Sleep pattern gender differences and fragmentation in postpartum parents of twins

Objective: Parents of newborn twins are at risk for both shortened sleep duration and sleep discontinuity. The purpose of this study was to characterize weekday and weekend sleep duration, sleep continuity, and awakenings in both mothers and fathers of newborn twins during the first 3 months at home. Material and Methods: Sleep-wake parameters were assessed at two time points using self-report diaries and actigraphy in 75 families with newborn twins. To assess sleep on weekdays and weekends with minimal subject burden, actigraphy recordings of both parents commenced at 9:00 p.m. Saturday and terminated at 9:00 p.m. Tuesday. Results: Mean sleep duration over 24 hours for parents of twins ranged between 6.7 and 7.5 hours during the first 3 months postpartum and did not significantly differ on weekdays or weekends for mothers. Weekend sleep was more fragmented for fathers at both one month and three months with more awakenings, compared to weekday sleep. Mothers had more fragmented night sleep compared to fathers at one month. In contrast, at three months postpartum fathers had shorter total sleep time and night sleep time, but fewer night awakenings on weekdays than mothers. No differences were observed in weekend sleep duration or sleep patterns between mothers and fathers at three months. Discussion: Consolidated sleep periods for both parents averages three hours or less during the first three months postpartum and sleep for both parents is fragmented. In families with newborn twins, the extent of sleep disruption for mothers and fathers is similar

Maximal Oxidative Capacity during Exercise Is Associated with Skeletal Muscle Fuel Selection and Dynamic Changes in Mitochondrial Protein Acetylation

SummaryMaximal exercise-associated oxidative capacity is strongly correlated with health and longevity in humans. Rats selectively bred for high running capacity (HCR) have improved metabolic health and are longer-lived than their low-capacity counterparts (LCR). Using metabolomic and proteomic profiling, we show that HCR efficiently oxidize fatty acids (FAs) and branched-chain amino acids (BCAAs), sparing glycogen and reducing accumulation of short- and medium-chain acylcarnitines. HCR mitochondria have reduced acetylation of mitochondrial proteins within oxidative pathways at rest, and there is rapid protein deacetylation with exercise, which is greater in HCR than LCR. Fluxomic analysis of valine degradation with exercise demonstrates a functional role of differential protein acetylation in HCR and LCR. Our data suggest that efficient FA and BCAA utilization contribute to high intrinsic exercise capacity and the health and longevity benefits associated with enhanced fitness

Application of combined omics platforms to accelerate biomedical discovery in diabesity

Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large-scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes

Physiological Adaptations to Progressive Endurance Exercise Training in Adult and Aged Rats: Insights from the Molecular Transducers of Physical Activity Consortium (MoTrPAC)

While regular physical activity is a cornerstone of health, wellness, and vitality, the impact of endurance exercise training on molecular signaling within and across tissues remains to be delineated. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to characterize molecular networks underlying the adaptive response to exercise. Here, we describe the endurance exercise training studies undertaken by the Preclinical Animal Sites Studies component of MoTrPAC, in which we sought to develop and implement a standardized endurance exercise protocol in a large cohort of rats. To this end, Adult (6-mo) and Aged (18-mo) female (n = 151) and male (n = 143) Fischer 344 rats were subjected to progressive treadmill training (5 d/wk, ∼70%-75% VO2max) for 1, 2, 4, or 8 wk; sedentary rats were studied as the control group. A total of 18 solid tissues, as well as blood, plasma, and feces, were collected to establish a publicly accessible biorepository and for extensive omics-based analyses by MoTrPAC. Treadmill training was highly effective, with robust improvements in skeletal muscle citrate synthase activity in as little as 1-2 wk and improvements in maximum run speed and maximal oxygen uptake by 4-8 wk. For body mass and composition, notable age- and sex-dependent responses were observed. This work in mature, treadmill-trained rats represents the most comprehensive and publicly accessible tissue biorepository, to date, and provides an unprecedented resource for studying temporal-, sex-, and age-specific responses to endurance exercise training in a preclinical rat model

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Death and organ procurement: public beliefs and attitudes

While brain death and the dead donor rule (patients must not be killed by organ retrieval) have been clinically and legally accepted in the US as a prerequisite to organ removal, there is little data about public attitudes and beliefs concerning these matters. To examine the public attitudes and beliefs about the determination of death and its relationship to organ transplantation, 1351 Ohio residents [greater-or-equal, slanted]18 years were randomly selected and surveyed using random digit dialing (RDD) sample frames. The RDD telephone survey was conducted using computer-assisted telephone interviews. The survey instrument was developed from information provided by 12 focus groups and a pilot study of the questionnaire. Three scenarios based on hypothetical patients were presented: brain dead, in a coma, or in a persistent vegetative state (PVS). Respondents' provided personal assessments of whether the patient in each scenario was dead and their willingness to donate that patient's organs in these circumstances. Over 98% of respondents had heard of the term brain death, but only one-third (33.7%) believed that someone who was brain dead was legally dead. The majority of respondents (86.2%) identified the brain dead patient in the first scenario as dead, 57.2% identified the patient in a coma as dead (Scenario 2), and 34.1% identified the patient in a PVS as dead (Scenario 3). Nearly, a third (33.5%) were willing to donate the organs of patients they classified as alive for at least one scenario, in seeming violation of the dead donor rule. Most respondents were not willing to violate the dead donor rule, although a substantial minority was. However, the majority of respondents were unaware, misinformed or held beliefs that were not congruent with current definitions of brain death. This study highlights the need for more public dialogue and education about brain death and organ donation.Organ procurement Brain death Public attitudes USA