bluuebunny/crossref_metadata_2025_split huggingface dataset:
1038 Sleep Architecture In Individuals Receiving Methadone For Medication-Assisted Treatment Of Opioid Use Disorder
Authors: Erwin J A , Wilson M , Finlay M , Hansen D A , Little-Gott A , Reynolds D , Quock R M , Layton M E , Van Dongen H 
Published: May 2020

Abstract

Introduction
It has been established that the use of opioids suppresses stage N3 sleep. For individuals with opioid use disorder (OUD), medication-assisted treatment (MAT) is a widely employed opioid replacement therapy used to mitigate withdrawal effects and drug cravings. We investigated sleep architecture in individuals receiving methadone-based MAT.

Methods
N=6 individuals (aged 43.8±12.8y; 5 females), who were within 90 days of methadone initiation, underwent in-laboratory overnight polysomnography (8h TIB; 22:00-06:00). Prior to bedtime, pain intensity and opioid withdrawal symptoms were assessed using the Numeric Pain Rating Scale (0-10) and the Clinical Opiate Withdrawal Scale (0-48). Sleep recordings were scored visually according to AASM guidelines.

Results
In this sample, subjects exhibited 87.4-93.0% (M: 92.2%) sleep efficiency (SE), 8.0-16.2min (M: 12.1min) sleep latency (SL), 5.5-7.5% (M: 6.5%) N1, 46.4-52.7% (M: 49.6%) N2, 20.7-30.6% (M: 25.6%) N3, 17.5-19.1% (M: 18.3%) REM, 28.0-38.5min (M: 33.3min) N3 latency, and 84.1-125.9min (M: 105.0min) REM latency. Subjects reported moderate pain intensity scores of 5-6 (M: 5.3) and mild to moderate withdrawal symptoms of 1-15 (M: 7.8).

Conclusion
Relative to published healthy sleeper norms, subjects showed more N1 and N3 and less REM sleep. The increased N3 was unexpected given that opioids (such as methadone) typically suppress N3; it may reflect subjects carrying a substantial sleep debt. Pain and withdrawal symptoms may be a factor increasing N1 and reducing REM sleep. Such potential sleep deficiencies may interfere with subjects achieving OUD recovery goals and are worthy of further investigation.

Support
Supported in part by a seed grant from the Washington State University Office of Research Advancement and Partnerships.
1046 Assessing Sleep-Related Hypertension Risks Using JNC 8 Guidelines: Analysis Of The National Health And Nutrition Examination Survey Data
Authors: Lough L , Seixas A , Avirappattu G , Robbins R , Rogers A , Williams S , Jean-Louis G 
Published: May 2020

Abstract

Introduction
Associations between self-reported sleep duration and risk of hypertension (HTN) are well established. The level of sleep-related HTN risk based on the new JNC 8 classification guidelines requires further research. In this study, we modeled the associations of insufficient sleep with HTN using the National Health and Nutrition Examination Survey (NHANES).

Methods
Data were extracted from the 2006-2016 NHANES (n=38,540), a nationally representative study of the US civilian population. Self-reported demographic and sleep duration were determined from household interview questions. Insufficient sleep was categorized as sleeping &lt;7hrs. Using 2017 ACC/AHA guidelines, HTN was classified as elevated (SBP:120-129mmHg and DBP &lt;80mmHg, Stage I (SBP:130-139mmHg and DBP:80-89mmHg), or Stage II (SBP ≥140mmHg and DBP ≥90mmHg). Logistic regression modeling was performed using R.

Results
Participants’ ages ranged from 18-85 years. Of the sample, 51% were female, 41% white, 22% black, 26% Hispanic, 8% others; 46% were married, and 25% completed &lt;high school. The model showed strong age and BMI-adjusted associations of insufficient sleep with HTN at all levels: (elevated: OR=1.079, CI=1.03-1.13; Stage I: OR=1.127, CI=1.07-1.18, and Stage II: OR=1.334, CI=1.17-1.52). Important sex and race/ethnicity differences in sleep-related HTN risks were observed: males (elevated: OR=1.024, CI=0.95-1.10; Stage I: OR=1.077, CI=1.01-1.15, and Stage II: OR=1.254, CI=1.06-1.48); females (elevated: OR=1.125, CI=1.05-1.21; Stage I: OR=1.170, CI=1.08-1.26, and Stage II: OR=1.445, CI=1.17-1.79); whites (elevated: OR=1.007, CI=0.93-1.08; Stage I: OR=1.030, CI=0.95-1.12, and Stage II: OR=1.131, CI=0.90-1.43); blacks (elevated: OR=1.047, CI=0.94-1.16; Stage I: OR=1.080, CI=0.97-1.20, and Stage II: OR=1.179, CI=0.95-1.46); and Hispanics (elevated: OR=1.066, CI=0.94-1.21; State I: OR=1.089, CI=0.96-1.24, and Stage II: OR=1.337, CI=0.92-1.92).

Conclusion
Our analyses showed that sleep-related HTN risks vary as a function of individual’s sex and race/ethnicity. Increasing sleep duration at all HTN severity level is important and males and Hispanics at Stage II HTN might benefit the most.

Support
This study was supported by funding from the NIH: R01MD007716,R01HL142066, R01AG056531, T32HL129953, K01HL135452 and K07AG052685
1047 Social Rhythm Instability Is Associated With Abdominal Adiposity After Involuntary Job Loss
Authors: Haynes P L , Apolinar G , Thomson C A , Quan S , Silva G E , Kobayashi U , Glickenstein D A 
Published: May 2020

Abstract

Introduction
Involuntary job loss is an acute stressor that disrupts daily time structure and activity and exacerbates economic hardship and psychological distress. Studies show that unemployment is associated with negative obesity-related health outcomes, such as metabolic syndrome. However, very little is known about daily routine, depression, and obesity in individuals who have recently experienced involuntary job loss. We hypothesized that individuals with less consistent daily routines, or unstable social rhythms, after job-loss would have more abdominal adiposity than individuals with more consistent social rhythms. We also hypothesized that this relationship would vary as a function of depressive symptoms.

Methods
Cross-sectional baseline data (n = 186) from the ongoing ADAPT study (Assessing Daily Activity Patterns through occupational Transitions) were analyzed using linear regression techniques. Participants were predominantly female (62%) with a mean age of 41.12 years (SD = 10.16 years); 31% were Hispanic or Latino. Over two weeks, participants completed the daily Social Rhythm Metric-17 (SRM), Beck Depression Inventory II (BDI), and waist circumference (adiposity) measurements (cm).

Results
A significant BDI x SRM interaction was detected in the prediction of waist circumference, B = .36, SE = .18, &lt;i&gt; p &lt;/i&gt; &lt; .05, 95% CI [.002, .709], R2 = .07). The SRM was inversely associated with waist circumference, B = -5.57, SE = 2.25, &lt;i&gt; p &lt;/i&gt; &lt; .05, 95% CI [-9.98, -1.13], only at lower levels of BDI (-1 SD below the mean). Results from the Johnson-Neyman technique identified that the conditional effect of SRM on waist circumference was statistically significant at a BDI raw score of 8.33 (0-13 points is minimal depression) with ~45% of cases within this region.

Conclusion
A less consistent daily routine was associated with a larger waist circumference among individuals with minimal depressive symptoms. These findings are the first to demonstrate a relationship between social rhythm stability and abdominal adiposity in adults at high risk for central obesity. Results highlight the moderating role of depression in obesity prevention. Future prospective analysis is necessary to examine causal pathways.

Support
#1R01HL117995-01A1
1050 Sleep, The Missing Domain For Cardiovascular Health?
Authors: Foster B E , Collen J F , Eliasson A H 
Published: May 2020

Abstract

Introduction
In developed countries, cardiovascular disease (CVD) is the leading cause of death. Identification and mitigation of CVD risk factors has been the chief strategy for prevention of CVD. To this end, the American Heart Association (AHA) and American College of Cardiology (ACC) suggest metrics of ideal cardiovascular (CV) health in seven domains and have published guidelines encouraging use of the “Simple Seven” to improve CV health. Sleep is notably absent from the Simple Seven despite substantial epidemiological evidence of its importance for CV health. We sought to measure the correlation of sleep parameters with CVD risk estimate by Framingham risk score (FRS).

Methods
Subjects prospectively enrolled in a CV Health Registry provided data for FRS, the seven domains of the “Simple Seven” including blood pressure, physical activity, total cholesterol, diet, weight, smoking, and glucose level, and completed validated questionnaires: Pittsburgh Sleep Quality Index (PSQI) and Berlin Questionnaire (BQ) for sleep apnea. Pearson correlations were analyzed between FRS and the “Simple Seven” as well as for the sleep questionnaires for comparison.

Results
Among the 646 subjects (mean age 55.4 ±9.9y, 43% men, 399W, 190B, 26H, 12A, 19other), FRS correlated substantially with blood glucose (R=0.398, p&lt;0.00001), moderately with blood pressure (R=0.220, p&lt;0.00001), and mildly with weight (r=0.145, p-0.0002) and physical activity (R=0.110, p=0.01). A negative (paradoxical) correlation was identified between total cholesterol and diet when compared to FRS. In comparison, FRS did correlate mildly with BQ (R=0.114, p=0.0002) and PSQI (r=-0.103, p=0.009).

Conclusion
This study suggests that sleep parameters, specifically BQ and PSQI, may contribute to risk assessment of CVD while also offering actionable information for improving CV health.

Support
1058 Modeling Self-reported Sleep Duration And Hypertension Using Deep Learning Network: Analysis Of The National Health And Nutrition Examination Survey Data
Authors: Williams S , Seixas A , Avirappattu G , Robbins R , Lough L , Rogers A , Beaugris L , Bernard M , Jean-Louis G 
Published: May 2020

Abstract

Introduction
Epidemiologic data show strong associations between self-reported sleep duration and hypertension (HTN). Modeling these associations is suboptimal when utilizing traditional logistic regressions. In this study, we modeled the associations of sleep duration and HTN using Deep Learning Network.

Methods
Data were extracted from participants (n=38,540) in the National Health and Nutrition Examination Survey (2006-2016), a nationally representative study of the US civilian non-institutionalized population. Self-reported demographic, medical history and sleep duration were determined from household interview questions. HTN was determined as SBP ≥ 130 mmHg and DBP ≥ 80 mmHg. We used a deep neural network architecture with three hidden layers with two input features and one binary output to model associations of sleep duration with HTN. The input features are the hours of sleep (limited to between 4 and 10 hours) and its square; and the output variable HTN. Probability predictions were generated 100 times from resampled (with replacement) data and averaged.

Results
Participants ranged from 18 to 85 years old; 51% Female, 41% white, 22% black, 26% Hispanic, 46% married, and 25% &lt; high school. The model showed that sleeping 7 hours habitually was associated with the least observed HTN probabilities (P=0.023%). HTN probabilities increased as sleep duration decreased (6hrs=0.05%; 5hrs=0.110%; 4hrs=0.16%); HTN probabilities for long sleepers were: (8hrs=0.027; 9hrs=0.024; 10hrs=0.022). Whites showed sleeping 7hrs or 9hrs was associated with lowest HTN probabilities (0.008 vs. 0.005); blacks showed the lowest HTN probabilities associated with sleeping 8hrs (0.07), and Hispanics showed the lowest HTN probabilities sleeping 7hrs (0.04).

Conclusion
We found that sleeping 7 hours habitually confers the least amount of risk for HTN. Probability of HTN varies as a function of individual’s sex and race/ethnicity. Likewise, the finding that blacks experience the lowest HTN probability when they sleep habitually 8 hours is of great public health importance.

Support
This study was supported by funding from the NIH: R01MD007716, R01HL142066, R01AG056531, T32HL129953, K01HL135452, and K07AG052685.
1062 The Role of Sleep in Sex and Racial/Ethnic Differences in 10-Year CVD Risk in the Sleep Heart Health Study: The Use of Machine-Learnt and Precision Insights to Understand Racial/Ethnic and Sex Differences in Sleep-CVD Disparity
Authors: Seixas A , Jin P , Liu M , Nunes J , Grandner M , Rogers A , McFarlane S , Jean-Louis G 
Published: May 2020

Abstract

Introduction
The current study investigated whether insufficient sleep (&lt;7 hrs.) explains differences in 10-year CVD risk, using Framingham risk (FRS) and Reynolds risk (RRS) scores, between blacks and whites and characterized risk and protective CVD risk profiles.

Methods
Using the Sleep Heart Health Study (SHHS) (N=6,441) data, we investigated the independent role of insufficient sleep in explaining differences in 10-years CVD between blacks and whites via a proportional odds model of four 10-year CVD risk groups: low (&lt;5%), low-medium (5% to &lt;10%), medium-high (10% to &lt;20%) and high (≥20%), adjusting for age, sex, and apnea-hypopnea index (AHI). We performed two levels of cluster analyses; via hierarchical cluster algorithm with entire sample (Level 1), and latent profiles in the low (protective profiles) and high (risk profiles) CVD risk groups (Level 2) to determine overall CVD risk, and risk and protective CVD profiles.

Results
Blacks had a higher prevalence of smoking behavior, diabetes, mean systolic blood pressure, body mass index, total cholesterol compared to whites. Conversely, whites had a higher mean HDL cholesterol, sleep hours, and sleep efficiency compared to blacks. Men had higher 10-year CVD risk than women. AHI and race/ethnicity-sleep interaction were positively associated, while sleep was negatively associated with FRS and RRS. Across all CVD risk groups, whites who slept less than 5.5 hrs. had a higher CVD risk and those who slept more than 6.5 hrs. had a lower CVD risk compared to blacks. In Level 1 cluster analyses, we found two clusters: Cluster 1 (n= 3233): 6.17 sleep hours, apnea-index 11.84, age 59, SBP 125.43, total cholesterol 209, HDL 51.39, BMI 29.03, and slightly more than 50% female; and Cluster 2 (n=1657): 5.61 sleep hours, apnea-index 13.41, age 74, SBP 131, total cholesterol 204, HDL 50.30, BMI 26.45, and slightly less than 50% female. In Level 2 cluster analyses, we found two profiles within the low and high CVD risk groups.

Conclusion
These findings suggest that blacks may not receive full protection from long-term CVD risk with longer sleep duration, as their white counterparts.

Support
K01HL135452, R01MD007716, R01HL142066, K07AG052685
1073 Sleep Disorders Contribute To Anger In Service Members With Posttraumatic Stress Disorder
Authors: Miles S R , Pruiksma K , Slavish D , Nakase-Richardson R , Nicholson K , Wardle S , Young-McCaughan S , Resick P , Williamson D , Dondanville K , Litz B , Mintz J , Keane T , Peterson A , Taylor D 
Published: May 2020

Abstract

Introduction
The emotion of anger and behavioral acts of aggression can lead to severe negative consequences, including family violence, legal charges, and death. Anger can be a symptom of posttraumatic stress disorder (PTSD), particularly in service members. Service members report difficulties managing their anger and trouble with the subsequent results. Factors that differentiate service members with PTSD who have anger related problems from those who do not are still unknown. Impaired sleep is associated with negative mood states in the general population and may be a risk factor for anger in those with PTSD. This project examines how sleep disorders commonly diagnosed in service members (i.e., obstructive sleep apnea and insomnia) relate to PTSD and anger.

Methods
Ninety-three service members with comorbid PTSD, insomnia, and nightmares (mean age = 35.86 years, SD = 8.38, 27% female, 45% white) completed polysomnography and other measures as part of a clinical trial. A multiple regression model examined how total Apnea Hypopnea Index (AHI), AHI during REM sleep, insomnia (Insomnia Severity Index), age, and race related to PTSD symptoms (Clinical Administered PTSD Scale-5: CAPS-5). A second multiple regression model examined the same variables’ associations with anger (Dimensions of Anger Reactions-5; DAR-5).

Results
More than a third of the sample (37%) met criteria for OSA (AHI scores&gt;5) and 99% met criteria for insomnia (ISI&gt;10). Total AHI and REM AHI were not associated with CAPS-5 scores or ISI. Across OSA and PSG indices, only greater REM AHI (b=.07, p&lt;.05) and Insomnia (b=.43, p&lt;.05) were positively associated with DAR-5 anger scores. Total AHI was unrelated to anger.

Conclusion
Elevated REM AHI and insomnia were associated with greater anger in service members with PTSD. Current treatments for anger are only moderately effective. Assessing and treating comorbid sleep disorders may reduce anger and enhance successful PTSD treatment.

Support
Consortium to Alleviate PTSD (W81XWH-13-2-0065), DVA (I01CX001136-01), GDHS (W91YTZ-13-C-0015) for DVBIC.
1079 Is PTSD In Young Women Associated With Rem Sleep Abnormalities?
Authors: Martinez D , Yeh M , Oliveira L , Coimbra B , Mello A F , Poyares D , Tufik S , Mello M F 
Published: May 2020

Abstract

Introduction
The increase in violence against young women has a high impact on the prevalence of Posttraumatic stress disorder (PTSD). The lifetime prevalence of PTSD is twice as high in women. However, most studies assessing sleep disturbances in PTSD were conducted predominantly in male samples and combat veterans. Objective: To analyze the sleep of young women with and without PTSD. Hypothesis: Women with PTSD have worse sleep quality, higher arousability, and higher muscle activity during REM sleep.

Methods
Case-controlled study with young women. Seventy-four women who suffered sexual assault and developed PTSD (DSM-5); and 64 women from the community without PTSD. Women were recruited from the PTSD outpatient clinic (Universidade Federal de São Paulo, Brazil).Clinician-Administered Posttraumatic Stress Scale (CAPS 5), Beck Depression and Anxiety Inventories (BDI) (BAI), full in-lab Polysomnography (PSG), Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Índex (PSQI), Fatigue Impact Scale (FIS), and Insomnia Severity Índex (ISI) were applied to all participants. Analysis of variance, regression models, and general linear modeling were used.

Results
Patients mean age was 28 vs 24 for the control group (p=0.004). CAPS mean score in PTSD-group was 42.5±9.1. BDI, BAI, FIS, PSQI, ISI scores were worse in PTSD-group (p&lt;0.05, all). Pittsburgh Sleep Quality Index (PSQI) score was significantly associated with CAPS 5 independently of depression, fatigue, and sleep fragmentation.
The PTSD women had lower total sleep time (p= 0.01) and lower REM sleep percentage (p=0.04). However, the control group had higher arousal index (p=.0.01) and had higher muscle activity during REM sleep (p=0.03) than PTSD.

Conclusion
Women with PTSD had significantly worse score in PSQI, FIS, and ISI. PSQI score was associated with PTSD severity. However, when PSG results are concerned, we found higher sleep fragmentation in the control group. We speculate that women with PTSD may have felt safer and taken care of in the lab, which might explain the difference between objective and subjective measures of sleep quality in PTSD.

Support
Acknowledgments: FAPESP: Fundação de Apoio à pesquisa de São Paulo, AFIP: Associação Incentivo a Pesquisa
1088 Heart Rate Variability During Sleep in Adults With and Without a History of Depression and the Influence of Antidepressant Use
Authors: Egeler M E , Bowman M , Thayer J F , Brindle R C , Hall M , Kline C E 
Published: May 2020

Abstract

Introduction
Individuals with depression have an increased risk for cardiovascular disease. While lower daytime HRV may be one mechanism of this association, it is less clear whether HRV during sleep (sHRV) differs between adults with and without depression. Examining sHRV is an important measure to test given evidence that nocturnal differences in physiology are strong predictors of cardiovascular disease.

Methods
118 adults who completed psychiatric interviews between the ages of 21-60 y (T1) returned 18.7 ± 4.5 years later for a re-evaluation of their mental health and assessment of sleep (T2). 71 participants were diagnosed with Major Depressive Disorder (MDD) at either T1 or T2, while 47 participants were free from depression at both time points. At T2, participants underwent an overnight polysomnogram with concurrent assessment of electrocardiography. The primary measures of sHRV were root mean square of successive differences (RMSSD) values during rapid eye movement (REM) sleep and non-REM (NREM) sleep stages N2 and N3. Lower values of RMSSD suggest lower parasympathetic activity. RMSSD values were natural log-transformed prior to analysis. Differences in RMSSD between depressive groups were examined using analysis of covariance (ANCOVA), adjusting for age, body mass index, gender, race, and antihypertensive medication use. Additional ANCOVA models adjusted for antidepressant use.

Results
Participants with current or a history of MDD had lower RMSSD values during REM sleep (P=.01) and a trend toward lower values during NREM (P=.06) compared to those without MDD. Antidepressant use was significantly associated with lower RMSSD during both REM (P=.002) and NREM sleep (P&lt;.001). Depression history was no longer associated with RMSSD during NREM or REM sleep following adjustment for antidepressant use (P&gt;.40).

Conclusion
These data indicate that adults with MDD exhibit lower parasympathetic activity during sleep compared to those without MDD. These associations were modified by antidepressant use, suggesting that antidepressants may partially explain the association between depression and sHRV. Future studies investigating the influence of specific antidepressants for modifying nocturnal physiology may help to better understand the link between depression and cardiovascular disease risk.

Support
This study was funded by National Institutes of Health (NIH) grants R01 HL104607 and K23 HL118318.
1092 Attention-deficit/hyperactivity Disorder Symptoms And Sleep Characteristics Within A Seasonal Affective Disorder Spectrum
Authors: Kim K N , Wescott D L , Franzen P L , Hasler B P , Roecklein K A 
Published: May 2020

Abstract

Introduction
Seasonal affective disorder (SAD) increases risk for attention-deficit/hyperactivity disorder (ADHD), although the mechanism linking SAD and ADHD is unknown. Prior research has identified insomnia and delayed sleep phase in both ADHD and SAD. We hypothesized that sleep duration and timing in SAD would be associated with the severity of ADHD symptoms.

Methods
Adults with SAD (n = 45) and subsyndromal SAD (S-SAD; n = 18) aged 19-66 years from Pittsburgh, PA., were assessed for ADHD symptoms, self-report sleep quality, depression severity, and daytime sleepiness in the Winter. Participants wore an Actiwatch for 4-14 days, from which we calculated sleep-onset latency, total sleep time, sleep midpoint, and sleep efficiency. We conducted a hierarchical multivariate linear regression to determine if sleep characteristics predict ADHD symptom severity in our sample while controlling for depressive symptoms. Age and gender were added in Step 1, seasonal depression severity in Step 2, actigraphy-based total sleep time, sleep onset latency, midpoint, and efficiency in Step 3, and self-reported sleep quality and daytime sleepiness in Step 4.

Results
Participants mostly scored in the “likely” or “highly likely” ADHD range (87.30%, n=55), higher than the national prevalence rate (4.4%). When controlling for age, gender, and depression severity, only shorter actigraphy-based total sleep time was associated with higher ADHD symptom severity (β=-0.30, p&lt;0.05). However, when self-reported sleep quality and daytime sleepiness were added as predictors, total sleep time was no longer a statistically-significant predictor of ADHD symptom severity and only daytime sleepiness predicted ADHD symptom severity (β=0.31, p&lt;0.05).

Conclusion
Our results suggest that individuals with SAD who experience daytime sleepiness and/or possibly shorter actigraphy-based sleep duration experience higher ADHD symptom severity. Treatments like Trans-C or CBT-I to improve daytime sleepiness and sleep duration may be indicated for SAD patients who present with comorbid ADHD symptoms.

Support
NIMH K.A.R. MH103303