The Activin/Follistatin-axis is severely deregulated in COVID-19 and independently associated with in-hospital mortality

Synolaki, Evgenia; Papadopoulos, Vasileios; Divolis, Georgios; Tsahouridou, Olga; Gavriilidis, Efstratios; Loli, Georgia; Gavriil, Ariana; Tsigalou, Christina; Tziolos, Nikolaos R; Sertaridou, Eleni; Kalra, Bhanu; Kumar, Ajay; Rafailidis, Petros; Pasternack, Arja; Boumpas, Dimitrios T; Germanidis, Georgios; Ritvos, Olli; Metallidis, Simeon; Skendros, Panagiotis; Sideras, Paschalis

BACKGROUND: Activins are members of the TGFβ-superfamily implicated in the pathogenesis of several immuno-inflammatory disorders. Based on our previous studies demonstrating that over-expression of Activin-A in murine lung causes pathology sharing key features of 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 levels were retrospectively analyzed in 574 serum samples from 263 COVID-19 patients hospitalized in three independent centers, and compared with common 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 novel disease scoring system, adding one point for each of Follistatin>6235pg/ml, Activin-A>591pg/ml, Activin-B>249pg/ml, CRP>10.3mg/dL, LDH>427U/L, Intensive Care Unit (ICU) admission, Neutrophil/Lymphocyte-Ratio>5.6, Age>61, Comorbidities>1 and D-dimers>1097ng/ml, efficiently predicted fatal outcome in an initial cohort (AUC: 0.951; 95%CI: 0.919-0.983, p<10 (-6)). Two independent cohorts that were used for validation indicated similar AUC values. CONCLUSIONS: This study unravels strong 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.