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BackgroundCoronavirus disease 2019 (COVID-19) leads to peripheral and central disorders, frequently with neurological implications. Blood-brain barrier disruption (BBBd) has been hypothesized as a mechanisms in the acute phase. We tested whether markers of BBBd, brain injury and inflammation could help identify a blood signature for disease severity and neurological complications. MethodsBiomarkers of BBBd (MMP-9, GFAP), neuronal damage (NFL) and inflammation (PPIA, IL-10, TNF) were measured by SIMOA, AlphaLISA and ELISA, in two COVID-19 patient cohorts with high disease severity (ICU Covid; n=79) and neurological complications (NeuroCovid; n=78), and in two control groups with no COVID-19 history: healthy subjects (n=20) and patients with amyotrophic lateral sclerosis (ALS; n=51). ResultsBiomarkers of BBBd and neuronal damage were high in COVID-19 patients, with levels similar to or higher than in ALS. NeuroCovid patients had lower levels of PPIA but higher levels of MMP-9 than ICU Covid patients. There was evidence of different temporal dynamics in ICU Covid compared to NeuroCovid patients with PPIA and IL-10 levels highest in ICU Covid patients in the acute phase. In contrast, MMP-9 was higher in the acute phase in NeuroCovid patients, with severity-dependency in the long term. We also found clear severity-dependency of NFL and GFAP. ConclusionsThe overall picture points to an increased risk of neurological complications in patients with high levels of biomarkers of BBBd. Our observations may provide hints for therapeutic approaches mitigating BBBd to reduce the neurological damage in the acute phase and potential dysfunction in the long term.
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BackgroundThe coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources. AimsTo externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients. MethodsTwo cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The primary endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed. ResultsThe C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort. ConclusionAlthough performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.
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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.
