Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study.

Background: Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. Objectives: To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings: 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Conclusions: Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

Detailed stratified GWAS analysis for severe COVID-19 in four European populations.

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 genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 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 region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes 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 including non-Caucasian individuals, 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.

A year living with SARS-CoV-2: an epidemiological overview of viral lineage circulation by whole-genome sequencing in Barcelona city (Catalonia, Spain).

Herein, we describe the genetic diversity of circulating SARS-CoV-2 viruses by whole-genome sequencing (WGS) in Barcelona city (Catalonia, Spain) throughout the first four pandemic waves. From weeks 11/2020-24/2021, SARS-CoV-2-positive respiratory samples were randomly selected per clinical setting (80% from primary care or 20% from the hospital), age group, and week. WGS was performed following the ARTICv3 protocol on MiSeq or NextSeq2000 Illumina platforms. Nearly complete consensus sequences were used for genetic characterization based on GISAID and PANGOLIN nomenclatures. From 2475 samples, 2166 (87%) were fully sequenced (78% from primary care and 22% from hospital settings). Multiple genetic lineages were co-circulating, but four were predominant at different periods. While B.1.5 (50.68%) and B.1.1 (32.88%) were the major lineages during the first pandemic wave, B.1.177 (66.85%) and B.1.1.7 (83.80%) were predominant during the second, third, and fourth waves, respectively. Almost all (96.4%) were carrying D614G mutation in the S protein, with additional mutations that define lineages or variants. But some mutations of concern, such as E484K from B.1.351 and P.1 lineages are currently under monitoring, together with those observed in the receptor-binding domain or N-terminal domain, such as L452R and T478K from B.1.617.2 lineage. The fact that a predominant lineage was observed in each pandemic wave suggests advantageous properties over other contemporary co-circulating variants. This genetic variability should be monitored, especially when a massive vaccination campaign is ongoing because the potential selection and emergence of novel antigenic SARS-CoV-2 strains related to immunological escapement events.