Nonlinear relationship between sleep midpoint and depression symptoms: a cross-sectional study of US adults

Yin, Jiahui; Wang, Huayang; Li, Siyuan; Zhao, Leiyong; You, Yanwei; Yang, Jiguo; Liu, Yuanxiang

doi:10.1186/s12888-023-05130-y

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…Previous studies on the association between sleep midpoint and depressive symptoms examined sleep midpoint at only one time point, which failed to capture long-term changes in individual sleep–wake rhythms 13 , 25 – 27 . Not surprisingly, variability of sleep midpoint between individuals can be considerable, particularly in the absence of social constraints 28 .…”

Section: Discussionmentioning

confidence: 99%

Sleep–wake changes and incident depressive symptoms in midlife women

Luo,

Lin

2024

Sci Rep

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Our study aimed to investigate the relationship between sleep–wake changes and depressive symptoms events among midlife women. We enrolled 1579 women aged 44–56 years who had no clinically relevant depressive symptoms at baseline. Depressive symptoms were assessed at each visit using the Center for Epidemiologic Studies Depression scale. At the third and fourth follow-up visits, women reported their sleep habits. The sleep midpoint was defined as the time to fall asleep plus one-half of the sleep duration. Sleep–wake changes were determined by the difference in the midpoint of sleep between the third and fourth visits, which were 1 year apart. The median follow-up time was 7 years (range 1–7 years). Cox proportional hazard models were fitted to calculate hazard ratios and 95% confidence intervals for the incidence of depressive symptoms associated with sleep–wake changes. After adjusting for potential confounding factors, the hazard ratio (95% confidence interval) of depressive symptoms for severe sleep midpoint changes was 1.51 (1.12, 2.05) compared with mild sleep midpoint changes. This relationship remained statistically significant and changed little when additionally controlling for sleep duration, sleep quality, insomnia symptoms, use of sleep medications, use of nervous medications, glucose, insulin, lipids, dietary energy intake, and C-reactive protein. Our findings indicate that exposure to long-term severe sleep–wake changes increases the risk of depressive symptoms in midlife women.

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Section: Discussionmentioning

confidence: 99%

Sleep–wake changes and incident depressive symptoms in midlife women

Luo,

Lin

2024

Sci Rep

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“…The exposure variable in this study was sleep duration. NHANES collected self-reported sleep duration data through their standardized questionnaire, which is administered to participants during in-person interviews 57 . In the survey, participants were asked, "How much sleep (hours) do you usually get during the weekdays or during workdays at night?…”

Section: Measurement Of Exposure and Outcome Variablesmentioning

confidence: 99%

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study

You,

Chen,

Liu

et al. 2024

Sci Rep

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Sleep is a modifiable behavior that can be targeted in interventions aimed at promoting healthy aging. This study aims to (i) identify the sleep duration trend in US adults; (ii) investigate the relationship between sleep duration and phenotypic age; and (iii) explore the role of exercise in this relationship. Phenotypic age as a novel index was calculated according to biomarkers collected from US adults based on the National Health and Nutrition Examination Survey (NHANES). Sleep information was self-reported by participants and discerned through individual interviews. The principal analytical method employed was weighted multivariable linear regression modeling, which accommodated for the complex multi-stage sampling design. The potential non-linear relationship was explored using a restricted cubic spline (RCS) model. Furthermore, subgroup analyses evaluated the potential effects of sociodemographic and lifestyle factors on the primary study outcomes. A total of 13,569 participants were finally included in, thereby resulting in a weighted population of 78,880,615. An examination of the temporal trends in sleep duration revealed a declining proportion of individuals with insufficient and markedly deficient sleep time since the 2015–2016 cycle. Taken normal sleep group as a reference, participants with extreme short sleep [β (95% CI) 0.582 (0.018, 1.146), p = 0.044] and long sleep [β (95% CI) 0.694 (0.186, 1.203), p = 0.010] were both positively associated with phenotypic age using the fully adjusted model. According to the dose–response relationship between sleep duration and phenotypic age, long sleep duration can benefit from regular exercise activity, whereas short sleep duration with more exercise tended to have higher phenotypic age. There is an inverted U-shaped relationship between short and long sleep durations and phenotypic age. This study represents an important step forward in our understanding of the complex relationship between sleep and healthy aging. By shedding light on this topic and providing practical exercise recommendations for promoting healthy sleep habits, researchers can help individuals live longer, healthier, and more fulfilling lives.

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“…Current evidence tended to show that extreme short sleep (e.g., sleep deprivation) was associated with several neurological diseases, such as stroke, 11 Alzheimer's, 12 Parkinson's disease, 13 and depression. 14 Amyloid‐β, a protein fragment, has been identified as a key player in the relationship between short sleep and cognitive decline. 15 , 16 Interestingly, emerging evidence suggested that one‐night short sleep duration may be associated with alterations in the clearance or production of amyloid‐β in the brain.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential relationship between short sleep risks and cognitive function from the perspective of inflammatory biomarkers and cellular pathways: Insights from population‐based and mice studies

You,

Li,

Zhang

et al. 2024

CNS Neurosci Ther

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AimsThe molecular mechanism of short‐sleep conditions on cognition remains largely unknown. This research aimed to investigate associations between short sleep, inflammatory biomarkers and cognitive function in the US population (NHANES data 2011–2014) and explore cellular mechanisms in mice.MethodsSystemic immune‐inflammation index (SII) was calculated using blood‐cell based biomarkers. Further, we employed integrated bioinformatics and single‐cell transcriptomics (GSE137665) to examine how short sleep exposure influenced the molecular pathways associated with inflammation in the brain. To explore the signaling pathways and biological processes of sleep deprivation, we carried out enrichment analyses utilizing the GO and KEGG databases.ResultsPopulation results showed that, compared with normal sleep group, severe short sleep was associated with lower cognitive ability in all the four tests. Moreover, a higher SII level was correlated with lower scores of cognitive tests. In mice study, elevated activation of the inflammatory pathway was observed in cell subgroups of neurons within the sleep deprivation and recovery sleep cohorts. Additionally, heightened expression of oxidative stress and integrated stress response pathways was noted in GABAergic neurons during sleep deprivation.ConclusionThis study contributed to the understanding of the influence of short sleep on cognitive function and its cellular mechanisms.

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Nonlinear relationship between sleep midpoint and depression symptoms: a cross-sectional study of US adults

Cited by 10 publications

References 44 publications

Sleep–wake changes and incident depressive symptoms in midlife women

Sleep–wake changes and incident depressive symptoms in midlife women

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study

Exploring the potential relationship between short sleep risks and cognitive function from the perspective of inflammatory biomarkers and cellular pathways: Insights from population‐based and mice studies

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