Breast Cancer Research and Treatment

, Volume 174, Issue 1, pp 209–218 | Cite as

Metabolic syndrome and risk of breast cancer mortality by menopause, obesity, and subtype

  • Daniel T. Dibaba
  • Kemi Ogunsina
  • Dejana Braithwaite
  • Tomi AkinyemijuEmail author



To investigate the association between metabolic syndrome (MetS) and risk of breast cancer mortality by menopausal status, obesity, and subtype.


Data from 94,555 women free of cancer at baseline in the National Institute of Health-American Association of Retired Persons Diet and Health Study cohort (NIH-AARP) were used to investigate the prospective associations of baseline MetS and components with risk of breast cancer mortality using Cox proportional hazard regression models adjusted for baseline behavioral and demographic covariates.


During a mean follow-up duration of 14 years, 607 women in the cohort died of breast cancer. Overall, MetS was associated with a 73% increased risk of breast cancer mortality (HR 1.73; 95% CI 1.09–2.75); the association remained significant among post-menopausal women overall (HR 2.07, 95% CI 1.32, 3.25), and among those with overweight/obesity (HR 1.15, 95% CI 0.81, 1.64). MetS was associated with increased risk of breast cancer mortality for ER+/PR+ (HR 1.28, 95% CI 0.52, 3.16) and lower risk for ER−/PR− (HR 0.44, 95% CI 0.11, 1.75) subtypes; however, the associations were not statistically significant. Of the individual MetS components, high waist circumference (HR 1.32, 95% CI 1.03, 1.70), high cholesterol (HR 1.24, 95% CI 1.05, 1.46), and hypertension (HR 1.24, 95% CI 1.05, 1.46) were independently associated with increased risk of breast cancer mortality.


MetS was associated with increased risk of breast cancer mortality, especially among post-menopausal women. Further studies with larger sample sizes are needed to definitively determine the extent to which these associations vary by breast cancer subtype.


Metabolic syndrome Breast cancer mortality Menopause Obesity Hormone-receptor subtypes 



We thank the participants in the NIH-AARP Diet and Health Study for their cooperation, and David Campbell and Jane Wang at Information management Services (Silver Spring, MD) for data support.

Author contributions

TA conceived and designed the study and oversaw statistical analysis and manuscript writing. DTD, KO, TA, and DB contributed to statistical analysis, drafting of the results, and critical review of the manuscript. All authors substantially contributed to the manuscript.


TA was funded by Grant K01TW010271 by the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The Special Studies Institutional Review Board (IRB) of the U.S. National Cancer Institute approved the NIH-AARP Diet and Health Study (Protocol Number: OH95CN025).


  1. 1.
    Nilsson PM, Engstrom G, Hedblad B (2007) The metabolic syndrome and incidence of cardiovascular disease in non-diabetic subjects—a population-based study comparing three different definitions. Diabet Med 24(5):464–472PubMedCrossRefGoogle Scholar
  2. 2.
    Athyros VG, Ganotakis ES, Elisaf MS, Liberopoulos EN, Goudevenos IA, Karagiannis A, Group G-MC (2007) Prevalence of vascular disease in metabolic syndrome using three proposed definitions. Int J Cardiol 117(2):204–210PubMedCrossRefGoogle Scholar
  3. 3.
    Koren-Morag N, Goldbourt U, Tanne D (2005) Relation between the metabolic syndrome and ischemic stroke or transient ischemic attack: a prospective cohort study in patients with atherosclerotic cardiovascular disease. Stroke 36(7):1366–1371PubMedCrossRefGoogle Scholar
  4. 4.
    Grundy SM (2008) Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol 28(4):629–636PubMedCrossRefGoogle Scholar
  5. 5.
    Abrahamson PE, Gammon MD, Lund MJ, Flagg EW, Porter PL, Stevens J, Swanson CA, Brinton LA, Eley JW, Coates RJ (2006) General and abdominal obesity and survival among young women with breast cancer. Cancer Epidemiol Biomark Prev 15(10):1871–1877CrossRefGoogle Scholar
  6. 6.
    Chen H-l, Ding A, Wang M-l (2016) Impact of central obesity on prognostic outcome of triple negative breast cancer in Chinese women. SpringerPlus 5(1):594PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Ogundiran TO, Huo D, Adenipekun A, Campbell O, Oyesegun R, Akang E, Adebamowo C, Olopade OI (2012) Body fat distribution and breast cancer risk: findings from the Nigerian breast cancer study. Cancer Causes Control 23(4):565–574PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Nicolucci A (2010) Epidemiological aspects of neoplasms in diabetes. Acta Diabetol 47(2):87–95PubMedCrossRefGoogle Scholar
  9. 9.
    Pereira A, Garmendia ML, Alvarado ME, Albala C (2012) Hypertension and the risk of breast cancer in Chilean women: a case-control study. Asian Pac J Cancer Prev 13(11):5829–5834PubMedCrossRefGoogle Scholar
  10. 10.
    Soler M, Chatenoud L, Negri E, Parazzini F, Franceschi S, la Vecchia C (1999) Hypertension and hormone-related neoplasms in women. Hypertension 34(2):320–325PubMedCrossRefGoogle Scholar
  11. 11.
    Calip GS, Malone KE, Gralow JR, Stergachis A, Hubbard RA, Boudreau DM (2014) Metabolic syndrome and outcomes following early-stage breast cancer. Breast Cancer Res Treat 148(2):363–377PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Berrino F, Villarini A, Traina A, Bonanni B, Panico S, Mano MP, Mercandino A, Galasso R, Barbero M, Simeoni M et al (2014) Metabolic syndrome and breast cancer prognosis. Breast Cancer Res Treat 147(1):159–165PubMedCrossRefGoogle Scholar
  13. 13.
    Maiti B, Kundranda MN, Spiro TP, Daw HA (2010) The association of metabolic syndrome with triple-negative breast cancer. Breast Cancer Res Treat 121(2):479–483PubMedCrossRefGoogle Scholar
  14. 14.
    Healy LA, Ryan AM, Carroll P, Ennis D, Crowley V, Boyle T, Kennedy MJ, Connolly E, Reynolds JV (2010) Metabolic syndrome, central obesity and insulin resistance are associated with adverse pathological features in postmenopausal breast cancer. Clin Oncol (R Coll Radiol) 22(4):281–288CrossRefGoogle Scholar
  15. 15.
    Bjorge T, Lukanova A, Jonsson H, Tretli S, Ulmer H, Manjer J, Stocks T, Selmer R, Nagel G, Almquist M et al (2010) Metabolic syndrome and breast cancer in the me-can (metabolic syndrome and cancer) project. Cancer Epidemiol Biomark Prev 19(7):1737–1745CrossRefGoogle Scholar
  16. 16.
    Emaus A, Veierod MB, Tretli S, Finstad SE, Selmer R, Furberg AS, Bernstein L, Schlichting E, Thune I (2010) Metabolic profile, physical activity, and mortality in breast cancer patients. Breast Cancer Res Treat 121(3):651–660PubMedCrossRefGoogle Scholar
  17. 17.
    Simon MS, Beebe-Dimmer JL, Hastert TA, Manson JE, Cespedes Feliciano EM, Neuhouser ML, Ho GYF, Freudenheim JL, Strickler H, Ruterbusch J et al (2018) Cardiometabolic risk factors and survival after breast cancer in the Women’s Health Initiative. Cancer 124(8):1798–1807PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Gathirua-Mwangi WG, Song Y, Monahan PO, Champion VL, Zollinger TW (2018) Associations of metabolic syndrome and C-reactive protein with mortality from total cancer, obesity-linked cancers and breast cancer among women in NHANES III. Int J Cancer 143(3):535–542PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Phipps AI, Malone KE, Porter PL, Daling JR, Li CI (2008) Reproductive and hormonal risk factors for postmenopausal luminal, HER2-overexpressing, and triple-negative breast cancer. Cancer 113(7):1521–1526PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Fan Y, Ding X, Wang J, Ma F, Yuan P, Li Q, Zhang P, Xu B (2015) Decreased serum HDL at initial diagnosis correlates with worse outcomes for triple-negative breast cancer but not non-TNBCs. Int J Biol Mark 30(2):e200–e207CrossRefGoogle Scholar
  21. 21.
    Suzuki R, Orsini N, Saji S, Key TJ, Wolk A (2009) Body weight and incidence of breast cancer defined by estrogen and progesterone receptor status–a meta-analysis. Int J Cancer 124(3):698–712PubMedCrossRefGoogle Scholar
  22. 22.
    Chan DS, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, Navarro Rosenblatt D, Thune I, Vieira R, Norat T (2014) Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol 25(10):1901–1914PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Chan DS, Norat T (2015) Obesity and breast cancer: not only a risk factor of the disease. Curr Treat Options Oncol 16(5):22PubMedCrossRefGoogle Scholar
  24. 24.
    Schatzkin A, Subar AF, Thompson FE, Harlan LC, Tangrea J, Hollenbeck AR, Hurwitz PE, Coyle L, Schussler N, Michaud DS et al (2001) Design and serendipity in establishing a large cohort with wide dietary intake distributions: the National Institutes of Health-American Association of Retired Persons Diet and Health Study. Am J Epidemiol 154(12):1119–1125PubMedCrossRefGoogle Scholar
  25. 25.
    Etemadi A, Sinha R, Ward MH, Graubard BI, Inoue-Choi M, Dawsey SM, Abnet CC (2017) Mortality from different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and Health Study: population based cohort study. BMJ 357.j1957Google Scholar
  26. 26.
    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr et al (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120(16):1640–1645PubMedCrossRefGoogle Scholar
  27. 27.
    Park Y, Leitzmann MF, Subar AF, Hollenbeck A, Schatzkin A (2009) Dairy food, calcium, and risk of cancer in the NIH-AARP diet and health study. Arch Intern Med 169(4):391–401PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Hiller L, Marshall A, Dunn J (2015) Assessing violations of the proportional hazards assumption in Cox regression: does the chosen method matter? Trials 16(Suppl 2):P134–P134PubMedCentralCrossRefGoogle Scholar
  29. 29.
    Rong SS, Chen LJ, Leung CKS, Matsushita K, Jia L, Miki A, Chiang SWY, Tam POS, Hashida N, Young AL et al (2016) Ethnic specific association of the CAV1/CAV2 locus with primary open-angle glaucoma. Sci Rep 6:27837PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Monzavi-Karbassi B, Gentry R, Kaur V, Siegel ER, Jousheghany F, Medarametla S, Fuhrman BJ, Safar AM, Hutchins LF, Kieber-Emmons T (2016) Pre-diagnosis blood glucose and prognosis in women with breast cancer. Cancer Metab 4:7PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Bandera EV, Maskarinec G, Romieu I, John EM (2015) Racial and ethnic disparities in the impact of obesity on breast cancer risk and survival: a global perspective. Adv Nutr 6(6):803–819PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Borugian MJ, Sheps SB, Kim-Sing C, Olivotto IA, Van Patten C, Dunn BP, Coldman AJ, Potter JD, Gallagher RP, Hislop TG (2003) Waist-to-hip ratio and breast cancer mortality. Am J Epidemiol 158(10):963–968PubMedCrossRefGoogle Scholar
  33. 33.
    Zhang M, Cai H, Bao P, Xu W, Qin G, Shu XO, Zheng Y (2017) Body mass index, waist-to-hip ratio and late outcomes: a report from the Shanghai Breast Cancer Survival Study. Sci Rep 7:6996PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Kang S, Song J, Kang H, Kim S, Lee Y, Park D (2003) Insulin can block apoptosis by decreasing oxidative stress via phosphatidylinositol 3-kinase- and extracellular signal-regulated protein kinase-dependent signaling pathways in HepG2 cells. Eur J Endocrinol 148(1):147–155PubMedCrossRefGoogle Scholar
  35. 35.
    Djiogue S,Nwabo, Kamdje AH, Vecchio L, Kipanyula MJ, Farahna M, Aldebasi Y, Seke Etet PF (2013) Insulin resistance and cancer: the role of insulin and IGFs. Endocr Relat Cancer 20(1):R1–R17PubMedCrossRefGoogle Scholar
  36. 36.
    Chumsri S, Howes T, Bao T, Sabnis G, Brodie A (2011) Aromatase, aromatase inhibitors, and breast cancer. J Steroid Biochem Mol Biol 125(1–2):13–22PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Subbaramaiah K, Howe LR, Bhardwaj P, Du B, Gravaghi C, Yantiss RK, Zhou XK, Blaho VA, Hla T, Yang P et al (2011) Obesity is associated with inflammation and elevated aromatase expression in the mouse mammary gland. Cancer Prev Res 4(3):329–346CrossRefGoogle Scholar
  38. 38.
    Lorincz AM, Sukumar S (2006) Molecular links between obesity and breast cancer. Endocr Relat Cancer 13(2):279–292PubMedCrossRefGoogle Scholar
  39. 39.
    Lobo RA (2008) Metabolic syndrome after menopause and the role of hormones. Maturitas 60(1):10–18PubMedCrossRefGoogle Scholar
  40. 40.
    Carr MC (2003) The emergence of the metabolic syndrome with menopause. J Clin Endocrinol Metab 88(6):2404–2411PubMedCrossRefGoogle Scholar
  41. 41.
    Monteiro R, Azevedo I (2010) Chronic inflammation in obesity and the metabolic syndrome. Mediat Inflamm 289645Google Scholar
  42. 42.
    Hsu M-C, Lee K-T, Hsiao W-C, Wu C-H, Sun H-Y, Lin I-L, Young K-C (2013) The dyslipidemia-associated SNP on the APOA1/C3/A5 gene cluster predicts post-surgery poor outcome in Taiwanese breast cancer patients: a 10-year follow-up study. BMC Cancer 13(1):330PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Chen SB, Lee YC, Ser KH, Chen JC, Chen SC, Hsieh HF, Lee WJ (2009) Serum C-reactive protein and white blood cell count in morbidly obese surgical patients. Obes Surg 19(4):461–466PubMedCrossRefGoogle Scholar
  44. 44.
    Allin KH, Nordestgaard BG, Flyger H, Bojesen SE (2011) Elevated pre-treatment levels of plasma C-reactive protein are associated with poor prognosis after breast cancer: a cohort study. Breast Cancer Res 13(3):R55PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Villaseñor A, Flatt SW, Marinac C, Natarajan L, Pierce JP, Patterson RE (2013) Postdiagnosis C-reactive protein and breast cancer survivorship: findings from the WHEL study. Cancer Epidemiol Biomark PrevGoogle Scholar
  46. 46.
    Zhou P, Li B, Liu B, Chen T, Xiao J (2018) Prognostic role of serum total cholesterol and high-density lipoprotein cholesterol in cancer survivors: a systematic review and meta-analysis. Clin Chim Acta 477:94–104PubMedCrossRefGoogle Scholar
  47. 47.
    Jeon JH, Kim SK, Kim HJ, Chang J, Ahn CM, Chang YS (2010) Lipid raft modulation inhibits NSCLC cell migration through delocalization of the focal adhesion complex. Lung Cancer 69(2):165–171PubMedCrossRefGoogle Scholar
  48. 48.
    Reverter M, Rentero C, Garcia-Melero A, Hoque M,Vilà, de Muga S, Álvarez-Guaita A, Conway JRW, Wood P, Cairns R, Lykopoulou L et al (2014) Cholesterol regulates syntaxin 6 trafficking at trans-golgi network endosomal boundaries. Cell Rep 7(3):883–897PubMedCrossRefGoogle Scholar
  49. 49.
    Hermansen SW, Leitzmann MF, Schatzkin A (2009) The impact on National Death Index ascertainment of limiting submissions to Social Security Administration Death Master File Matches in epidemiologic studies of mortality. Am J Epidemiol 169(7):901–908PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EpidemiologyUniversity of KentuckyLexingtonUSA
  2. 2.Markey Cancer CenterUniversity of KentuckyLexingtonUSA
  3. 3.Department of Public Health SciencesUniversity of MiamiMiamiUSA
  4. 4.Department of OncologyGeorgetown UniversityWashingtonUSA
  5. 5.College of Public Health and Markey Cancer CenterUniversity of KentuckyLexingtonUSA

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