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This article is from Frontiers in Neurology , volume 3 . Abstract The human electroencephalogram (EEG) during non-rapid eye movement sleep (NREM) is characterized mainly by high-amplitude (>75 μV), slow-frequency ( 0.25 s). These NREM oscillations play a crucial role in brain plasticity, and importantly, NREM sleep oscillations change considerably with aging. This review discusses the association between NREM sleep oscillations and cerebral plasticity as well as the functional impact of age-related changes on NREM sleep oscillations. We propose that age-related reduction in sleep-dependent memory consolidation may be due in part to changes in NREM sleep oscillations.

Biological aging is an umbrella term that refers to a complex biological process which entails the aging of cells, their organelles and DNA (Hägg & Jylhävä, 2021). Biological aging processes have been associated with a variety of diseases, including cancer and atherosclerosis (Adams & White, 2004; Dugué et al., 2018), and are a good predictor for higher morbidity and mortality rates (Perna et al., 2016). Therefore, a growing body of research is focused on potential factors affecting biological aging. Biological aging can be operationalized by different biomarkers, including telomere length and epigenetic clocks (Belsky et al., 2018; Pearce et al., 2022). Telomeres are repetitive TTAGGG sequences at the end of chromosomes that protect the chromosomes from shortening during cell division. Telomeres shorten throughout the lifespan, and accelerated telomere shortening is an index of accelerated biological aging (Tempaku et al., 2015; Vaiserman & Krasnienkov, 2021). Furthermore, the epigenetic clock concerns DNA methylation levels, namely the accumulation of methyl groups to DNA molecules, and is an estimator of chronical age (Chen et al., 2016; Jylhävä et al., 2017). These clocks capture methylation of certain sets of CpG sites in the DNA, and methylation levels correlate with age. Accordingly, the biological age of the cells can be calculated in years (Chen et al., 2016; Horvath et al., 2012; McEwen et al., 2020). It has been found that, besides genetic factors (Gibson et al., 2019), also environmental factors, such as smoking and diet, are associated with both of the previous biomarkers of accelerated biological aging (Gao et al., 2016; Horvath et al., 2014; Huang et al., 2019; Vaiserman & Krasnienkov, 2021). Accelerated biological aging in terms of accelerated telomere attrition and advanced epigenetic age has also been associated with sleeping problems in adults (Carskadon et al., 2019; Carroll & Prather, 2021; Cribbet et al., 2014; Gao et al., 2022; James et al., 2017; Jin et al., 2022). Sleep is a complex behavioral and homeostatically regulated state of all mammals (Zielinski et al., 2016). Sleep has many vital functions, including development, modulation of immune responses, memory consolidation and endocrine functions (Krueger et al., 2016; Zielinski et al., 2016). Previous research has demonstrated associations between poor quality of sleep, shorter sleep duration, and delayed sleep onset with shortened telomere lengths (Cribbet et al., 2014; Wynchank et al., 2019). Additionally, reduced sleep duration and poor sleep quality have also been linked to the acceleration of the epigenetic clock (Carskadon et al., 2019; Gao et al., 2022) This association between sleep and accelerated biological aging was not only observed in adults. James and colleagues (2017) found an association of shorter telomeres with shorter sleep durations in nine-year-old children, indicating that the relationship between biological aging and sleep already emerges early in development (James et al., 2017). However, to our knowledge, this is the only study that has looked at children’s sleep in relation to markers for biological aging. Therefore, to date, there is a lack of research on the association of sleep quality with biological aging in children. Additionally, the previously mentioned study on this association in children was cross-sectional, hence the directionality of the relationship between biological aging and sleep quality in children remains unclear. On the one hand, previous studies in adults have largely assumed that more sleeping problems cause alterations in neuro-chemical processes, which, in turn, cause accelerated aging. But on the other hand, studies on the elderly population suggest that accelerated aging can cause significant alterations in the sleep stages and in the quantity and quality of sleep (Tempaku et al., 2015), indicating that aging could also bring about sleep changes. Accordingly, Carrol and Prather (2021) suggest that there is also a possibility that accelerated biological aging causes alterations in brain areas that regulate sleep states, hereby causing sleep difficulties (Carroll & Prather, 2021). Since previous research on the association of sleep and biological aging in children was solely cross-sectional, the directionality of the association remains to be assessed. In the current study we assess the association of sleep with accelerated biological aging in children bi-directionally between chronological ages 6 years (T1) and 10 years (T2). As telomere length and epigenetic clocks have been shown to capture different biological aging processes (Belsky et al., 2018; Pearce et al., 2022), both will be used in this study to measure biological aging. In previous studies, the Pediatric-Buccal-Epigenetic (PedBE) clock has been reported to be the most reliable epigenetic clock for pediatrics (McEwen et al., 2020), and will hence be used in the current study. In this study, we will assess the following goals: 1) Our first goal is to assess cross-sectional associations at T1 and T2 between our biomarkers of biological aging (telomere length and PedBE clock) with child sleeping problems. 2) Our second goal is to determine whether sleeping problems at T1 forecasts biological aging, defined as a) telomere length at T2; b) telomere erosion between T1 – and T2; c) PedBE clock at T2. 3) Our third goal is to determine whether our biomarkers of biological aging (a, b & c) predict sleeping problems at T2. a) telomere length; b) telomere erosion from T1 to T2; c) PedBE clock at T1. 4) Lastly, we will assess the directionality between sleeping problems and biological aging by comparing the explained variance between question 2 and 3. Note that we do not include the change in PedBE from T1 to T2, as the PedBE clock already incorporates the difference between biological aging and chronological aging as an age acceleration. Given that telomere length and/or erosion varies by sex (Barrett & Richardson, 2011), z-score body mass index index (zBMI; Clemente et al., 2019; Gielen et al., 2018), and maternal education (Broer et al., 2013), we will statistically control for these aspects in our analyses. This will be done by using child sex determined at birth, child zBMI at T2 and maternal education defined as the highest level of education completed when assessed at T2 (Beijers, Hartman, et al., 2020; Shalev et al., 2013).

This is a pre-registration for a secondary data analysis. See the attachment for more details.

The effects on performance across forty eight hours (including two night of sleep loss) were studied. The experimental variables were subject age and repetition of deprivation periods. Six 18-22 year old subjects were subjected to five repeated 48 h session with an intervening three weeks between periods. Subjective measures and vigilance tasks showed substantial deprivation effects: cognitively demanding tasks were less affected. Where repetition resulted in change the effects were decremental rather than offsetting. The performance of 10 subjects who were 40-50 years of age was compared with younger subjects (above) using a single deprivation. The older subjects, who generally exhibited superior performance, were also more affected by acute sleep deprivation.

The effects on performance across forty eight hours (including two night of sleep loss) were studied. The experimental variables were subject age and repetition of deprivation periods. Six 18-22 year old subjects were subjected to five repeated 48 h session with an intervening three weeks between periods. Subjective measures and vigilance tasks showed substantial deprivation effects: cognitively demanding tasks were less affected. Where repetition resulted in change the effects were decremental rather than offsetting. The performance of 10 subjects who were 40-50 years of age was compared with younger subjects (above) using a single deprivation. The older subjects, who generally exhibited superior performance, were also more affected by acute sleep deprivation.

This article is from Psychiatry Investigation , volume 9 . Abstract Objective: This study aims to analyze how much heart rate variability (HRV) indices discriminatively respond to age and severity of sleep apnea in the obstructive sleep apnea syndrome (OSAS). Methods: 176 male OSAS patients were classified into four groups according to their age and apnea-hypopnea index (AHI). The HRV indices were compared via analysis of covariance (ANCOVA). In particular, the partial correlation method was performed to identify the most statistically significant HRV indices in the time and frequency domains. Stepwise multiple linear regressions were further executed to examine the effects of age, AHI, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and sleep parameters on the significant HRV indices. Results: The partial correlation analysis yielded the NN50 count (defined as the number of adjacent R-wave to R-wave intervals differing by more than 50 ms) and low frequency/high frequency (LF/HF) ratio to be two most statistically significant HRV indices in both time and frequency domains. The two indices showed significant differences between the groups. The NN50 count was affected by age (p

Sleep and Aging

This article is from PLoS ONE , volume 8 . Abstract Background: Sleep disorders causes a significant negative effect on mental and physical health, particularly among the elderly. The disease burden and risk factors of poor sleep quality of the elderly need to be verified using a validated form of measurement in urban mainland China. Methods: This study included 1086 community residents aged ≥60 years who completed the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Poor sleeper was defined by a CPSQI global score of >5. Subjects also accepted the neurological and neuropsychological assessments, including the Mini-Mental State Examination, Center for Epidemiological Studies Depression Scale, and Zung Self-Rating Anxiety Scale (ZSAS). A history of chronic diseases was confirmed by the medical records of each participant. Results: The prevalence of poor sleep quality in this population was 41.5% (95% confidence interval (CI) = 38.6–44.5%), with a higher rate observed in elderly females (45.8% [95% CI = 41.9–49.7%]) than that in elderly males (35.8% [95% CI = 31.4–40.1%]). The prevalence rate increased with age, from 32.1% (95% CI = 27.8–36.4%) in those aged 60–69 years to 52.5% (95% CI = 45.9–59.1%) in those aged ≥80 years (p value for trend

This article is from PLoS ONE , volume 8 . Abstract Background: Sleep disorders causes a significant negative effect on mental and physical health, particularly among the elderly. The disease burden and risk factors of poor sleep quality of the elderly need to be verified using a validated form of measurement in urban mainland China. Methods: This study included 1086 community residents aged ≥60 years who completed the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Poor sleeper was defined by a CPSQI global score of >5. Subjects also accepted the neurological and neuropsychological assessments, including the Mini-Mental State Examination, Center for Epidemiological Studies Depression Scale, and Zung Self-Rating Anxiety Scale (ZSAS). A history of chronic diseases was confirmed by the medical records of each participant. Results: The prevalence of poor sleep quality in this population was 41.5% (95% confidence interval (CI) = 38.6–44.5%), with a higher rate observed in elderly females (45.8% [95% CI = 41.9–49.7%]) than that in elderly males (35.8% [95% CI = 31.4–40.1%]). The prevalence rate increased with age, from 32.1% (95% CI = 27.8–36.4%) in those aged 60–69 years to 52.5% (95% CI = 45.9–59.1%) in those aged ≥80 years (p value for trend