Birth weight-breast cancer revisited: is the association confounded by familial factors?

Cancer Epidemiol Biomarkers Prev. 2009 Sep;18(9):2447-52. doi: 10.1158/1055-9965.EPI-09-0123. Epub 2009 Aug 18.

Abstract

Purpose: The study aimed to investigate whether the association between birth weight and the risk of breast cancer can be confounded by familial factors, such as shared environment and common genes.

Materials and methods: Eligible were all female like-sexed twins of the Swedish Twin Registry, born during the period 1926-1958 and alive in 1973. Data were obtained from birth records, and the final study population with reliable birth weight data was made up of 11,923 twins. Hazard ratios (HR) for breast cancer according to birth weight were estimated through Cox regression, using robust SE to account for the dependence within twin pairs. Paired analysis was done to account for potential confounding by familial factors.

Results: In the cohort analysis, a birth weight >or=3,000 g was associated with an increased risk of breast cancer diagnosed at or before 50 years [adjusted HR, 1.57; 95% confidence interval (95% CI), 1.03-2.42] but not with breast cancer with a later onset (adjusted HR, 0.80; 95% CI, 0.57-1.12). From >or=2,500 g, a 500-g increase in birth weight conferred a HR of 1.62 (95% CI, 1.16-2.27) for breast cancer diagnosed at or before 50 years. This risk remained in analysis within twin pairs (HR, 1.57; 95% CI, 1.00-2.48).

Conclusion: In the present study, findings indicate that the association between birth weight and breast cancer risk, seen only in women diagnosed early (<or=50 years), is not confounded by familial factors.

Publication types

  • Research Support, Non-U.S. Gov't
  • Twin Study

MeSH terms

  • Birth Weight*
  • Breast Neoplasms / epidemiology*
  • Breast Neoplasms / genetics
  • Cohort Studies
  • Diseases in Twins / epidemiology*
  • Diseases in Twins / genetics
  • Environment
  • Female
  • Genetic Predisposition to Disease
  • Humans
  • Pregnancy
  • Proportional Hazards Models
  • Risk Factors
  • Sweden / epidemiology