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
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care. Trial registrationGerman Clinical Trials Register DRKS00021688
ABSTRACT
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.
ABSTRACT
OBJECTIVES: To assess magnetic resonance imaging (MRI) with conventional chemical shift-based sequences with and without T2* correction for the evaluation of steatosis hepatitis (SH) in the presence of iron. METHODS: Thirty-one patients who underwent MRI and liver biopsy because of clinically suspected diffuse liver disease were retrospectively analysed. The signal intensity (SI) was calculated in co-localised regions of interest (ROIs) using conventional spoiled gradient-echo T1 FLASH in-phase and opposed-phase (IP/OP). T2* relaxation time was recorded in a fat-saturated multi-echo-gradient-echo sequence. The fat fraction (FF) was calculated with non-corrected and T2*-corrected SIs. Results were correlated with liver biopsy. RESULTS: There was significant difference (P < 0.001) between uncorrected and T2* corrected FF in patients with SH and concomitant hepatic iron overload (HIO). Using 5 % as a threshold resulted in eight false negative results with uncorrected FF whereas T2* corrected FF lead to true positive results in 5/8 patients. ROC analysis calculated three threshold values (8.97 %, 5.3 % and 3.92 %) for T2* corrected FF with accuracy 84 %, sensitivity 83-91 % and specificity 63-88 %. CONCLUSIONS: FF with T2* correction is accurate for the diagnosis of hepatic fat in the presence of HIO. Findings of our study suggest the use of IP/OP imaging in combination with T2* correction. KEY POINTS: ⢠Magnetic resonance helps quantify both iron and fat content within the liver ⢠T2* correction helps to predict the correct diagnosis of steatosis hepatitis ⢠"Fat fraction" from T2*-corrected chemical shift-based sequences accurately quantifies hepatic fat ⢠"Fat fraction" without T2* correction underestimates hepatic fat with iron overload.
