bluuebunny/crossref_metadata_2025_split huggingface dataset:
0601 Predicting Incident Outcomes from the Microstructure of Sleep
Authors: Sun Haoqi , Adra Noor , Ayub Muhammad , Ye Elissa , Ganglberger Wolfgang , Thomas Robert , Westover M Brandon 
Published: May 2022

Abstract

Introduction
Sleep contains rich information about health status, relevant to future health outcomes including dementia, cerebrocardiovascular diseases, psychiatric diseases, and mortality. We hypothesized that the risk of these outcomes is predictable from quantitative analysis of sleep microstructure.

Methods
We included participants who underwent a diagnostic study and age older than 18 years. We excluded participants with missing demographics or PSGs &lt; 2.5 hours in duration. We considered 11 outcomes including dementia, mild cognitive impairment or dementia, ischemic stroke, intracranial hemorrhage, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. The outcomes were determined using ICD codes, brain imaging reports, medications, and/or cognition scores. We extracted 86 spectral and time-domains features from overnight sleep EEG recordings, including 57 features from NREM sleep epochs, 21 from REM, and 8 covariates including age, sex, body mass index, and medication prescriptions including benzodiazepines, antidepressants, sedatives, antiseizure medications, and stimulants. We modeled risk using Cox survival analysis with death as a competing risk. Model calibration was assessed using the difference in 10-year cumulative incidence (10y-CI) between Cox estimate vs. Aalon-Johansen estimate (ground truth).

Results
There were 8673 participants with an average age of 51 years; 51% were female. Participants were partitioned into three groups: poor sleep (hazard &gt; 3rd quartile (Q3)), average sleep (Q1 ≤ hazard ≤ Q3), and good sleep (hazard &lt; Q1). The model was able to predict the 10y-CI not significantly different from the ground truth, except for the risk of intracranial hemorrhage in the poor sleep group. The outcome-wise mean prediction difference in 10y-CI was 2.3% for the poor sleep group, 0.5% for the average sleep group, and 1.3% for the good sleep group. The outcomes with the top three poor-to-average risk ratios (RR) were dementia (RR = 6.2 95% confidence interval [4.5 – 9.3]), mortality (RR = 5.7 [5.0 – 7.5]), and MCI or dementia (RR = 4.0 [3.2 – 4.9]).

Conclusion
Sleep EEGs contain decodable information about the risk for future incidence of mortality, dementia, cerebrocardiovascular and psychiatric diseases. The findings strengthen the concept of sleep as a window into brain and general health.

Support (If Any)
This work is supported by the AASM Foundation 2019 Strategic Research Award.
0617 Association Between Green, Blue, and Open Spaces and Sleep Health in a Black Population: An Analysis of the MetSO Dataset
Authors: Moore Jesse , Jin Peng , Briggs Anthony , Grisby Diana , Seixas Azizi , Jean-Louis Girardin 
Published: May 2022

Abstract

Introduction
Blacks have a high burden of poor sleep health outcomes. Environmental determinants, such as green space or open environments, represent an underexplored contributor to sleep burden among Blacks. The extent these environmental factors affect sleep health outcomes within this population has not been adequately explored. To fill this gap in the literature, we investigated associations between environmental factors and sleep outcomes among Blacks in a large urban city. Objectives included (1) examine if zip-code derived open spaces (defined as proportion of open space in residential area,) green spaces (defined as open tree coverage of the ground) and blue spaces (proportion of water space) sleep apnea risk, and insomnia symptoms; (2) Examine if open, blue, and green spaces predict sleep outcomes independent of sex, age, and education level.

Methods
Our study used data from the Metabolic Syndrome Cohort Study (2009-2014), a studythat examined behavioral intervention methods to improve sleep apnea outcomes among Blacks. Sleep Apnea was assessed with the ARES (apnea risk) scale and insomnia status was collected through self-report (“Do you have difficulty staying/falling asleep or waking up?”) in a subset of 344 participants. Logistic regression analyses were performed to predict the effect green, blue, and open spaces had on sleep outcomes. To account for within zip-code correlation, mixed effects models (unadjusted and adjusted) account for sex, age, and education were considered.

Results
We found that none of the green, blue, or open space variables predicted sleep outcomes in the unadjusted model. In adjusted models, green space predicted sleep apnea risk scores, (OR=1.03, P&lt;.05), but not insomnia.

Conclusion
Our study examined the extent which green, blue, and open spaces predicted insomnia and sleep apnea in urban blacks. We found that only green spaces were associated with sleep apnea, and none of our environmental variables predicted insomnia. Given the large amount of literature detailing a complex and multifactorial process on how environment affects sleep outcomes, our findings suggest that the link between urban environments, green spaces, and sleep outcomes may not be as definitive as they seem. Further research should explore the differential effect environment has on diverse populations’ sleep outcomes.

Support (If Any)
NIH R01HL142066, R01HL095799, RO1MD004113, R01HL152453
0621 Overcoming Obstacles to Recruitment and Community Engagement During COVID-19 and Development of a Digital Community Outreach program
Authors: Oliveira Bruno Carucci , Vidal Crystal , Pichardo Yakira , Hahn Kaitlin , Locklear Clarence , Moore Jesse Jesse , Jean-Louis Girardin , Seixas Azizi , Blanc Judite 
Published: May 2022

Abstract

Introduction
COVID-19 disrupted traditional research infrastructures and processes most notably in-person community recruitment, especially in underrepresented populations like racial ethnic minorities. To find creative and effective strategies, our group implemented and tested the efficacy of a culturally tailored community outreach plan (COP) developed during the US COVID-19 pandemic.

Methods
In February 2021, we developed an 11 step culturally-tailored community outreach program to support the implementation of three NIH funded community-based sleep studies. The following steps include: (1) description of the situation statement, 2) definition of goals, 3) engagement of audience/stakeholders, 4) tailoring message, 5) defining incentives, 6) choice of outreach methods, 7) identification of spokesperson, 8) choice of tools to assess progress, 9) identification of media outlets, 10) creation of study timeline, and 11) implementation of the plan. The studies leveraged several recruitment channels: 1) community settings (Place of worship, “community recruiter”, health fairs, word of mouth, & healthcare providers/doctors’ clinics), 2) online platforms (Facebook, Twitter, LinkedIn and Research Match), and 3) preexisting datasets in NYC.

Results
All three studies successfully met recruitment goals. ESSENTIAL [n= 224, 69% females, mean age= 36], MOSAIC [n=109, 61% females; mean age= 64] and Latinx/Hispanics: DORMIR[n=260, 61.3% of female; 32.4]. Among the three NYC cohorts, the most common recruitment channels were: preexisting datasets (74%), community settings (19%), & online platform (7%) for ESSENTIAL; preexisting datasets (85%) & community settings (15%) for MOSAIC, and (71.7%) online platform for DORMIR. However, the Miami cohorts came mostly from community settings 90% for Essential and 97% for MOSAIC.

Conclusion
Overall, the TSCS community outreach plan seems to be an effective tool to engage minoritized populations in greater NY and Miami. Our current field experience indicates that recruitment channels must be adapted to age, and community resources. Limited access to technology, particularly among older Blacks seem to be a major barrier for field staff to successfully engage the disenfranchised communities.

Support (If Any)
NIH R01HL142066-04; R01HL152453-01 R01HL142066, R01HL095799, RO1MD004113
0647 DSM-V Diagnosed Post-traumatic Stress Disorder (PTSD) is Associated with Reported Dream Enactment Independently from Gender, Race or Education in a Psychiatric Outpatient Population
Authors: Bliwise Donald , Greer Sophia , Black Kathryn , Wise Anna , Rauch Sheila , Rothbaum Barbara 
Published: May 2022

Abstract

Introduction
PTSD has been associated with PSG-derived measurements of REM without atonia suggestive of RBD (Elliott et al, Sleep 2020 Mar 12;43(3):zsz237). Those findings have been reported in men in their mid-50’s. In a relatively younger, diverse, help-seeking, psychiatric outpatient population enriched with PTSD, we examined the impact of demographics (self-reported gender, race, education) on the association between primary diagnosis of PTSD and probable RBD assessed with a validated questionnaire.

Methods
Patients (n = 1,658) were enrolled in a non-VA based treatment program for veterans. Patients underwent detailed diagnostic interviews and psychometrics. About 48% (549 men, 251 women; mean age 40.0 [SD = 9.6] years; 381 non-white) met criteria for PTSD. Typical other diagnoses included Axis I defined major depression and various anxiety disorders (without re-experiencing). Dream enactment was assessed with the previously validated University of Michigan RBD Questionnaire (UMRBDQ), completed either by patient or bedpartner. Mean (SD) and 50th% UMRBDQ scores were 0.52 (SD = 0.26) and 0.50. We defined likelihood of RBD by the 90th% of UMRBDQ score (&gt; .80), based on previously published psychometrics, and used multiple logistic regression to define predictors of high vs low UMRBDQ scores. Predictors were diagnosis, self-reported gender (male), race (non-white [vs white]) and education (at least partial college).

Results
PTSD diagnosis was strongly associated with positive UMRBDQ (31.5% vs 22.0%; OR = 1.77; 95% CI 1.40-2.23). Significant predictors were male gender (OR = 1.93, 95% CI 1.45-2.58) and non-white race (OR = 1.81; 95% CI 1.03-1.65) but not education (OR = 0.86; 95% CI 0.68-1.09). Models of 2-way interactions were all non-significant. Linear models employing UMRBDQ as continuous scores yielded similar results.

Conclusion
These results confirmed that PTSD may be associated with higher likelihood of reported dream enactment and that these results were not dependent on self-reported gender, race or education.

Support (If Any)
Wounded Warrior Project
0662 Comparative efficacy of digital CBT-I versus stepped-care CBT-I to prevent depression
Authors: Cheng Philip , Kalmbach David , Sultan Zain , Fellman-Couture Cynthia , Drake Christopher 
Published: May 2022

Abstract

Introduction
Insomnia is a robust risk factor for depression. Treating insomnia with digital CBT-I (dCBT-I) has been shown to prevent future episodes of depression; however, remission rate of insomnia following dCBT-I is lower compared to face-to-face CBT-I (fCBT-I), which may reduce the effect on depression prevention. A stepped-care model can optimize care by starting with a least resource intensive intervention (step 1: dCBT-I) and stepping-up non-remitters to specialized treatment (step 2: face-to-face CBT-I). This study examined the efficacy of a stepped-care approach to prevent depression.

Methods
341 individuals with DSM-5 insomnia and no depression were randomized into two conditions at step 1: dCBT-I (n=161), or an online sleep education control (n=188). Participants in the dCBT-I condition who did not show remission for insomnia (ISI&gt;9) were further randomized to either face-to-face CBT-I (n=40) or sleep education (n=47). Rates of clinically significant depression was assessed post-step 2.

Results
Insomnia remission rates were higher in the dCBT-I group (46%) compared to the control group (22%); however, 54% remained symptomatic after dCBT-I and were rerandomized to step 2 treatments (fCBT-I or sleep education control). Insomnia remission rates following fCBT-I was 70% compared to 23% in the step 2 control.Rate of depression in those who received step 2 control (following non-remission to dCBT-I in step 1; 15%) was not significantly different from the step 1 control group (20%). In contrast, those who received step 2 fCBT-I reported significantly lower rates of depression (5%, OR=.22, p &lt; .05) compared to the step 1 control. Those who remitted to dCBT-I in step 1 also reported comparably low rates of depression (8%, OR=.36, p &lt; .05)

Conclusion
Preliminary evidence from this study provide supported that a stepped-care approach may produce greater protection against incident depression than dCBT-I alone.

Support (If Any)
R01MH122636
0663 Association of Insomnia Phenotypes based on Objective Sleep Duration with Suicide Attempts, Ideation and Completion
Authors: Saulnier Kevin , Singh Rupsha , Lenker Kristina , Calhoun Susan , Liao Duanping , Bixler Edward , Vgontzas Alexandros , Fernandez-Mendoza Julio 
Published: May 2022

Abstract

Introduction
Sleep disturbances, including insomnia and short sleep duration, are known risk factors for suicidal ideation, attempts, and death. Insomnia is a heterogeneous disorder, with phenotypes of short and normal sleep duration based on objective sleep measures showing differential pathophysiology, natural course, cardiometabolic and neurocognitive morbidity. However, little is known about the association of these insomnia phenotypes with suicidality in adults.

Methods
We analyzed data from the Penn State Adult Cohort (N = 1741, M age = 52.46, SD = 13.43, 57.4% female), a randomly selected population-based sample who underwent a thorough clinical history and in-lab polysomnography (PSG). Suicidality was ascertained by a lifetime history of suicide attempts, suicidal ideation or suicide as cause of death by December 31 2018 (n = 102). Insomnia symptoms were defined as a complaint of moderate-to-severe difficulties initiating or maintaining sleep, early morning awakening and non-restorative sleep, or chronic insomnia (n = 719). Short sleep duration was defined as &lt; 6-h of in-lab PSG-measured sleep (n = 879). Binary logistic regression was used to examine the association between insomnia phenotypes with suicidality, while controlling for sex, age, race/ethnicity, and medical and psychiatric comorbidities. Given the low suicidality prevalence in this sample, 1000 bootstrapped samples were drawn to provide stable estimates.

Results
Compared to normal sleepers who slept &gt; 6-h, subjects with insomnia symptoms who slept &lt; 6-h and those who slept &gt; 6-h were associated with 1.96-fold (95%CI = 1.04-4.00) and 2.46-fold (95%CI = 1.30-5.04) increased odds of suicidality, respectively. After further adjusting for substance use, subjects with insomnia symptoms who slept &lt; 6-h and those who slept &gt; 6-h were associated with 1.72-fold (95%CI = 0.90-3.58) and 2.22-fold (95%CI = 1.15-4.60) increased odds of suicidality, respectively. Normal sleepers who slept &lt; 6-h were not associated with significantly increased odds of suicidality (OR = 1.32; 95%CI = 0.56-2.94).

Conclusion
Adults with insomnia, particularly those with normal sleep duration, were associated with increased suicidality. These data further support that objectively-defined insomnia phenotypes may confer risk for differential adverse health outcomes (e.g., cardiometabolic vs. psychopathologic) via distinct mechanistic pathways.

Support (If Any)
American Heart Association (14SDG19830018)
0698 Genetic susceptibility to elevated C-reactive protein and risk of obstructive sleep apnea in US men and women
Authors: Huang Tianyi , Goodman Matthew , Wang Heming , Sofer Tamar , Tworoger Shelley , Stampfer Meir , Saxena Richa , Redline Susan 
Published: May 2022

Abstract

Introduction
Obstructive sleep apnea (OSA) may trigger inflammation. However, growing evidence suggests a role for inflammation in predisposing OSA through increased upper airway size/collapsibility and altered ventilatory control. Inflammation also promotes sleep disturbance and excessive daytime sleepiness (EDS). Genetic analysis may provide further insights into the causal relationship between chronic inflammation and OSA incidence.

Methods
In 33,171 participants of European ancestry from the Nurses’ Health Study (NHS), NHS2 and the Health Professionals Follow-up Study, we quantified genetic predisposition to inflammation using a weighted polygenic risk score (PRS) based on 51 loci identified for C-reactive protein (CRP) in a recent genome-wide association meta-analysis. OSA was determined using self-reported diagnoses, which were demonstrated to reliably indicate moderate-to-severe OSA in these cohorts. EDS was defined based on self-reports of disrupted daily activities due to sleepiness ≥4 days/week. Multivariable logistic regression was used to estimate the odds ratio (OR) for OSA risk according to the number of risk alleles, adjusted for age, sex, BMI, genotyping platforms and 10 genetic principal components. Mendelian randomization using individual-level participant data was performed to evaluate the association between genetically determined CRP levels and OSA risk.

Results
A total of 3,163 participants (9.5%) had clinically diagnosed OSA (575 with EDS). While the CRP PRS (explained 5.0% variance in circulating levels) was not associated with OSA risk overall (OR per increment of 5 risk alleles: 1.00; 95% CI: 0.96, 1.04; p=0.96), a significant positive association was observed for OSA with concurrent EDS (Comparable OR: 1.13; 95% CI: 1.04, 1.23; p=0.003). In contrast, among participants without clinically diagnosed OSA, there was no association between the PRS and EDS (Comparable OR for EDS: 1.02; 95% CI: 0.98, 1.06; p=0.28). In Mendelian randomization, each doubling of genetically elevated CRP was associated with 44% higher odds of OSA with EDS (95% CI: 1.13, 1.82).

Conclusion
Chronic inflammation (characterized by elevated CRP levels) may be casually associated with risk of developing symptomatic OSA with EDS, but not OSA or EDS alone. To confirm our findings, future investigations are needed to evaluate the genetic associations with objective measures of OSA severity across diverse populations and elucidate mechanisms driven by specific inflammatory mediators.

Support (If Any)
NIH grants UM1CA186107, U01CA176726, U01CA167552, K01HL143034, R35HL135818
0704 Association of high-risk obstructive sleep apnea with inflammatory markers in asymptomatic young and middle-aged adults in Miami Heart (MiHeart) Study at Baptist Health South Florida
Authors: Walia Harneet , Saxena Anshul , Ali Shozab , Valero-Elizondo Javier , Cainzos-Achirica Miguel , Feldman Theodore , Nasir Khurram , Fialkow Jonathan 
Published: May 2022

Abstract

Introduction
Obstructive Sleep Apnea (OSA) is associated with elevated inflammatory markers in those with cardiovascular disease (CVD). In contrast, there are limited data to support this association in asymptomatic individuals. The Miami Heart Study measured high sensitivity C-reactive protein (hs-CRP), Interleukin 6 (IL-6) and TNF- Alpha in a cohort of asymptomatic individuals from the general population. We hypothesized that there will be significant association of high OSA risk with inflammatory markers in Miami Heart Study cohort free of CVD.

Methods
We analyzed data for 2359 clinical CVD-free participants from the Miami Heart Study, age 40-65 years (May 2015-Sept 2018). High OSA risk included those with an OSA diagnosis and/or those with high risk using the Berlin questionnaire. Poisson regression analyses were utilized to examine the associations between high OSA risk (reference: low risk) and hs-CRP, IL-6 and TNF alpha levels (continuous), in univariate and multivariate models (adjusting for age, sex, race/ethnicity [model 2], and BMI, diabetes, hypertension, high cholesterol and smoking [model 3]).

Results
800 (34%) participants were categorized as high OSA risk. Those with high OSA risk tended to be Hispanic, male, and with a higher CVD risk factor burden, especially obesity (64% vs 17%, p &lt; 0.005) when compared to those with low OSA risk. Patients with high OSA risk had higher median values of hs-CRP (2.1 vs 1.0), IL-6 (2.0 vs 1.4), and TNF-alpha (1.2 vs 1.1) when compared to those with low OSA risk (all p &lt; 0.001). When adjusting for age, sex, and race/ethnicity, the mean difference between patients with high and low OSA risk in hs-CRP was 1.86 (95% CI 1.69, 2.02), and 0.85 (95% CI 0.71, 0.99) in IL6. When further adjusting for CVD risk factors, these differences were attenuated, but statistically significant (hs-CRP [0.32, 95% CI 0.16, 0.48]; IL6 [0.37, 95% CI 0.21, 0.53]). In adjusted analyses, TNF-alpha was not statistically different between OSA risk populations.

Conclusion
Individuals at high risk for OSA had significant higher levels of hs-CRP and IL6, signaling to potential role of OSA in mediating the increased inflammatory markers in asymptomatic CVD risk free individuals.

Support (If Any)
Baptist Health South Florida
0708 Stage-dependent Differences in Central Sleep Apnea (CSA) Predominate in remedē System Pivotal Trial Participants
Authors: Schwartz Alan , Germany Robin , Meyer Timothy , McKane Scott 
Published: May 2022

Abstract

Introduction
Differences in neuroventilatory control can impact the type and severity of sleep disordered breathing between non-REM and REM sleep. We examined the distribution of central apneic episodes in polysomnograms from subjects with predominantly central sleep apnea, who received phrenic nerve stimulation.

Methods
Baseline in-lab polysomnograms from patients enrolled in the remedē System Pivotal Trial were scored by a central core laboratory (n=151). Participants with predominantly CSA were enrolled if the apnea-hypopnea index (AHI)≥20/hr, the central apnea index (CAI) exceeded the obstructive apnea index (OAI), and the OAI did not exceed 20% of the total AHI. This post-hoc analysis compared sleep apnea indices in REM and non-REM sleep in those with ≥5 minutes REM sleep (n=131). Within-patient median non-REM - REM differences were calculated and compared.

Results
REM sleep time was 40 [Q1=28, Q3=64] minutes, and non-REM sleep time was 301 [Q1=269,Q3=344] minutes. AHI in REM sleep was 22/hr [Q1=9,Q3=44] and was 46/hr (Q1=33,Q3=60) in non-REM sleep, yielding a within-patient difference between non-REM and REM sleep of 22/hr ([Q1=6, Q3=34], p&lt;.001). CAI in REM was only 4/hr (Q1=0, Q3=11), but in non-REM was 25/hr [Q1=16,Q3=43] with all patients having a CAI≥5/hr during non-REM. In REM, 70% had a CAI&gt;0/hr and 46% had a CAI ≥5/hr. The CAI difference between non-REM and REM sleep was 18/hr [Q1=10, Q3=30; p&lt;.001]. The OAI and mixed apnea index (MAI) differed by &lt;1/hr (p=0.235 and &lt;.001, respectively). Of note, the hypopnea index [HI] did not differ between REM and non-REM sleep (12/hr [Q1=2,Q3=22] vs. 11/hr [Q1=4, Q3=20], respectively, p=0.273), yet hypopneas constituted a greater proportion of total AHI during REM compared to non-REM sleep.

Conclusion
Among subjects with predominantly CSA, the prevalence and severity of CSA was greater in non-REM than REM sleep, yet low-level CSA persisted in REM sleep. Stage-related differences in CAI but not OAI, MAI or HI can be attributed to alterations in ventilatory rather than upper airway control in this CSA cohort. These differences in the type and severity of sleep disordered breathing episodes comprise a key diagnostic signature, for which specific CSA therapeutic strategies are indicated.

Support (If Any)
ZOLL Respicardia, Inc. and NIH R01 HL144859