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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer. Author manuscript; available in PMC Oct 1, 2013.
Published in final edited form as:
PMCID: PMC3448867
NIHMSID: NIHMS348183
Fat or Fit: The Joint Effects of PA, Weight Gain, and Body Size on Breast Cancer Risk
Lauren E. McCullough,1 Sybil M. Eng,2 Patrick T. Bradshaw,1 Rebecca J. Cleveland,3 Susan L. Teitelbaum,4 Alfred I. Neugut,2,5 and Marilie D. Gammon1
1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina
2Department of Epidemiology, Columbia University
3Department of Medicine, University of North Carolina
4Department of Community Medicine and Prevention, Mt. Sinai School of Medicine
5Department of Medicine, Columbia University
Correspondence: Lauren E. McCullough, Department of Epidemiology, CB #7435, McGavran-Greenberg Hall, University of North Carolina, Chapel Hill, NC 27599-7435, USA. Phone:(919)966-7421. Fax:(919)966-2089. lauren.mccullough/at/unc.edu
Background
While physical activity reduces breast cancer risk, issues critical to providing clear public health messages remain to be elucidated. These include: the minimum duration and intensity necessary for risk reduction; the optimal time period for occurrence; as well as subgroup effects, particularly with regard to tumor heterogeneity and body size.
Methods
The purpose of this report is to investigate the relationship between recreational physical activity (RPA) and breast cancer risk, in addition to characterizing the joint effects of activity level, weight gain and body size, using a population-based sample of 1504 cases (N=233 in-situ, N=1271 invasive) and 1555 controls (aged 20–98 years) from the Long Island Breast Cancer Study Project.
Results
We observed a non-linear dose response association between breast cancer risk and RPA during the reproductive period and after menopause. Women in the third quartile of activity experienced the greatest benefit with an approximate 30% risk reduction for reproductive (odds ratio [OR]=0.67; 95% confidence interval [CI], 0.48–0.94) and postmenopausal activity (OR=0.70; 95% CI, 0.52–0.95). We observed little to no difference by intensity or hormone receptor status. Joint assessment of RPA, weight gain and body size revealed that women with unfavorable energy balance profiles were at increased breast cancer risk. We observed a significant multiplicative interaction between RPA and adult weight gain (p=0.033).
Conclusions
RPA at any intensity level during the reproductive and postmenopausal years have the greatest benefit for reducing breast cancer risk. Substantial postmenopausal weight gain may eliminate the benefits of regular activity.
Keywords: Breast cancer, physical activity, weight gain, BMI, epidemiology
Despite the recent downward trend in the rates of breast cancer, with over 200,000 women newly diagnosed with this disease per year1 it remains an important public health concern. While there has been considerable research in identifying risk factors for breast cancer, a small proportion are amenable to intervention. Physical Activity (PA) appears to play an important role in the reduction of both pre- and postmenopausal breast cancer risk25. Given that three-quarters of the United States (US) population participates in some PA6; it is conceivably one of the most important lifestyle risk factors associated with the breast cancer.
A number of important issues critical to developing public health messages and/or interventions to reduce breast cancer risk, with respect to PA, remain to be elucidated. These include the minimum duration and intensity of PA necessary for risk reduction, as well as the optimal time periods at which activity should occur. A more comprehensive understanding of how this association varies within subgroups may allow us to better target susceptible populations of women for public health messaging and intervention. It may also advance our knowledge of breast cancer etiology. Analyses of subgroups defined by hormone receptor (HR) status may better inform us about the impact of PA in heterogeneous breast tumors. Given the strong association between adiposity and PA7, simultaneous examination of body weight, PA, and breast cancer risk could help to uncover mechanisms through which PA acts.
The purpose of this report was to: (1) investigate the association between recreational physical activity (RPA), at several points throughout the life-course, and risk of developing breast cancer using data from a large population-based case-control study; (2) explore the association between RPA and HR status to understand if RPA preferentially reduces the risk of HR-positive breast cancers; and (3) characterize the joint effects of activity level and weight gain or body size. We hypothesized that RPA across the life-course was most important for breast cancer risk reduction and that RPA preferentially reduces the risk of HR-positive breast cancers. We also anticipated that women with unfavorable energy balance profiles would be at increased risk of breast malignancy.
The Long Island Breast Cancer Study Project (LIBCSP) is a population-based study conducted among adult English-speaking female residents of Nassau and Suffolk counties, Long Island, NY. Data used for the analysis reported here include: participants of the case-control study; the case-control interview; and the medical record review. Details of the study methods have been described previously8. Institutional Review Board approval was obtained from all participating institutions.
Study Population
Eligible LIBCSP cases were women of all ages (age range 20–98 years) and races newly diagnosed with first primary in-situ or invasive breast cancer between August 1, 1996, and July 31, 1997. Cases were identified through daily or weekly contact to 28 hospitals on Long Island and three large tertiary care hospitals in New York City. Eligible controls were women without a personal history of breast cancer and were frequency matched to the expected age distribution of cases by 5-year age group. Controls were identified through random digit dialing for women under age 65 years and the Health Care Finance Administration rosters for women age 65 years and greater. All data were collected through a two-hour interviewer-administered structured questionnaire conducted by a trained interviewer in the respondent’s home. Interview response among eligible cases and controls were 82.1% (N=1508) and 62.8% (N=1556), respectively.
RPA Assessment
As part of the LIBCSP case-control questionnaire, interviewers asked participants about their involvement in RPA using a modified instrument developed by Bernstein and colleagues9. Respondents were asked about all RPA in which they had engaged for at least one hour per week and three months or more in any year over the life-course. Participants who replied never having participated in RPA were classified as having no RPA. Among women who replied ever having participated in RPA, interviewers obtained the activity name, the ages the activity was started and stopped, and the number of hours per week (hrs/wk) and months per year the activity was performed. These data were summed across all activities for each year of a woman’s life, providing a lifetime composite score of exercise duration from menarche (left truncated) to reference date. Similarly, for women classified as ever having participated in RPA, metabolic equivalents of energy expenditure (MET) scores were assigned to each reported activity according to a published database10. Scores were multiplied by the number of hrs/wk the participant reported engaging in the activity and were summed across all activities. For this ancillary study complete RPA data were obtained for 1,504 cases and 1,555 controls.
Average hrs/wk and average MET hrs/wk of RPA were evaluated in four etiologically relevant time periods based on known windows of breast cancer susceptibility: from menarche to first birth (among parous women), to approximate activity in adolescence and early adulthood; from first birth to menopause (among parous postmenopausal women), to approximate activity during the reproductive years; from menopause to reference date (among postmenopausal women), to approximate activity during the postmenopausal years; and from menarche to reference date, to approximate activity across the lifespan.
Covariate Assessment
In addition to RPA, the case-control questionnaire queried women on demographic characteristics; reproductive, medical and environmental histories; cigarette and alcohol use; use of exogenous hormones; energy intake; and select anthropometric measurements.
Body size assessments included weight, height, and weight change by decade of life. Participants self-reported height to the nearest inch and weight to the nearest pound at age 20 and 1-year prior to reference date. Respondents also reported their weight in each decade of life starting at age 20. Change in weight during two time periods were calculated using methods previously described11. Weight change from the 20s to 1-year prior to reference date was used to estimate adulthood weight change, while weight change from the 50s to 1-year prior to reference date estimated postmenopausal weight change. Body mass index (BMI) at age 20 and reference date were calculated for each participant based on the following formula: weight (kg)/height(m)2.
Among eligible cases clinical data on the characteristics of their breast cancer diagnosis, including HR status, were obtained from medical records.
Statistical Methods
Unconditional logistic regression12 was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between breast cancer and RPA. All statistical models were implemented in SAS v.9.1 (Cary, NC).
RPA for the four time periods were evaluated independently as dichotomous variables (using the control median), quartiles, deciles, and as continuous variables. We additionally explored flexible modeling using quadratic functions and linear splines to determine which construction best described the shape of the data. Results produced using quartiles are reported here, as it most adequately captured the dose-response shape of the data employing the fewest number of parameters. To optimize study power for assessment of heterogeneity by HR status and effect measure modification by weight change and BMI, RPA was classified based on the control median (<control median=low RPA; ≥control median=high RPA). Among women who gained weight we classified participants as high (≥control median) or low (<control median) weight gainers for the time periods from age 20 to referent date and from age 50 to referent date. These two groups, along with weight maintainers (+/− 3kg) and weight losers, define our weight change variable. We categorized BMI using the standard World Health Organization (WHO) classifications (<18.5; 18.5–24.9; 25.0–29.9; and ≥30).
We identified potential confounders through the known epidemiology of breast cancer and analysis of causal diagrams13. Education (categorical), first degree family history of breast cancer (yes/no), history of benign breast disease (yes/no), income (categorical), lactation history (ever/never), use of oral contraceptives (ever/never), parity (categorical), and smoking history (never, current, former) were considered potential confounders. Covariates resulting in greater than a 10% change in the regression coefficient when added to the model, compared to a model without the covariate, were considered confounders in our final analysis14. None of the variables assessed met our criteria. Thus, final multivariable models were adjusted only for the frequency matching factor, 5-year age group.
The main effect of RPA on breast cancer risk was assessed among all women combined as well as within strata of menopausal status. The effect of RPA among postmenopausal women in all four time periods was evaluated by HR status stratifying cases into two groups using information on estrogen receptor (ER) and progesterone receptor (PR) status15: women with tumors that showed any hormone responsiveness (ER+/PR+, ER+/PR−, and ER−/PR+) versus none (ER−/PR−).
We examined interactions between RPA, weight change, BMI and postmenopausal breast cancer risk using multiplicative and additive scales. Weight change and BMI were investigated as potential effect measure modifiers based on our a priori study aims. Participants who reported not partaking in RPA over any period during the life-course and had neither gained nor lost weight as an adult were used as a common referent group for the RPA-weight change interactions, while women with no life-course RPA and BMI between 18.5 and 24.99 were used as a common referent group for the RPA-BMI interaction. Departures from the multiplicative null were assessed using the likelihood ratio test, employing α ≤ 0.2 as a cutoff for statistical significance16. Departures from the additive null were evaluated by estimating the interaction contrast ratio (ICR)17. Using indicator terms for RPA, weight change, and BMI variables the magnitude of the additive interaction effect was determined by estimating the adjusted ICR based on the following formula: ICR OR11–OR01–OR10+1 and its respective confidence interval obtained by ICR±1.96 SE(ICR)18.
Breast cancer risk among all women was only slightly reduced with ever participation in RPA (OR=0.94; 95% CI, 0.79–1.12) and varied modestly by menopausal status (OR=1.15; 95% CI, 0.80–1.64 for premenopausal women and OR=0.87; 95% CI, 0.71–1.08 for postmenopausal women). While we observed no difference in effects when intensity levels were considered (average MET hours/week) compared to the analysis including duration alone (data not shown), we did observe some variation by timing of RPA (Table 1). Among premenopausal women neither RPA during adolescence and early adulthood nor RPA over the life-course were associated with breast cancer risk. In contrast, consistent inverse associations between RPA and breast cancer risk were observed among postmenopausal women. Compared to the other time periods, RPA during the reproductive years showed the strongest association. Parous postmenopausal women in the third quartile of activity (10 to 19 hrs/wk) had an age-adjusted OR of 0.67 (95% CI, 0.48–0.94) compared to women who were inactive. The association was also apparent for postmenopausal women in the third quartile of activity performed after menopause (OR: 0.70; 95% CI: 0.52–0.95). Even upon restricting our analyses to parous women creating comparable groups across time periods, we find that the most relevant period for breast cancer risk reduction is during the reproductive period and following menopause (data not shown). Similarly, we found comparable effect estimates for invasive and in-situ cases (data not shown), and thus report findings for total breast cancer.
TABLE 1
TABLE 1
Age Adjusted Odds Ratios and 95% Confidence Intervals for the Association between Recreational Physical Activity at four Time Periods and Breast Cancer Risk in the Long Island Breast Cancer Study Project (1996–1997).
Among parous postmenopausal women, RPA during the reproductive period was associated with a 25% risk reduction of HR-positive breast cancer (OR=0.75; 95% CI, 0.55–1.03), and a 4% risk reduction among HR-negative cases (OR=0.96; 95% CI, 0.58–1.58) (Table 2). While we observed no statistical differences between HR-positive and HR-negative cases, our exploratory analysis suggests that breast cancer risk reduction from reproductive RPA may apply to women who have the most common types of postmenopausal breast cancer, namely HR-positive tumors. We found no striking results among any of the other three time periods assessed. We also estimated the association between RPA and ER status and found little differences in the two outcome groups. Our data indicate a 15% risk reduction for ER− breast tumors and a 20% risk reduction for ER+ breast tumors among postmenopausal women who were classified as having high reproductive RPA (data not shown).
TABLE 2
TABLE 2
Age Adjusted Odds Ratios and 95% Confidence Intervals for the Association between Recreational Physical Activity Hormone Receptor Status among Postmenopausal Women in the Long Island Breast Cancer Study Project (1996–1997).
Our data show that within strata of weight change or BMI women who engage in high activity are at a lower risk of breast cancer than women in the same classification who are inactive (Table 3). High adulthood weight gain was associated with a 28% increased risk of breast cancer among women who engaged in no activity over the life-course (95% CI, 0.68–2.39), while approximately null associations were observed among high gainers reporting high levels of RPA during the same time period (OR=1.02; 95% CI, 0.55–1.87). Similar patterns were observed for high postmenopausal weight gain and obesity although RPA was not shown to mitigate the deleterious effects of high postmenopausal weight gain on breast cancer risk. We found a statistically significant interaction (p=0.033) between lifetime RPA and weight gain from age 20 on the multiplicative scale (Table 3). There was no evidence of interaction on the additive scale.
TABLE 3
TABLE 3
Age Adjusted Odds Ratios and 95% Confidence Intervals for the Joint Effect of Body Size and Recreational Physical Activity among Postmenopausal Women on Breast Cancer Risk in the Long Island Breast Cancer Study Project (1996–1997)
We observed stronger effects for PA among postmenopausal women compared to premenopausal women, which is consistent with previously published data25. The effects obtained using average MET-hours as a composite measure of intensity and duration were similar to estimates observed for duration alone. Our results did indicate some variations in risk based on timing of RPA occurrence. We found that RPA over the life-course, particularly during the reproductive (among parous women) and postmenopausal years, decreases breast cancer risk. The observed inverse associations are consistent with most other studies that have examined the effect of PA on breast cancer risk reduction4,5,19,20 where an average 25% risk reduction is reported5,19.
Results from the current study show that RPA during the reproductive years and after menopause are most critical for risk reduction. Our observations likely reflect the role of PA in energy balance and obesity mediated mechanisms (e.g. insulin resistance and inflammation) which most commonly manifest after adolescence. We found little evidence of benefit from early-life activity which is consistent with most 2124, but not all25,26, investigations. While few studies are able to assess activity across the life-course, those that have a comprehensive lifetime assessment of PA report inverse associations2,27. Using a similar PA assessment to the current study, Bernstein and colleagues report a 17% risk reduction among women in the highest quartile of lifetime activity compared inactive women27. We also found a 17% risk reduction for lifetime RPA among the most active women despite our wide age distribution (age 20–98 vs. age 35–64). Given the noticeable difference in age distribution for this study, we re-assed our main effects restricted to women age <80, <70, and <60 years, respectively. We found no difference between these restricted analyses and the analyses among women of all ages (data not shown).
Contrary to a recent review which reports an inverse dose-response association between PA and breast cancer risk5, the observed effect for RPA did not decrease in a dose-response manner. A lack of linear dose-response could be interpreted as weak evidence of an association between RPA and breast cancer risk. However, it is possible that the RPA breast cancer association may follow a J- or U-shape. In the LIBCSP, a substantial proportion of women reported high levels of RPA permitting us to consider a wide range of effects. Studies indicate that sustained PA is a strong inducer of lipid peroxidation and reactive oxygen species28,29. These changes may cause DNA damage, mutations in proto-oncogenes or tumor-suppressor genes and, if unrepaired, transformation of normal epithelium to a malignant phenotype30,31. Studies also show that vigorous physical exercise may depress immune function32. Inconsistencies in dose-response may therefore reflect the underlying distribution of RPA among study participants. For example, the median lifetime RPA in the Women’s Contraceptive and Reproductive Experiences study was 1.2 hrs/week27, while the median lifetime RPA was 6.35 hrs/week in the current study. Our results, in combination with animal and clinical data, suggest that sustained involvement in vigorous activity may mitigate the known protective effect of RPA resulting in a J- or U-shaped association.
Although our results do not show statistically significant associations among HR-positive or HR-negative cases, they do suggest that RPA during the reproductive period may preferentially decrease HR-positive tumors. This further supports the hypothesis that breast cancer is a heterogeneous disease with varying etiologic pathways33. When we assessed ER-negative cases compared to ER-positive cases the patterns of association were not markedly different between groups (OR=0.80 and 0.85, respectively), suggesting that the role of PA in risk reduction is not entirely mediated through an estrogen pathway. It should be noted that in these analyses the cell sizes were particularly small for ER−/PR− cases in comparison with any positive cases. Our findings are comparable to other reports that find no difference in the PA-breast cancer association by hormone receptor status27,34,35, but in contrast to some studies which report greater decreases among ER- cases compared to ER+ cases35,36, and still others which show stronger associations for ER+ tumors37,38.
In this large case-control study we found that breast cancer risk was generally the greatest among women jointly classified as having high levels of adiposity and little RPA. While the results were consistent with our hypothesis, we found only one multiplicative interaction and no evidence of additive interaction among our indicators of energy balance. It is noteworthy that postmenopausal RPA reduced the adverse effects of obesity on breast cancer risk to approximately null, but did not completely obliterate the effect of postmenopausal weight gain. These observations likely reflect differences in the effect of weight maintenance vs. weight gain39 with the latter being potentially more deleterious during the postmenopausal years11. The timing of weight gain may therefore be an important factor in understanding the weight-PA interaction among postmenopausal populations. While stratification would help us better uncover these associations, even with a study sample of 3000 women we did not have adequate statistical power to evaluate the three-way joint effects of weight gain, BMI and RPA. Few studies have examined modification by weight change4044. In the only other study42 to assess the joint effects of weight gain and PA using the common referent analysis, investigators also reported that high PA did not eliminate the excess breast cancer risk caused by weight gain.
Studies of PA and breast cancer risk have mixed results on the modifying effects of BMI. Several investigations have found risk reductions only among physically active lean women4348, while others report risk reductions in all BMI categories42,49. A 2008 review of 16 studies estimated that risk reductions were approximately 25% among women with BMI between 22 and 25 kg/m2 and 20% among women with BMI ≥25 kg/m2. There were near null effects of PA on breast cancer risk among women with BMI ≥30 kg/m2, although few studies reported effects in this stratum5. Our results are consistent with this review.
The strengths of our study are numerous and include its population-based design and large sample size which increased our power to detect small associations, assess subgroup analyses, and evaluate joint effects of weight indicators and RPA. Our RPA assessment provided a wide range of activities that contribute to energy expenditure in this population of women. Multiple time periods throughout the lifespan were evaluated as well as several parameters of RPA. While in this analysis we were unable to assess all potential sources of PA, a comprehensive 2008 review of physical activity parameters and breast cancer risk showed that the greatest risk reductions were for RPA (20% risk reduction). Activity related to occupation, transportation and living each resulted in ~14% risk reduction5. Few studies have considered PA from all sources. Given the high socioeconomic status and of Long Island women8 we expected little variation by alternative sources of activity and anticipate that any additional variation would result in a more pronounced risk reduction. Although our RPA measurement has not been validated, this instrument was useful in revealing important relationships between exercise and breast cancer risk in other epidemiologic studies9,50.
Despite the large overall sample size a limitation of this study was its relatively homogenous population. Study participants were on average more affluent and educated than the US population. Results are therefore not readily applicable to all women. Our study population included few women who were nulliparous8. We were therefore unable to perform a stratified analysis by nulliparity when assessing the effect of timing on breast cancer risk. Errors in reporting or differential reporting by cases and controls have the potential to bias the study results. A spurious inverse association could have occurred if PA was systematically underreported by cases or over reported by controls. While regular PA, like other healthy behaviors, may result in some social desirability, we suspect that these biases would persist in both case and control groups resulting in non-differential misclassification of RPA. Given the exposure variable was not simply dichotomous however, the direction of such a bias would be unpredictable51. To reduce the probability of recall bias and misclassification, Long Island investigators used a comprehensive questionnaire to obtain detailed information on most study variables enhancing our ability to assess RPA and control for confounding by important breast cancer risk factors.
In this large population-based study that included a life-course assessment of RPA and body size, we found that frequent episodes of RPA (10 to 19 hrs/wk) at any intensity level during the reproductive and postmenopausal years may have the greatest benefit for reducing the risk of breast cancer. Our data indicate, however, that substantive postmenopausal weight gain may eliminate the benefits of regular RPA. Collectively, these results suggest that women with can still reduce their breast cancer risk later in life by maintaining their weight and engaging in moderate amounts of activity. Future investigations should include populations with wide distributions of PA to confirm the non-linear dose-response found in this study.
Acknowledgements
This work was supported in part by awards from: the Department of Defense Breast Cancer Research Program (Predoctoral Traineeship Award BC093608); and the National Cancer Institute and the National Institute of Environmental Health Science (CA/ES66572 and ES10126)
Footnotes
Financial Disclosures: The authors have no financial or non-financial conflicts of interest to declare.
1. Howlader N, Noone AM, Krapcho M, et al. SEER cancer statistics review. 1975–2008 http://seer.cancer.gov/csr/1975_2008/. Updated 2011.
2. Dorn J, Vena J, Brasure J, et al. Lifetime physical activity and breast cancer risk in pre-and postmenopausal women. Med Sci Sports Exerc. 2003;35(2):278–285. [PubMed]
3. Friedenreich CM. Physical activity and breast cancer risk: The effect of menopausal status. Exerc Sport Sci Rev. 2004;32(4):180–184. [PubMed]
4. Monninkhof EM, Elias SG, Vlems FA, et al. Physical activity and breast cancer: A systematic review. Epidemiology. 2007;18(1):137–157. [PubMed]
5. Friedenreich CM, Cust AE. Physical activity and breast cancer risk: Impact of timing, type and dose of activity and population subgroup effects. Br J Sports Med. 2008;42(8):636–647. [PubMed]
6. Centers for Disease Control - Office of Surveillance, Epidemiology, and Laboratory Services. Prevalence and trend data - exercise 2010. [Accessed 06/15, 2011];Behavorial Risk Factor Surveillance System Web site. http://apps.nccd.cdc.gov/BRFSS/list.asp?cat=EX&yr=2010&qkey=4347&state=All. Updated 2010.
7. McTiernan A, Ulrich C, Slate S, et al. Physical activity and cancer etiology: Associations and mechanisms. Cancer Causes Control. 1998;9(5):487–509. [PubMed]
8. Gammon MD, Neugut AI, Santella RM, et al. The long island breast cancer study project: Description of a multi-institutional collaboration to identify environmental risk factors for breast cancer. Breast Cancer Res Treat. 2002;74(3):235–254. [PubMed]
9. Bernstein L, Henderson BE, Hanisch R, et al. Physical exercise and reduced risk of breast cancer in young women. J Natl Cancer Inst. 1994;86(18):1403–1408. [PubMed]
10. Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: Classification of energy costs of human physical activities. Med Sci Sports Exerc. 1993;25(1):71–80. [PubMed]
11. Eng SM, Gammon MD, Terry MB, et al. Body size changes in relation to postmenopausal breast cancer among women on long island, new york. Am J Epidemiol. 2005;162(3):229–237. [PubMed]
12. Hosmer DW, Lemeshow S. Applied logistic regression. New Youk: Wiley; 1989.
13. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 2008;8:70. [PMC free article] [PubMed]
14. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health. 1989;79(3):340–349. [PubMed]
15. Yang XR, Chang-Claude J, Goode EL, et al. Associations of breast cancer risk factors with tumor subtypes: A pooled analysis from the breast cancer association consortium studies. J Natl Cancer Inst. 2011;103(3):250–263. [PMC free article] [PubMed]
16. Breslow NE, Day NE. Statistical methods in cancer research. volume I - the analysis of case-control studies. IARC Sci Publ. 1980;32(32):5–338. [PubMed]
17. Rothman K, Greenland S. Modern epidemiology. 2nd ed. Philadelphia: Maple Press; 1998.
18. Assmann SF, Hosmer DW, Lemeshow S, et al. Confidence intervals for measures of interaction. Epidemiology. 1996;7(3):286–290. [PubMed]
19. Friedenreich CM. Physical activity and breast cancer: Review of the epidemiologic evidence and biologic mechanisms. Recent Results Cancer Res. 2011;188:125–139. [PubMed]
20. McTiernan A. Mechanisms linking physical activity with cancer. Nat Rev Cancer. 2008;8(3):205–211. [PubMed]
21. Hu YH, Nagata C, Shimizu H, et al. 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;43(1):65–72. [PubMed]
22. Taioli E, Barone J, Wynder EL. A case-control study on breast cancer and body mass. the american health foundation--division of epidemiology. Eur J Cancer. 1995;31A(5):723–728. [PubMed]
23. McTiernan A, Stanford JL, Weiss NS, et al. Occurrence of breast cancer in relation to recreational exercise in women age 50–64 years. Epidemiology. 1996;7(6):598–604. [PubMed]
24. D'Avanzo B, Nanni O, La Vecchia C, et al. Physical activity and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1996;5(3):155–160. [PubMed]
25. Frisch RE, Wyshak G, Albright NL, et al. Lower lifetime occurrence of breast cancer and cancers of the reproductive system among former college athletes. Am J Clin Nutr. 1987;45(1 Suppl):328–335. [PubMed]
26. Mittendorf R, Longnecker MP, Newcomb PA, et al. Strenuous physical activity in young adulthood and risk of breast cancer (united states) Cancer Causes Control. 1995;6(4):347–353. [PubMed]
27. Bernstein L, Patel AV, Ursin G, et al. Lifetime recreational exercise activity and breast cancer risk among black women and white women. J Natl Cancer Inst. 2005;97(22):1671–1679. [PubMed]
28. Guerra A, Rego C, Castro E, et al. LDL peroxidation in adolescent female gymnasts. Rev Port Cardiol. 2000;19(11):1129–1140. [PubMed]
29. Guerra A, Rego C, Laires MJ, et al. Lipid profile and redox status in high performance rhythmic female teenagers gymnasts. J Sports Med Phys Fitness. 2001;41(4):505–512. [PubMed]
30. Kang DH. Oxidative stress, DNA damage, and breast cancer. AACN Clin Issues. 2002;13(4):540–549. [PubMed]
31. Behrend L, Henderson G, Zwacka RM. Reactive oxygen species in oncogenic transformation. Biochem Soc Trans. 2003;31(Pt 6):1441–1444. [PubMed]
32. Hoffman-Goetz L, Apter D, Demark-Wahnefried W, et al. Possible mechanisms mediating an association between physical activity and breast cancer. Cancer. 1998;83(3 Suppl):621–628. [PubMed]
33. Potter JD, Cerhan JR, Sellers TA, et al. Progesterone and estrogen receptors and mammary neoplasia in the iowa women's health study: How many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev. 1995;4(4):319–326. [PubMed]
34. Leitzmann MF, Moore SC, Peters TM, et al. Prospective study of physical activity and risk of postmenopausal breast cancer. Breast Cancer Res. 2008;10(5):R92. [PMC free article] [PubMed]
35. Peters TM, Schatzkin A, Gierach GL, et al. Physical activity and postmenopausal breast cancer risk in the NIH-AARP diet and health study. Cancer Epidemiol Biomarkers Prev. 2009;18(1):289–296. [PMC free article] [PubMed]
36. Dallal CM, Sullivan-Halley J, Ross RK, et al. Long-term recreational physical activity and risk of invasive and in situ breast cancer: The california teachers study. Arch Intern Med. 2007;167(4):408–415. [PubMed]
37. Bardia A, Hartmann LC, Vachon CM, et al. Recreational physical activity and risk of postmenopausal breast cancer based on hormone receptor status. Arch Intern Med. 2006;166(22):2478–2483. [PubMed]
38. Suzuki R, Iwasaki M, Kasuga Y, et al. Leisure-time physical activity and breast cancer risk by hormone receptor status: Effective life periods and exercise intensity. Cancer Causes Control. 2010;21(11):1787–1798. [PubMed]
39. Eliassen AH, Colditz GA, Rosner B, et al. Adult weight change and risk of postmenopausal breast cancer. JAMA. 2006;296(2):193–201. [PubMed]
40. Dirx MJ, Voorrips LE, Goldbohm RA, et al. Baseline recreational physical activity, history of sports participation, and postmenopausal breast carcinoma risk in the netherlands cohort study. Cancer. 2001;92(6):1638–1649. [PubMed]
41. Breslow RA, Ballard-Barbash R, Munoz K, et al. Long-term recreational physical activity and breast cancer in the national health and nutrition examination survey I epidemiologic follow-up study. Cancer Epidemiol Biomarkers Prev. 2001;10(7):805–808. [PubMed]
42. Shin A, Matthews CE, Shu XO, et al. Joint effects of body size, energy intake, and physical activity on breast cancer risk. Breast Cancer Res Treat. 2009;113(1):153–161. [PubMed]
43. Shoff SM, Newcomb PA, Trentham-Dietz A, et al. Early-life physical activity and postmenopausal breast cancer: Effect of body size and weight change. Cancer Epidemiol Biomarkers Prev. 2000;9(6):591–595. [PubMed]
44. Patel AV, Callel EE, Bernstein L, et al. Recreational physical activity and risk of postmenopausal breast cancer in a large cohort of US women. Cancer Causes Control. 2003;14(6):519–529. [PubMed]
45. McTiernan A, Kooperberg C, White E, et al. Recreational physical activity and the risk of breast cancer in postmenopausal women: The women's health initiative cohort study. JAMA. 2003;290(10):1331–1336. [PubMed]
46. Luoto R, Latikka P, Pukkala E, et al. The effect of physical activity on breast cancer risk: A cohort study of 30,548 women. Eur J Epidemiol. 2000;16(10):973–980. [PubMed]
47. Verloop J, Rookus MA, van der Kooy K, et al. Physical activity and breast cancer risk in women aged 20–54 years. J Natl Cancer Inst. 2000;92(2):128–135. [PubMed]
48. Moradi T, Nyren O, Zack M, et al. Breast cancer risk and lifetime leisure-time and occupational physical activity (sweden) Cancer Causes Control. 2000;11(6):523–531. [PubMed]
49. Tehard B, Friedenreich CM, Oppert JM, et al. Effect of physical activity on women at increased risk of breast cancer: Results from the E3N cohort study. Cancer Epidemiol Biomarkers Prev. 2006;15(1):57–64. [PubMed]
50. Carpenter CL, Ross RK, Paganini-Hill A, et al. Lifetime exercise activity and breast cancer risk among post-menopausal women. Br J Cancer. 1999;80(11):1852–1858. [PMC free article] [PubMed]
51. Dosemeci M, Wacholder S, Lubin JH. Does nondifferential misclassification of exposure always bias a true effect toward the null value? Am J Epidemiol. 1990;132(4):746–748. [PubMed]