Background: Anthropometric and reproductive factors have been reported as being established risk factors for breast cancer (BC). This study explores the contribution of anthropometric and reproductive factors in UK females developing BC in a large longitudinal cohort.
Methods: Data from the UK Biobank prospective study of 273,467 UK females were analyzed. Relative risks (RRs) and 95% confidence intervals (CIs) for each factor were adjusted for age, family history of BC and deprivation score. The analyses were stratified by the menopausal status.
Results : Over the 9 years of follow up the total number of BC cases were 14,231 with 3,378 (23.7%) incident cases with an incidence rate of 2.09 per 1000 person-years. In pre-menopausal, increase in age, height, having low BMI, low waist to hip ratio, first degree family history of BC, early menarche age, nulliparous, late age at first live birth, high reproductive interval index, and long contraceptive use duration were all significantly associated with an increased BC risk. In post-menopausal, getting older, being taller, having high BMI, first degree BC family history, nulliparous, late age at first live birth, and high reproductive interval index were all significantly associated with an increased risk of BC. The population attributable fraction (PAF) suggested that an early first live birth, lower reproductive interval index and increased number of children can contribute to BC risk reduction up to 50%.
Conclusions: This study utilizes the UK Biobank study to confirm associations between anthropometric and reproductive factors and the risk of breast cancer development. Result of attributable fraction of risk contributed by each risk factor suggested that lifetime risk of BC can be reduced by controlling weight, reassessing individual approaches to the timing of childbirth and options for contraception and considering early screening for women with family history in the first degree relative.
Development and validation of risk prediction model for breast and ovarian cancers
Ovarian and breast cancer are hormonally dependent cancers. Breast cancer is the most common female cancer in the UK. Although ovarian cancer has a lower incidence, its 5 year survival rate is half of that for breast cancer. Prevention and/or early detection are important in both diseases. Risk prediction models can be used to assess individual risk. So far, most risk prediction models for these cancers often include clinical indices with only very limited basic epidemiological factors such as family history, age etc. This proposal therefore aims to build a risk calculator for breast and ovarian cancers. We will utilize data on lifestyle/environmental factors, biomarkers and genetic markers. Data (not samples) from the full female cohort is required for women (ovarian and breast cancers and controls). In each cancer type, we will build risk models that predict individual risk. This research fits UK Biobank?s stated purpose in that it is in the public interest. For models developed using non-UK Biobank data the entire female cohort will also be used in order to perform prospective model validation. We will build one or more optimised risk prediction models fit for predicting risk in both sporadic and familial cases (including genetic markers, biomarkers, lifestyle/environmental factors collected within the female cohort). Familial cases are defined as breast and/or ovarian cancer cases with first degree relatives affected with breast and/or ovarian cancer. We will also explore whether ?any cancer? in first degree relatives also add to the prediction models. Both cohort and nested case control methods will be used. We will require data only (not samples) from the whole female cohort including full genetic data (not samples)and biomarker data (not samples) once available. We would like to receive lifestyle/environmental data first and in due course the genetic and biomarker data (not samples) when they become available.
|Professor Kenneth Muir
|University of Manchester