From apnea and insomnia in Iceland to season and slow wave sleep

Abstract

Tacit assumptions and beliefs influence our research designs and interpretation of the data. Sometimes we have to admit that commonly held beliefs about sleep are based on scant data or no data at all. The current issue of the Journal of Sleep Research contains papers covering a wide range of issues relevant to sleep medicine and basic sleep research. Some of these papers challenge assumptions and common beliefs, others provide new insights. Apnea and insomnia are both highly prevalent and at first sight very distinct, disorders. Apnea is more common in men, and women suffer more frequently from insomnia. Recently, evidence that these disorders may co-exist has emerged. Bjornsdottir et al. (2012) have taken a closer look at the extent of co-existence in the general population in Iceland. Difficulties maintaining sleep were reported by 31% of women and 31% of men in the control group and 52% (men) and 62% (women) in the untreated obstructive sleep apnoea group. As reported by men, difficulties initiating sleep did not differ significantly between apnoea patients (12.6%) and controls (9.4%), but were much higher in women with apnea (27.3%) than in control women (27.3%). How to interpret these data? The authors point out that it may be that arousals associated with sleep apnea have a negative effect on sleep maintenance and sleep initiation. Therefore, it may be that sleep apnea is, at least in part, the underlying cause of the insomnia complaint, even though the degree of sleep apnea did not associate with insomnia complaints. Nevertheless, the data show that it is important to screen for insomnia in apnea patients, because the data also indicate that those apnea patients with insomnia report a greater reduction in quality of life than those apnea patients without insomnia. The current issue of the Journal also contains a report on differences in protein expression in serum samples of apnea patients (Jurado-Gamez et al., 2012). These differences were correlated with disease severity and were implicated primarily in lipid and vascular pathways. This, of course, fits neatly with the observation that sleep-disordered breathing is a risk factor for cardiovascular disease, and is a further step in understanding the pathogenesis underlying these associations. Svensson et al. (2012) also investigated some of the potential mechanisms underlying the associations between apnea and cardiovascular disease by assessing sleep, breathing and markers of inflammation in a population-based sample of 400 women in Sweden. A main finding of that study is that, after controlling for confounders such as central obesity, the percentage of sleep time with oxygen saturation below 90% appears to be the best predictor of inflammation. Of course, we sleep much longer during these long dark winter nights, and it is difficult to get up in the morning when it is freezing, snowing or just grey and drizzly? And should it not be expected that these effects of season are much greater on free days than during work days and much greater in Norway than in Ghana? Friborg et al. (2012) investigated these questions by asking a total of 330 participants in Norway and Ghana to complete a sleep diary for 1 week in August and January. Several surprising and unexpected findings emerged. Interaction between season and latitude were detected only during weekdays, not during weekends. No seasonal effect on sleep duration was detected. In the two countries, only aspects of sleep timing and insomnia were affected differentially by season. This is a relatively small study which raises many questions about the importance of variation in the duration of the natural photoperiod, the contribution of artificial light in the evening and social factors in the determination of sleep timing and quality which, to a large extent, are driven by circadian clocks. Individual differences in slow wave sleep (SWS) are considerable and stable within an individual. Several correlates of differences in slow wave activity have been reported recently (e.g. Buchmann et al., 2011), and it is often assumed that SWS is primarily for the brain. In the current issue, Mokhlesi et al. (2012) analyse data from more than 900 individuals who were referred to a sleep laboratory for suspected sleep apnea. Multiple regression analyses showed that age, gender, obesity and ethnicity, as well as sleep apnea severity, are all predictors of SWS. Some of these effects are comparable to or greater than the effects of total sleep deprivation. The challenge will now be to link the mechanisms by which individual differences in SWS relate to differences in the risk for developing, for example, diabetes or hypertension.