Shift-work can harm health, affects men and women differently

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In a new study from the University of Waterloo, researchers found that shift work and irregular work schedules can cause several health-related issues and affect our defense against infection.

These health-related issues occur because the body’s natural clock, called the circadian clock, can be disrupted by inconsistent changes in the sleep-wake schedule and feeding patterns often caused by shift work.

In the study, the team developed a mathematical model to look at how a disruption in the circadian clock affects the immune system in fighting off illness.

They also created computational models, separately for men and women, which simulate the interplay between the circadian clock and the immune system.

The model is composed of the core clock genes, their related proteins, and the regulatory mechanism of pro- and anti-inflammatory mediators. By adjusting the clock, the models can simulate male and female shift-workers.

The results of these computer simulations showed that the immune response varies with the time of infection. Model simulation suggests that the time before we go to bed is the “worst” time to get an infection.

That is the period of the day when our body is least prepared to produce the pro- and anti-inflammatory mediators needed during an infection. Just as importantly, an individual’s sex impacts the severity of the infection.

The team says shift work likely affects men and women differently. Compared to females, the immune system in males is more prone to overactivation, which can increase their chances of sepsis following an ill-timed infection.

If you care about sleep health, please read studies about are melatonin supplements safe for good sleep? and findings of popular sleep and anxiety drug may cause addiction.

For more information about sleep and wellness, please see recent studies about a new way to improves sleep in people with insomnia and results showing that this common diet may increase sleep loss, insomnia.

The study is published in PLoS Computational Biology. One author of the study is Anita Layton.

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