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A study by Dr Nathalie Conrad and Prof Kazaem Rahimi at the George Institute (Nuffield Department of Women's and Reproductive Health) finds that women and older people are most likely to be exposed to shortcomings in heart failure care.
Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease
ObjectiveTo improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults.MethodsUsing free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machine-learning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.ResultsIn leave-one-participant-out analysis, our machine-learning method classified free-living movement behaviours with mean accuracy 88% (95% CI 87% to 89%) and Cohen’s kappa 0.80 (95% CI 0.79 to 0.82). Among 87 498 UK Biobank participants, there were 4105 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with lower CVD risk. For an average individual, reallocating 20 min/day to MVPA from all other behaviours proportionally was associated with 9% (95% CI 7% to 10%) lower risk, while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with 5% (95% CI 3% to 7%) higher risk.ConclusionMachine-learning methods classified movement behaviours accurately in free-living accelerometer data. Reallocating time from other behaviours to MVPA, and from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD, and should be promoted by interventions and guidelines.
AIMS: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. METHODS AND RESULTS: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01-0.05) at P
AbstractMiscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10−8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10−8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10−9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10−8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.
Remarkably low affinity of CD4/peptide-major histocompatibility complex class II protein interactions
The αβ T-cell coreceptor CD4 enhances immune responses more than 1 million-fold in some assays, and yet the affinity of CD4 for its ligand, peptide-major histocompatibility class II (pMHC II) on antigen-presenting cells, is so weak that it was previously unquantifiable. Here, we report that a soluble form of CD4 failed to bind detectably to pMHC II in surface plasmon resonance-based assays, establishing a new upper limit for the solution affinity at 2.5 mM. However, when presented multivalently on magnetic beads, soluble CD4 bound pMHC II-expressing B cells, confirming that it is active and allowing mapping of the native coreceptor binding site on pMHC II. Whereas binding was undetectable in solution, the affinity of the CD4/pMHC II interaction could be measured in 2D using CD4- and adhesion molecule-functionalized, supported lipid bilayers, yielding a 2D Kd of ∼5,000 molecules/μm2. This value is two to three orders of magnitude higher than previously measured 2D Kd values for interacting leukocyte surface proteins. Calculations indicated, however, that CD4/pMHC II binding would increase rates of T-cell receptor (TCR) complex phosphorylation by threefold via the recruitment of Lck, with only a small, 2–20% increase in the effective affinity of the TCR for pMHC II. The affinity of CD4/pMHC II therefore seems to be set at a value that increases T-cell sensitivity by enhancing phosphorylation, without compromising ligand discrimination.
Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis
Ambient air pollution is projected to become a major environmental risk in sub-Saharan Africa (SSA). Research into its health impacts is hindered by limited data. We aimed to investigate the cross-sectional relationship between particulate matter with a diameter ≤ 2.5 μm (PM2.5) and prevalence of cough or acute lower respiratory infection (ALRI) among children under five in SSA. Data were collected from 31 Demographic and Health Surveys (DHS) in 21 SSA countries between 2005–2018. Prior-month average PM2.5 preceding the survey date was assessed based on satellite measurements and a chemical transport model. Cough and ALRI in the past two weeks were derived from questionnaires. Associations were analysed using conditional logistic regression within each survey cluster, adjusting for child’s age, sex, birth size, household wealth, maternal education, maternal age and month of the interview. Survey-specific odds ratios (ORs) were pooled using random-effect meta-analysis. Included were 368,366 and 109,664 children for the analysis of cough and ALRI, respectively. On average, 20.5% children had reported a cough, 6.4% reported ALRI, and 32% of children lived in urban areas. Prior-month average PM2.5 ranged from 8.9 to 64.6 μg/m3. Pooling all surveys, no associations were observed with either outcome in the overall populations. Among countries with medium-to-high Human Development Index, positive associations were observed with both cough (pooled OR: 1.022, 95%CI: 0.982–1.064) and ALRI (pooled OR: 1.018, 95%CI: 0.975–1.064) for 1 μg/m3 higher of PM2.5. This explorative study found no associations between short-term ambient PM2.5 and respiratory health among young SSA children, necessitating future analyses using better-defined exposure and health metrics to study this important link.
OBJECTIVES: We aimed to investigate whether digital home monitoring with centralised specialist support for remote management of heart failure (HF) is more effective in improving medical therapy and patients' quality of life than digital home monitoring alone. METHODS: In a two-armed partially blinded parallel randomised controlled trial, seven sites in the UK recruited a total of 202 high-risk patients with HF (71.3 years SD 11.1; left ventricular ejection fraction 32.9% SD 15.4). Participants in both study arms were given a tablet computer, Bluetooth-enabled blood pressure monitor and weighing scales for health monitoring. Participants randomised to intervention received additional regular feedback to support self-management and their primary care doctors received instructions on blood investigations and pharmacological treatment. The primary outcome was the use of guideline-recommended medical therapy for chronic HF and major comorbidities, measured as a composite opportunity score (total number of recommended treatment given divided by the total number of opportunities the treatment should have been given, with a score 1 indicating 100% adherence to recommendations). Co-primary outcome was change in physical score of Minnesota Living with Heart Failure questionnaire. RESULTS: 101 patients were randomised to 'enhanced self-management' and 101 to 'supported medical management'. At the end of follow-up, the opportunity score was 0.54 (95% CI 0.46 to 0.62) in the control arm and 0.61 (95% CI 0.52 to 0.70) in the intervention arm (p=0.25). Physical well-being of participants also did not differ significantly between the groups (17.4 (12.4) mean (SD) for control arm vs 16.5 (12.1) in treatment arm; p for change=0.84). CONCLUSIONS: Central provision of tailored specialist management in a multi-morbid HF population was feasible. However, there was no strong evidence for improvement in use of evidence-based treatment nor health-related quality of life. TRIAL REGISTRATION NUMBER: ISRCTN86212709.
AbstractToday, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0–13.2% (in terms of average precision scores for different tasks), over the existing state-of-the-art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre-)training results in disease and patient representations can be useful for future studies (i.e., transfer learning).