How staying up all night can dramatically influence over 100 proteins in your blood
New research from scientists at the University of Colorado Boulder has, for the first time, studied how protein levels in human blood can vary over a 24-hour period depending on when a person is sleeping and eating. The striking results found that when a person stays up all night, the patterns of over 100 different proteins in the blood are disrupted.
For some years now, we've seen a growing body of evidence associating irregular sleep patterns, or night-shift work, with negative health consequences. Last month saw the release of a major observational study following nearly half a million people finding that night owls have a 10 percent higher risk of dying sooner than those with a preference for getting to bed early. Back in 2007, the International Agency for Research on Cancer even went so far as to classify shift work that disrupts circadian rhythms as a probable human carcinogen.
This new study set out to examine exactly how night-shift work patterns could alter the expressions and levels of certain proteins in the blood. Six male subjects were recruited and they spent six days in a research center where meals, sleep and light exposure were controlled. Blood was drawn every four hours to monitor changes in the patterns of 1,129 different proteins.
After two days of a normal schedule, the participants moved into a simulated night shift pattern, sleeping for eight hours during the day and eating at night. Pretty quickly the researchers discovered the natural 24-hour cycles of up to 129 proteins were altered.
"By the second day of the misalignment we were already starting to see proteins that normally peak during the day peaking at night and vice versa," says lead author Christopher Depner.
These proteins were noted as being associated with a variety of biological pathways known to be related to immune functions, metabolism and cancer. Thirty proteins were specifically found to be regulated significantly by circadian rhythms, normally peaking between 2 pm and 9 pm.
One disrupted protein, called glucagon, was seen to surge to high levels during the night after the participants were pushed into simulated night-shift patterns. Glucagon is a protein that is known to trigger the release of sugar into the bloodstream via the liver. The researchers hypothesize that this particular disruption could help explain the higher rates of diabetes seen in night-shift workers.
Another specific example observed was a decrease in fibroblast growth factor 19 after night-shift patterns were introduced. In animal models this particular protein has been found to increase energy expenditure and calorie burning. The reduction in its levels correlated with the finding that subjects reduced their overall ability to burn calories by around 10 percent when subjected to the circadian disruption.
Like other similar studies, these results are measured from acute alterations in a subject's circadian patterns, so it isn't entirely clear to what degree the body can adapt to these kinds of night-shift patterns over a long term. While some of these biological disruptions could certainly return to a more normal balance over a longer period of disruption, longitudinal studies of night-shift workers do certainly indicate higher rates of conditions such as diabetes and cancer. This suggests the human body may only be able to adapt to fundamental circadian disruptions to a limited degree.
Depner alludes to some fascinating outcomes from his research, including the suggestion that certain medical treatments may be more effective if timed at points in the day that better correspond with certain protein levels. For future diagnostic blood tests that rely on accurately measuring protein levels, this research also importantly shows how much the timing of the test matters. Another outcome is the potential of being able to develop treatments for night shift workers to specifically protect them from harmful protein fluctuations that could be leading to negative health effects.
The study was published in the journal PNAS.
Source: University of Colorado Boulder
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