BMC Cancer (Jul 2023)

Association of reproductive risk factors and breast cancer molecular subtypes: a systematic review and meta-analysis

  • Xihua Mao,
  • Chioma Omeogu,
  • Shama Karanth,
  • Ashwini Joshi,
  • Clare Meernik,
  • Lauren Wilson,
  • Amy Clark,
  • April Deveaux,
  • Chunyan He,
  • Tisha Johnson,
  • Karen Barton,
  • Samantha Kaplan,
  • Tomi Akinyemiju

DOI
https://doi.org/10.1186/s12885-023-11049-0
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 29

Abstract

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Abstract Background Associations between reproductive factors and breast cancer (BC) risk vary by molecular subtype (i.e., luminal A, luminal B, HER2, and triple negative/basal-like [TNBC]). In this systematic review and meta-analysis, we summarized the associations between reproductive factors and BC subtypes. Methods Studies from 2000 to 2021 were included if BC subtype was examined in relation to one of 11 reproductive risk factors: age at menarche, age at menopause, age at first birth, menopausal status, parity, breastfeeding, oral contraceptive (OC) use, hormone replacement therapy (HRT), pregnancy, years since last birth and abortion. For each reproductive risk factor, BC subtype, and study design (case–control/cohort or case-case), random-effects models were used to estimate pooled relative risks and 95% confidence intervals. Results A total of 75 studies met the inclusion criteria for systematic review. Among the case–control/cohort studies, later age at menarche and breastfeeding were consistently associated with decreased risk of BC across all subtypes, while later age at menopause, later age of first childbirth, and nulliparity/low parity were associated with increased risk of luminal A, luminal B, and HER2 subtypes. In the case-only analysis, compared to luminal A, postmenopausal status increased the risk of HER2 and TNBC. Associations were less consistent across subtypes for OC and HRT use. Conclusion Identifying common risk factors across BC subtypes can enhance the tailoring of prevention strategies, and risk stratification models can benefit from subtype specificity. Adding breastfeeding status to current BC risk prediction models can enhance predictive ability, given the consistency of the associations across subtypes.

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