bluuebunny/crossref_metadata_2025 huggingface dataset:
0204 Spindle Detection in Polysomnography of Toddlers: Comparing Manual Feature Detection and an Automated Algorithm
Authors: Horger Melissa , Swift Allison , Kainec Kyle , Holmes Jennifer , Spencer Rebecca
Published: April 2024

Introduction

Sleep spindles are a feature of NREM sleep distinctly related to memory (Friedrich et al., 2019; Kurdziel et al., 2013) and later outcomes (Jaramillo et al., 2023). They have a protracted developmental trajectory and, in toddlers, spindles are defined as 10-16 Hz sinusoidal waves with a minimum duration of 500 ms. Manual identification of spindles is time-intensive and subjective. A variety of automated algorithms, utilizing machine learning techniques, have been developed to combat this problem. However, these vary in the extent to which they are trained on robust datasets, accessible to researchers, and customizable. One option, meeting all three criteria, is the Yet Another Spindle Algorithm (YASA). YASA was trained on over 3,000 adult PSG records, is open-access and extensively documented, and is modifiable (Vallat & Walker, 2021). The goal of the current research was to examine the feasibility of YASA to detect spindles in a much younger population- toddlers. This requires direct assessment because spindles are qualitatively different in this age group.

Methods

Ten toddlers (Range = 21.07-23.5 months; M = 21.84 months) underwent overnight PSG. The first 30 minutes of artifact-free NREM was isolated and scored for sleep spindles. Spindles were identified manually by an expert sleep scorer and automatically via YASA. One adjustment was made to the YASA standard parameters, expanding the range of detectable spindles from 12-15Hz to 10-16Hz.

Results

Manual scoring served as the ground truth. YASA consistently detected fewer spindles than scorers (C3: Range_YASA = 0-6; M_YASA = 1.6 vs. Range:manual= 0-50; M_manual = 11.2). In one record, zero spindles were identified by both methods. Among the remaining records, the percent agreement, or total spindles identified by both methods out of the total identified by scorers, ranged from 0-44% (M = 8%, SD = 15%). False alarms were present in 3 records (Range = 1-5 spindles).

Conclusion

Results suggest YASA is not a reliable substitute for manual spindle detection in toddlers. However, additional modifications to the base code may be necessary. We will unpack these modifications and extend this analysis to other auto-detection approaches.

Support (if any)

This research was funded by NIH R21 HD108913.
0205 Exploring the Relationship Between Social Jetlag, Sleep Quality, and Developmental Outcomes in Preschoolers
Authors: Abbas Leen , Harsh John , Choubai Sophia , Wong Sachi , Hartstein Lauren , LeBourgeois Monique
Published: April 2024

Introduction

Social jetlag (SJL), defined as the weekend-weekday discrepancy in the midpoint of the nocturnal sleep episode, is indicative of circadian misalignment. In adolescents, SJL is associated with poor sleep quality and impaired daytime and academic functioning. However, data on the prevalence and impact of SJL in preschoolers is limited. The present study examines the prevalence of SJL in preschoolers and its association with sleep, sleepiness, and developmental outcomes.

Methods

Data consisted of a cross-sectional sample of 841 children aged 2-5 years (M = 3.6 years, 50.3% female, 27.7% Black) in Mississippi between 2001-2003. Parents completed a questionnaire about child demographics, sleep habits, and developmental history. In a sub-sample of preschoolers, linear regression was used to examine the relationship between SJL and (i) measures of sleep quality (Children’s Sleep Wake Scale (CSWS)); (ii) daytime sleepiness (propensity to fall asleep), (iii) school readiness (Bracken School Readiness Assessment (BSRA)), and (iv) social skills (Social Skills Rating System (SSRS)).

Results

Following exploratory analysis, the prevalence of SJL within the sample was categorized as: no SJL (25.1%), < 90 mins of SJL (58.6%), and >90 mins of SJL (13.6%). Compared to no SJL and < 90 mins of SJL, >90 mins of SJL was associated with greater problems “going to bed” (R² = 0.012, F = 3.5, p = 0.031) and “returning to wakefulness” (R² = 0.092, F = 21.0, p < 0.001) on the CSWS. Additionally, >90 mins of SJL was associated with greater daytime sleepiness (R² = 0.097, F = 22.0, p < 0.001), relatively lower school readiness (R² = 0.075, F = 4.1, p = 0.020), and poorer social skills (R² = 0.054, F = 8.2, p < 0.001).

Conclusion

Social jetlag is highly prevalent among preschool children. Greater than 90 mins of SJL may be a risk factor for impaired daytime functioning and poor developmental outcomes. Future research is needed to identify moderators and mediators of these relationships. These findings could inform health intervention programs targeting sleep in early childhood.

Support (if any)

NSF-BSC0079435, UH3-0D023313
0206 The Family Environment: A Potential Driver in Children’s School-summer Sleep Differences
Authors: Fergason Kyla , Moreno Jennette
Published: April 2024

Introduction

More than 1/3 of US children do not sleep adequately. Our previous work has shown that during summer, 5-8-year-old children go to bed later, obtain less sleep, have a lower Entrainment Sleep Regularity Index (ESRI), and have lower sleep efficiency compared to the school year. Good sleep hygiene helps support adequate sleep. We hypothesized that school year-summer differences in the family environment would explain differences in children’s sleep timing, efficiency, and ESRI.

Methods

119 parents of 5-8-year-olds completed measures of child temperament, sleep hygiene, and parenting practices. Child sleep was measured for 8 days using wrist actigraphs in the summer and school year. Sleep timing was determined using the Sadeh algorithm and sleep diaries. Paired samples t-tests examined school year-summer differences in family variables. Multivariate analyses were performed to calculate predictors of sleep midpoint, total sleep time, sleep efficiency, and ESRI.

Results

Compared to the school year, during summer parents reported less consistent bedtime routines (t(108)=3.388, p<.001), increased average daily screen use (t(107)=-3.194, p=.001), less parent-child sleep conflict (t(108)=3.388, p<.001) and greater child surgency (impulsivity) (t(108)=-2.960, p=.004). During the school year, chronotype (β=3.639, p<.001), greater sleep reinforcement (β=-2.005, p=.041), greater caffeine use (β=5.312, p=.02), lower negative affect (β=-10.156, p=.039), and greater effortful control (β=14.253, p=.014) predicted later sleep midpoint. Greater bedtime routine consistency predicted lower sleep efficiency (β=-0.154, p=.032). During the summer, chronotype (β=5.542, p<.001) and decreased bedtime routine consistency predicted later sleep midpoints (β=-2.438, p=.016). Greater maladaptive bedtime activities (β=-.009, p=.004), greater sleep reinforcement (β=-.007, p=.036), and greater screen media use (β=-.003, p=.019) predicted lower ESRI.

Conclusion

Concerningly, maladaptive bedtime behaviors and screen media use predicted lower ESRI during the summer, which has been associated with increases in children’s BMI over the summer. Overall, these results suggest factors that contribute to unhealthy sleep habits differ during the school year and summer. During the school year, it may be important to encourage families to limit caffeine while during summer, promoting consistent bedtime routines and limiting screen media may help improve children’s sleep, with potential BMI effects.

Support (if any)

Texas Children’s Pediatric Pilot Award
0207 NREM Sleep Oscillations and Their Relations to Mood Symptoms in Children with Anxiety Disorders
Authors: Arpaci Hazal , Tripathy Aditi , Becic Intisar , Holowicki Anna , Firouzshahi Narges , Lott Morgan , Maeder Avery , Baran Bengi
Published: April 2024

Introduction

Sleep disturbances are common in children and adolescents with anxiety disorders. NREM sleep oscillations have been associated with affect and emotional wellbeing and undergo drastic maturational changes during puberty. The primary objective of the current study was to quantify NREM microstructure in children with anxiety disorders (Generalized Anxiety Disorder, Separation Anxiety Disorder or Social Anxiety Disorder) and examine their relations with severity of mood symptoms.

Methods

Forty-two participants (9-13 years, mean age: 11.33 years, n(anxiety)=16, n(comparison)=26) completed an in-lab overnight polysomnography session (with a 32-channel EEG acquisition device). Sleep spindles and slow oscillations during N2 and N3 sleep were detected with a validated wavelet-based algorithm and characterized using custom scripts. Symptom severity was measured based on parent reports on Child Behavior Checklist (CBCL) and Revised Children's Anxiety and Depression Scale (RCADS). Regressions between symptoms and NREM oscillation characteristics were based on 3 representative electrodes (Cz, Fz, Pz)

Results

Spindle duration was significantly reduced in children with anxiety (7 electrodes, tsum = -17.04, pcorrected = 0.04). In the entire sample, spindle duration was associated with severity of depression such that shorter spindle duration correlated significantly with higher depressive symptoms (frontal: p <.001, central: p =.03, parietal: p =.02). We didn’t observe any other differences in NREM oscillation characteristics. There were no group differences in sleep duration, quality or architecture.

Conclusion

In a sample of mostly prepubertal children with anxiety disorders, we observed a reduction in spindle duration compared to a demographically matched group of typically developing children. This finding was in the context of normal sleep duration, quality and architecture. Reduced spindle duration was associated with heightened depressive symptoms. Spindle duration has been associated with general cognitive ability and has been shown to decrease linearly with age during adolescence. A recent study on older adolescents at clinical high risk for psychotic disorders also reveals a significant reduction in spindle duration. These findings suggest that alterations in spindle duration may be a marker of general psychopathology in adolescence. Our future plans include repeating sleep monitoring longitudinally in these participants to trace the developmental trajectory of NREM oscillations across different stages of puberty.

Support (if any)

Iowa Neuroscience Institute Pilot Grant (Ideas Lab)
0208 Sleep Irregularity Predicts Emotional Change After Sleep Restriction Among School-aged Children
Authors: Myers Annika , Kim Jinu , Rech Megan , Vieira Alyssa , Cifre Anthony , Rizvi Sarah , Alfano Candice
Published: April 2024

Introduction

Sleep irregularity has been associated with more negative emotional outcomes in adolescents and young adults. Although commonly used, standard deviation of sleep duration may be a less sensitive metric of sleep regularity in school-aged children given increased need for sleep and parental influence on sleep timing during childhood. We therefore examined another metric of sleep regularity, mean sum of successive differences (MSSD), in association with children’s responses to emotional stimuli when rested and after partial sleep restriction.

Methods

Healthy children (N = 50, 7-11 years, M age = 9.65; 56% female) wore actigraphs for one week from which MSSD of sleep time (TST) and midsleep point (MSP) was calculated. Both after a night of full sleep and after two night of sleep restriction (SR; 7 and 6 hours sleep), participants were asked to suppress their facial expressions in response to negatively valanced movies while respiratory sinus arrythmia (RSA), a measure of physiological emotional regulation, was monitored. Participants also rated difficulty of emotional suppression (ES). Hierarchical linear regressions were run with change in RSA during negative movies and difficulty of ES while rested and after SR as dependent variables, and regularity metrics as independent variables. Average TST and pubertal status were entered as covariates, as well as resting RSA and change in resting RSA after SR in relevant models.

Results

Sleep regularity did not predict emotional variables when children were rested. However, greater TST MSSD (β=0.26, p=0.02) predicted increased RSA during movies after SR, suggesting greater difficulty regulating emotional responses (overall model: F(4,28)=3.12, p=0.03, addition of TST MSSD: ΔR2 = 0.15, F(1,28)=6.21, p=0.02). Additionally, greater MSP MSSD (β=-0.9, p=0.0003) predicted less ES difficulty post SR, indicative of blunted affective expression (overall model: F(3,46)=6.22, p=0.001, addition of MSP MSSD: ΔR2=0.24, F(1,46)=15.27, p=0.0003).

Conclusion

Irregularity in TST may exert the greatest impacts on school-aged children’s physiological emotional responses when sleep duration is inadequate. Analyses also suggest a blunting effect of irregular sleep timing on emotional expression after inadequate sleep duration. Our findings warrant further exploration.

Support (if any)

Data collection was supported by grant #R21MH099351
0211 The Intersection of Sleep, Pain, and Executive Function in Girls Following the Menarcheal Transition
Authors: Khatoon Sofia , Adornetti Julianna , Hagy Hannah , Crowley Stephanie , Hellman Kevin , Tu Frank , Kmiecik Matthew
Published: April 2024

Introduction

Adolescence is marked by shifts in sleep, both in terms of timing and quality. Little is known about how these disruptions in sleep relate to pain and executive functioning (EF) following the onset of menstruation (e.g., menarche) in females. This study examines the relations between 1) sleep disturbances and pain, 2) sleep disturbances and EF, and 3) pain and EF in a sample of adolescents who experienced menarche in the past year.

Methods

A community sample of biological females (n = 159; M age = 12.8; SD age = 1.1; 74% white) and their caregivers were recruited from a Midwestern metropolitan area. Caregivers reported on demographics, sleep disturbance (PROMIS Sleep Disturbance Short Form), and EF problems (BRIEF scales: Inhibitory Control (IC), Emotion Control (EC), Shift (S), Working Memory (WM), and Task Switching (TS) subscales). Adolescents reported on somatic symptoms (Children’s Somatic Symptom Inventory: CSSI) and period pain (“Over the last three months, what was the average cramping pain you experienced with your period?”; rating scale 0 [no pain] to 10 [worst pain imaginable]).

Results

A series of linear regressions investigated the relation between sleep, pain, and EF. Participants with higher somatic symptoms, but not greater period pain, experienced more sleep disturbance (β =.098, p=.032). More sleep disturbance was associated with more EF problems including EC (β = .016, p<.001), S (β =.169, p<.001), WM (β = .136, p=.01), and TS (β = .169, p<.001). There were no significant associations between either somatic symptoms or period pain and EF problems. Thus, adolescents who experienced more sleep disturbance exhibited higher levels of somatic symptoms and more difficulty with emotional regulation, cognitive flexibility, and memory.

Conclusion

Preliminary results indicate that sleep disturbance in the year following menarche is related to more somatic symptoms, but not more period pain. Further, sleep disturbance appears to be closely linked to EF problems. Data collection is ongoing, and actigraphy data on sleep (duration, quality) will be used to further investigate how sleep relates to pain and EF during this important developmental transition.

Support (if any)
0213 Latent Typologies of College Students’ Sleep-related Habits and Behaviors
Authors: Peltz Jack , Rogge Ronald
Published: April 2024

Introduction

With 43% of college students obtaining less than the recommended 7-9 hours of sleep per night and with over 75% reporting feeling tired/sleepy most days of the week (American College Health Association, 2023), this population is at significant risk for mental health, physical, and academic problems (Gaultney, 2010; Peltz & Rogge, 2016). Research has tended to focus on links between deficient sleep and negative outcomes via variable-center approaches (e.g., linear modeling), which may fail to capture individuals who exhibit multivariate sleep patterns (Yue et al., 2022). The present study used a latent profile analysis to identify subpopulations based on an extensive group of sleep-related habits and behaviors endorsed by a large sample of college students.

Methods

The current sample’s mean age (N=638, 82.4% female) was 21.3 years (SD=2.4; range 18-34), and 64.3% of participants were white, with 19.6% Asian/Pacific Islander, 6.9% Black, 6.7% Hispanic/Latinx, and 2.5% multi-racial or “other.” Approximately 60.5% of the sample lived on campus, and 56.8% maintained part- or full-time employment while attending school. The sleep-related habits and behaviors included in the latent profile analysis included the following scales: sleep disturbance, daytime impairment, environmental noise, sleep environment, sleep competency, sleep hygiene, work hours, problematic smartphone use, chronotype, and melatonin usage.

Results

Five sleep classes were identified including two more adaptive groups (great sleepers-23% & typical sleepers-35%), and three groups reporting greater sleep challenges (poor but conscientious sleepers-19%, poor sleepers-20%, & self-sabotaging sleepers-3%). The two adaptive groups reported fewer depressive/anxiety symptoms and higher GPAs, whereas the groups reporting disrupted sleep reported greater depressive/anxiety symptoms, life stress, and lower GPAs. The poor but conscientious sleepers were distinguished by better sleep environments and sleep hygiene (with slightly lower impairment), whereas the sabotaged sleepers were distinguished by poorer sleep hygiene, sleep environments, and greater problematic phone and alcohol use (with slightly greater impairment). Regressions predicting individual, interpersonal, and adaptive functioning highlighted that the typologies contributed unique predictive validity toward understanding college student functioning.

Conclusion

Our results highlight the diversity of sleep typologies present in college students. Students falling into poor/problematic sleep groups remain at risk for negative psychosocial and other health-related outcomes.

Support (if any)
0215 Optogenetic Rescue of Sleep Slows Alzheimer's Progression in a Mouse Model
Authors: Kastanenka Ksenia
Published: April 2024

Introduction

Alzheimer’s disease (AD) patients exhibit memory disruptions and sleep disturbances, such as disruption of deep non-rapid eye movement (NREM) sleep. Slow-wave activity (SWA) is a restorative feature of NREM sleep and is important for memory consolidation.

Methods

We made a mouse model where GABAergic interneurons could be targeted in the presence of APPswe/PS1dE9 (APP) amyloidosis, APP-GAD-Cre mice. An electroencephalography (EEG) / electromyography (EMG) telemetry system was used to monitor sleep disruptions in these animals. Optogenetic stimulation of GABAergic interneurons in the anterior cortex targeted with channelrhodopsin-2 (ChR2) allowed us to examine examining the role GABAergic interneurons play in sleep deficits. We also examined the effect of optogenetic stimulation on amyloid plaques, neuronal calcium as well as sleep-dependent memory consolidation. In addition, microglial morphological features and functions were assessed using confocal microscopy and flow cytometry. Finally, we performed sleep deprivation during optogenetic stimulation to investigate whether sleep restoration was necessary to slow AD progression.

Results

APP-GAD-Cre mice exhibited impairments in sleep architecture including decreased time spent in NREM sleep, decreased delta power, and increased sleep fragmentation compared to nontransgenic (NTG) NTG-GAD-Cre mice. Optogenetic stimulation of cortical GABAergic interneurons increased SWA and rescued sleep impairments in APP-GAD-Cre animals. Furthermore, it slowed AD progression by reducing amyloid deposition, normalizing neuronal calcium homeostasis, and improving memory function. These changes were accompanied by increased numbers and a morphological transformation of microglia, elevated phagocytic marker expression, and enhanced amyloid β (Aβ) phagocytic activity of microglia. Sleep was necessary for amelioration of pathophysiological phenotypes in APP-GAD-Cre mice.

Conclusion

In summary, our study shows that optogenetic targeting of GABAergic interneurons rescues sleep, which then ameliorates neuropathological as well as behavioral deficits by increasing clearance of Aβ by microglia in an AD mouse model.

Support (if any)

NIH, BrightFocus Foundation, Alzheimer's Association
0218 Sleep and Circadian Polygenic Risk Scores Predict Actigraphy-derived Traits and Cognition in Older Adults
Authors: Maués Cássia , Zayats Tetyana , Blankenship Anneka , Bock Karen , Watts Amber , Hunt Suzanne , Siengsukon Catherine , Veatch Olivia , Honea Robyn , Morris Jill , Santoro Marcos , Mazzotti Diego
Published: April 2024

Introduction

Sleep disturbances are prevalent in older adults and have been associated with major pathophysiological characteristics of Alzheimer’s disease. Genome-wide association studies (GWAS) of sleep and circadian traits identified hundreds of genetic loci influencing variability in these phenotypes. GWAS summary statistics can be used to derive polygenic risks scores (PRS), a quantitative measure of genetic risk for a particular phenotype. In this study, we assessed whether sleep and circadian PRS can predict actigraphy-derived measures and cognition scores among older adults followed at the University of Kansas Alzheimer’s Disease Research Center.

Methods

We collected 7-days of actigraphy and cognitive assessments of verbal memory, attention, and executive function among participants of European ancestry with genome-wide genotyping data. Linkage disequilibrium reference panels and GWAS summary statistics for insomnia, chronotype, and sleep duration were used to calculate PRS using a Bayesian regression approach with continuous shrinkage priors (PRS-CS). We used Spearman correlations and general linear models adjusted for age, sex, and education to test for associations of PRS with actigraphy-derived traits (sleep duration, sleep efficiency, wake after sleep onset [WASO], number of awakenings, and sleep midpoint) and cognitive factors.

Results

The final dataset consisted of 61 participants (mean [standard deviation (SD)] age 74.1 [6.5] years; 39.3% female; mean 16 [3] years of education). No associations were observed between early chronotype PRS and early sleep midpoint, nor sleep duration PRS and objective sleep duration. However, early chronotype PRS was negatively correlated with WASO (ρ=-0.32; p=0.012) and positively correlated sleep efficiency (ρ=0.30; p=0.018). Adjusted analyses suggest that a one SD increase in early chronotype PRS was associated with lower WASO (β[95%CI]=-15.0[-29.3;-0.76] minutes; p=0.043) and higher sleep efficiency (2.9[0.6;5.1] %; p=0.014). Furthermore, one SD increase in sleep duration PRS was associated with better performance on attention (2.9 [0.6;5.1] SD; p=0.009). No significant associations with insomnia PRS were observed.

Conclusion

This study identified novel associations between early chronotype and sleep duration PRS and relevant actigraphy-derived traits and attention in a clinical cohort of older adults. Results may inform the potential role of genetically informed sleep traits towards understanding their impact on cognition.

Support (if any)

NIA P30AG035982; General Faculty Research Fund (University of Kansas)
0219 Sleep Disturbances, Cortisol, and Neuroimaging Correlates Among Memory Clinic Patients
Authors: Sørensen Charlotte , Kåreholt Ingemar , Kalpouzos Grégoria , Udeh-Momoh Chinedu T , Holleman Jasper , Aspö Malin , Hagman Göran , Spulber Gabriela , Kivipelto Miia , Solomon Alina , Sindi Shireen
Published: April 2024

Introduction

Several sleep disturbance parameters as well as abnormal cortisol secretion levels are increasingly acknowledged as risk factors for Alzheimer’s disease (AD). Currently, the mechanisms between sleep disturbances and AD, and the interplay with abnormal cortisol levels, are still not understood. This study examines how self-reported sleep disturbances are associated with structural brain measures and diurnal cortisol dysregulation in a cohort of memory clinic patients.

Methods

The study was based on a cohort of 146 memory clinic patients diagnosed with either mild cognitive impairment or subjective cognitive impairment. The Karolinska Sleep Questionnaire (KSQ) was used to measure self-reported sleep. Neuroimaging (MRI or CT) was used to quantify structural brain measures using four visual rating scales (Scheltens, Pasquier, Koedam, and Fazekas scales). Salivary cortisol was sampled to measure diurnal cortisol patterns through five computed measures of awakening, bedtime, total, AM/PM cortisol ratio, and cortisol awakening response.

Results

Some associations were found between sleep, structural brain measures and cortisol. Increased apnea index (based on the KSQ) was associated with greater odds of posterior brain atrophy (OR=1.20, p=0.015) measured by the Koedam visual rating scale. Further, increased apnea index was associated with reduced awakening cortisol (β=-0.03; p=0.045, and an increased daytime sleepiness index (based on the KSQ) was associated with both reduced awakening cortisol (β=-0.03; p=0.025) and a reduced AM/PM cortisol ratio (β=-0.04; p=0.021).

Conclusion

In a memory clinic cohort, self-reported apnea disturbances are associated with a neuroimaging correlate of increased posterior atrophy and lower awakening cortisol, while daytime sleepiness index is associated with lower awakening cortisol and a reduced AM/PM cortisol ratio. These findings add insights into the early identification of modifiable AD risk factors an¬d their mechanisms.

Support (if any)
0221 Effect of Acutely Induced OSA During SWS on AD Plasma Biomarkers
Authors: Kam Korey , Mullins Anna , Parekh Ankit , Bubu Omonigho , Valencia Daphne , Berkalieva Asem , Ayappa Indu , Rapoport David , Osorio Ricardo , Varga Andrew
Published: April 2024

Introduction

OSA induces both sleep fragmentation and intermittent hypoxemia and has been associated with AD progression. We hypothesized that SWS-specific OSA can influence plasma AD biomarkers.

Methods

We developed a model of SWS-specific CPAP-withdrawal in subjects with AHI4% ≥ 20/hour to create 3 polysomnologically (PSG)-verified conditions per subject: 1) stable-SWS on CPAP, 2) SWS-fragmentation with intermittent hypoxemia (OSAsws), and 3) SWS-fragmentation with reduced hypoxemia (OSAsws+O2). We examined post-PSG morning plasma Aβ42 and Aβ40 by mass-spectrometry and plasma T-tau, P-tau181, NfL and GFAP by SIMOA in a study of 34 patients. Wilcoxon signed rank and Kruskal Wallace tests were used to compare SWS-specific OSA metrics and plasma measures across PSG conditions.

Results

In 34 patients (57 years, 32% female) CPAP withdrawal caused sleep disruption and recurrence of underlying OSA such that the OSAsws and OSAsws+O2 conditions caused significant increases in AHI4 and arousals during SWS [CPAP: 0 ±0, OSAsws: 16.1 ±15.7, OSAsws+O2: 16.2 ±12.0 in evts/hr, p< 0.0001], and arousal index during SWS [CPAP: 0.9 ±2.0, OSAsws: 11.7 ±14.3, OSAsws+O2: 11.7 ±15.5 in evts/hr, p< 0.0001] compared to CPAP treatment. Furthermore, the minimum SpO2 desaturation level of OSAsws was lower than OSAsws+O2 (OSAsws: 89.9 ±4.0, OSAsws+O2: 90.1 ±5.9 in % SpO2, p= 0.007). No change was observed between PSG conditions in Aβ42 (CPAP: 22.9 ±6.2, OSAsws: 21.3 ±6.2, OSAsws+O2: 21.2 ±6.8 in pg/mL), Aβ40 (CPAP: 232.3 ±39.9, OSAsws: 233.2 ±89.6, OSAsws+O2: 197.7 ±114.6), the ratio of Aβ42 to Aβ40 (CPAP: 0.10 ±0.01, OSAsws: 0.10 ±0.02, OSAsws+O2: 0.10 ±0.01), T-tau (CPAP: 3.1 ±0.3, OSAsws: 3.3 ±1.1, OSAsws+O2: 2.9 ±0.8 in pg/mL), P-tau181 (CPAP: 1.4 ±1.0, OSAsws: 1.5 ±0.5, OSAsws+O2: 1.6 ±0.9 in pg/mL), NfL (CPAP: 8.7 ±4.4, OSAsws: 6.0 ±3.0, OSAsws+O2: 7.8 ±6.0 in pg/mL), and GFAP (CPAP: 42.9 ±33.2, OSAsws: 30.9 ±25.6, OSAsws+O2: 39.5 ±36.9 in pg/mL).

Conclusion

Although we were able to recapitulate OSA in SWS, the degree of OSA severity was less than that on subjects’ diagnostic studies, and breathing was normal in N1, N2, and REM sleep, factors which possibly account for the lack of significant differences in biomarkers between conditions.

Support (if any)

R01AG056682, R01AG066870, RF1AG083975, R01AG080609, R01AG082278, K24HL109156, T32HL160511, K23AG068534, K25HL151912
0223 Temporal Correlations in Motor Activity Fluctuations and Risk of Parkinson’s Disease in Middle-to-older Aged Adults
Authors: Zheng Xi , Li Peng , Gao Lei , Hu Kun , Haghayegh Shahab
Published: April 2024

Introduction

Physiological outputs such as motor activity display complex fluctuations with a delicate pattern balanced between randomness and excessive regularity. Altered fractal patterns were observed in patients with Parkinson’s disease (PD). We examined whether perturbed motor activity fluctuation patterns are associated with the risk of developing PD in middle-to-older aged adults.

Methods

Actigraphy recordings (up to 7 days) were collected from more than 100,000 participants in the UK Biobank between 2013-2015. Participants were followed after actigraphy assessments for up to 7.5 years (median: 5 years). The detrended fluctuation analysis was performed to obtain a scaling exponent, α, that quantifies temporal correlations in activity fluctuations at timescales ~6-90 min: α close to 1 indicates the highest complexity or the balance between randomness and excessive regularity as observed in health young adults; deviations from 1 indicate reduced complexity — more randomness (close to 0.5) or excessive regularity or rigidity (close to 1.5) as occurred with aging or in diseases. We performed Cox proportional hazards models to examine the association of |α-1| with incident PD (ascertained by ICD-10 codes) during the follow-up while adjusting for age, sex, education, ethnicity, obesity, sleep apnea, alcohol intake, smoking status, morbidity burden, circulatory system disorder, and Townsend Deprivation Index (TDI).

Results

In total, 94,041 participants (56.4% females; age: 62.4±7.83 [SD], range 43.5-79.0 years) who had valid actigraphy or had no PD diagnosis at baseline were included. Among them, 290 participants (~0.3%) developed PD after 3.7±1.8 [SD] years from baseline. Older age (hazard ratio [HR] per 1 year increase=1.15, 95% CI: 1.12-1.17, p< 0.0001) and being male (HR= 2.42, 95% CI: 1.88-3.13, p< 0.0001) were associated with a higher risk of developing PD. The mean and SD of the squared root transformed |α-1| were 0.2±0.1. With a square root transform on |α-1| (to account for the right skewed distribution) and after controlling for co-variables, larger |α-1| was associated with increased risk of developing PD (for 1-SD increase, HR=1.15, 95% CI: 1.02-1.28, p=0.017; Q4 vs. Q1: HR=1.46, 95% CI: 1.05-2.04, p=0.025).

Conclusion

Perturbed balance between randomness and regularity in motor activity fluctuations was associated with higher PD risk.

Support (if any)

5T32HL007901-25, NIH RF1AG064312, BrightFocus A2020886S.
0225 Actigraphy-Assessed Daytime Napping Links to Mild Cognitive Impairment and Dementia in Middle-to-Older Aged Adults
Authors: Gao Chenlu , Haghayegh Shahab , Zheng Xi , Cai Ruixue , Rutter Martin K , Bennett David , Gao Lei , Hu Kun , Li Peng
Published: April 2024

Introduction

Prior studies of older adults have demonstrated that the development and progression of Alzheimer’s disease are tightly linked with longer daytime naps in a potentially bidirectional manner. It remains unclear whether daytime sleep behaviors in middle-to-older aged adults are linked to the risk for dementia.

Methods

We studied 85,037 cognitively intact non-shift-workers (baseline age=63 years, SD=8, range: 43-69; 57% female) in the UK Biobank cohort who completed 7-day actigraphy and had been followed for up to 8 years. We implemented the Cole-Kripke algorithm to identify daytime sleep episodes and considered three types of napping metrics: (1) mean nap duration between 9am-7pm; (2) intra-individual variability (individual SD) in the nap duration between 9am-7pm; (3) timing of napping quantified as the percentage of nap duration in each 2-h bin between 9am-7pm (9-11am, 11am-1pm, 1-3pm, 3-5pm, 5-7pm). Incidences of all-cause dementia and mild cognitive impairment (MCI) were identified via ICD-10 codes. Nap variables were square-root transformed to correct for skewness. We conducted Cox proportional hazards models, adjusting for demographic characteristics, lifestyle, comorbidities, polygenic genetic risk score for Alzheimer's disease, nighttime sleep, and chronotype.

Results

Almost all participants (99.97%) napped at least once. Median nap duration was 0.40 hours per day (IQR=0.20-0.78), and intra-individual variability of nap duration was 0.39 (IQR=0.19-0.69) hours. Twenty-eight percent (IQR=12%-52%) of the naps were taken between 9-11am, 5% (IQR=0%-17%) between 11am-1pm, 9% (IQR=0%-22%) between 1-3pm, 16% (IQR=1%-29%) between 3-5pm, and 17% (IQR=4%-32%) between 5-7pm. During follow-up, 557 (0.66%) participants developed MCI or dementia on average 4.62 (range: 0.01-7.98) years after baseline. Longer nap duration (for 1-SD, HR=1.21, 95%CI: 1.12-1.31, p< 0.0001), larger intra-individual variability (for 1-SD, HR=1.14, 95%CI: 1.04-1.24, p=0.004), and higher percentage of naps between 1-3pm (for 1-SD, HR=1.11, 95%CI: 1.02-1.22, p=0.017) were associated with increased risk for MCI/dementia.

Conclusion

Longer and increased intra-individual variability in daytime nap as well as higher percentage of naps in the early afternoon are associated with a greater risk of MCI/dementia in middle-to-older aged adults. These findings highlight the potential importance of monitoring napping in screening for the risk for developing dementia.

Support (if any)

AASM (290-FP-22), Alzheimer’s Association (AARFD-22-928372), NIH 5T32HL007901-25, BRI Fund to Sustain Research Excellence.
0226 A Novel GABAergic Dietary Supplement Enhances Sleep Quality in Ovariectomized Mice
Authors: Bernsley Devon , Sahin Kazim , Kilic Ertugrul , Korkusuz Ahmet Kayhan , Orhan Cemal , Er Besir , Komorowski James
Published: April 2024

Introduction

Sleep disruptions affect 56% of the population. Sleep disorders are common symptoms facing peri- and post-menopausal women. Ovariectomized rodents are a common study model for research on menopausal symptoms. The primary objective of this investigation was to evaluate the effects of BH_SLP_01 (Bonafide Health Sleep Product) on sleep quality and brain wave patterns in ovariectomized mice.

Methods

Five female BALB/c mice (age: 8 weeks, body mass: 180±20g) were allocated per treatment arm. After an adaptation period, all mice were ovariectomized (OVX). Treatment groups included caffeine control (control) and caffeine + BH_SLP_01. Both groups were treated with caffeine to induce sleep disturbance. Caffeine (7.5mg/kg) was injected intraperitoneally at the 15th minute of recordings, and second injections (saline or combinations) were performed at the 30th minute (min). Brain electrical activity was monitored and recorded for two hours total using electrocorticography (ECoG). Serum melatonin, serotonin, and dopamine were measured by ELISA. Brain levels of MDA were measured by HPLC and protein concentrations were determined using Western Blot analysis. Study treatments were compared using an ANOVA, Tukey’s post-hoc test, and Student's unpaired t-test. Significance levels were set at < 0.05.

Results

Analysis of ECoG recording data revealed notable findings. Significant differences in amplitude were seen at 15-30, 45-60, 60-75, and 75-90 min time points for the BL_SLP_01 group compared to control. Significant differences in frequency were seen at 15-30, 30-45, 45-60, 60-75, 75-90, and 90-105 min timepoints for the BH_SLP_01 group compared to control. Additionally, serotonin, dopamine, melatonin, brain MDA, GABA-A R2, GABA-B R1, GABA-B R2, 5-HT1A, GluA1, GluN1, GluN2A, Bax, Bcl-2, and Caspase-3 levels were significantly improved by BH_SLP_01 compared to control (p< 0.05).

Conclusion

This study provides evidence for the efficacy of BH_SLP_01 to improve impaired sleep in a menopausal model. This conclusion is supported by notable enhancements in sleep architecture demonstrated by ECoG recording data. Furthermore, the results show an upregulation of GABA, glutamine receptors, and natural melatonin, all of which collectively contribute to sleep quality. These findings underscore the potential of BH_SLP_01 as a promising therapeutic intervention for menopausal women experiencing sleep stress and disturbances.

Support (if any)

Research was supported by Bonafide Health, LLC.
0228 Chemogenetic Activation of Noradrenergic A7 and SubCoeruleus Neurons Inhibits Generation of REM Sleep in Aging Mice
Authors: FrassondosReis Leonardo , Yadav Raghunandan Kumar , Fenik Victor , Rukhadze Irma
Published: April 2024

Introduction

The generation of REM sleep involves mutually inhibitory interactions between brainstem aminergic and cholinergic neurons. The silencing of Locus Coeruleus and A5, noradrenergic neurons during REM sleep have been proposed to play a permissive role in the occurrence of REM sleep. Since noradrenergic SubCoeruleus (SubC) and A7 neurons have similar sleep-wake pattern of activity, we tested whether these neurons also participate in the regulation of the sleep-wake cycle in young and aging mice.

Methods

Fourteen adult DBH-cre mice (young 3-6 month-old, 4 males and 3 females; and aging 21-26 month-old, 5 males and 2 females) were bilaterally injected with excitatory M3-DREADD into A7/SubC pontine noradrenergic region. In two weeks, mice were implanted with EEG and neck EMG electrodes to record sleep-wake behavior. Four weeks following the DREADD injections, the mice were habituated to sleep-wake recording system. The EEG and neck EMG were collected from 9 am to 7 pm, and randomized CNO (0.3 mg/kg, i.p.) or saline injections were administered at 12 pm during experimental sessions. Sleep scoring was performed using SleepSign and sleep-wake states were manually defined in 5 s epochs. The duration of time spent in wakefulness, NREM, and REM sleep was calculated per hour for saline and CNO sessions. Pontine sections were processed for tyrosine hydroxylase (a marker for noradrenergic neurons) immunohistochemistry, and mCherry-positive (DREADD) A7/SubC neurons were counted.

Results

CNO tended to reduce total duration of REM sleep compared to saline injections within the first two hours after the injections in young (2.5 + 0.8 (SE) min vs. 4.6 + 0.8 min, n=7, p=0.078) and significantly decreased in old (2.5 + 0.6 min vs. 5.6 + 0.6 min, n=7, p< 0.001, paired t-tests) mice. The total duration of REM sleep was negatively correlated with the number of mCherry-positive, M3-expressing noradrenergic neurons.

Conclusion

The obtained results suggest that activation of A7/SubC noradrenergic neurons negatively affect REM sleep generation, which was more pronounced in older animals. These findings suggest a novel role for both SubC and A7 noradrenergic neurons in the regulation of REM sleep.

Support (if any)

NIIH/NIA R01AG065233
0231 Exploring Age Differences in Relationships Between Prior Sleep Duration and Sleep Latency on MSLTs
Authors: Keohane Lara , McCullar Katie , Dijk Derk-Jan , Klerman Elizabeth
Published: April 2024

Introduction

We investigated whether the relationship between daytime sleepiness and previous night’s sleep duration changes with age using a protocol in which individuals were studied both after habitual sleep duration and after multiple days with extended sleep opportunities.

Methods

35 younger (18-32 years old, 18 F) and 18 older (60-76 years old, 6 F) healthy participants were studied using polysomnography in an in-lab protocol (Klerman and Dijk, Curr. Biol. 2008). During night 1 (SP1), participants were scheduled to sleep based on their at-home sleep schedules. On day 2, the 5 sessions of a MSLT (MSLT_pre) began 2 hours after SP1 ended .Participants then began a schedule with 16 hrs (12 hrs at night and 4 hrs during day) of sleep opportunity in bed per 24 hrs for 7 days, followed by a second set of MSLTs (MSLT-post). Total sleep time (TST) for the nighttime sleep episodes prior to both sets of MSLTs was calculated. The median sleep latency and percent of the MSLT sessions in which participants fell asleep (PctSlept) within MSLT_pre and MSLT_post were calculated.

Results

Total sleep time on SP1 ranged from 5.4-9.7hr; only Younger participants had a TST >8.0 hr. Therefore, for analyses comparing Younger and Older participants, only TST of < 8.0 hr was used (21 Younger, 18 Older). All participants fell asleep at least once during MSLT_pre. During MSLT_post, 32% of Younger and 8% of Older participants did not fall asleep during any of the MSLT sessions (i.e., PctSlept=0); therefore, latency-based analyses cannot be done for MSLT_post data. There was a negative correlation between SP1 TST and median sleep latency for MSLT_pre for Younger (Pearson R=0.35; p=0.049) but not Older participants. There was a negative correlation between SP1 TST and PctSlept for Younger (Pearson R=-0.47, p=0.006), but not Older participants. There was no relationship between TST before MSLT_post and the PctSlept for either Younger or Older participants. Repeated measures ANOVA testing (MSLT_pre vs MSLT_post) found a time (p< 0.001) and time*agegroup (p=0.008) effect for PctSlept.

Conclusion

The relationship between the prior night’s sleep and daytime sleepiness may change with age.

Support (if any)

NIH R01- HL166205, R01-HD107064
0232 Association of Sleep Quality and Sleep Duration with Cognitive Frailty in Older Chinese Adults
Authors: Cai Ruixue , Gao Chenlu , Gao Lei , Hu Kun , Li Peng
Published: April 2024

Introduction

Cognitive frailty refers to the co-occurrence of physical frailty and cognitive impairment. The aim of this study is to examine the associations of sleep quality and sleep duration with cognitive frailty among older Chinese adults.

Methods

We analyzed data from the 2008 wave of the Chinese Longitudinal Healthy Longevity Survey. In total, 11,293 participants aged ≥ 65 years old (mean age 85 years; 6032 females) who had cognition and physical frailty assessments at baseline were included. Participants were considered physically frail when meeting ≥3 of the following criteria: shrinking, weakness, slowness, exhaustion, and inactivity; and were considered mild cognitive impairment if the Mini-Mental State Examination score ◻22. Cognitive frailty was identified as the presence of both physical frailty and mild cognitive impairment. Sleep quality was assessed based on answers to the question “How about the quality of your sleep?” A score “poor” was rendered if the answer was “so so”, “bad”, or “very bad”, and a score “good” was rendered otherwise. Sleep duration was estimated based on the answers to the question “How many hours do you sleep normally?”, was further categorized into short (< 6 h), normal (6-9 h), and long (>9 h). Multivariate logistic regression models were performed to assess the associations of sleep quality and sleep duration with cognitive frailty adjusting for age, sex, education, residence, economic status, marital status, and multimorbidity.

Results

Among all participants, 1,297 (11.5%) were identified as having cognitive frailty. Poor sleep quality was found in 3,794 (33.6 %) participants. Numbers of participants who reported short, normal, or long sleep duration were 1,325 (11.7%), 7,200 (63.8%), and 2,768 (24.5%). Compared with those who had good quality, participants who had poor sleep quality were at a higher risk for cognitive frailty (odds ratio [OR]=1.36, 95%CI=1.19-1.56, p< 0.001). Compared to participants who had normal sleep duration, the odds of having cognitive frailty were also higher in those with short (OR=1.29, 95%CI=1.05-1.59, p< 0.05) or long sleep duration (OR=1.52, 95%CI=1.32-1.74, p< 0.001).

Conclusion

Poor sleep quality and abnormal sleep duration were associated with cognitive frailty among older Chinese adults.

Support (if any)

BrightFocus Foundation (A2020886S), NIH RF1AG064312
0233 Actigraphy-derived Sleep Profiles in Older Men: Insights into Cognitive and Cardiovascular Health Risks
Authors: Cavailles Clemence , Wallace Meredith , Leng Yue , Stone Katie , Ancoli-Israel Sonia , Yaffe Kristine
Published: April 2024

Introduction

While growing attention has been given to understanding the multidimensionality of sleep, research has primarily focused on individual sleep characteristics. Therefore, the link between actigraphy-derived 24-hour sleep/circadian profiles and the incidence of dementia and cardiovascular disease (CVD) events in healthy older men remains unexplored.

Methods

We analyzed actigraphy-derived sleep/circadian data gathered from 2,667 men participating in the Osteoporotic Fractures in Men (MrOS) Sleep study. Sleep/circadian profiles were determined using multiple coalesced generalized hyperbolic mixture modeling, based on 20 sleep parameters and circadian activity rhythms variables. Multivariable Cox models were performed to investigate how these sleep profiles related to the incidence of dementia and CVD events over a 12-year period.

Results

We identified three sleep profiles: active healthy sleepers (characterized by high sleep efficiency; strong circadian rhythmicity; and high activity during wake periods), fragmented poor sleepers (low sleep efficiency; high sleep fragmentation; short sleep duration; and weak circadian rhythmicity), and nappers (more frequent and longer naps; and high sleep efficiency). Over the 12-year follow-up, compared to active healthy sleepers, fragmented poor sleepers had significantly increased risks of dementia and CVD events (Hazard Ratio (HR)= 1.47, 95% confidence interval (CI)= 1.08;1.98 and HR=1.34, 95%= 1.11;1.63, respectively), whereas nappers had a higher risk of CVD events (HR= 1.18, 95% CI= 1.00; 1.40) but not dementia incidence (HR= 1.14, 95%CI= 0.87;1.49).

Conclusion

In older men, fragmented poor sleepers’ profile was associated with less favorable cognitive and cardiovascular health over 12-year follow-up, indicating a potential target for sleep intervention. Nappers’ profile was linked to increased risk of CVD events incidence, but not with the incidence of dementia. These results suggest that different sleep profiles may influence cognitive and cardiovascular health through distinct mechanisms.

Support (if any)