Hypertrophic cardiornyopathy: from gene defect to clinical disease

Major advances have been made over the last decade in our understanding of the molecular basis of several cardiac conditions. Hypertrophic cardiomyopathy (HCM) was the first cardiac disorder in which a genetic basis was identified and as such, has acted as a paradigm for the study of an inherited cardiac disorder. HCM can result in clinical symptoms ranging from no symptoms to severe heart failure and premature sudden death. HCM is the commonest cause of sudden death in those aged less than 35 years, including competitive athletes. At least ten genes have now been identified, defects in which cause HCM. All of these genes encode proteins which comprise the basic contractile unit of the heart, i.e. the sarcomere. While much is now known about which genes cause disease and the various clinical presentations, very little is known about how these gene defects cause disease, and what factors modify the expression of the mutant genes. Studies in both cell culture and animal models of HCM are now beginning to     

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study

BackgroundDuring the Coronavirus Disease 2019 (COVID-19) pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies.Methods and findingsThe repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study.The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants’ age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or overreport the number of contacts they have made.ConclusionsIn this study, we observed that recorded contacts reduced dramatically compared to prepandemic levels (as measured in the POLYMOD study), with changes in reported contacts correlated with government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, the mean number of reported contacts only returned to about half of that observed prepandemic at its highest recorded level. The CoMix survey provides a unique repeated cross-sectional data set for a full year in England, from the first day of the first lockdown, for use in statistical analyses and mathematical modelling of COVID-19 and other diseases.

In a repeated cross-sectional study, Amy Gimma and colleagues study social contact patterns in the context of lockdown periods and government interventions in England during the first year of the COVID-19 pandemic.     

Location of the gene causing hyperimmunoglobulinemia D and periodic fever syndrome differs from that for familial mediterranean fever

The hyperimmunoglobulinemia D and periodic fever (hyper-IgD) syndrome is typified by recurrent febrile attacks with abdominal distress, joint involvement (arthralgias/arthritis), headache, skin lesions, and an elevated serum IgD level (>100U/ml). This familial disorder has been diagnosed in 56 subjects worldwide. As the hyper-IgD syndrome resembles familial Mediterranean fever, one could speculate that both result from mutations in the same gene. The gene causing familial Mediterranean fever (MEF) has been located on chromosome 16p. We have studied 10 families with 19 affected and 28 non-affected subjects. The clinical findings and IgD determinations from these families are compatible with autosomal recessive inheritance. Using highly polymorphic markers surrounding the MEF gene, only negative Lod scores were obtained, whereas haplotype analysis excluded this locus as the cause of the hyper-IgD syndrome. In addition, no indication for linkage was obtained with markers from other candidate gene regions on chromosomes 17q and 14q.Participants listed in the appendix     

Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective,Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study

Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included death, multiple organ failure, substantial bleeding, and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion patients enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on patient outcomes related to the volume of the trauma hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in patient characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for patients who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies.