中老年人睡眠时光谱功率的纵向轨迹

IF 1.7 Q3 CLINICAL NEUROLOGY Aging brain Pub Date : 2023-01-01 DOI:10.1016/j.nbas.2022.100058
Chenlu Gao , Michael K. Scullin
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引用次数: 1

摘要

与年龄相关的睡眠变化似乎会导致认知衰老和痴呆。然而,目前对整个生命周期睡眠的大多数理解都是基于横断面证据。利用睡眠心脏健康研究的数据,我们调查了睡眠微观结构的纵向变化,重点关注这种与年龄相关的变化是否在个体之间一致。参与者为2202名成年人(年龄基线=62.40±10.38,55.36%为女性,87.92%为白人),他们在两次研究访问中完成了家庭多导睡眠图评估,两次访问间隔5.23年(范围:4-7年)。我们分析了每个0.5 Hz频率仓的NREM和REM频谱功率密度,包括慢振荡(0.5–1 Hz)、delta(1–4 Hz)、theta(4–8 Hz)、alpha(8–12 Hz)、sigma(12–15 Hz)和beta-1(15–20 Hz)频带。纵向比较显示,NREMδ(p<.001)和NREM西格玛功率密度(p<0.001)在5年内下降,θ,纵向轨迹在个体之间变化很大。在个体内,NREM和REM功率密度的5年变化是强相关的(慢振荡:r=0.46;δ:r=0.67;θr=0.78;αr=0.66;西格玛:r=0.71;β-1:r=0.73;ps<;0.001)。NREM和快速眼动活动的纵向轨迹的收敛可能反映了与年龄相关的神经去分化和/或补偿过程。未来的研究应该调查睡眠微观结构纵向变化的神经认知影响,并测试改善关键睡眠微观结构特征的干预措施(如NREMδ和西格玛活动)是否也会随着时间的推移对认知有益。
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Longitudinal trajectories of spectral power during sleep in middle-aged and older adults

Age-related changes in sleep appear to contribute to cognitive aging and dementia. However, most of the current understanding of sleep across the lifespan is based on cross-sectional evidence. Using data from the Sleep Heart Health Study, we investigated longitudinal changes in sleep micro-architecture, focusing on whether such age-related changes are experienced uniformly across individuals. Participants were 2,202 adults (ageBaseline = 62.40 ± 10.38, 55.36 % female, 87.92 % White) who completed home polysomnography assessment at two study visits, which were 5.23 years apart (range: 4–7 years). We analyzed NREM and REM spectral power density for each 0.5 Hz frequency bin, including slow oscillation (0.5–1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), sigma (12–15 Hz), and beta-1 (15–20 Hz) bands. Longitudinal comparisons showed a 5-year decline in NREM delta (p <.001) and NREM sigma power density (p <.001) as well as a 5-year increase in theta power density during NREM (p =.001) and power density for all frequency bands during REM sleep (ps < 0.05). In contrast to the notion that sleep declines linearly with advancing age, longitudinal trajectories varied considerably across individuals. Within individuals, the 5-year changes in NREM and REM power density were strongly correlated (slow oscillation: r = 0.46; delta: r = 0.67; theta r = 0.78; alpha r = 0.66; sigma: r = 0.71; beta-1: r = 0.73; ps < 0.001). The convergence in the longitudinal trajectories of NREM and REM activity may reflect age-related neural de-differentiation and/or compensation processes. Future research should investigate the neurocognitive implications of longitudinal changes in sleep micro-architecture and test whether interventions for improving key sleep micro-architecture features (such as NREM delta and sigma activity) also benefit cognition over time.

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Aging brain
Aging brain Neuroscience (General), Geriatrics and Gerontology
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