This analysis investigated whether reproductive factors such as age at menarche, parity, and timing and outcomes of pregnancies were associated with survival among women with breast cancer younger than 55 years. Female residents of Atlanta, Georgia, and central New Jersey who were diagnosed with a primary, incident invasive breast cancer between 1990 and 1992 and enrolled in a population-based study (n = 1,264) were followed for 8-10 years. Detailed exposure and covariate information was collected via in-person interviews administered shortly after diagnosis. Vital status as of January 1, 2000 was ascertained through the National Death Index via the state cancer registries (n = 292 deaths). Cox regression methods were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) adjusted for confounders. Parity of 4 or more births, as compared with nulliparity, was positively associated with all-cause mortality, [HR (95% CI) = 1.71 (1.09-2.67)]. Increased mortality was associated with having given birth within 5 years prior to diagnosis (5 years) [1.78 (1.28-2.47)], and was more pronounced among women with a pre-diagnostic body mass index of <25 kg/m2 [2.54 (1.61-4.00)]. Early age at menarche and early age at first birth also modestly increased mortality; history of miscarriage, induced abortion, and ever breastfeeding were not related to survival. These results may help elucidate breast cancer progression mechanisms and enable a better understanding of how reproductive characteristics influence breast cancer survival.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10549-006-9346-1DOI Listing

Publication Analysis

Top Keywords

breast cancer
12
reproductive factors
8
association reproductive
4
factors breast
4
cancer
4
cancer survival
4
survival younger
4
younger women
4
women analysis
4
analysis investigated
4

Similar Publications

Triple-negative breast cancer (TNBC) remains a significant global health challenge, emphasizing the need for precise identification of patients with specific therapeutic targets and those at high risk of metastasis. This study aimed to identify novel therapeutic targets for personalized treatment of TNBC patients by elucidating their roles in cell cycle regulation. Using weighted gene co-expression network analysis (WGCNA), we identified 83 hub genes by integrating gene expression profiles with clinical pathological grades.

View Article and Find Full Text PDF

Background: Breast cancer screening (BCS) inequities are evident at national and local levels, and many health systems want to address these inequities, but may lack data about contributing factors. The objective of this study was to inform health system interventions through an exploratory analysis of potential multilevel contributors to BCS inequities using health system data.

Methods: The authors conducted a cross-sectional analysis within a large academic health system including 19,774 individuals who identified as Black (n = 1445) or White (n = 18,329) race and were eligible for BCS.

View Article and Find Full Text PDF

Background: To date, 11 DNA polymerase epsilon (POLE) pathogenic variants have been declared "hotspot" mutations. Patients with endometrial cancer (EC) characterized by POLE hotspot mutations (POLEmut) have exceptional survival outcomes. Whereas international guidelines encourage deescalation of adjuvant treatment in early-stage POLEmut EC, data regarding safety in POLEmut patients with unfavorable characteristics are still under investigation.

View Article and Find Full Text PDF

Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of these genes. A meta-analysis combining the reported risk estimates can provide an overall estimate of age-specific risk of developing BC, that is, penetrance for a gene.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!