bluuebunny/crossref_metadata_2025_split huggingface dataset:
0040 Relationship Between Inflammatory Markers and Sleep in Healthy Adolescents
Authors: Reddy A , Li L 
Published: May 2020

Abstract

Introduction
Multiple studies in different countries show a trend of adolescents having insufficient sleep. Review of literature strongly suggests role of cytokines in sleep regulation. Different inflammatory markers like tumor necrosis factor (TNF)α, C-reactive protein (CRP) and interleukins (IL) are sleep regulatory substances. Most of the studies showing relation between cytokines and sleep are seen in adults. In our study, we were interested in finding the relationship between sleep quality and inflammatory markers in healthy adolescents.

Methods
Twenty eight female and male, African American and White, healthy adolescents aged 15–18 completed the study. Sleep quality was measured using the Pediatric Sleep Questionnaires (PSQ), including snoring, daytime sleepiness and hyperactive behavior. Blood sample was collected from each participant for measuring the inflammatory factors.

Results
Partial Pearson correlation analysis showed that global PSQ score and hyperactive behavior were significantly correlated with TNF α (r=0.37 for both). Snoring was significantly correlated with leptin, CRP and IL-6 in healthy adolescents. No other correlations were observed.

Conclusion
Consistent with findings in adults, we have observed an association between inflammatory markers and poor sleep in healthy adolescents. Our findings suggest the importance to improve sleep quality in adolescents for better health outcomes.

Support
None of the authors have any conflict of interest. This research was supported by awards, P30DK056336 and P30DK079626, from the National Institute of Diabetes And Digestive And Kidney Diseases to Nutrition Obesity Research Center and Diabetes Research Center, respectively, at the University of Alabama at Birmingham.
0043 BDNF Genotype Modulates Circadian Response to Simulated Nightwork in IL-6
Authors: Satterfield B C , Savenkova M I , Karatsoreos I N , Van Dongen H 
Published: May 2020

Abstract

Introduction
Brain Derived Neurotrophic Factor (BDNF) is involved in synaptic plasticity and may be involved in sleep and circadian regulation. The BDNF gene has a single nucleotide polymorphism (Val66Met) resulting in decreased BDNF secretion. The Met allele is associated with reduced nocturnal slow wave activity following sleep loss, and may also enable more flexible adaptation to circadian misalignment. We used IL-6, a pleiotropic cytokine with biphasic circadian secretion, to compare the response of Val and Met carriers to circadian misalignment.

Methods
15 healthy men (26.7±4.9y) participated in a 16-day laboratory study of simulated nightwork with two nighttime duty cycles and an intervening 58h restart break. The study began with two baseline 10h time in bed (TIB) nighttime sleep opportunities (22:00-08:00) and a 5h TIB afternoon nap (15:00-20:00). Each duty cycle consisted of five nighttime waking periods and four 10h TIB daytime sleeps (10:00-20:00). The restart break began with a 5h TIB morning nap (10:00-15:00), followed by two 10h TIB nighttime sleeps (22:00-08:00). The middle two daytime sleep periods from each duty cycle were monitored polysomnographically and visually scored. During the baseline and restart periods, blood samples were collected at 08:25 and then at 2h intervals until 20:00. Plasma IL-6 was measured by means of ELISA. BDNF Val66Met genotype was determined from whole blood.

Results
The genotype distribution was in Hardy-Weinberg equilibrium (Val/Val:5; Val/Met:10; Met/Met:0). On average, Met carriers slept 52.2min longer (p=0.052) and displayed 27.7min more REM (p=0.013) than Val/Val subjects during the 10h TIB daytime sleeps. Baseline IL-6 concentrations were similar between genotypes. However, during the restart break, Val/Met subjects showed reduced IL-6 compared to baseline and to Val/Val subjects (p=0.022).

Conclusion
BDNF Val66Met heterozygotes exhibited blunted IL-6 levels following simulated nightwork, suggesting this genotype experienced a substantial circadian shift and/or altered response to sleep loss associated with daytime sleep.

Support
FMCSA award DTMC75-07-D-00006; Elliot D. Weitzman, M.D. Research Grant from the Sleep Research Society Foundation.
0048 The Association Between REM Sleep and Risk of Mortality in Three Independent Cohorts
Authors: Leary E B , Watson K T , Ancoli-Israel S , Redline S , Yaffe K , Ravelo L A , Peppard P E , Zou J , Goodman S , Mignot E , Stone K L 
Published: May 2020

Abstract

Introduction
Sleep disorders and sleep characteristics have been linked to higher risk of mortality. Despite the emerging evidence of a sleep-mortality association, the relationship between sleep architecture and mortality aren’t well understood. We hypothesize that reduced REM is associated with increased mortality risk.

Methods
The Osteoporotic Fractures in Men (MrOS) study is a population-based study of 2,675 older men. Cox regression was used to evaluate the association between %REM and mortality rate. Potential covariates were evaluated using 6-fold cross validation. Sensitivity analyses were performed to rule out alternative explanations. Wisconsin Sleep Cohort (WSC) and Sleep Heart Health Study (SHHS) data were used to replicate the findings.

Results
The MrOS sample mean age was 76.3 years (SD=5.51) and the median follow-up time was 12.1 years. There was a 13% higher rate of mortality for every absolute 5% reduction in REM sleep (HR=1.13, 95%CI, 1.08–1.19) after adjusting for multiple demographic, sleep, and health covariates. The association persisted for cardiovascular disease-related mortality (CVD) (HR=1.18, 95%CI, 1.09–1.28), cancer-related mortality (HR=1.14, 95%CI, 1.03–1.26), and other mortality (HR=1.19, 95%CI, 1.10–1.28). The WSC included 45.7% women. The mean age of the 1,388 individuals analyzed was 51.5 (SD=8.5); the median follow-up time was 20.8 years. The effect size for 5% reduction in REM on rate of all-cause mortality was similar in this cohort despite the younger age, inclusion of women, and longer follow-up period (HR=1.17, 95%CI, 1.03–1.34). SHHS data is still being analyzed; however the unadjusted model is consistent with the other cohorts.

Conclusion
We found an association between reduced REM and mortality in two, possibly three independent cohorts, which persisted across different causes of death and multiple sensitivity analyses. Mechanistic studies are needed and strategies to preserve REM may influence clinical therapies and reduce mortality risk.

Support
NHLBI provides funding for the MrOS Sleep ancillary study “Outcomes of Sleep Disorders in Older Men” under grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. Wisconsin Sleep Cohort was supported by R01HL62252, RR03186, and R01AG14124 from the NIH. Dr. Redline was partially supported by NHLBI R35 HL135818.
0055 Physiological Based Predictive Models of Vigilance
Authors: Daley M S , Gever D H , Chon K H , Posada-Quintero H , Bolkhovsky J B 
Published: May 2020

Abstract

Introduction
The Naval Submarine Medical Research Laboratory (NSMRL) is developing predictive models to examine how non-invasive, non-disruptive physiological monitoring can be used to track performance decrements due to sleep deficiency. Utilizing biometrics extracted from physiological measures to track performance changes would allow for automated tracking of fatigue and alleviate the overhead necessary to monitor individual schedules and sleep patterns.

Methods
NSMRL collaborated with the University of Connecticut to run a sleep deprivation study that deprived 20 participants of sleep for a period of up to 25 hours. During this time, subjects completed multiple tasks, including the Psychomotor Vigilance Test (PVT) every few hours. A non-invasive monitoring system collected physiological data from participants, which includes eye tracking, electrocardiography, electrodermal activity, and facial tracking (e.g., blink metrics, heart rate variability, skin conductance levels, facial action units). Using this multimodal approach, biometrics were extracted and evaluated to determine their predictive power on PVT performance. Multiple linear regression, using predictors selected via sequential forward selection, was used to develop a model of performance at an individual level based on a subset of these metrics chosen using principal component regression.

Results
Thirty-eight biometrics were extracted from the collected data and used to produce a predictive model of PVT performance. Sequential forward selection was used to select 11 primary biometrics. The criteria for primary metric inclusion in the model was minimization of root mean squared error. The resultant model had a correlation coefficient (r) of 0.71 (p &lt; 0.001) with a root mean squared error (RMSE) of 49.8 ms between the predicted reaction time and true reaction time for each subject.

Conclusion
Non-invasive, non-disruptive monitoring could be used to track individual cognitive performance decrement due to sleep deficiency. This study examined the capability of combining the data from four physiological monitors that can be contained within a wrist worn device and a desk or helmet mounted camera. Utilizing 11 biometrics obtained from these monitors a stepwise regression model was developed that significantly correlates with PVT reaction time at both an individual and group level.

Support
This work was supported by the Military Operational Medicine Research Program.
0061 Does Combining M1 M2 Reference Influence Amplitude of Slow Waves?
Authors: Walker N A , Roth H L , Fan Z , Vaughn B V 
Published: May 2020

Abstract

Introduction
Slow wave amplitudes are critical to determining Stage N3 sleep yet ECG artifact frequently interferes with accurate amplitude measurement. This artifact may be lessened by using a combined M1-M2 reference however theoretically this may decrease the amplitude due to shorter inter-electrode distance (predicted 27% loss). The AASM Scoring Manual recommends scoring slow wave activity using F4-M1 channel or alternatively F3-M2, but does not recognize a combined reference. This study measures the differences in slow wave amplitude using contralateral versus combine reference.

Methods
12 polysomnograms were randomly selected for analysis of amplitude of slow wave using contralateral and combined reference channels. Six separate EEG channels (F3-M1, F3-M2, F3-M1+M2, F4-M1, F4-M2, and F4-M1+M2) were used to analyze 25 different slow waves from each polysomnogram. Individual slow waves from Stage N3 sleep were analyzed using the Natus Sleepworks Amplitude Measurement Tool if their peak and trough were free EKG artifact. Averages and standard deviations of the waveforms were calculated for each patient and channel. Differences were normalized by dividing by the amplitude of the original wave using the contralateral reference.

Results
Subjects age ranged from 30–69 yrs, with 6 being females. Mean amplitudes were as follows: F3-M2 was 131.75µV, F3-M1+M2 125.84 µV, F4-M1 130.57 µV, and F4-M1+M2 128.22µV. The overall average difference of F4-M1 to F4-M1+M2 was 0.92% and the average difference of F3-M2 to F3-M1+M2 was 3.52% with the average standard deviation of 8.47%.

Conclusion
This study shows the average loss in amplitude of converting F4-M1 to F4-M1+M2 was less than 1% and 3.5% for F3-M2 to F3-M1+M2. Combining M1M2 reference may be a valuable alternative to reduce EKG artifact.

Support
None
0087 Childhood Naps Promote Short-Term Destabilization but Long-Term Consolidation of Emotional Memories
Authors: Hanron O , Mason G , Holmes J F , Spencer R M 
Published: May 2020

Abstract

Introduction
Early childhood naps have been shown to support emotional memory consolidation, but this benefit only emerges the following day. It is unknown whether consolidation occurs during the nap itself, or if napping only prepares memories for overnight consolidation. In this study, we used a forced-choice recognition task to determine whether naps protect emotional memories against post-nap interference, which would indicate the occurrence of consolidation.

Methods
Preschool children (33–67 months; N=63) viewed neutral faces paired with negative or neutral descriptions. Following a nap or an equal interval awake (within-subjects, order counterbalanced, ~1 week apart), half of these participants (N=33) were presented with an interfering set of faces and descriptions, while the other half (N=30) did not receive interference. For all participants, recognition of the original faces was probed after encoding, after the nap or wake interval, and the next morning.

Results
To assess the influence of napping on changes in emotional memory, 2 (stimulus valence: negative vs. neutral) x 2 (condition: nap vs. wake) repeated-measures ANOVAs were performed. Recall of negative and neutral items did not immediately differ between the nap and wake conditions for the participants who received no interference. 24 hours later, these children trended towards recalling negative and neutral items better if they had napped the previous day (condition main effect: F(1,29)=3.539, p=0.070). In contrast, participants who received interference recalled fewer negative items than neutral items immediately following a nap (p=0.034), while this difference did not emerge following an interval awake.

Conclusion
Our results suggest that naps initially destabilize emotional memories rather than protecting them against interference. However, this initial destabilization may reflect the partial processing of memories during naps, perhaps allowing for enhanced long-term consolidation. Overall, our findings provide important insight into the mechanism of nap-dependent emotional processing.

Support
Supported by NIH R01 HL111695 and an Honors Research Grant from Commonwealth Honors College
0090 Stage-Specific Sleep Disruption and its Effect on Spatial Navigational Memory
Authors: Parekh A A , Kam K , Mullins A , Fakhoury A , Castillo B , Roberts Z , Fleysher L , Rapoport D M , Ayappa I , Varga A 
Published: May 2020

Abstract

Introduction
The mechanisms by which sleep disruption impact memory may depend on sleep stage, as rapid eye movement (REM) and slow wave sleep (SWS) differ in several significant ways, including degree of neuronal synchrony and frequency of cortical local field potential oscillations. Here we sought to examine the relationship between stage-specific disruption of sleep and its effect on spatial navigational memory.

Methods
9 healthy adult subjects participated in this study which involved 3 in-lab polysomnograms (normal, REM-disruption, and SWS-disruption) accompanied by pre- and post-sleep functional neuroimaging of brain during a spatial navigational memory task. Graded auditory stimuli consisting of 0.5 second bursts of high-frequency tones (300-3000Hz) were used to disrupt sleep (REM/SWS) in real time. Primary metrics to ascertain the effect of these auditory tones on sleep were time in sleep stage (REM/SWS) as a % of total sleep time (TST), bout length. The primary metric for spatial navigational memory was %change in overnight completion time on a first-person-experience 3D maze task.

Results
Sleep macrostructure was normal during the normal night (TST:379.9±56.6 min; SWS:19.5±7.6%; REM:19.4±5.3%; mean±std). Stage-specific disruption of sleep was achieved using auditory tones during a) SWS-disruption condition (TST:388.9±47.4 mins; SWS:6.6±4.8%; REM:18.7±5.2%) and b) REM-disruption condition (TST:365.3±69.8 mins; SWS:17.1±7.7%; REM:12.1±6.6%). SWS-disruption reduced mean bout length of SWS as compared to no disruption (1.3±0.8 mins vs. 10.3±8.2 mins; p&lt;0.01) and REM-disruption reduced mean bout length of REM as compared to no disruption (2.2±1.7 vs. 10.6±5.2 mins; p&lt;0.01). When sleep was not disrupted, subjects achieved overnight improvements in performance (25.3±17%) which remained unchanged during REM-disruption (18.8±29.6%, p=0.5) and during SWS-disruption (38.8±24.4%; p=0.2). Morning psychomotor vigilance was also unaffected by condition.

Conclusion
Stage specific disruption of sleep can be achieved using graded auditory tones. While performance on a virtual 3D maze remain unchanged with stage specific sleep disruption, lower sample size may have limited our ability to detect the change. Activation patterns from functional neuroimaging that were acquired during the spatial navigation task may elucidate the interaction between stage-specific sleep disruption and performance.

Support
NIH R21AG059179
1005 Characterizing Sleep and Glycemia in Emerging Adults with Type 1 Diabetes
Authors: Griggs S , Redeker N S , Grey M 
Published: May 2020

Abstract

Introduction
Type 1 Diabetes (T1D) affects 1.25 million Americans, and only 14% of emerging adults (ages 18-30 years) achieve targets for glycemic control (A1C &lt; 7.0%). Sleep deficiency, less than 6.5 hours total sleep time (TST), is associated with poorer glycemic control.

Methods
Emerging adults with T1D wore a wrist actigraph and their own or provided continuous glucose monitor (CGM) concurrently 24 hours/day for 6-8 days. Participants completed a 10-minute psychomotor vigilance test (PVT) on a device, 3-minute Trail Making Test on paper, and questionnaires including twice daily Pittsburgh sleep diaries in Research Electronic Data Capture (REDCap). TST, sleep onset latency (SOL), sleep efficiency (SE), wake after sleep onset (WASO), and sleep fragmentation index (SFI) were determined via actigraphy, glycemic control via A1C, and glucose variability via CGM. The purpose of this descriptive study was to explore associations between TST, sleep variability (SD of TST), neurocognitive function (psychomotor vigilance and executive function) and diabetes outcomes (glycemia and distress).

Results
The sample included 36 emerging adults (mean age 22.8±3.1; 30.6% male; 91.7% White, 86.1% non-Hispanic; A1C mean 7.1±1.0%, BMI 27.3±4.8 kg/m2). Mean TST was 7.1±1.2 hours, SOL was 19.7±13.5 minutes, SE was 85.5±4.6%, WASO was 34.7±18.2 minutes, and SFI was 17.7±6.2. Shorter TST was associated with more severe sleepiness (r=-0.48,p=0.004) and more diabetes distress (r=-0.37, p=0.03). More sleep variability was associated with more severe sleepiness (r=0.36, p=0.03), longer response times (RT) ≥ 500ms (rho=0.39, p=0.02) measured via PVT, more nocturnal glucose variability (r=0.38, p=0.04), greater mean daily differences in glucose levels (r=0.42, p=0.02). Shorter mean RT was associated with more time in glucose range (rho=-0.37, p=0.04).

Conclusion
Improving TST and sleep variability are potential therapeutic targets to improve glucoregulation in this high-risk population. Researchers should consider within-person multi-level modeling to inform our understanding of the true nature of the sleep-glucose association in emerging adults with T1D.

Support
T32 NR0008346, Sigma Theta Tau International, Dexcom provided continuous glucose monitors (G4) free of charge for participants who did not have their own device.
1036 Does Diary and Actigraphy Measured Sleep Differ Between Good and Poor Sleepers During Breast Cancer Treatment?
Authors: Tulk J , Garland S N , Rash J , Lester R , Laing K 
Published: May 2020

Abstract

Introduction
Women may enter in breast cancer (BCa) treatment with poor sleep, or it may begin during treatment. We assessed how subjective and objective sleep changes during the first year of treatment for women with BCa. Further, we examined whether this differs between previously good and poor sleepers and whether there was agreement between subjective and objective measures of sleep.

Methods
Sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) were measured among 100 patients with newly diagnosed, non-metastatic BCa using 7 days of diary and actigraphy collected at 4 time points: pre-treatment, 4, 8, and 12 months. Women with a score ≥5 on the Pittsburgh Sleep Quality Index at treatment onset were classified as poor sleepers. A 4 (time: 0-, 4-, 8-, 12-months) by 2 (sleep measure: sleep diary, actigraphy) by 2 (group: good, poor sleepers) mixed model ANOVAs was performed for each sleep parameter.

Results
There was a time by sleep measure by group interaction for TST, [F(3,294)= 3.014, p = .03). Good sleepers reported greater TST on diaries- than actigraphy at pre-treatment and 12 months, whereas there were no differences in poor sleepers. There was a group by time effect for good vs. poor sleepers [F(3,294)= 2.909, p = .035]. Good sleepers experienced decreased TST and SE from pre-treatment through 4-mo, followed by increases. Poor sleepers showed the opposite pattern. Neither group returned to pre-treatment levels. Sleep diaries and actigraphy are concordant over time for TST, but not SOL, WASO, or SE.

Conclusion
Sleep parameters worsen during the first year following onset of BCa and concordance between sleep diaries and actigraphy differ between good or poor sleepers.

Support
Dr. Garland is supported by a Scotiabank New Investigator Award and seed funding from the Beatrice Hunter Cancer Research Institute (BHCRI).
1039 Sleep And Daytime Activity Among Mechanically Ventilated Adults During Early Critical Illness
Authors: Liang Z , Elias M N , Ji M , Munro C L 
Published: May 2020

Abstract

Introduction
The purpose of this study is to report 5 consecutive days’ descriptive data for sleep efficiency (SE), total sleep time (TST), and daytime activity ratio (DAR) among critically ill mechanically ventilated adults from 9 intensive care units (ICU) across two hospitals. To our knowledge, this is the first study to describe sleep and activity patterns among mechanically ventilated adults during the early critical illness period.

Methods
We enrolled 31 critically ill mechanically ventilated subjects within 48 hours of ICU admission. Daytime periods were defined as 06:00-21:59; nighttime periods were defined as 22:00-05:59. Actigraphy estimated nighttime SE, TST, and the DAR. We calculated mean DARs [DAR = (daytime activity count per minute / 24-hour activity count per minute) x 100], which may be an indicator of altered rest/activity cycles. In our study, a DAR of &gt;80% was used to define normal rest/activity patterns. Descriptive analyses were used for this sub-analysis of our parent randomized controlled trial.

Results
Among the 31 subjects included, the mean age was 59.6 ± 17.3 years, 41.9% were male, 80.6% were White, and 67.7% were Hispanic/Latino. The mean nighttime SE and TST over the 5-day ICU period were 83.1% ± 16.1 and 6.6 ± 1.3 hours, respectively. The mean DAR over the 5-day ICU period was 66.5% ± 19.2. Only 17.5% of subject days (14 days out of a total of 80 recorded days) met the definition of normal rest/activity patterns (DAR &gt;80%).

Conclusion
Throughout the early ICU period, among mechanically ventilated patients, both the sleep/wake as well as the rest/activity cycle were disturbed. Intervention studies targeting the optimization of nighttime sleep consolidation and daytime activity should be investigated.

Support
This project was supported by a federal grant from the National Institutes of Health/National Institute of Nursing Research (R01NR016702). This clinical trial is registered with ClinicalTrials.gov (NCT03128671).