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Testing the Light-at-Night (LAN) Theory for Breast Cancer Causation

The “light-at-night” (LAN) theory began with a focus on the mystery of breast cancer: why is risk so high in the modern world and why does risk increase dramatically as societies industrialize (Stevens, 1987, 2009)? Known risk factors account for less than half the incidence in the high-risk societies, and there is no scientific consensus on what exposure(s) is driving the breast cancer pandemic (Madigan et al., 1995; Nagata et al., 1997). This stands in stark contrast to our understanding of cancers of the lung, liver, stomach, cervix, and others, for which the major causative agents have been discovered. The LAN theory is based on the idea of “circadian disruption” (Stevens & Rea, 2001; Stevens et al., 2007, 2011) in which artificial lighting has displaced much of the population of the modern world from the solar cycle of light and dark to a constant twilight; there is little sun during the day inside buildings where more and more people work, and there is light during the night for all manner of activity. It is now becoming evident that artificial lighting, and the circadian disruption that can result, may also influence the risk of some other diseases of modern life, such as prostate cancer in men, adultonset diabetes, and obesity (Stevens et al., 2007). But, how to test this theory and how to gather reliable evidence upon which to base effective intervention and mitigation, if needed? The first step was to make predictions based on the theory. If LAN increases risk, then women on a non-day shift should be at higher risk, blind women should be at lower risk, long sleep duration (as a surrogate for hours of dark) should lower risk, and community nighttime light level should codistribute with breast cancer incidence (Stevens, 2009). As the epidemiological studies have moved forward, two lines of biological research have been making remarkable advances in the last decade: (1) phototransduction for the circadian system (e.g., Gooley et al., 2010), and (2) the molecular genetics of the clock mechanism (e.g., Duguay & Cermakian, 2009; Takahashi et al., 2008). These have important implications for exposure assessment in epidemiological studies, and for proposed disease mechanisms and endpoints.