The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource ...(NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.
We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.
The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.
The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.
Diabetes is currently classified based on pathogenetic mechanisms and Type 2 diabetes (T2DM) can be considered a residual heterogeneous category. Factor analysis (FA) identifies a limited number of ...calculated variables related to a larger number of measured parameters, capable of explaining most of their variance. Aim of the present study was to verify the feasibility of the application of FA for the development of pathogenetic models of individual cases of T2DM, using three available databases.
Firstly, a model of FA was applied to an existing dataset of non-diabetic patients, identifying three factors associated with fasting or post-prandial hyperglycemia. These factors were then calculated in: - patients enrolled in a retrospective observational study, assessing time to failure to diabetes treatment in three cohorts of patients (metformin or sulfonylurea monotherapy, or no pharmacological therapy); - in a retrospective cohort of patients failing to dual oral therapy and initiating treatment with DPP4 inhibitors; - in patients enrolled in a case-control study onincident cancer in T2DM subjects initiating insulin treatment.
Despite limitations, our results confirm the feasibility of approaching the characterization of T2DM through the identification of dimensional factors, providing additional and complementary information to that obtained with cluster analysis.
Abstract Objectives To compare the accuracy of the commercial Fitbit Flex device (FF) with polysomnography (PSG; the gold-standard method) in insomnia disorder patients and good sleepers. Methods ...Participants wore an FF and actigraph while undergoing overnight PSG. Primary outcomes were intraclass correlation coefficients (ICCs) of the total sleep time (TST) and sleep efficiency (SE), and the frequency of clinically acceptable agreement between the FF in normal mode (FFN) and PSG. The sensitivity, specificity, and accuracy of detecting sleep epochs were compared among FFN, actigraphy, and PSG. Results The ICCs of the TST between FFN and PSG in the insomnia (ICC = 0.886) and good-sleepers (ICC = 0.974) groups were excellent, but the ICC of SE was only fair in both groups. The TST and SE were overestimated for FFN by 6.5 min and 1.75%, respectively, in good sleepers, and by 32.9 min and 7.9% in the insomnia group with respect to PSG. The frequency of acceptable agreement of FFN and PSG was significantly lower ( p = 0.006) for the insomnia group (39.4%) than for the good-sleepers group (82.4%). The sensitivity and accuracy of FFN in an epoch-by-epoch comparison with PSG was good and comparable to those of actigraphy, but the specificity was poor in both groups. Conclusions The ICC of TST in the FFN–PSG comparison was excellent in both groups, and the frequency of agreement was high in good sleepers but significantly lower in insomnia patients. These limitations need to be considered when applying commercial sleep trackers for clinical and research purposes in insomnia.
This study evaluated the daily, temporal associations between sleep and daytime physical activity and sedentary behavior among adolescents from the Fragile Families & Child Wellbeing Study. A ...sub-sample of the cohort at age 15 (N = 417) wore actigraphy monitors for one week during the school year from which we derived daily minutes in sedentary and moderate-to-vigorous physical activity (MVPA) and nighttime sleep measures. Multilevel models tested temporal associations of nightly sleep onset, offset, duration, and sleep maintenance efficiency, with daily MVPA and sedentary behavior. More MVPA than an individual's average was associated with earlier sleep onset (p < 0.0001), longer duration (p = 0.03), and higher sleep maintenance efficiency (p < 0.0001). On days with more sedentary behavior than an individual's average, sleep onset and offset were delayed (p < 0.0001), duration was shorter (p < 0.0001), and sleep maintenance efficiency was higher (p = 0.0005). Conversely, nights with earlier sleep onset predicted more next-day sedentary behavior (p < 0.0001), and nights with later sleep offset and longer sleep duration were associated with less MVPA (p < 0.0001) and less sedentary time (p < 0.0001, p = 0.004) the next day. These bidirectional associations between sleep and physical activity suggest that promoting MVPA may help to elicit earlier bedtimes, lengthen sleep duration, and increase sleep efficiency, critical for healthy adolescent development.
Summary Despite the marked improvement in the understanding of molecular mechanisms and classification of apocrine carcinoma, little is known about its specific molecular genetic alterations and ...potentially targetable biomarkers. In this study, we explored immunohistochemical and molecular genetic characteristics of 37 invasive apocrine carcinomas using immunohistochemistry (IHC), fluorescent in situ hybridization (FISH), multiplex ligation-dependent probe amplification (MLPA), and next-generation sequencing (NGS) assays. IHC revealed frequent E-cadherin expression (89%), moderate (16%) proliferation activity Ki-67, phosphohistone H3, infrequent (~10%) expression of basal cell markers CK5/6, CK14, p63, caveolin-1, loss of PTEN (83%), and overexpression of HER2 (32%), EGFR (41%), cyclin D1 (50%), and MUC-1 (88%). MLPA assay revealed gene copy gains of MYC , CCND1 , ZNF703 , CDH1 , and TRAF4 in 50% or greater of the apocrine carcinomas, whereas gene copy losses frequently affected BRCA2 (75%), ADAM9 (54%), and BRCA1 (46%). HER2 gain, detected by MLPA in 38% of the cases, was in excellent concordance with HER2 results obtained by IHC/FISH ( κ = 0.915, P < .001). TOP2A gain was observed in one case, while five cases (21%) exhibited TOP2A loss. Unsupervised hierarchical cluster analysis revealed two distinct clusters: HER2- positive and HER2- negative ( P = .03 and .04, respectively). NGS assay revealed mutations of the TP53 (2 of 7, 29%), BRAF/KRAS (2 of 7, 29%), and PI3KCA/PTEN genes (7 of 7, 100%). We conclude that morphologically defined apocrine carcinomas exhibit complex molecular genetic alterations that are consistent with the “luminal-complex” phenotype. Some of the identified molecular targets are promising biomarkers; however, functional studies are needed to prove these observations.
The cardiovascular system exhibits strong circadian rhythms to maintain normal functioning. Irregular sleep schedules, characterized by high day-to-day variability in sleep duration or timing, ...represent possibly milder but much more common and chronic disruption of circadian rhythms in the general population than shift work.
This study aimed to prospectively examine the association between sleep regularity and risk of cardiovascular disease (CVD).
In MESA (Multi-Ethnic Study of Atherosclerosis), 1,992 participants free of CVD completed 7-day wrist actigraphy for sleep assessment from 2010 to 2013 and were prospectively followed through 2016. The study assessed sleep regularity using the SD of actigraphy-measured sleep duration and sleep-onset timing across 7 days. Incident CVD included nonfatal and fatal cardiovascular events. A Cox proportional hazards model was used to estimate hazard ratios (HRs) for incident CVD according to SD of sleep duration and timing, adjusted for traditional CVD risk factors (including CVD biomarkers) and other sleep-related factors (including average sleep duration).
During a median follow-up of 4.9 years, 111 participants developed CVD events. The multivariable-adjusted HRs (95% confidence intervals) for CVD across categories of sleep duration SD were 1.00 (reference) for ≤60 min, 1.09 (0.62 to 1.92) for 61 to 90 min, 1.59 (0.91 to 2.76) for 91 to 120 min, and 2.14 (1.24 to 3.68) for >120 min (p trend = 0.002). Similarly, compared with participants with a sleep timing SD ≤30 min, the HRs (95% confidence intervals) for CVD were 1.16 (0.64 to 2.13) for 31 to 60 min, 1.52 (0.81 to 2.88) for 61 to 90 min, and 2.11 (1.13 to 3.91) for >90 min (p trend = 0.002). Exclusion of current shift workers yielded similar results.
Irregular sleep duration and timing may be novel risk factors for CVD, independent of traditional CVD risk factors and sleep quantity and/or quality.
We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night ...electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.
Slow wave (or stage N3) sleep has been linked to a variety of cognitive processes. However, the role of stage N3 in the elderly is debated. The link between stage N3 and episodic memory may be ...weakened or changed in the older adult population, possibly due to several altered mechanisms impacting the cellular structure of the brain. The bases for the age-related dissociation between stage N3 and cognition are not understood. Since APOEε4 status is the strongest genetic risk factor for cognitive decline, we assessed whether the ε4 allele is associated with stage N3 sleep. Participants were from the population-based Osteoporotic Fractures in Men (MrOS) cohort with polysomnography and APOEε4 genotype data (n = 2,302, 100% male, mean age 76.6 years). Sleep stages were objectively measured using overnight in-home polysomnography and central electroencephalogram data were used to score stage N3 sleep. Cognitive function was assessed using the Modified Mini Mental State Exam (3MS). The APOE rs429358 single nucleotide polymorphism, which defines the APOEε4 allele, was genotyped using a custom genotyping array. Total time in stage N3 sleep was significantly higher (p<0.0001) among the 40 MrOS participants carrying two copies of the ε4 allele (62±5.2 minutes) compared with 43±1.5 minutes for carriers of one ε4 allele (n = 515) and 40±0.8 minutes for ε4 non-carriers (n = 1747). All results were independent of sleep efficiency, number of sleep cycles, and apnea hypopnea index. These findings support an association between APOEε4 genotype and sleep stage N3 in the elderly. Increased total stage N3 duration among ε4/ε4 carriers does not appear to reflect compensation for prior cognitive decline and may reflect overactive downscaling of synapses during sleep. If confirmed, these results might in part explain the high risk of age-related cognitive decline and AD among APOE ε4/ε4 carriers.
The CD95 (APO-1/Fas) system is an important mediator of T-cell cytotoxicity. We investigated this system in 22 hepatocellular carcinomas (HCCs) from patients. All HCCs had partially or completely ...lost the expression of the CD95 receptor constitutively expressed by normal liver cells and might thus evade CD95-mediated killing. We also considered a new mechanism of immune evasion, namely, the active destruction of T-lymphocytes by tumor cells expressing CD95 ligand (CD95L). CD95L messenger RNA and protein could be detected in the HCCs. In coculture experiments, HepG2 hepatoblastoma cells, expressing CD95L mRNA after treatment with cytostatic drugs, killed CD95+ Jurkat lymphocytes. Our data suggest that tumor cells can evade immune attack by down-regulation of the CD95 receptor and killing of lymphocytes through expression of CD95L.
PDF
