characteristics, self-reported infertility due to an ovulatory disorder, or probable PCOS. The BMI at age 18 years, however, explained part of the association between current BMI and breast cancer incidence. In several studies, an inverse association has been reported between body size and premenopausal breast cancer. Of studies in which the relation between BMI at ages 16 to 25 years and the risk of breast cancer was explored, a significant inverse association with premenopausal breast cancer risk was found in some11,18-20 but not all.1,14,21-26 High current BMI has been linked to a reduced risk of breast cancer in premenopausal women or in women younger than 50 to 55 years in numerous studies,1-12,14,18,19 but this link was not confirmed in other studies.20,21,24-38 In a pooled analysis13 of 7 prospective cohort studies including 337 819 women and 723 incident cases of invasive premenopausal breast cancer, a nonlinear inverse association between BMI and breast cancer was found; women with a BMI exceeding 31 had a relative risk of 0.54 (95% CI, 0.34-0.85) compared with women with a BMI of 21 or less. In 3 studies,22,39,40 a positive association between BMI and breast cancer diagnosed before menopause or before age 55 years was observed. Key and Pike41 suggested that the mechanism underlying the inverse association between BMI and premenopausal breast cancer is anovulation in the heavier women, resulting in decreased estradiol and progesterone levels. We cannot exclude a possible role of anovulation because we cannot measure anovulation directly. However, because adjustment for menstrual cycle patterns, infertility due to ovulatory disorder, probable PCOS, and use of oral contraceptives did not even slightly attenuate the association with BMI, anovulation does not seem to be a primary explanation for the reduced risk in heavier women. Among women with no history of infertility due to an ovulatory disorder, the inverse association between BMI and premenopausal breast cancer incidence persisted, lending further support to the role of mechanisms other than anovulation. In the same population, women with infertility due to an ovulatory disorder had a lower incidence of premenopausal breast cancer,42 whereas menstrual cycle pattern was not associated with breast cancer incidence except in women younger than 40 years.43 In this population of premenopausal women, BMI during earlier periods of adult life was more consistently associated with breast cancer incidence than BMI during adulthood. Body fatness during childhood has also been related to a lower incidence of breast cancer in the premenopausal women of the NHS II.44 A high BMI during adulthood is highly correlated with a high body mass during adolescence, which may be more important for the development of breast cancer before menopause. Although a high birth weight has been fairly consistently linked to an increase in the risk of premenopausal breast cancer,45 the BMI–breast cancer association seems to reverse at some point during the first years of life, only to revert back after menopause.46 Because BMI was more clearly related to ER-positive than ER-negative breast cancer, a role of sex steroid hormones is likely. High plasma estradiol levels have been associated with an increased risk of premenopausal breast cancer in some studies47-50 but not in others.51-53 Most studies collected samples at any time in the menstrual cycle, making interpretation more difficult given the fluctuation of estradiol levels and other sex steroid hormone levels throughout the menstrual cycle. In the NHS II, 18 506 premenopausal women provided blood samples in the early follicular and midluteal phases; higher levels of follicular total and free estradiol levels were associated with an increased incidence of breast cancer, whereas no association was apparent for estradiol levels in the luteal phase.50 Premenopausal overweight women have been found to have lower estradiol levels than lean women, but the evidence is inconsistent.54-74 In studies in which the collection of blood specimens was timed during the menstrual cycle,54-65,71 a significant inverse association between total estradiol concentration and BMI was found in 454-57 and a nonsignificant inverse association was found in another.65 In the NHS II, we also observed a significant inverse association between total estradiol level during the follicular and luteal phases and BMI at blood sampling but not between free estradiol level and BMI.75 Obese women have decreased levels of sex hormone–binding globulin.54-57,60-74 Because sex hormone–binding globulin is the main protein carrier of estradiol, free estradiol levels should increase with higher BMI, but the pituitary gland and hypothalamus regulate free estradiol in premenopausal women; hence ovarian production of estradiol may be kept low.76 Potischman et al55 suggested that more free estradiol may be cleared by the liver and other tissues in obese women. Alternatively, obese women may experience ovulatory insufficiency, resulting in compromised estradiol production capacity.75 Whether decreased estrogen blood levels in obese premenopausal women explain the inverse association with breast cancer remains to be determined. The observation that this inverse association is stronger for ER-positive breast cancer lends support to this mechanism. Finally, detection bias has to be considered as a possible explanation for the observed associations. Obese women are less likely to seek breast cancer screening than normal-weight women.77-79 It is possible that obese women with preclinical breast cancer delay their diagnosis, moving the detection of their cancer from the premenopausal to the postmenopausal phase. In conclusion, a large body size during early adulthood is inversely related to the incidence of breast cancer in premenopausal women. Factors related to ovulation, such as menstrual cycle characteristics, infertility due to an ovulatory disorder, and probable PCOS, do not seem to explain this association. Correspondence: Karin B. Michels, ScD, PhD, Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA 02115. Accepted for Publication: August 18, 2006. Author Contributions:Study concept and design: Michels. Acquisition of data: Michels and Willett. Analysis and interpretation of data: Michels and Terry. Drafting of the manuscript: Michels. Critical revision of the manuscript for important intellectual content: Michels, Terry, and Willett. Statistical analysis: Michels and Terry. Obtained funding: Michels and Willett. Study supervision: Michels. Financial Disclosure: None reported. Funding/Support: This project was supported by a grant from the Massachusetts Breast Cancer Research Grants Program of the Massachusetts Department of Public Health (Dr Michels). The NHS II is supported by Public Health Service grant CA50385 from the National Cancer Institute, National Institutes of Health, US Department of Health and Human Services. References 1. Brinton LASwanson CA Height and weight at various ages and risk of breast cancer. Ann Epidemiol 1992;2597- 609PubMedGoogle ScholarCrossref 2. Harris RENamboodiri KKWynder EL Breast cancer risk: effects of estrogen replacement therapy and body mass. J Natl Cancer Inst 1992;841575- 1582PubMedGoogle ScholarCrossref 3. Vatten LJKvinnsland S Prospective study of height, body mass index and risk of breast cancer. Acta Oncol 1992;31195- 200PubMedGoogle ScholarCrossref 4. Bruning PFBonfrer JMHart AA et al. Body measurements, estrogen availability and the risk of human breast cancer: a case-control study. Int J Cancer 1992;5114- 19PubMedGoogle ScholarCrossref 5. Petrek JAPeters MCirrincione CRhodes DBajorunas D Is body fat topography a risk factor for breast cancer? Ann Intern Med 1993;118356- 362PubMedGoogle ScholarCrossref 6. Tornberg SACarstensen JM Relationship between Quetelet's index and cancer of breast and female genital tract in 47,000 women followed for 25 years. Br J Cancer 1994;69358- 361PubMedGoogle ScholarCrossref 7. Taioli EBarone JWynder EL A case-control study on breast cancer and body mass. Eur J Cancer 1995;31A723- 728PubMedGoogle ScholarCrossref 8. Franceschi SFavero ALa Vecchia C et al. Body size indices and breast cancer risk before and after menopause. Int J Cancer 1996;67181- 186PubMedGoogle ScholarCrossref 9. Hu YHNagata CShimizu HKaneda NKashiki Y Association of body mass index, physical activity, and reproductive histories with breast cancer: a case-control study in Gifu, Japan. Breast Cancer Res Treat 1997;4365- 72PubMedGoogle ScholarCrossref 10. Tavani AGallus SLa Vecchia C et al. Risk factors for breast cancer in women under 40 years. Eur J Cancer 1999;351361- 1367PubMedGoogle ScholarCrossref 11. Peacock SLWhite EDaling JRVoigt LFMalone KE Relation between obesity and breast cancer in young women. Am J Epidemiol 1999;149339- 346PubMedGoogle ScholarCrossref 12. Hall IJNewman BMillikan RCMoorman PG Body size and breast cancer risk in black women and white women: the Carolina Breast Cancer Study. Am J Epidemiol 2000;151754- 764PubMedGoogle ScholarCrossref 13. van den Brandt PASpiegelman DYaun SS et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 2000;152514- 527PubMedGoogle ScholarCrossref 14. de Vasconcelos ABAzevedo e Silva Mendonca GSichieri R Height, weight, weight change and risk of breast cancer in Rio de Janeiro, Brazil. Sao Paulo Med J 2001;11962- 66PubMedGoogle ScholarCrossref 15. Friedenreich CM Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev 2001;1015- 32PubMedGoogle ScholarCrossref 16. Cox DR Regression models and life tables (with discussion). J R Stat Soc Ser B 1972;34187- 220Google Scholar 17. Marshall RJChisholm EM Hypothesis testing in the polychotomous logistic model with an application to detecting gastrointestinal cancer. Stat Med 1985;4337- 344PubMedGoogle ScholarCrossref 18. Huang ZHankinson SEColditz GA et al. Dual effects of weight and weight gain on breast cancer risk. JAMA 1997;2781407- 1411PubMedGoogle ScholarCrossref 19. Coates RJUhler RJHall HI et al. Risk of breast cancer in young women in relation to body size and weight gain in adolescence and early adulthood. Br J Cancer 1999;81167- 174PubMedGoogle ScholarCrossref 20. Folsom ARKaye SAPrineas RJPotter JDGapstur SMWallace RB Increased incidence of carcinoma of the breast associated with abdominal adiposity in postmenopausal women. Am J Epidemiol 1990;131794- 803PubMedGoogle Scholar 21. Lund EAdami HOBergstrom RMeirik O Anthropometric measures and breast cancer in young women. Cancer Causes Control 1990;1169- 172PubMedGoogle ScholarCrossref 22. Mayberry RM Age-specific patterns of association between breast cancer and risk factors in black women, ages 20 to 39 and 40 to 54. Ann Epidemiol 1994;4205- 213PubMedGoogle ScholarCrossref 23. Ursin GPaganini-Hill ASiemiatycki JThompson WDHaile RW Early adult body weight, body mass index, and premenopausal bilateral breast cancer: data from a case-control study. Breast Cancer Res Treat 1995;3375- 82PubMedGoogle ScholarCrossref 24. Hirose KTajima KHamajima N et al. Association of family history and other risk factors with breast cancer risk among Japanese premenopausal and postmenopausal women. Cancer Causes Control 2001;12349- 358PubMedGoogle ScholarCrossref 25. Shu XOJin FDai Q et al. Association of body size and fat distribution with risk of breast cancer among Chinese women. Int J Cancer 2001;94449- 455PubMedGoogle ScholarCrossref 26. Wenten MGilliland FDBaumgartner KSamet JM Associations of weight, weight change, and body mass with breast cancer risk in Hispanic and non-Hispanic white women. Ann Epidemiol 2002;12435- 444PubMedGoogle ScholarCrossref 27. McNee RKMason BHNeave LMKay RG Influence of height, weight, and obesity on breast cancer incidence and recurrence in Auckland, New Zealand. Breast Cancer Res Treat 1987;9145- 150PubMedGoogle ScholarCrossref 28. Schatzkin APalmer JRRosenberg L et al. Risk factors for breast cancer in black women. J Natl Cancer Inst 1987;78213- 217PubMedGoogle Scholar 29. Parazzini Fla Vecchia CNegri EBruzzi PPalli DBoyle P Anthropometric variables and risk of breast cancer. Int J Cancer 1990;45397- 402PubMedGoogle ScholarCrossref 30. Mannisto SPietinen PPyy MPalmgren JEskelinen MUusitupa M Body-size indicators and risk of breast cancer according to menopause and estrogen-receptor status. Int J Cancer 1996;688- 13PubMedGoogle ScholarCrossref 31. Chie WCLi CYHuang CSChang KJLin RS Body size as a factor in different ages and breast cancer risk in Taiwan. Anticancer Res 1998;18565- 570PubMedGoogle Scholar 32. Galanis DJKolonel LNLee JLe Marchand L Anthropometric predictors of breast cancer incidence and survival in a multi-ethnic cohort of female residents of Hawaii, United States. Cancer Causes Control 1998;9217- 224PubMedGoogle ScholarCrossref 33. Kaaks RVan Noord PADen Tonkelaar IPeeters PHRiboli EGrobbee DE Breast-cancer incidence in relation to height, weight and body-fat distribution in the Dutch “DOM” cohort. Int J Cancer 1998;76647- 651PubMedGoogle ScholarCrossref 34. Tung HTTsukuma HTanaka H et al. Risk factors for breast cancer in Japan, with special attention to anthropometric measurements and reproductive history. Jpn J Clin Oncol 1999;29137- 146PubMedGoogle ScholarCrossref 35. Enger SMRoss RKPaganini-Hill ACarpenter CLBernstein L Body size, physical activity, and breast cancer hormone receptor status: results from two case-control studies. Cancer Epidemiol Biomarkers Prev 2000;9681- 687PubMedGoogle Scholar 36. Adebamowo CAOgundiran TOAdenipekun AA et al. Waist-hip ratio and breast cancer risk in urbanized Nigerian women. Breast Cancer Res 2003;5R18- R24PubMedGoogle ScholarCrossref 37. Adebamowo CAOgundiran TOAdenipekun AA et al. Obesity and height in urban Nigerian women with breast cancer. Ann Epidemiol 2003;13455- 461PubMedGoogle ScholarCrossref 38. Lahmann PHHoffmann KAllen N et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer 2004;111762- 771PubMedGoogle ScholarCrossref 39. Chu SYLee NCWingo PASenie RTGreenberg RSPeterson HB The relationship between body mass and breast cancer among women enrolled in the Cancer and Steroid Hormone Study. J Clin Epidemiol 1991;441197- 1206PubMedGoogle ScholarCrossref 40. Chang SBuzdar AUHursting SD Inflammatory breast cancer and body mass index. J Clin Oncol 1998;163731- 3735PubMedGoogle Scholar 41. Key TJPike MC The role of oestrogens and progestagens in the epidemiology and prevention of breast cancer. Eur J Cancer Clin Oncol 1988;2429- 43PubMedGoogle ScholarCrossref 42. Terry KLWillett WCRich-Edwards JWMichels KB A prospective study of infertility due to ovulatory disorders, ovulation induction, and incidence of breast cancer. Arch Intern Med In pressGoogle Scholar 43. Terry KLWillett WCRich-Edwards JWHunter DJMichels KB Menstrual cycle characteristics and incidence of premenopausal breast cancer. Cancer Epidemiol Biomarkers Prev 2005;141509- 1513PubMedGoogle ScholarCrossref 44. Baer HJColditz GARosner BA et al. Body fatness during childhood and adolescence and incidence of breast cancer in premenopausal women. Breast Cancer Res 2005;7R314- R325PubMedGoogle ScholarCrossref 45. Michels KBXue F Role of birthweight in the etiology of breast cancer. Int J Cancer 2006;1192007- 2025PubMedGoogle ScholarCrossref 46. Michels KBWillett WC Breast cancer: early life matters. N Engl J Med 2004;3511679- 1681PubMedGoogle ScholarCrossref 47. Rosenberg CRPasternack BSShore REKoenig KLToniolo PG Premenopausal estradiol levels and risk of breast cancer: a new method of controlling for day of the menstrual cycle. Am J Epidemiol 1994;140518- 525PubMedGoogle Scholar 48. Thomas HVKey TJAllen DS et al. A prospective study of endogenous serum hormone concentrations and breast cancer risk in premenopausal women on the island of Guernsey. Br J Cancer 1997;751075- 1079PubMedGoogle ScholarCrossref 49. Kabuto MAkiba SStevens RGNeriishi KLand CE A prospective study of estradiol and breast cancer among Japanese women. Cancer Epidemiol Biomarkers Prev 2000;9575- 579PubMedGoogle Scholar 50. Eliassen AHMissmer SATworoger SS et al. Endogenous steroid hormone concentrations and risk of breast cancer among premenopausal women. J Natl Cancer Inst 2006;981406- 1415Google ScholarCrossref 51. Wysowski DKComstock GWHelsing KJLau HL Sex hormone levels in serum in relation to the development of breast cancer. Am J Epidemiol 1987;125791- 799PubMedGoogle Scholar 52. Helzlsouer KJAlberg AJBush TLLongcope CGordon GBComstock GW A prospective study of endogenous hormones and breast cancer. Cancer Detect Prev 1994;1879- 85PubMedGoogle Scholar 53. Kaaks RBerrino FKey T et al. Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2005;97755- 765PubMedGoogle ScholarCrossref 54. Grenman SRonnemaa TIrjala KKaihola HLGronroos M Sex steroid, gonadotropin, cortisol, and prolactin levels in healthy, massively obese women: correlation with abdominal fat cell size and effect of weight reduction. J Clin Endocrinol Metab 1986;631257- 1261PubMedGoogle ScholarCrossref 55. Potischman NSwanson CASiiteri PHoover RN Reversal of relation between body mass and endogenous concentrations with menopausal status. J Natl Cancer Inst 1996;88756- 758PubMedGoogle ScholarCrossref 56. Randolph JFSowers MGold EB et al. Reproductive hormones in the early menopausal transition: relationship to ethnicity, body size, and menopausal status. J Clin Endocrinol Metab 2003;881516- 1522PubMedGoogle ScholarCrossref 57. Verkasalo PKThomas HVAppleby PNDavey GKKey TJ Circulating levels of sex hormones and their relation to risk factors for breast cancer: a cross-sectional study in 1092 pre- and postmenopausal women. Cancer Causes Control 2001;1247- 59PubMedGoogle ScholarCrossref 58. Zumoff B Relationship of obesity to blood estrogens. Cancer Res 1982;42 ((8 suppl)) 3289s- 3294sPubMedGoogle Scholar 59. Trichopoulos DPolychronopoulou ABrown JMacMahon B Obesity, serum cholesterol, and estrogens in premenopausal women. Oncology 1983;40227- 231PubMedGoogle ScholarCrossref 60. Haffner SMKatz MSStern MPDunn JF Relationship of sex hormone binding globulin to overall adiposity and body fat distribution in a biethnic population. Int J Obes 1989;131- 9PubMedGoogle Scholar 61. Dorgan JFReichman MEJudd JT et al. The relation of body size to plasma levels of estrogens and androgens in premenopausal women (Maryland, United States). Cancer Causes Control 1995;63- 8PubMedGoogle ScholarCrossref 62. De Pergola GZamboni MSciaraffia M et al. Body fat accumulation is possibly responsible for lower dehydroepiandrosterone circulating levels in premenopausal obese women. Int J Obes Relat Metab Disord 1996;201105- 1110PubMedGoogle Scholar 63. Westhoff CGentile GLee JZacur HHelbig D Predictors of ovarian steroid secretion in reproductive-age women. Am J Epidemiol 1996;144381- 388PubMedGoogle ScholarCrossref 64. Nagata CKaneda NKabuto MShimizu H Factors associated with serum levels of estradiol and sex hormone-binding globulin among premenopausal Japanese women. Environ Health Perspect 1997;105994- 997PubMedGoogle ScholarCrossref 65. Thomas HVKey TJAllen DS et al. Re: Reversal of relation between body mass and endogenous estrogen concentrations with menopausal status [letter]. J Natl Cancer Inst 1997;89396- 397PubMedGoogle ScholarCrossref 66. Ivandic APrpic-Krizevac ISucic MJuric M Hyperinsulinemia and sex hormones in healthy premenopausal women: relative contribution of obesity, obesity type, and duration of obesity. Metabolism 1998;4713- 19PubMedGoogle ScholarCrossref 67. Kraemer RRSynovitz LBGimpel TKraemer GRJohnson LGCastracane VD Effect of estrogen on serum DHEA in younger and older women and the relationship of DHEA to adiposity and gender. Metabolism 2001;50488- 493PubMedGoogle ScholarCrossref 68. Allen NEAppleby PNKaaks RRinaldi SDavey GKKey TJ Lifestyle determinants of serum insulin-like growth-factor-I (IGF-I), C-peptide and hormone binding protein levels in British women. Cancer Causes Control 2003;1465- 74PubMedGoogle ScholarCrossref 69. Kok PRoelfsema FFrolich MMeinders AEPijl H Prolactin release is enhanced in proportion to excess visceral fat in obese women. J Clin Endocrinol Metab 2004;894445- 4449PubMedGoogle ScholarCrossref 70. Tufano AMarzo PEnrini RMorricone LCaviezel FAmbrosi B Anthropometric, hormonal and biochemical differences in lean and obese women before and after menopause. J Endocrinol Invest 2004;27648- 653PubMedGoogle ScholarCrossref 71. Lukanova ALundin EZeleniuch-Jacquotte A et al. Body mass index, circulating levels of sex-steroid hormones, IGF-I and IGF-binding protein-3: a cross-sectional study in healthy women. Eur J Endocrinol 2004;150161- 171PubMedGoogle ScholarCrossref 72. Bezemer IDRinaldi SDossus L et al. C-peptide, IGF-I, sex-steroid hormones and adiposity: a cross-sectional study in healthy women within the European Prospective Investigation into Cancer and Nutrition (EPIC). Cancer Causes Control 2005;16561- 572PubMedGoogle ScholarCrossref 73. Kopelman PGPilkington TRWhite NJeffcoate SL Abnormal sex steroid secretion and binding in massively obese women. Clin Endocrinol (Oxf) 1980;12363- 369PubMedGoogle ScholarCrossref 74. Pasquali RAntenucci DMelchionda N et al. Sex hormones in obese premenopausal women and their relationships to body fat mass and distribution, B cell function and diet composition. J Endocrinol Invest 1987;10345- 350PubMedGoogle ScholarCrossref 75. Tworoger SSEliassen AHMissmer SA et al. Birthweight and body size throughout life in relation to sex hormones and prolactin concentrations in premenopausal women. Cancer Epidemiol Biomarker Prev In pressGoogle Scholar 76. Yen SJaffe RBarbieri R Reproductive Endocrinology: Physiology, Pathophysiology, and Clinical Management. Philadelphia, Pa WB Saunders Co1999; 77. Wee CCMcCarthy EPDavis RBPhillips RS Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000;132697- 704PubMedGoogle ScholarCrossref 78. Wee CCMcCarthy EPDavis RBPhillips RS Obesity and breast cancer screening. J Gen Intern Med 2004;19324- 331PubMedGoogle ScholarCrossref 79. Amy NKAalborg ALyons PKeranen L Barriers to routine gynecological cancer screening for white and African-American obese women. Int J Obes 2006;30147- 155Google ScholarCrossref

Journal

Archives of Internal MedicineAmerican Medical Association

Published: Nov 27, 2006

Keywords: premenopause,breast cancer,infertility,follow-up,body mass index procedure,ovulatory dysfunction,nurses' health study,menstrual cycle

References