The potential association between shift work involving night work and the risk of cancer, specifically breast cancer, has been extensively debated over the last decades. Although some epidemiological studies have linked long-term night work to an increased incidence of breast cancer, causal evidence and insight into the mechanisms responsible for the association is currently lacking. More specifically, it is currently unclear what the separate and combined effects of type, duration, frequency, intensity, and rotation direction of shift work are. Secondly, it is unclear whether diurnal preference (i.e. chronotype) and genetic factors (i.e. polymorphisms in circadian genes) modify the associations, as they affect both a person’s ability to adapt to circadian disruption and breast cancer risk.
In 2011 the Nightingale Study was established; a nationwide prospective cohort of 59,947 female nurses, aged 18–65 years old. All participants completed a questionnaire including full job history, period-specific information on all domains of shift work and potential confounding factors. Toenail clippings were collected as a source of DNA for genetic analyses. The modifying role of single nucleotide polymorphisms (SNPs) in circadian and melatonin metabolism genes will be assessed in a nested case-cohort design. In 2016, a follow-up questionnaire will be enrolled to update information on menopausal status, lifestyle factors and work schedules since baseline. Breast cancer incidence will be assessed through linkage with the Netherlands cancer registry and the national pathology database.
If individual susceptibility and adaptability are effect modifiers or confounders in the association between shift work and breast cancer risk, this may (partly) explain the associations observed in previous studies. The Nightingale Study provides the opportunity to study these important questions and will contribute to more specific and evidence-based recommendations regarding the prevention of breast cancer related to shift work, for example through optimisation of work schedules.