ABSTRACT
Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of a well-characterized cohort of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen (HLA) region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a highly pleiotropic [~]0.9-Mb inversion polymorphism and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.
ABSTRACT
BACKGROUND: Congestive heart failure (HF) is a chronic, frequent and disabling condition but with a modifiable course and a large potential for improving. The aim of this project was to develop a clinical prediction model of biological and non biological factors in patients with first diagnosis of HF that facilitates the risk-stratification and decision-making process at the point of care. METHODS AND RESULTS: Historical cohort analysis of 600 patients attended at three tertiary hospitals and diagnosed of a first episode of HF according Framingham criteria. There were followed 1 year. We analyzed sociodemographic, clinical and laboratory data with potential prognostic value. The modelling process concluded into a logistic regression multivariable analysis and a predictive rule: PREDICE SCORE. Age, dependency for daily basic activities, creatinine clearance, sodium levels at admission and systolic dysfunction diagnosis (HF with left ventricular ejection fraction ã 40%) were the selected variables. The model showed a c-statistic of 0.763. PREDICE Score, has range of 22 points to stratifications of 1-year mortality. CONCLUSIONS: The follow-up of 600 patients hospitalized by a first episode of congestive HF, allowed us to obtain a predictive 1 year mortality model from the combination of demographic data, routine biochemistry and easy handling social and functional variables at the point of care. The variables included were non-invasive, undemanding to collect, and widely available. It allows for risk stratification and therapeutical targeting and may help in the clinical decisions process in a sustainable way.