Sleeping Pattern Biologically determined
Individual Differences In Sleep Structure Are Biologically Determined
Sleeping pattern variability has long been attributed to differences in several non-biological factors. Now a study from the Sleep and Performance Research Center at Washington State University Spokane, Wash., has shown that these individual differences are in large part biologically determined and may even prove to be genetic in origin.
Researchers have long observed significant differences in normal people’s sleep. Some are light sleepers, whereas others sleep deeply. Some fall asleep right away, while others take their time.
Such sleeping pattern variability has long been attributed solely to differences in circumstances, habits, and other non-biological factors. But now a study led by Hans Van Dongen, associate research professor and assistant director of the Sleep and Performance Research Center at Washington State University Spokane, has shown that these individual differences constitute traits—that is, they are in large part biologically determined and may even prove to be genetic in origin.
“This is the first study to reveal that substantial differences in sleep patterns exist even among healthy adults who are good sleepers,” said Van Dongen, who emphasized that normal sleep covers a wide terrain and that many different sleep patterns qualify as good sleep. “How much sleep people need and what the structure of their sleep looks like depends in large measure on their biology.”
The results of the NIH-funded research study were published in the June 2007 issue of the Journal of Sleep Research, with WSU graduate student Adrienne Tucker as the lead author. The study, which was conducted in large part at the University of Pennsylvania and was recently continued at Washington State University, assessed the presence and magnitude of biologically determined individual differences in the structure of sleep for a group of 21 carefully screened healthy young adults, and compared these individual differences to the effect of prior sleep deprivation on the structure of their sleep.
Over 11 consecutive days and nights, study participants were monitored continuously in a strictly controlled laboratory environment. They spent eight nights sleeping for up to 12 hours. These nights were interspersed with three 36-hour sleep deprivation periods—that is, three nights without any sleep. During the eight nights when sleep was allowed, polysomnographic recordings—which show brain waves, eye movements, and muscle tone—were done.
The researchers assessed 18 standard sleep parameters, including sleep duration, time to fall asleep, and the amount of time in each sleep stage (stages 1 through 4 and REM sleep). They found large individual differences in these sleep parameters, which showed up consistently across the eight nights with sleep—regardless of whether or not there had been sleep deprivation in the night before. This meant that the individual differences were not driven by circumstance, but were at least partially biologically determined. For deep sleep (stages 3 and 4) in particular, the individual differences were overwhelmingly biological in nature.
“In this group of healthy young adults, the wide variation in the duration and structure of their sleep was, to a large extent, biological in nature. The next logical step is to look for genes that may be responsible for these large individual differences,” Van Dongen said.
The physiological or functional significance of these sleep traits remains a mystery. The fact that all subjects were healthy, young adults and good sleepers seems to rule out any immediate clinical relevance of the differences among them. However, Van Dongen thinks that the sleep differences may be predictive of future clinical conditions.
“Recognition of trait individual differences in sleep may help to understand the increasing evidence for a functional link between sleep and health,” he said.
J Sleep Res. 2007 Jun;16(2):170-80
Trait interindividual differences in the sleep physiology of healthy young adults.
Sleep and Performance Research Center, Washington State University, Spokane, WA 99210-1495, USA.
Despite decades of sleep research by means of polysomnography (PSG), systematic interindividual differences in PSG-assessed sleep parameters have been scarcely investigated. The present study is the first to quantify interindividual variability in standard PSG-assessed variables of sleep structure in terms of stability and robustness as well as magnitude. Twenty-one carefully screened healthy young adults were studied continuously in a strictly controlled laboratory environment, where their PSGs were recorded for eight nights interspersed with three separate 36 h sleep deprivation periods. All PSG records were scored blind to subject and condition, using conventional criteria, and delta power in the non-REM sleep EEG was computed for four electrode derivations. Interindividual differences in sleep variables were examined for stability and robustness, respectively, by comparing results across equivalent nights (e.g. baseline nights) and across experimentally differentiated nights (baseline nights versus recovery nights following sleep deprivation). Among 18 sleep variables analyzed, all except slow-wave sleep (SWS) latency were found to exhibit significantly stable and robust–i.e. trait-like–interindividual differences. This was quantified by means of intraclass correlation coefficients (ICCs), which ranged from 36% to 89% across physiologic variables, and were highest for SWS (73%) and delta power in the non-REM sleep EEG (78-89%). The magnitude of the trait interindividual differences was considerable, consistently exceeding the magnitude of the group-average effect on sleep structure of 36 h total sleep deprivation. Notably, for non-REM delta power–a putative marker of sleep homeostasis–the interindividual differences were from 9.9 to 12.8 times greater than the group-average increase following sleep deprivation relative to baseline. Physiologic sleep variables did not vary among subjects in a completely independent manner–61.1% of their combined variance clustered in three trait dimensions, which appeared to represent sleep duration, sleep intensity, and sleep discontinuity. Any independent functional significance of these sleep physiologic phenotypes remains to be determined.