Sleep最新文献

Study objectives: Sleep/wakefulness is regulated by intracellular signaling pathways composed of protein kinases such as salt-inducible kinase 3 (Sik3). Sik3-deficiency in neurons decreases nonrapid eye movement (NREM) sleep time and electroencephalogram (EEG) delta power during NREM sleep, while Sik3Slp mice lacking a protein kinase A (PKA)-phosphorylation site, S551, show hypersomnia phenotype. In this study, we examined how a phosphomimetic mutation of the 221st threonine residue (T221E), which provides a partial (weak) constitutive activity of the kinase, affects sleep/wakefulness and circadian behavior. We also examined the effect of T221E substitution on the hypersomnia phenotype of Sik3Slp mice.

Methods: We examined the sleep/wake behavior of heterozygous and homozygous Sik3T221E mice and Sik3T221E;Slp mice using EEG and electromyogram recording. We also examined the circadian behavior of Sik3T221E mice using a running wheel under the light-dark cycle and constant darkness.

Results: Heterozygous and homozygous Sik3T221E mice showed normal sleep time and sleep homeostatic responses. Homozygous Sik3T221E mice exhibited a delayed onset of wakefulness at the early dark phase and longer circadian periods. Sik3T221E;Slp mice showed decreased NREM sleep time and homeostatic responses compared to Sik3Slp mice.

Conclusions: Our results suggest that the peak onset of wakefulness is sensitive to disturbed kinase activity of SIK3, and the relationship between phosphorylation at T221 and S551 is critical for regulating sleep need.

Study objectives: The diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (CFS) is based on a constellation of symptoms which center around fatigue. However, fatigue is commonly reported in the general population by people without CFS. Does the biology underlying fatigue in patients with CFS also drive fatigue experienced by individuals without CFS?

Methods: We used UK Biobank actigraphy data to characterize differences in physical activity patterns and daily temperature rhythms between participants diagnosed with CFS compared to controls. We then tested if single nucleotide variants (SNVs) previously associated with CFS are also associated with the variation of these actigraphic CFS correlates and/or subjective fatigue symptoms in the general population.

Results: Participants diagnosed with CFS (n = 295) had significantly decreased overall movement (Cohen's d = 0.220, 95% CI of -0.335 to -0.106, p-value = 2.42 × 10-15), lower activity amplitudes (Cohen's d = -0.377, 95% CI of -0.492 to -0.262, p-value = 1.74 × 10-6), and lower wrist temperature amplitudes (Cohen's d = -0.173, 95% CI of -0.288 to -0.059, p-value = .002) compared to controls (n = 63,133). Of 30 tested SNVs associated in the literature with CFS, one was associated in the control population with subjective fatigue and one with actigraphic measurements (FDR < 0.05).

Conclusions: The genetic overlap of CFS risk with actigraphy and subjective fatigue phenotypes suggests that some biological mechanisms underlying pathologic fatigue in patients with CFS also underlie fatigue symptoms at a broader population level. Therefore, understanding the biology of fatigue in general may inform our understanding of CFS pathophysiology.

Study objectives: Insufficient sleep costs the US economy over $411 billion per year. However, most studies investigating the economic costs of sleep rely on one-time measures of sleep, which may be prone to recall bias and cannot capture variability in sleep. To address these gaps, we examined how sleep metrics captured from daily sleep diaries predicted medical expenditures.

Methods: Participants were 391 World Trade Center (WTC) responders enrolled in the WTC Health Program (mean age = 54.97 years, 89% men). At baseline, participants completed 14 days of self-reported sleep and stress measures. Mean sleep, variability in sleep, and a novel measure of sleep reactivity (i.e. how much people's sleep changes in response to daily stress) were used to predict the subsequent year's medical expenditures, covarying for age, race/ethnicity, sex, medical diagnoses, and body mass index.

Results: Mean sleep efficiency did not predict mental healthcare utilization. However, greater sleep efficiency reactivity to stress (b = $191.75, p = .027), sleep duration reactivity to stress (b = $206.33, p = .040), variability in sleep efficiency (b = $339.33, p = .002), variability in sleep duration (b = $260.87, p = .004), and quadratic mean sleep duration (b = $182.37, p = .001) all predicted greater mental healthcare expenditures. Together, these sleep variables explained 12% of the unique variance in mental healthcare expenditures. No sleep variables were significantly associated with physical healthcare expenditures.

Conclusions: People with more irregular sleep, more sleep reactivity, and either short or long sleep engage in more mental healthcare utilization. It may be important to address these individuals' sleep problems to improve mental health and reduce healthcare costs.