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Disentangling the body weight-bone mineral density association among breast cancer survivors: an examination of the independent roles of lean mass and fat mass

BMC Cancer volumeĀ 13, ArticleĀ number:Ā 497 (2013) Cite this article

Abstract

Background

Bone mineral density (BMD) and lean mass (LM) may both decrease in breast cancer survivors, thereby increasing risk of falls and fractures. Research is needed to determine whether lean mass (LM) and fat mass (FM) independently relate to BMD in this patient group.

Methods

The Health, Eating, Activity, and Lifestyle Study participants included 599 women, ages 29ā€“87Ā years, diagnosed from 1995ā€“1999 with stage 0-IIIA breast cancer, who underwent dual-energy X-ray absorptiometry scans approximately 6-months postdiagnosis. We calculated adjusted geometric means of total body BMD within quartiles (Q) of LM and FM. We also stratified LM-BMD associations by a fat mass index threshold that tracks with obesity (lower body fat: ā‰¤12.9Ā kg/m2; higher body fat: >12.9Ā kg/m2) and stratified FM-BMD associations by appendicular lean mass index level corresponding with sarcopenia (non-sarcopenic: ā‰„ā€‰5.45Ā kg/m2 and sarcopenic: <ā€‰5.45Ā kg/m2).

Results

Higher LM (Q4 vs. Q1) was associated with higher total body BMD overall (1.12Ā g/cm2 vs. 1.07Ā g/cm2, p-trendā€‰<ā€‰0.0001), and among survivors with lower body fat (1.13Ā g/cm2 vs. 1.07Ā g/cm2, p-trendā€‰<ā€‰0.0001) and higher body fat (1.15Ā g/cm2 vs. 1.08Ā g/cm2, p-trendā€‰=ā€‰0.004). Higher FM (Q4 vs. Q1) was associated with higher total body BMD overall (1.12Ā g/cm2 vs. 1.07Ā g/cm2, p-trendā€‰<ā€‰0.0001) and among non-sarcopenic survivors (1.15Ā g/cm2 vs. 1.08Ā g/cm2, pā€‰<ā€‰0.0001), but the association was not significant among sarcopenic survivors (1.09Ā g/cm2 vs. 1.04Ā g/cm2, p-trendā€‰=ā€‰0.18).

Conclusion

Among breast cancer survivors, higher LM and FM were independently related to higher total body BMD. Future exercise interventions to prevent bone loss among survivors should consider the potential relevance of increasing and preserving LM.

Peer Review reports

Background

In the United States (US), over 2.5 million women are living with a personal history of breast cancer [1, 2]. Age-related changes in body composition include a decrease in lean mass (LM), and loss and weakening of bone, leading to an increased risk of hip fractures and other fractures [3]. These changes are often accelerated by cancer and its treatment, including hormone therapies such as aromatase inhibitors [3]. Skeletal weakening is a particular concern for breast cancer survivors [4]. Compared to postmenopausal osteoporotic women without cancer, non-pathologic hip fractures in breast cancer survivors present at an earlier age and occur paradoxically at higher bone mineral density (BMD) [5]. Research has shown that after a diagnosis of breast cancer, survivors may have a 15% increased risk of falls and 55% increased risk of hip fracture [6], compared to postmenopausal women without cancer. The sequelae of fractures lead to many adverse events such as major surgery, increased morbidity and mortality, increased cost of disease management, and reduced quality of life [7].

Body weight has been proposed to be one of the best determinants of BMD [8]. Heavier women have a higher BMD because of the mechanical stress of weight on the skeleton [9]. However, for cancer survivors, being overweight or obese adversely affects quality of life, may worsen prognosis [10], and may increase the risk of chronic diseases, such as diabetes, hypertension, and coronary heart disease [11]. Further, obesity has been associated with greater risk of fall-related injury [12] and clinical fractures [13]. These latter associations may be due to difficulty maintaining postural stability [14] and/or diseases such as diabetes [14, 15] which accompany obesity and are well-known to be associated with neuropathies and poor foot health. Because an increase in body fat over time has been shown to be common among women being treated for breast cancer [16ā€“25], and because body weight does not necessarily track with increases in adipose tissue, [26] it is important to also understand the relationship of fat mass and BMD among women with breast cancer.

Among postmenopausal breast cancer survivors, targeted exercise training has been related to preservation of BMD [27, 28]. Targeted exercise training can additionally help to prevent weight gain and concurrent losses in LM that result from cancer and its treatment, adding to its attractiveness as a lifestyle intervention of choice for survivors at risk for fractures and falls. However, the tailoring and testing of these targeted interventions is an area in progress [29], and given body weightā€™s strong relationship with BMD, it is useful to evaluate how the main components of weight, lean mass (LM) and fat mass (FM), relate to BMD so that interventions can be tailored as such to improve LM while reducing obesity. We explored this critical question in the Health, Eating, Activity, and Lifestyle (HEAL) Study of US women with early-stage breast cancer. We hypothesized that LM and FM would be independently related to total body BMD.

Methods

Study setting and participants

The HEAL study is a multi-ethnic prospective cohort study that has enrolled 1,183 women with first primary breast cancer drawn from Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries in New Mexico, Western Washington State, and Los Angeles County. The study was designed to determine whether lifestyle, hormones, and other exposures affect breast cancer prognosis, and details of the study have been published, including physical activity levels of the population [30ā€“32]. Around 6Ā months postdiagnosis, a subset of participants who were enrolled at New Mexico and Washington underwent a whole-body dual-energy X-ray absorptiometry (DXA) scan. To answer the study questions for this particular analysis, our sample included the women with this measure.

In New Mexico, we recruited 615 women aged 18Ā years or older, diagnosed with in situ, localized, and regional breast cancer between July 1996 and March 1999, and living in Bernalillo, Santa Fe, Sandoval, Valencia, or Taos counties. In Western Washington, we recruited 202 women between ages 40 and 64Ā years, diagnosed with in situ to regional breast cancer between September 1997 and September 1998, and living in King, Pierce, or Snohomish counties. The age range for the Washington patients was restricted due to other ongoing breast cancer studies. Patients were eligible if they were less than 12Ā months post-diagnosis. None of the patients used aromatase inhibitors; these drugs were not licensed for clinical practice at the time of their treatment. The study was approved by institutional review boards at all sites and informed consent was obtained from all participants. In Western Washington, a subset of 109 of the 202 participants were offered and participated in DXA scans; in New Mexico, all 615 participants were offered DXA scans and 499 participated. Of the 608 women who received DXA scans, we excluded one participant who was missing data on weight, and six participants missing data on current use of postmenopausal hormone therapy (estrogen or estrogen plus progestin). Our final sample included 599 women.

Data collection

DXA. We used DXA to measure whole and regional body composition (New Mexico site: Lunar model DPX, Lunar Radiation Corporation, Madison, WI; Washington site: Hologic QDR 1500, Hologic, Inc., Bedford, MA). Data from the DXA scan were used to measure total body LM (g), FM (g), and BMD (g/cm2). Appendicular lean mass index (ALMI) was calculated as the sum of lean mass (fat-free, non-bone) in the arms and legs divided by height in m2[33]. We calculated fat mass index (FMI) as fat mass in kg / height in m2. DXA provides a highly reproducible and accurate measure for FM and LM and is a validated and accepted method for assessing body composition [34, 35].

Additional risk factors

Breast cancer stage at diagnosis was obtained from cancer registry records, and detailed information on treatment and surgical procedures was abstracted from cancer registry, physician, and hospital records. Height and weight were measured in-person. Information on age, race/ethnicity, smoking status, tamoxifen use, and postmenopausal hormone therapy use was determined via self-report using a standardized protocol. We categorized participantsā€™ race/ethnicity as non-Hispanic white (nā€‰=ā€‰406); Hispanic (nā€‰=ā€‰94); and a combined category (nā€‰=ā€‰15) which included women who were Asian, American Indian, or ā€œotherā€ race. Menopausal status was determined via self-report and blood hormone levels of estradiol, estrone, and follicle-stimulating hormone.

Statistical analysis

Log total body BMD values were regressed on quartiles (Q) of LM and FM in multivariate models, and beta scores were exponentiated and expressed as geometric means. We also performed the test for linear trend across categories of LM and FM, by assigning participants the median value of their categories and entering it as a continuous term in a regression model.

We also stratified the LM-BMD association by a FMI threshold for body that tracks with obesity status (lower body fat: <=12.9Ā kg/m2; higher body fat: >12.9Ā kg/m2) [36]. Similarly, we stratified the FM-BMD association by sarcopenia status using the ALMI cut point (non-sarcopenic: ā‰„ā€‰5.45Ā kg/m2; sarcopenic: <ā€‰5.45Ā kg/m2) [37, 38].

For model building, we identified risk factors that when added to exposure-outcome models acted as confounders (changing beta estimates by ā‰„ā€‰10%) and were statistically significant (pā€‰<ā€‰0.05). Age and study site met these criteria for all relationships. LM-BMD models were adjusted for FMI and FM-BMD models were adjusted for ALMI. To enable comparability to the literature we also adjusted for menopausal status, cancer treatment, tamoxifen use, postmenopausal hormone therapy use, and race/ethnicity. Inclusion of these covariates in the models did not result in substantial changes to the beta values obtained in age-adjusted models. To further assure comparability with the extant literature focusing on skeletal health among postmenopausal women, we repeated analyses restricted to postmenopausal women.

All analyses were performed in SAS version 9.2 (Cary, NC). All tests were two-sided and statistical significance was set at pā€‰<ā€‰0.05.

Results

At 6-months postdiagnosis, the mean age of participants was 57 (Ā±11) years, and the majority of women were postmenopausal, non-Hispanic White, and not currently using postmenopausal hormone therapy (TableĀ 1). At this time, about half of the women were taking tamoxifen, and none were using aromatase inhibitors.

Table 1 Demographic, clinical and lifestyle characteristics of 599 women in the health, eating, activity and lifestyle study, 6Ā months postdiagnosis
Full size table

As shown in TableĀ 2, higher vs. lower (Q4 vs. Q1) LM was associated with higher BMD (1.12Ā g/cm2 vs. 1.07Ā g/cm2, p-trendā€‰<ā€‰0.0001) and higher vs. lower (Q4 vs. Q1) FM was associated with higher BMD (1.12Ā g/cm2 vs. 1.07Ā g/cm2, p-trendā€‰<ā€‰0.0001).

Table 2 Multivariate adjusted geometric means and 95% confidence intervals of bone mineral density (BMD) by quartiles of lean mass and fat mass
Full size table

As shown in TableĀ 3, in stratified analyses by body fatness and sarcopenia, higher vs. lower LM was associated with BMD both in survivors with lower body fat (1.13Ā g/cm2 vs. 1.07Ā g/c, p-trendā€‰<ā€‰0.0001) and higher body fat (1.15Ā g/cm2 vs. 1.08Ā g/cm2, p-trendā€‰=ā€‰0.004). Higher vs. lower FM was associated with BMD among non-sarcopenic women (1.15Ā g/cm2 vs. 1.08Ā g/cm2, pā€‰<ā€‰0.0001); however, among women with sarcopenia, the FM-BMD relationship was in a similar direction and of comparable magnitude but was not statistically significant (1.09Ā g/cm2 vs. 1.04Ā g/cm2, p-trendā€‰=ā€‰0.18). When we repeated analyses among postmenopausal women, results were similar (data not shown).

Table 3 Multivariate adjusted geometric means and 95% confidence intervals of bone mineral density (BMD) by quartiles of lean mass and fat mass by body fatness and sarcopenia status
Full size table

In univariate models, LM also explained more variance in BMD among those who were not obese (10%) than among those who were obese (3%), and FM explained more variance in BMD among those who were not sarcopenic (3%) than among those who were sarcopenic (0.005%) (data not shown). In this study with its wide age range, age explained more variance (8-18%) in BMD in all univariate models than other variables (data not shown).

Discussion

Our results demonstrate the independent association of LM and total body BMD among breast cancer survivors, after controlling for relevant confounders, and confirmed the role of FM in relation to total body BMD. This finding showing the importance LM in relation to BMD is biologically plausible, because dynamic rather than static loads promote bone formation and retention [39, 40], and adipose tissue (FM) predominantly applies a static load on the bone; in contrast, muscle tissue (LM) exerts a dynamic strain on bone [15].

Rates of true bone loss among postmenopausal women are reported to be approximately 3% per year [41]. The cross-sectional differences in total body BMD observed in our study for those in the highest vs. lowest quartiles of LM (5%) and FM (5%) suggest clinical relevance. However, since this is the first investigation on this topic among breast cancer survivors, these observations require validation. Among survivors, some physical activity interventions have promise in affecting both LM and BMD. Among cancer survivors, postdiagnosis weight-bearing physical activity and resistance training have been shown to increase LM [29], and among postmenopausal breast cancer survivors at risk for bone loss, postdiagnosis strength/weight training has been shown to prevent loss of BMD [27, 42]. More comprehensive and longitudinal research is needed to understand how and the extent to which different types and doses of physical activity affect both LM and BMD.

Key strengths of this study include our large group of breast cancer survivors ascertained through US population-based cancer registries and the use of DXA to assess body composition. This study also has some limitations. Although this study also included a fairly diverse sample of US Non-Hispanic White and Hispanic women, the generalizability of our findings may be limited, and it will be important to extend this research to populations from other cultures and race/ethnicities where some research suggests that the association between body composition and BMD may vary from that observed in white populations. In addition, our cohort only included women with breast cancer, so we are unable to compare associations observed in survivors with those observed in women without cancer. Many of the body composition changes experienced by breast cancer patients happen during and after treatment, and the DXA measures collected in our study occurred at one point in the time, approximately 6Ā months post-diagnosis. Because our study only had a measure of total body BMD, we were not able to examine associations with spine or hip BMD, which would be most clinically relevant. Our measure of total fat mass did not allow us to break down the associations by types of adipose tissue, such as visceral fat. Future studies with multiple, comprehensive measurements of body composition throughout the cancer treatment process could identify critical periods of rapid bone loss.

We did not have data on recent bisphosphonate use, osteoporosis, or comorbidities at the time of the 6-months post-diagnosis assessment, so we were unable to control for these factors. In addition, the cohort accrued participants before the widespread use of aromatase inhibitors; therefore our study could not address associations among women treated with these medications. Lastly, we had very few patients with sarcopenia (nā€‰=ā€‰84), which limited our ability to draw conclusions about the association of FM-BMD in this group, or the heterogeneity by sarcopenia status.

Conclusion

Within this large study of early-stage breast cancer survivors, we found that higher LM was independently related to higher total body BMD. Given breast cancer survivorsā€™ increased risk for falls and fractures, replication of our results in other cohort studies could determine whether preservation of LM results in meaningful reductions in bone loss among breast cancer patients. Future exercise interventions to prevent bone loss among survivors should consider the potential relevance of increasing and preserving LM.

Abbreviations

ALMI:

Appendicular lean mass index

BMD:

Bone mineral density

CI:

Confidence interval

DXA:

Dual-energy X-ray absorptiometry

FM:

Fat mass

FMI:

Fat mass index

HEAL:

Health, Eating, Activity, and Lifestyle Study

LM:

Lean mass

Q:

Quartile.

References

  1. Altekruse S, Kosary C, Krapcho M, Neyman N, Aminou R, Waldron W, Ruhl J, Howlader N, Tatalovich Z, Cho H, et al: (eds.): SEER Cancer Statistics Review, 1975ā€“2007. 2010, National Cancer Institute: Bethesda, MD

    Google ScholarĀ 

  2. American Cancer Society: Breast Cancer Facts and Figures. 2009, Atlanta, GA

    Google ScholarĀ 

  3. Saad F, Adachi JD, Brown JP, Canning LA, Gelmon KA, Josse RG, Pritchard KI: Cancer treatment-induced bone loss in breast and prostate cancer. J Clin Oncol. 2008, 26 (33): 5465-5476. 10.1200/JCO.2008.18.4184.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  4. Winters-Stone KM, Schwartz AL, Hayes SC, Fabian CJ, Campbell KL: A prospective model of care for breast cancer rehabilitation: bone health and arthralgias. Cancer. 2012, 118 (8 Suppl): 2288-2299.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  5. Edwards BJ, Raisch DW, Shankaran V, McKoy JM, Gradishar W, Bunta AD, Samaras AT, Boyle SN, Bennett CL, West DP, et al: Cancer therapy associated bone loss: implications for hip fractures in mid-life women with breast cancer. Clin Cancer Res. 2011, 17 (3): 560-568. 10.1158/1078-0432.CCR-10-1595.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  6. Chen Z, Maricic M, Aragaki A, Mouton C, Arendell L, Lopez A, Bassford T, Chlebowski R: Fracture risk increases after diagnosis of breast or other cancers in postmenopausal women: results from the Womenā€™s Health Initiative. Osteoporos Int. 2009, 20 (4): 527-536. 10.1007/s00198-008-0721-0.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  7. Body JJ: Increased fracture rate in women with breast cancer: a review of the hidden risk. BMC Cancer. 2011, 11: 384-10.1186/1471-2407-11-384.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  8. Hannan MT, Felson DT, Anderson JJ: Bone mineral density in elderly men and women: results from the Framingham osteoporosis study. J Bone Miner Res. 1992, 7 (5): 547-553.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  9. Rubin CT, Lanyon LE: Regulation of bone mass by mechanical strain magnitude. Calcif Tissue Int. 1985, 37 (4): 411-417. 10.1007/BF02553711.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  10. Eheman C, Henley SJ, Ballard-Barbash R, Jacobs EJ, Schymura MJ, Noone A-M, Pan L, Anderson RN, Fulton JE, Kohler BA, et al: Annual Report to the Nation on the status of cancer, 1975ā€“2008, featuring cancers associated with excess weight and lack of sufficient physical activity. Cancer. 2012, 118 (9): 2338-2366. 10.1002/cncr.27514.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  11. Demark-Wahnefried W, Platz EA, Ligibel JA, Blair CK, Courneya KS, Meyerhardt J, Ganz PA, Rock CL, Schmitz KH, Wadden T, et al: The Role of Obesity in Cancer Survival and Recurrence. Cancer Epidemiology Biomarkers Prev. 2012, 21 (8): 1244-1259. 10.1158/1055-9965.EPI-12-0485.

    ArticleĀ  Google ScholarĀ 

  12. Finkelstein EA, Chen H, Prabhu M, Trogdon JG, Corso PS: The relationship between obesity and injuries among U.S. adults. Am J Health Promot. 2007, 21 (5): 460-468. 10.4278/0890-1171-21.5.460.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  13. Compston JE, Watts NB, Chapurlat R, Cooper C, Boonen S, Greenspan S, Pfeilschifter J, Silverman S, DĆ­ez-PĆ©rez A, Lindsay R, et al: Obesity Is Not Protective against Fracture in Postmenopausal Women: GLOW. Am J Med. 2011, 124 (11): 1043-1050. 10.1016/j.amjmed.2011.06.013.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  14. Mignardot JB, Olivier I, Promayon E, Nougier V: Obesity impact on the attentional cost for controlling posture. PLoS One. 2010, 5 (12): e14387-10.1371/journal.pone.0014387.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  15. Faje A, Klibanski A: Body composition and skeletal health: too heavy? too thin?. Curr Osteoporos Rep. 2012, 10 (3): 208-216. 10.1007/s11914-012-0106-3.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  16. Campbell KL, Lane K, Martin AD, Gelmon KA, McKenzie DC: Resting energy expenditure and body mass changes in women during adjuvant chemotherapy for breast cancer. Cancer Nurs. 2007, 30 (2): 95-100. 10.1097/01.NCC.0000265004.64440.5f.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  17. Cheney CL, Mahloch J, Freeny P: Computerized tomography assessment of women with weight changes associated with adjuvant treatment for breast cancer. Am J Clin Nutr. 1997, 66 (1): 141-146.

    CASĀ  PubMedĀ  Google ScholarĀ 

  18. Demark-Wahnefried W, Hars V, Conaway MR, Havlin K, Rimer BK, McElveen G, Winer EP: Reduced rates of metabolism and decreased physical activity in breast cancer patients receiving adjuvant chemotherapy. Am J Clin Nutr. 1997, 65 (5): 1495-1501.

    CASĀ  PubMedĀ  Google ScholarĀ 

  19. Demark-Wahnefried W, Peterson BL, Winer EP, Marks L, Aziz N, Marcom PK, Blackwell K, Rimer BK: Changes in weight, body composition, and factors influencing energy balance among premenopausal breast cancer patients receiving adjuvant chemotherapy. J Clin Oncol. 2001, 19 (9): 2381-2389.

    CASĀ  PubMedĀ  Google ScholarĀ 

  20. Freedman RJ, Aziz N, Albanes D, Hartman T, Danforth D, Hill S, Sebring N, Reynolds JC, Yanovski JA: Weight and body composition changes during and after adjuvant chemotherapy in women with breast cancer. J Clin Endocrinol Metab. 2004, 89 (5): 2248-2253. 10.1210/jc.2003-031874.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  21. Gordon AM, Hurwitz S, Shapiro CL, LeBoff MS: Premature ovarian failure and body composition changes with adjuvant chemotherapy for breast cancer. Menopause. 2011, 18 (11): 1244-1248. 10.1097/gme.0b013e31821b849b.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  22. Irwin ML, McTiernan A, Baumgartner RN, Baumgartner KB, Bernstein L, Gilliland FD, Ballard-Barbash R: Changes in body fat and weight after a breast cancer diagnosis: influence of demographic, prognostic, and lifestyle factors. J Clin Oncol. 2005, 23 (4): 774-782. 10.1200/JCO.2005.04.036.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  23. Kutynec CL, McCargar L, Barr SI, Hislop TG: Energy balance in women with breast cancer during adjuvant treatment. J Am Diet Assoc. 1999, 99 (10): 1222-1227. 10.1016/S0002-8223(99)00301-6.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  24. Nissen MJ, Shapiro A, Swenson KK: Changes in weight and body composition in women receiving chemotherapy for breast cancer. Clin Breast Cancer. 2011, 11 (1): 52-60. 10.3816/CBC.2011.n.009.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  25. Winters-Stone KM, Nail L, Bennett JA, Schwartz A: Bone Health and Falls: Fracture Risk in Breast Cancer Survivors With Chemotherapy-Induced Amenorrhea. Oncol Nurs Forum. 2009, 36 (3): 315-325. 10.1188/09.ONF.315-325.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  26. Sheean PM, Hoskins K, Stolley M: Body composition changes in females treated for breast cancer: a review of the evidence. Breast Cancer Res Treat. 2012, 135 (3): 663-680. 10.1007/s10549-012-2200-8.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  27. Winters-Stone KM, Dobek J, Nail L, Bennett JA, Leo MC, Naik A, Schwartz A: Strength training stops bone loss and builds muscle in postmenopausal breast cancer survivors: a randomized, controlled trial. Breast Cancer Res Treat. 2011, 127 (2): 447-456. 10.1007/s10549-011-1444-z.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  28. Irwin ML, Alvarez-Reeves M, Cadmus L, Mierzejewski E, Mayne ST, Yu H, Chung GG, Jones B, Knobf MT, DiPietro L: Exercise improves body fat, lean mass, and bone mass in breast cancer survivors. Obesity (Silver Spring). 2009, 17 (8): 1534-1541. 10.1038/oby.2009.18.

    ArticleĀ  Google ScholarĀ 

  29. Winters-Stone KM, Schwartz A, Nail LM: A review of exercise interventions to improve bone health in adult cancer survivors. J Cancer Surviv. 2010, 4 (3): 187-201. 10.1007/s11764-010-0122-1.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  30. Irwin ML, McTiernan A, Bernstein L, Gilliland FD, Baumgartner R, Baumgartner K, Ballard-Barbash R: Physical activity levels among breast cancer survivors. Med Sci Sports Exerc. 2004, 36 (9): 1484-1491.

    PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  31. McTiernan A, Rajan KB, Tworoger SS, Irwin M, Bernstein L, Baumgartner R, Gilliland F, Stanczyk FZ, Yasui Y, Ballard-Barbash R: Adiposity and sex hormones in postmenopausal breast cancer survivors. J Clin Oncol. 2003, 21 (10): 1961-1966. 10.1200/JCO.2003.07.057.

    ArticleĀ  CASĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  32. Wayne SJ, Baumgartner K, Baumgartner RN, Bernstein L, Bowen DJ, Ballard-Barbash R: Diet quality is directly associated with quality of life in breast cancer survivors. Breast Cancer Res Treat. 2006, 96 (3): 227-232. 10.1007/s10549-005-9018-6.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  33. Baumgartner RN: Body composition in healthy aging. Ann N Y Acad Sci. 2000, 904: 437-448.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  34. Kiebzak GM, Leamy LJ, Pierson LM, Nord RH, Zhang ZY: Measurement precision of body composition variables using the lunar DPX-L densitometer. J Clin Densitom. 2000, 3 (1): 35-41. 10.1385/JCD:3:1:035.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  35. Link J, Glazer C, Torres F, Chin K: International Classification of Diseases coding changes lead to profound declines in reported idiopathic pulmonary arterial hypertension mortality and hospitalizations: implications for database studies. Chest. 2011, 139 (3): 497-504. 10.1378/chest.10-0837.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  36. Kelly TL, Wilson KE, Heymsfield SB: Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS ONE. 2009, 4 (9): e7038-10.1371/journal.pone.0007038.

    ArticleĀ  PubMedĀ  PubMed CentralĀ  Google ScholarĀ 

  37. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, Garry PJ, Lindeman RD: Epidemiology of Sarcopenia among the Elderly in New Mexico. Am J Epidemiol. 1998, 147 (8): 755-763. 10.1093/oxfordjournals.aje.a009520.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  38. VillaseƱor A, Ballard-Barbash R, Baumgartner K, Baumgartner R, Bernstein L, McTiernan A, Neuhouser ML: Prevalence and prognostic effect of sarcopenia in breast cancer survivors; the HEAL Study. J Cancer Surviv. In press

  39. Lanyon LE, Rubin CT: Static vs dynamic loads as an influence on bone remodelling. J Biomech. 1984, 17 (12): 897-905. 10.1016/0021-9290(84)90003-4.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  40. Liskova M, Hert J: Reaction of bone to mechanical stimuli. 2. Periosteal and endosteal reaction of tibial diaphysis in rabbit to intermittent loading. Folia Morphol (Praha). 1971, 19 (3): 301-317.

    CASĀ  Google ScholarĀ 

  41. Riggs BL, Khosla S, Melton LJ: Sex steroids and the construction and conservation of the adult skeleton. Endocr Rev. 2002, 23 (3): 279-302. 10.1210/er.23.3.279.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  42. Waltman NL, Twiss JJ, Ott CD, Gross GJ, Lindsey AM, Moore TE, Berg K, Kupzyk K: The effect of weight training on bone mineral density and bone turnover in postmenopausal breast cancer survivors with bone loss: a 24-month randomized controlled trial. Osteoporos Int. 2010, 21 (8): 1361-1369. 10.1007/s00198-009-1083-y.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

Pre-publication history

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Acknowledgements

We would like to thank the HEAL study participants, the HEAL study staff, and Todd Gibson of Information Management Systems. This study was funded by National Cancer Institute Grants N01-CN-75036-20, NO1-CN-05228, and NO1-PC-67010.

Author information

Authors and Affiliations

  1. Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, 20892, USA

    Stephanie M George,Ā Ashley W SmithĀ &Ā Rachel Ballard-Barbash

  2. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Anne McTiernanĀ &Ā Marian L Neuhouser

  3. Moores UCSD Cancer Center, Cancer Prevention and Control Program, University of California, San Diego, CA, USA

    Adriana VillaseƱor

  4. Office of Cancer Survivorship, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA

    Catherine M Alfano

  5. Division of Chronic Disease Epidemiology, MD Yale School of Public Health, New Haven, CT, USA

    Melinda L Irwin

  6. Department of Epidemiology and Population Health, University of Louisville, Louisville, KY, USA

    Richard N BaumgartnerĀ &Ā Kathy B Baumgartner

  7. Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA

    Leslie Bernstein

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  1. Stephanie M George
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  2. Anne McTiernan
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  3. Adriana VillaseƱor
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  4. Catherine M Alfano
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Corresponding author

Correspondence to Stephanie M George.

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Competing interests

The authors declare that they have no competing interests.

Authorsā€™ contributions

Substantial contributions to the conception and design: SMG, RBB, AV, AM, MLN, and RNB. Acquisition of data RBB, AM, MLN, AV, RNB, KBB, LB. Interpretation of the data: SMG, AM, AV, CMA, MLI, MLN, RNB, KBB, LB, AWS, and RBB. Drafting of the manuscript: SMG, AM, AV, CMA, MLI, MLN, RNB, KBB, LB, AWS, and RBB. Critical revisions: SMG and RBB. Final approval of the version to be published: SMG, AM, AV, CMA, MLI, MLN, RNB, KBB, LB, AWS, and RBB.

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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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George, S.M., McTiernan, A., VillaseƱor, A. et al. Disentangling the body weight-bone mineral density association among breast cancer survivors: an examination of the independent roles of lean mass and fat mass. BMC Cancer 13, 497 (2013). https://doi.org/10.1186/1471-2407-13-497

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  • DOI: https://doi.org/10.1186/1471-2407-13-497

Keywords

BMC Cancer

ISSN: 1471-2407

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