Showing papers by "Susan Redline published in 2020"
Alexander G. Bick, Joshua S. Weinstock1, Satish K. Nandakumar2, Satish K. Nandakumar3 +162 more•Institutions (49)
TL;DR: Analysis of high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine programme enables simultaneous identification of germline and somatic mutations that predispose individuals to clonal expansion of haematopoietic stem cells.
Abstract: Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown1. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer2-4 and coronary heart disease5-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)6. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.
300 citations
TL;DR: Irregular sleep duration and timing may be novel risk factors for CVD, independent of traditional CVD risk factors and sleep quantity and/or quality.
185 citations
TL;DR: Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways, increasing understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.
Abstract: Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P
158 citations
Harvard University1, Southwestern University of Finance and Economics2, University of Texas Health Science Center at Houston3, University of Texas MD Anderson Cancer Center4, University of Kentucky5, University of Alabama at Birmingham6, Baylor College of Medicine7, University of Michigan8, The University of Texas Rio Grande Valley9, Wake Forest University10, University of Washington11, University of Mississippi Medical Center12, Boston University13, Los Angeles Biomedical Research Institute14, Qatar University15, Fred Hutchinson Cancer Research Center16, University of Virginia17, George Washington University18, Johns Hopkins University School of Medicine19, Brown University20, University of Maryland, Baltimore21, Veterans Health Administration22, University of Massachusetts Medical School23, Broad Institute24, Kaiser Permanente25, Beth Israel Deaconess Medical Center26, Brigham and Women's Hospital27, University of Minnesota28, University of Pittsburgh29, Regeneron30
TL;DR: STAAR is a powerful rare variant association test that incorporates variant functional categories and complementary functional annotations using a dynamic weighting scheme based on annotation principal components and is scalable for analyzing large whole-genome sequencing studies.
Abstract: Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
116 citations
TL;DR: The results demonstrate that, for nonnormally distributed traits, INT‐based tests outperform the standard untransformed association test, both in terms of power and type I error rate control.
Abstract: Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.
93 citations
TL;DR: Electronic health record data from a large New England healthcare system was analyzed to ask whether sleep apnea is an unrecognized risk factor for COVID-19–related death, hospitalization, ventilator use, and ICU admission among patients with positive CO VID-19 diagnostic testing.
Abstract: A report describing 5,700 patients with coronavirus disease (COVID-19) identified that common risk factors for poor outcomes are older age, minority ethnicity, obesity, hypertension, and diabetes (1). However, mortality and hospitalizations, estimated by the CDC COVID-19 Response Team to occur in 1.8–3.4% and 20.7–31.4% of COVID-19–positive individuals, respectively (2), are not fully explained by recognized risk factors. Sleep apnea—prevalent in older, obese, and minority individuals—increases risk for COVID-19 comorbidities and may contribute to poor outcomes by exacerbating or causing endothelial dysfunction, inflammation, oxidative stress, microaspiration, and lung injury (3–8). Although prior reports of COVID-19 risk factors have not identified sleep apnea as a prevalent risk factor, data were from healthcare systems where clinical recognition of sleep apnea is markedly underrecognized. Given its association with recognized COVID-19 comorbidities and physiological plausibility, we analyzed electronic health record (EHR) data (9) from a large New England healthcare system to ask whether sleep apnea is an unrecognized risk factor for COVID-19–related death, hospitalization, ventilator use, and ICU admission among patients with positive COVID-19 diagnostic testing. Methods The sample was adult nonemployee participants with positive COVID-19 RNA PCR diagnostic results who had available demographic data, a minimum of two clinical notes, two encounters, and three International Classification of Disease (ICD) diagnoses of any disease (to minimize the effect of minimal EHR documentation in participants with out-of-network care). Participants were further restricted to include those with either zero or two or more ICD-9 or ICD-10 diagnoses of sleep apnea or obstructive sleep apnea on different dates (to minimize the effect of rule-out diagnosis codes). Natural language processing (10) was used to obtain documentation of continuous positive airway pressure (CPAP) usage in the year before the first COVID-19 test.
88 citations
TL;DR: In men, the hypoxic burden of sleep apnea was associated with incident HF after accounting for demographic factors, smoking, and co-morbidities and the findings suggest that quantification of an easily measured index ofSleep Apnea related hypoxias may be useful for identifying individuals at risk for heart disease while also suggesting targets for intervention.
73 citations
TL;DR: There are significant sex differences in NREM-AHI levels and in physiological endotypes, and definitions that use 4%-desaturation criteria under-estimate AHI in women.
Abstract: STUDY OBJECTIVES The bases for sex disparities in obstructive sleep apnea (OSA), is poorly understood. We quantified the influences of event definitions, sleep-state, and body position on apnea-hypopnea indices (AHIs) in men and women, and evaluated sex differences in pathophysiological endotypes. METHODS Polysomnography (PSG) data were analyzed from 2057 participants from the multi-ethnic study of atherosclerosis. Alternative AHIs were compared using various desaturation and arousal criteria. Endotypes (loop gain, airway collapsibility, arousal threshold) were derived using breath-by-breath analysis of PSG signals. Regression models estimated the extent to which endotypes explained sex differences in AHI. RESULTS The sample (mean 68.5 ± 9.2 years) included 54% women. OSA (AHI4P ≥15/h, defined by events with ≥4% desaturations) was found in 41.1% men and 21.8% women. Compared to AHI4P, male/female AHI ratios decreased by 5%-10% when using 3%-desaturation and/or arousal criteria; p < 0.05. REM-OSA (REM-AHI ≥15/h) was similar in men and women regardless of event desaturation criteria. REM-AHI4P ≥15/h was observed in 57% of men and women each. In NREM, AHI4P in men was 2.49 (CI95: 2.25, 2.76) of that in women. Women demonstrated lower loop gain, less airway collapsibility, and lower arousal threshold in NREM (ps < 0.0005). Endotypes explained 30% of the relative sex differences in NREM-AHI4P. CONCLUSIONS There are significant sex differences in NREM-AHI levels and in physiological endotypes. Physiological endotypes explained a significant portion of the relative sex differences in NREM-AHI. Definitions that use 4%-desaturation criteria under-estimate AHI in women. Combining NREM and REM events obscures OSA prevalence in REM in women.
71 citations
University of Maryland, Baltimore1, Boston University2, University of Michigan3, Broad Institute4, Brigham and Women's Hospital5, University of Colorado Denver6, University of Pittsburgh7, Harvard University8, Beth Israel Deaconess Medical Center9, National Institutes of Health10, Johns Hopkins University11, McGill University12, University of Mississippi Medical Center13, University of Washington14, United States Department of Veterans Affairs15
TL;DR: The most diverse human de novo mutation call set to date is provided, and it is used to quantify the genome-wide relationship between local mutation rate and population-level rare genetic variation, and a significant positive correlation between local recombination rate and local DNM rate is found.
Abstract: De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains <1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability (h 2), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.
66 citations
TL;DR: Delayed high school start times could extend adolescent school night sleep duration and lessen their need for catch-up sleep on weekends, and these findings suggest that later start time could be a durable strategy for addressing population-wide adolescent sleep deficits.
Abstract: Importance Sleep is a resource that has been associated with health and well-being; however, sleep insufficiency is common among adolescents. Objective To examine how delaying school start time is associated with objectively assessed sleep duration, timing, and quality in a cohort of adolescents. Design, Setting, and Participants This observational cohort study took advantage of district-initiated modifications in the starting times of 5 public high schools in the metropolitan area of Minneapolis and St Paul, Minnesota. A total of 455 students were followed up from grade 9 (May 3 to June 3, 2016) through grade 11 (March 15 to May 21, 2018). Data were analyzed from February 1 to July 24, 2019. Exposures All 5 participating schools started early (7:30amor 7:45am) at baseline (2016). At follow-up 1 (2017) and continuing through follow-up 2 (2018), 2 schools delayed their start times by 50 and 65 minutes, whereas 3 comparison schools started at 7:30amthroughout the observation period. Main Outcomes and Measures Wrist actigraphy was used to derive indices of sleep duration, timing, and quality. With a difference-in-difference design, linear mixed-effects models were used to estimate differences in changes in sleep time between delayed-start and comparison schools. Results A total of 455 students were included in the analysis (among those identifying sex, 225 girls [49.5%] and 219 boys [48.1%]; mean [SD] age at baseline, 15.2 [0.3] years). Relative to the change observed in the comparison schools, students who attended delayed-start schools had an additional mean 41 (95% CI, 25-57) objectively measured minutes of night sleep at follow-up 1 and 43 (95% CI, 25-61) at follow-up 2. Delayed start times were not associated with falling asleep later on school nights at follow-ups, and students attending these schools had a mean difference-in-differences change in weekend night sleep of −24 (95% CI, −51 to 2) minutes from baseline to follow-up 1 and −34 (95% CI, −65 to −3) minutes from baseline to follow-up 2, relative to comparison school participants. Differences in differences for school night sleep onset, weekend sleep onset latency, sleep midpoints, sleep efficiency, and the sleep fragmentation index between the 2 conditions were minimal. Conclusions and Relevance This study found that delaying high school start times could extend adolescent school night sleep duration and lessen their need for catch-up sleep on weekends. These findings suggest that later start times could be a durable strategy for addressing population-wide adolescent sleep deficits.
53 citations
TL;DR: In community-dwelling older men, T90 is an independent predictor of CV mortality and is not only a consequence of frank desaturations, but also reflects non-specific drifts in oxygen saturation, both contributing towards the association with CV death.
Abstract: Aims To investigate the composition of nocturnal hypoxaemic burden and its prognostic value for cardiovascular (CV) mortality in community-dwelling older men. Methods and results We analysed overnight oximetry data from polysomnograms obtained in 2840 men from the Outcomes of Sleep Disorders in Older Men (MrOS Sleep) study (ClinicalTrials.gov Identifier: NCT00070681) to determine the number of acute episodic desaturations per hour (oxygen desaturation index, ODI) and time spent below 90% oxygen saturation (T90) attributed to acute desaturations (T90desaturation) and to non-specific drifts in oxygen saturation (T90non-specific), respectively, and their relationship with CV mortality. After 8.8 ± 2.7 years follow-up, 185 men (6.5%) died from CV disease. T90 [hazard ratio (HR) 1.21, P 12 min were at an elevated risk of CV mortality (HR 1.59; P = 0.006). Approximately 20.7 (5.7-48.5) percent of the variation in T90 could be attributed to non-specific drifts in oxygen saturation. T90desaturation and T90non-specific were individually associated with CV death but combining both variables did not improve the prediction. Conclusion In community-dwelling older men, T90 is an independent predictor of CV mortality. T90 is not only a consequence of frank desaturations, but also reflects non-specific drifts in oxygen saturation, both contributing towards the association with CV death. Whether T90 can be used as a risk marker in the clinical setting and whether its reduction may constitute a treatment target warrants further study.
TL;DR: There is consistent evidence of associations of sleep variability with increased risk for adiposity, glucose dysregulation, T2D, and MetS, and there is support to recommend stabilizing sleep patterns for cardiometabolic risk prevention.
Abstract: Night-to-night variability in sleep patterns leads to circadian disruption and, consequently, could increase cardiometabolic risk. The purpose of this review is to summarize findings from studies published between 2015 and 2020 examining various measures of night-to-night variability in sleep in relation to metabolic syndrome (MetS), type 2 diabetes (T2D), and their risk factors. We illustrate a potential causal pathway between irregular sleep patterns and T2D, highlighting knowledge gaps along the way. Across different measures of sleep variability, irregular sleep patterns were associated with poorer cardiometabolic outcomes. Higher standard deviations (SD) across nights of sleep duration and onset or midpoint of sleep were associated with increased odds of having MetS and clusters of metabolic abnormalities as well as greater adiposity and poorer glycemic control. Conversely, greater regularity of rest-activity patterns related to lower risk for T2D. Social jetlag was associated with glycemic dysregulation, adiposity, T2D, and MetS. These associations are often observed in both metabolically healthy and unhealthy individuals; both higher SD of sleep duration and social jetlag relate to poorer glucose regulation in individuals with diabetes. There is consistent evidence of associations of sleep variability with increased risk for adiposity, glucose dysregulation, T2D, and MetS. Although experimental evidence is needed to determine causation, there is support to recommend stabilizing sleep patterns for cardiometabolic risk prevention.
Boston University1, University of Michigan2, Washington University in St. Louis3, University of North Carolina at Chapel Hill4, Icahn School of Medicine at Mount Sinai5, University of Edinburgh6, University of Mississippi Medical Center7, Leiden University Medical Center8, Outcomes Research Consortium9, University of Colorado Denver10, Veterans Health Administration11, Brigham and Women's Hospital12, University of Iceland13, National Institutes of Health14, Erasmus University Medical Center15, University of Kentucky16, Broad Institute17, Harvard University18, Mashhad University of Medical Sciences19, Fred Hutchinson Cancer Research Center20, University of Aberdeen21, Pennington Biomedical Research Center22, Johns Hopkins University23, Beth Israel Deaconess Medical Center24, Los Angeles Biomedical Research Institute25, New York Academy of Medicine26, University of Dundee27, VA Boston Healthcare System28
TL;DR: The identification of loci exhibiting potential interaction with baseline smoking status provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.
Abstract: Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.
TL;DR: Reduced percentage REM sleep was associated with greater risk of all-cause, cardiovascular, and other noncancer-related mortality in 2 independent cohorts and a random forest model identified REM sleep as the most important sleep stage associated with survival.
Abstract: Importance Rapid eye movement (REM) sleep has been linked with health outcomes, but little is known about the relationship between REM sleep and mortality. Objective To investigate whether REM sleep is associated with greater risk of mortality in 2 independent cohorts and to explore whether another sleep stage could be driving the findings. Design, Setting, and Participants This multicenter population-based cross-sectional study used data from the Outcomes of Sleep Disorders in Older Men (MrOS) Sleep Study and Wisconsin Sleep Cohort (WSC). MrOS participants were recruited from December 2003 to March 2005, and WSC began in 1988. MrOS and WSC participants who had REM sleep and mortality data were included. Analysis began May 2018 and ended December 2019. Main Outcomes and Measures All-cause and cause-specific mortality confirmed with death certificates. Results The MrOS cohort included 2675 individuals (2675 men [100%]; mean [SD] age, 76.3 [5.5] years) and was followed up for a median (interquartile range) of 12.1 (7.8-13.2) years. The WSC cohort included 1386 individuals (753 men [54.3%]; mean [SD] age, 51.5 [8.5] years) and was followed up for a median (interquartile range) of 20.8 (17.9-22.4) years. MrOS participants had a 13% higher mortality rate for every 5% reduction in REM sleep (percentage REM sleep SD = 6.6%) after adjusting for multiple demographic, sleep, and health covariates (age-adjusted hazard ratio, 1.12; fully adjusted hazard ratio, 1.13; 95% CI, 1.08-1.19). Results were similar for cardiovascular and other causes of death. Possible threshold effects were seen on the Kaplan-Meier curves, particularly for cancer; individuals with less than 15% REM sleep had a higher mortality rate compared with individuals with 15% or more for each mortality outcome with odds ratios ranging from 1.20 to 1.35. Findings were replicated in the WSC cohort despite younger age, inclusion of women, and longer follow-up (hazard ratio, 1.17; 95% CI, 1.03-1.34). A random forest model identified REM sleep as the most important sleep stage associated with survival. Conclusions and Relevance Decreased percentage REM sleep was associated with greater risk of all-cause, cardiovascular, and other noncancer-related mortality in 2 independent cohorts.
15 Apr 2020-Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
TL;DR: None Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools.
Abstract: Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelli...
University of Michigan1, University of Florida2, Vanderbilt University Medical Center3, University of Pittsburgh4, Beth Israel Deaconess Medical Center5, Harvard University6, Rutgers University7, Washington University in St. Louis8, Veterans Health Administration9, University of California, Los Angeles10, Mayo Clinic11, University Hospitals of Cleveland12, Wayne State University13
TL;DR: Although the most apparent and immediate application of AI in this field is the assisted scoring of PSG, there are potential clinical use cases that transcend the sleep laboratory and are expected to deepen the understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase the knowledge of the role of sleep in health at a population level.
Abstract: Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is t...
TL;DR: In this biracial, community-based population, evening chronotype was independently associated with obesity, specifically among white participants, and further research is needed to identify behavioral, endocrine, nutritional and genetic pathways which underlie these associations.
Abstract: Research indicates that sleep duration and quality are inter-related factors that contribute to obesity, but few studies have focused on sleep chronotype, representing an individual’s circadian pro...
TL;DR: Sleep fragmentation, defined by low sleep efficiency, was associated with higher odds of migraine on day 1, and short sleep duration and low sleep quality were not temporally associated with migraine.
Abstract: Objective To test the hypotheses that insufficient duration, high fragmentation, and poor sleep quality are temporally associated with migraine onset on the day immediately following the sleep period (day 0) and the following day (day 1). Methods In this prospective cohort study of 98 adults with episodic migraine, participants completed twice-daily electronic diaries on sleep, headaches, and other health habits, and wore wrist actigraphs for 6 weeks. We estimated the incidence of migraine following nights with short sleep duration, high fragmentation, or low quality compared to nights with adequate sleep with conditional logistic regression models stratified by participant and adjusted for caffeine intake, alcohol intake, physical activity, stress, and day of week. Results Participants were a mean age of 35.1 ± 12.1 years. We collected 4,406 days of data, with 870 headaches reported. Sleep duration ≤6.5 hours and poor sleep quality were not associated with migraine on day 0 or day 1. Diary-reported low efficiency was associated with 39% higher odds of headache on day 1 (odds ratio [OR] 1.39, 95% confidence interval [CI] 1.06–1.81). Actigraphic-assessed high fragmentation was associated with lower odds of migraine on day 0 (wake after sleep onset >53 minutes, OR 0.64, 95% CI 0.48–0.86; efficiency ≤88%, OR 0.74, 95% CI 0.56–0.99). Conclusion Short sleep duration and low sleep quality were not temporally associated with migraine. Sleep fragmentation, defined by low sleep efficiency, was associated with higher odds of migraine on day 1. Further research is needed to understand the clinical and neurobiologic implications of sleep fragmentation and risk of migraine.
TL;DR: Higher duty cycle was associated with lower SBP overall and hypoxic burden with higher SBP and DBP among non-BP medication users, suggesting changes in both respiratory effort and oxygenation during sleep influence BP.
Abstract: Background Mechanisms underlying blood pressure (BP) changes in obstructive sleep apnoea (OSA) are incompletely understood. We assessed the associations between BP and selected polysomnography (PSG) traits: sleep depth, airflow limitation measurements and OSA-specific hypoxic burden. Methods This cross-sectional analysis included 2055 participants from the Multi-Ethnic Study of Atherosclerosis who underwent PSG and BP measurements in 2010–2013. Sleep depth was assessed using the ‘OR product’, a continuous measure of arousability. Airflow limitation was assessed by duty cycle (Ti/Tt) and % of breaths with flow limitation, and hypoxia by ‘hypoxic burden’. Primary outcomes were medication-adjusted systolic BP (SBP) and diastolic BP (DBP). We used generalised linear models adjusted for age, sex, race/ethnicity, smoking, education, body mass index, alcohol use, periodic limb movements and alternative physiological disturbances. Results The sample had a mean age of 68.4 years and apnoea–hypopnoea index of 14.8 events/hour. Sleep depth was not significantly associated with BP. Every 1 SD increment in log-transformed non-rapid eye movement duty cycle was associated with 0.9% decrease in SBP (95% CI: 0.1% to 1.6%), even after adjusting for sleep depth and hypoxic burden. Every 1 SD increment in log-transformed hypoxic burden was associated with a 1.1% increase in SBP (95% CI: 0.1% to 2.1%) and 1.9% increase in DBP (95% CI: 1.0% to 2.8%) among those not using hypertension medications. Conclusions Higher duty cycle was associated with lower SBP overall and hypoxic burden with higher SBP and DBP among non-BP medication users. These findings suggest changes in both respiratory effort and oxygenation during sleep influence BP.
Brigham and Women's Hospital1, Harvard University2, Radboud University Nijmegen3, McGill University4, University of Colorado Denver5, University of Mississippi Medical Center6, University of California, Berkeley7, Broad Institute8, Brown University9, University of Maryland, Baltimore10, United States Department of Veterans Affairs11, Boston University12, University of Kentucky13, University of Texas at Austin14, University of Texas Health Science Center at Houston15, Human Genome Sequencing Center16, Tulane University17, Oklahoma Medical Research Foundation18, Washington University in St. Louis19, Los Angeles Biomedical Research Institute20, University of Washington21, National Yang-Ming University22, University of Virginia23, Wake Forest University24, University of Michigan25, University of Maryland, College Park26
TL;DR: It is demonstrated that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage.
Abstract: Mechanistic processes underlying human germline mutations remain largely unknown. Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage.
TL;DR: Persistent short sleep duration or changes in sleep duration from early to middle adulthood were associated with higher risk of type 2 diabetes in later life, and these associations were weaker after obesity and metabolic comorbidities were accounted for.
Abstract: OBJECTIVE To identify sleep duration trajectories from early to middle adulthood and their associations with incident type 2 diabetes. RESEARCH DESIGN AND METHODS Using a group-based modeling approach, we identified sleep duration trajectories based on sleep duration in ages 20–25, 26–35, 36–45, and 46+ years, which were retrospectively assessed in 2009 among 60,068 women from the Nurses’ Health Study II (median age 54.9 years) who were free of diabetes, cardiovascular disease, and cancer. We investigated the prospective associations between sleep duration trajectories and diabetes risk (2009–2017) using multivariable Cox proportional hazards models. RESULTS We documented 1,797 incident diabetes cases over a median follow-up of 7.8 years (442,437 person-years). Six sleep duration trajectories were identified: persistent 5-, 6-, 7-, or 8-h sleep duration and increased or decreased sleep duration. After multivariable adjustment for diabetes risk factors, compared with the persistent 7-h sleep duration group, the hazard ratio was 1.43 (95% CI 1.10, 1.84) for the 5-h group, 1.17 (1.04, 1.33) for the 6-h group, 0.96 (0.84, 1.10) for the 8-h group, 1.33 (1.09, 1.61) for the increased sleep duration group, and 1.32 (1.10, 1.59) for the decreased sleep duration group. Additional adjustment for time-updated comorbidities and BMI attenuated these associations, although a significantly higher risk remained in the decreased sleep duration group (1.24 [1.03, 1.50]). CONCLUSIONS Persistent short sleep duration or changes in sleep duration from early to middle adulthood were associated with higher risk of type 2 diabetes in later life. These associations were weaker after obesity and metabolic comorbidities were accounted for.
TL;DR: In this paper, the authors investigated the relation of snoring to risks of stroke and other major cardiovascular (CV) events in patients with moderate-to-severe OSA and established coronary or cerebral CV disease.
University of Michigan1, Brigham and Women's Hospital2, University of Virginia3, Columbia University4, University of Leicester5, Harvard University6, Columbia University Medical Center7, University of Texas Health Science Center at Houston8, University of Colorado Denver9, Boston University10, University of Washington11, Johns Hopkins University12, National Institutes of Health13, University of North Carolina at Chapel Hill14, Kaiser Permanente15, University of Mississippi Medical Center16, Los Angeles Biomedical Research Institute17, University of Vermont18, Duke University19, Broad Institute20, University of Southern California21, Baylor College of Medicine22, Beth Israel Deaconess Medical Center23, University of Maryland, Baltimore24, University of Illinois at Chicago25, University of Miami26, Fred Hutchinson Cancer Research Center27, Albert Einstein College of Medicine28, National Institute for Health Research29
TL;DR: This study performs whole genome sequence analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine Program.
Abstract: Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.
TL;DR: It is demonstrated that automated CAP analysis of large-scaled databases can lead to new findings on CAP and its subcomponents, and it is shown that sleep disturbance indices are associated with CAP rate.
Abstract: Study objectives To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people. Methods We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea-hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance. Results Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3). Conclusions We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture.
TL;DR: To determine if sleep‐disordered breathing (SDB), daytime sleepiness, insomnia, and sleep duration predict seven‐year neurocognitive decline in US Hispanics/Latinos (N = 5247), sleeping patterns are studied to help predict this decline.
Abstract: Introduction To determine if sleep-disordered breathing (SDB), daytime sleepiness, insomnia, and sleep duration predict seven-year neurocognitive decline in US Hispanics/Latinos (N = 5247). Methods The exposures were baseline SDB, daytime sleepiness, insomnia, and sleep duration. The outcomes were change in episodic learning and memory (B-SEVLT-Sum and SEVLT-Recall), language (word fluency [WF]), processing speed (Digit Symbol Substitution), and a cognitive impairment screener (Six-item Screener [SIS]). Results Mean age was 63 ± 8 years, with 55% of the population being female with 7.0% Central American, 24.5% Cuban, 9.3% Dominican, 35.9% Mexican, 14.4% Puerto Rican, and 5.1% South American background. Long sleep (>9 hours), but not short sleep ( Conclusion Long sleep duration predicted seven-year cognitive decline.
TL;DR: A pathway wherein sleep fragmentation raises inflammatory-related white blood cell counts, thereby increasing atherosclerosis severity, is described, even when other common risk factors have been accounted for.
Abstract: Why does poor-quality sleep lead to atherosclerosis? In a diverse sample of over 1,600 individuals, we describe a pathway wherein sleep fragmentation raises inflammatory-related white blood cell counts (neutrophils and monocytes), thereby increasing atherosclerosis severity, even when other common risk factors have been accounted for. Improving sleep quality may thus represent one preventive strategy for lowering inflammatory status and thus atherosclerosis risk, reinforcing public health policies focused on sleep health.
TL;DR: In a sample of adults with cLBP, virtually all with poor sleep quality prior to intervention, modest but statistically significant improvements in sleep quality were observed with both yoga and PT.
Abstract: Poor sleep is common among adults with chronic low back pain (cLBP), but the influence of cLBP treatments, such as yoga and physical therapy (PT), on sleep quality is under studied. Evaluate the effectiveness of yoga and PT for improving sleep quality in adults with cLBP. Secondary analysis of a randomized controlled trial. Academic safety-net hospital and 7 affiliated community health centers. A total of 320 adults with cLBP. Twelve weekly yoga classes, 1-on-1 PT sessions, or an educational book. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) global score (0–21) at baseline, 12 weeks, and 52 weeks. Additionally, we also evaluated how the proportion of participants who achieved a clinically meaningful improvement in sleep quality (> 3-point reduction in PSQI) at 12 weeks varied by changes in pain and physical function at 6 weeks. Among participants (mean age = 46.0, 64% female, 82% non-white), nearly all (92%) reported poor sleep quality (PSQI > 5) at baseline. At 12 weeks, modest improvements in sleep quality were observed among the yoga (PSQI mean difference [MD] = − 1.19, 95% confidence interval [CI] − 1.82, − 0.55) and PT (PSQI MD = − 0.91, 95% CI − 1.61, − 0.20) groups. Participants who reported a ≥ 30% improvement in pain or physical function at 6 weeks, compared with those who improved < 10%, were more likely to be a sleep quality responder at 12 weeks (odds ratio [OR] = 3.51, 95% CI 1.73, 7.11 and OR = 2.16, 95% CI 1.18, 3.95, respectively). Results were similar at 52 weeks. In a sample of adults with cLBP, virtually all with poor sleep quality prior to intervention, modest but statistically significant improvements in sleep quality were observed with both yoga and PT. Irrespective of treatment, clinically important sleep improvements at the end of the intervention were associated with mid-intervention pain and physical function improvements. ClinicalTrials.gov Identifier: NCT01343927
TL;DR: Average event duration varies across population groups and provides information on ventilatory features and airway collapsibility not captured by the AHI.
Abstract: Rationale: Symptoms and morbidities associated with obstructive sleep apnea (OSA) vary across individuals and are not predicted by the apnea–hypopnea index (AHI). Respiratory event duration is a he...
TL;DR: Sleep‐disordered breathing, overnight hypoxemia, and sleep fragmentation were associated with higher blood glucose levels among blacks.
Abstract: Background Characterizing associations of sleep characteristics with blood‐glucose–level factors among blacks may clarify the underlying mechanisms of impaired glucose metabolism and help identify ...
TL;DR: Associations of sleep duration and social jetlag with cardiometabolic outcomes were associated with healthier behaviors and a lower odds of being overweight and short sleep duration was associated with less physical activity but did not relate to other cardiometric outcomes.