Objectives: Meditation practices positively influence the neural, hormonal and autonomic systems. We have demonstrated that long-term practice of mindfulness meditation increases N3 and rapid eye movement (REM) sleep stages and bring efficient autonomic modulation during sleep. In the present study, the probable humoral correlation that could bring about these changes is evaluated.
Material And Methods: Long-term Vipassana meditators (n=41) and controls (n=24) (males, 30-60 years of age) underwent a two-day consecutive whole night polysomnography recording. During the second day, with exposure to 100Lux brightness, blood was sampled from the antecubital vein between 8-9 PM and in subsequent early morning. Sleep stage was scored as per American Society of Sleep Medicine (ASSM) guidelines for the second-day recording. Sleep-related hormones were estimated - melatonin by radioimmunoassay; dehydroepiandrosterone (DHEA), cortisol, growth hormone (GH) and prolactin with enzyme-linked immunosorbent assay (ELISA); DHEA/cortisol ratio was calculated. Percentage of sleep stages and hormonal levels were compared between both groups using independent 't' test and Pearson's correlation was estimated between sleep stages and hormonal levels.
Results: Meditators showed increased N3, REM sleep stages. Though evening cortisol was comparable between the two groups; early morning cortisol, diurnal DHEA and melatonin were significantly higher in meditators. Diurnal DHEA correlated significantly with the N3 sleep stage in meditators.
Discussion: Higher diurnal DHEA despite variations in corresponding cortisol in meditators demonstrates that long-term Vipassana meditation practice modulates the hypothalamicpituitary-adrenal (HPA) axis and thereby influences sleep. Thus, the study provides evidence to explore the mechanism most likely involved with mindfulness meditation intervention in insomnia.
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http://dx.doi.org/10.5935/1984-0063.20220039 | DOI Listing |
Sensors (Basel)
December 2024
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming.
View Article and Find Full Text PDFNutrients
December 2024
Gastroenterology Department, Hospital de Vila Franca de Xira, 2600-009 Vila Franca de Xira, Portugal.
Earth's rotation around its axis has pressured its inhabitants to adapt to 24 h cycles of day and night. Humans adapted their own circadian rhythms to the Earth's rhythms with a light-aligned awake-sleep cycle. As a consequence, metabolism undergoes drastic changes throughout the circadian cycle and needs plasticity to cope with opposing conditions in the day (when there is an increase in energy demands and food availability), and during the night (when prolonged fasting couples with cyclic changes in the energy demands across the sleep stages).
View Article and Find Full Text PDFCells
December 2024
Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL 32306, USA.
Fragile X Syndrome (FXS) presents with a constellation of phenotypes, including trouble regulating emotion and aggressive behaviors, disordered sleep, intellectual impairments, and atypical physical development. Genetic study of the X chromosome revealed that substantial repeat expansion of the 5' end of the gene fragile X messenger ribonucleoprotein 1 () promoted DNA methylation and, consequently, silenced expression of . Further analysis proved that shorter repeat expansions in also manifested in disease at later stages in life.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Institute of Physics, University of Zielona Góra, 65-069 Zielona Góra, Poland.
This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. Utilizing 31 polysomnographic recordings, we focused exclusively on electrocardiogram (ECG) data, specifically the RR interval time series, to explore heart rate dynamics associated with different sleep stages. Employing both statistical techniques and machine learning models, with the Generalized Estimating Equation model as the foundational approach, we assessed the effectiveness of HRA in identifying and differentiating sleep stages and transitions.
View Article and Find Full Text PDFBrain Sci
November 2024
Department of Neurology, Beth Isreal Deaconess Medical Center, Harvard Medical School, Harvard University, Cambridge, MA 02215, USA.
: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.
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