背景:生殖因素与乳腺癌(BC)风险之间的关联因分子亚型而异(即,管腔A,管腔B,HER2和三阴性/基底样[TNBC])。在这篇系统综述和荟萃分析中,我们总结了生殖因素与BC亚型之间的关联.
方法:如果对与11个生殖风险因素之一相关的BC亚型进行检查,则包括2000年至2021年的研究:初潮年龄,更年期的年龄,第一胎的年龄,更年期状态,奇偶校验,母乳喂养,口服避孕药(OC)的使用,激素替代疗法(HRT),怀孕,自上次出生和堕胎以来。对于每个生殖风险因素,BC亚型,和研究设计(病例对照/队列或病例),随机效应模型用于估计合并相对风险和95%置信区间.
结果:共有75项研究符合系统评价的纳入标准。在病例对照/队列研究中,月经初潮和母乳喂养的年龄越晚与所有亚型的BC风险降低始终相关。而更年期的年龄更高,第一次分娩的年龄较晚,无效胎次/低胎次与腔A的风险增加有关,管腔B,和HER2亚型。在仅案例分析中,与管腔A相比,绝经后状态增加了HER2和TNBC的风险.OC和HRT使用的亚型之间的关联不太一致。
结论:确定不同BC亚型的常见危险因素可以增强预防策略的定制,和风险分层模型可以受益于亚型特异性。将母乳喂养状态添加到当前的BC风险预测模型中可以增强预测能力,考虑到不同亚型之间关联的一致性。
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.
CONCLUSIONS: 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.