Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Epidemiology

A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses’ Health Studies

Abstract

Background

Adiposity is consistently positively associated with postmenopausal breast cancer and inversely associated with premenopausal breast cancer risk, though the reasons for this difference remain unclear.

Methods

In this nested case–control study of 1649 breast cancer cases and 1649 matched controls from the Nurses’ Health Study (NHS) and the NHSII, we selected lipid and polar metabolites correlated with BMI, waist circumference, weight change since age 18, or derived fat mass, and developed a metabolomic score for each measure using LASSO regression. Logistic regression was used to investigate the association between this score and breast cancer risk, adjusted for risk factors and stratified by menopausal status at blood draw and diagnosis.

Results

Metabolite scores developed among only premenopausal or postmenopausal women were highly correlated with scores developed in all women (r = 0.93–0.96). Higher metabolomic adiposity scores were generally inversely related to breast cancer risk among premenopausal women. Among postmenopausal women, significant positive trends with risk were observed (e.g., metabolomic waist circumference score OR Q4 vs. Q1 = 1.47, 95% CI = 1.03–2.08, P-trend = 0.01).

Conclusions

Though the same metabolites represented adiposity in pre- and postmenopausal women, breast cancer risk associations differed suggesting that metabolic dysregulation may have a differential association with pre- vs. postmenopausal breast cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spearman correlations of metabolites with adiposity measures, by class of metabolite.

Similar content being viewed by others

Data availability

The data generated in this study are not publicly available due to participant confidentiality and privacy concerns but are available upon request. Further information including the procedures to obtain and access data from the Nurses’ Health Studies is described at https://www.nurseshealthstudy.org/researchers.

References

  1. Lahmann PH, Hoffmann K, Allen N, van Gils C, Khaw KT, Tehard B, et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer And Nutrition (EPIC). Int J Cancer. 2004;111:762–71.

    Article  CAS  PubMed  Google Scholar 

  2. Morimoto LM, White E, Chen Z, Chlebowski RT, Hays J, Kuller L, et al. Obesity, body size, and risk of postmenopausal breast cancer: the Women’s Health Initiative (United States). Cancer Causes Control. 2002;13:741–51.

    Article  PubMed  Google Scholar 

  3. Cheraghi Z, Poorolajal J, Hashem T, Esmailnasab N, Doosti, Irani A. Effect of body mass index on breast cancer during premenopausal and postmenopausal periods: a meta-analysis. PLoS ONE. 2012;7:e51446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371:569–78.

    Article  PubMed  Google Scholar 

  5. Premenopausal Breast Cancer Collaborative Group, Schoemaker MJ, Nichols HB, Wright LB, Jones ME, O’Brien KM, Adami HO, et al. Association of body mass index and age with subsequent breast cancer risk in premenopausal women. JAMA Oncol. 2018;4:e181771.

    Article  Google Scholar 

  6. Luo J, Chen X, Manson JE, Shadyab AH, Wactawski-Wende J, Vitolins M, et al. Birth weight, weight over the adult life course and risk of breast cancer. Int J Cancer. 2020;147:65–75.

    Article  CAS  PubMed  Google Scholar 

  7. Hartz A, He T, Rimm A. Comparison of adiposity measures as risk factors in postmenopausal women. J Clin Endocrinol Metab. 2012;97:227–33.

    Article  CAS  PubMed  Google Scholar 

  8. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Sun Q, et al. Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Br J Nutr. 2017;118:858–66.

    Article  CAS  PubMed  Google Scholar 

  9. Moore SC, Mazzilli KM, Sampson JN, Matthews CE, Carter BD, Playdon MC, et al. A metabolomics analysis of postmenopausal breast cancer risk in the cancer prevention study II. Metabolites. 2021;11:95.

  10. Moore SC, Playdon MC, Sampson JN, Hoover RN, Trabert B, Matthews C, et al. A metabolomics analysis of body mass index and postmenopausal breast cancer risk. J Natl Cancer Inst. 2018;110:588–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Moore SC, Matthews CE, Sampson JN, Stolzenberg-Solomon RZ, Zheng W, Cai Q, et al. Human metabolic correlates of body mass index. Metabolomics. 2014;10:259–69.

    Article  CAS  PubMed  Google Scholar 

  12. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125:2222–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gaudet MM, Falk RT, Stevens RD, Gunter MJ, Bain JR, Pfeiffer RM, et al. Analysis of serum metabolic profiles in women with endometrial cancer and controls in a population-based case-control study. J Clin Endocrinol Metab. 2012;97:3216–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mascanfroni ID, Takenaka MC, Yeste A, Patel B, Wu Y, Kenison JE, et al. Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α. Nat Med. 2015;21:638–46. https://doi.org/10.1038/nm.3868

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. O’Sullivan JF, Morningstar JE, Yang Q, Zheng B, Gao Y, Jeanfavre S, et al. Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes. J Clin Invest. 2017;127:4394–402.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Paynter NP, Balasubramanian R, Giulianini F, Wang DD, Tinker LF, Gopal S, et al. Metabolic predictors of incident coronary heart disease in women. Circulation. 2018;137:841–53.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Townsend MK, Clish CB, Kraft P, Wu C, Souza AL, Deik AA, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem. 2013;59:1657–67.

    Article  CAS  PubMed  Google Scholar 

  18. Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.

    Article  Google Scholar 

  19. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. “mediation: R package for causal Mediation analysis.”. J Stat Softw. 2014;59:1–38.

    Article  Google Scholar 

  20. Ho JE, Larson MG, Ghorbani A, Cheng S, Chen MH, Keyes M, et al. Metabolomic profiles of body mass index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes. PLoS ONE. 2016;11:e0148361.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9:311–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang Q, Holmes MV, Davey Smith G, Ala-Korpela M. Genetic support for a causal role of insulin resistance on circulating branched-chain amino acids and inflammation. Diabetes Care. 2017;40:1779–86.

    Article  CAS  PubMed  Google Scholar 

  23. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chen H-H, Tseng YJ, Wang S-Y, Tsai Y-S, Chang C-S, Kuo T-C, et al. The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity. Int J Obes. 2015;39:1241–8.

    Article  CAS  Google Scholar 

  25. Rebouche CJ. Kinetics, pharmacokinetics, and regulation of L-carnitine and acetyl-L-carnitine metabolism. Ann N. Y Acad Sci. 2004;1033:30–41. https://doi.org/10.1196/annals.1320.003

    Article  CAS  PubMed  Google Scholar 

  26. Dickerman BA, Ebot EM, Healy BC, Wilson KM, Eliassen AH, Ascherio A, et al. A Metabolomics analysis of adiposity and advanced prostate cancer risk in the health professionals follow-up study. Metabolites. 2020;10:99.

  27. Schooneman MG, Vaz FM, Houten SM, Soeters MR. Acylcarnitines: reflecting or inflicting insulin resistance? Diabetes. 2013;62:1–8.

    Article  CAS  PubMed  Google Scholar 

  28. Srikanthan K, Feyh A, Visweshwar H, Shapiro JI, Sodhi K. Systematic review of metabolic syndrome biomarkers: a panel for early detection, management, and risk stratification in the west virginian population. Int J Med Sci. 2016;13:25–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Dibaba DT, Braithwaite D, Akinyemiju T. Metabolic syndrome and the risk of breast cancer and subtypes by race, menopause and BMI. Cancers. 2018;10:299.

  30. Zeleznik OA, Balasubramanian R, Ren Y, Tobias DK, Rosner BA, Peng C, et al. Branched chain amino acids and risk of breast cancer. JNCI Cancer Spectr. 2021;5:pkab059.

  31. Tobias DK, Hazra A, Lawler PR, Chandler PD, Chasman DI, Buring JE, et al. Circulating branched-chain amino acids and long-term risk of obesity-related cancers in women. Sci Rep. 2020;10:16534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li M, Song L, Yuan J, Zhang D, Zhang C, Liu Y, et al. Association between serum insulin and C-peptide levels and breast cancer: an updated systematic review and meta-analysis. Front Oncol. 2020;10:553332.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Verheus M, Peeters PHM, Rinaldi S, Dossus L, Biessy C, Olsen A, et al. Serum C-peptide levels and breast cancer risk: results from the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer. 2006;119:659–67.

    Article  CAS  PubMed  Google Scholar 

  34. Eliassen AH, Tworoger SS, Mantzoros CS, Pollak MN, Hankinson SE. Circulating insulin and c-peptide levels and risk of breast cancer among predominately premenopausal women. Cancer Epidemiol Biomark Prev. 2007;16:161–4.

    Article  CAS  Google Scholar 

  35. His M, Viallon V, Dossus L, Gicquiau A, Achaintre D, Scalbert A, et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med. 2019;17:178.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Davies SS, Roberts LJ. F2-isoprostanes as an indicator and risk factor for coronary heart disease. Free Radic Biol Med. 2011;50:559–66.

    Article  CAS  PubMed  Google Scholar 

  37. Il’yasova D, Wang F, Spasojevic I, Base K, D’Agostino RB, Wagenknecht LE. Urinary F2-isoprostanes, obesity, and weight gain in the IRAS cohort. Obesity. 2012;20:1915–21.

    Article  PubMed  CAS  Google Scholar 

  38. Nemoto S, Finkel T. Ageing and the mystery at Arles. Nature. 2004;429:149–52.

    Article  CAS  PubMed  Google Scholar 

  39. Dai Q, Gao Y-T, Shu X-O, Yang G, Milne G, Cai Q, et al. Oxidative stress, obesity, and breast cancer risk: results from the Shanghai Women’s Health Study. J Clin Oncol. 2009;27:2482–8.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Sisti JS, Lindström S, Kraft P, Tamimi RM, Rosner BA, Wu T, et al. Premenopausal plasma carotenoids, fluorescent oxidation products, and subsequent breast cancer risk in the nurses’ health studies. Breast Cancer Res Treat. 2015;151:415–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nichols HB, Anderson C, White AJ, Milne GL, Sandler DP. Oxidative stress and breast cancer risk in premenopausal women. Epidemiology. 2017;28:667–74.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Fortner RT, Katzke V, Kühn T, Kaaks R. Obesity and breast cancer. Recent Results Cancer Res. 2016;208:43–65.

    Article  CAS  PubMed  Google Scholar 

  43. Simpson ER. Sources of estrogen and their importance. J Steroid Biochem Mol Biol. 2003;86:225–30.

    Article  CAS  PubMed  Google Scholar 

  44. Zhou C, Zhou D, Esteban J, Murai J, Siiteri PK, Wilczynski S, et al. Aromatase gene expression and its exon I usage in human breast tumors. Detection of aromatase messenger RNA by reverse transcription-polymerase chain reaction. J Steroid Biochem Mol Biol. 1996;59:163–71.

    Article  CAS  PubMed  Google Scholar 

  45. Sasano H, Nagura H, Harada N, Goukon Y, Kimura M. Immunolocalization of aromatase and other steroidogenic enzymes in human breast disorders. Hum Pathol. 1994;25:530–5.

    Article  CAS  PubMed  Google Scholar 

  46. Brown KA, Iyengar NM, Zhou XK, Gucalp A, Subbaramaiah K, Wang H, et al. Menopause is a determinant of breast aromatase expression and its associations with BMI, inflammation, and systemic markers. J Clin Endocrinol Metab. 2017;102:1692–701.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rich-Edwards JW, Spiegelman D, Garland M, Hertzmark E, Hunter DJ, Colditz GA, et al. Physical activity, body mass index, and ovulatory disorder infertility. Epidemiology. 2002;13:184–90.

    Article  PubMed  Google Scholar 

  48. Tworoger SS, Eliassen AH, Missmer SA, Baer H, Rich-Edwards J, Michels KB, et al. Birthweight and body size throughout life in relation to sex hormones and prolactin concentrations in premenopausal women. Cancer Epidemiol Biomark Prev. 2006;15:2494–501.

    Article  CAS  Google Scholar 

  49. Potischman N, Swanson CA, Siiteri P, Hoover RN. Reversal of relation between body mass and endogenous estrogen concentrations with menopausal status. J Natl Cancer Inst. 1996;88:756–8.

    Article  CAS  PubMed  Google Scholar 

  50. Houghton LC, Sisti JS, Hankinson SE, Xie J, Xu X, Hoover RN, et al. Estrogen metabolism in premenopausal women is related to early life body fatness. Cancer Epidemiol Biomark Prev. 2018;27:585–93.

    Article  CAS  Google Scholar 

  51. Xu K, Shi L, Zhang B, Mi B, Yang J, Sun X, et al. Distinct metabolite profiles of adiposity indices and their relationships with habitual diet in young adults. Nutr Metab Cardiovasc Dis. 2021;31:2122–30.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the participants and staff of the Nurses’ Health Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA and WY. The authors assume full responsibility for the analyses and interpretation of these data.

Funding

This study was funded by the National Institutes of Health (NIH)/National Cancer Institute (NCI) with the following grants: UM1 CA186107, P01 CA87969, R01 CA49449, R01 CA050385, U01 CA176726, R01 CA67262 and T32 CA009001.

Author information

Authors and Affiliations

Authors

Contributions

KB and AHE designed the study. CC and JA performed metabolomic profiling. KB and OZ performed the statistical analysis. BD, RB, RT, BR and AHE performed interpretation of results. AHE, BR and RT supervised the study. All authors reviewed the manuscript and approved its final version.

Corresponding author

Correspondence to Kristen D. Brantley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The return of the self-administered questionnaires and blood samples were considered to imply consent. The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. The study was performed in accordance with the Declaration of Helsinki.

Consent to publish

Not applicable.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brantley, K.D., Zeleznik, O.A., Dickerman, B.A. et al. A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses’ Health Studies. Br J Cancer 127, 1076–1085 (2022). https://doi.org/10.1038/s41416-022-01873-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-022-01873-9

Search

Quick links