Novel Circadian Exposure Metrics for Shift Workers

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 DESCRIPTION (provided by applicant): Shift work is a predictor of chronic disease risk. However, to date, the critical dose of exposure to shift work required to impact health remains unclear. We seek to develop improved exposure metrics to better capture the occupational strain linked to work schedules, and, in particular, shift work. One factor thought to underlie the association between shift work and adverse health outcomes is the interference of work times with the individual 24h-rhythm in physiology and behavior: because activity, food intake and light exposure take place at inappropriate biological times (i.e. during the individual biological night) work schedules induce a certain level of circadian strain (or disruption). Yet 24h-rhythms exhibit large inter-individual variability as far as timing of sleep and wake behavior are concerned; this variability is also referred to as "chronotype". For example, early chronotypes - if free of social and work constraints - tend to fall asleep and wake up significantly earlier in the 24h day than late chronotypes. It appears therefore plausible that occupational circadian strain depends not only on the work schedule itself, but also on an individual's respective chronotype. We aim to address this question by developing novel exposure metrics that rely on the conjunct assessment of chronotype and work schedules and linking them to BMI - a prime mediator of chronic disease risk. In the Nurses' Health Study 3 (NHS3), we implemented highly detailed work schedule assessments. In addition, we also introduced the two prime measures of chronotype. Based on these measures, we will develop two circadian strain metrics that, ultimately, could be applied and generalized to a wide range of studies. We will conduct a systematic comparison of the two chronotype measures and the newly developed circadian strain metrics across work schedules. Next, we will apply these metrics to the Nurses' Health Study 2 and NHS3 to examine associations between occupational circadian strain and body mass index, a central chronic disease mediator that has previously been associated with circadian strain in animal and experimental studies. In sum, we seek to develop widely applicable metrics for personalized occupational risk assessment, reflecting personalized circadian strain as one of the central biological mechanisms linking shift work and adverse health outcomes. Ultimately, these newly developed metrics can be used to examine the relationship between work schedule-associated circadian strain and health and safety outcomes, and identify critical doses for prevention strategies, across industry settings and sectors.