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Oxford University Press (OUP), The Journal of Infectious Diseases, 9(223), p. 1544-1554, 2021

DOI: 10.1093/infdis/jiab108

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The Activin/Follistatin Axis Is Severely Deregulated in COVID-19 and Independently Associated With In-Hospital Mortality

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

Abstract Background Activins are members of the transforming growth factor-β superfamily implicated in the pathogenesis of several immunoinflammatory disorders. Based on our previous studies demonstrating that overexpression of activin-A in murine lung causes pathology sharing key features of coronavirus disease 2019 (COVID-19), we hypothesized that activins and their natural inhibitor follistatin might be particularly relevant to COVID-19 pathophysiology. Methods Activin-A, activin-B, and follistatin were retrospectively analyzed in 574 serum samples from 263 COVID-19 patients hospitalized in 3 independent centers, and compared with demographic, clinical, and laboratory parameters. Optimal scaling with ridge regression was used to screen variables and establish a prediction model. Result The activin/follistatin axis was significantly deregulated during the course of COVID-19, correlated with severity and independently associated with mortality. FACT-CLINYCoD, a scoring system incorporating follistatin, activin-A, activin-B, C-reactive protein, lactate dehydrogenase, intensive care unit admission, neutrophil/lymphocyte ratio, age, comorbidities, and D-dimers, efficiently predicted fatal outcome (area under the curve [AUC], 0.951; 95% confidence interval, .919−.983; P <10−6). Two validation cohorts indicated similar AUC values. Conclusions This study demonstrates a link between activin/follistatin axis and COVID-19 mortality and introduces FACT-CLINYCoD, a novel pathophysiology-based tool that allows dynamic prediction of disease outcome, supporting clinical decision making.