TGF Beta as a Prognostic Biomarker of COVID-19 Severity in Patients with NAFLD-A Prospective Case-Control Study

Frano Susak, Nina Vrsaljko, Adriana Vince, Neven Papic, Frano Susak, Nina Vrsaljko, Adriana Vince, Neven Papic

Abstract

Non-alcoholic fatty liver disease (NAFLD), the leading cause of chronic liver disease in Western countries, has been identified as a possible risk factor for COVID-19 severity. However, the immunological mechanisms by which NAFLD exacerbates COVID-19 remain unknown. Transforming growth factor-beta 1 (TGF-β1) has an important immunomodulatory and pro-fibrotic role, which has already been described in NAFLD. However, the role of TGF-β1 in COVID-19 remains unclear, and could also be the pathophysiology link between these two conditions. The aim of this case-control study was to analyze the expression of TGF-β1 in COVID-19 patients depending on the presence of NAFLD and COVID-19 severity. Serum TGF-β1 concentrations were measured in 60 hospitalized COVID-19 patients (30 with NAFLD). NAFLD was associated with higher serum TGF-β1 concentrations that increased with disease severity. Admission TGF-β1 concentrations showed good discriminative accuracy in predicting the development of critical disease and COVID-19 complications (need for advanced respiratory support, ICU admission, time to recovery, development of nosocomial infections and mortality). In conclusion, TGF-β1 could be an efficient biomarker for predicting COVID-19 severity and adverse outcomes in patients with NAFLD.

Keywords: COVID-19; NAFLD; SARS-CoV-2; TGF-β1; cytokines; inflammation; non-alcoholic fatty liver disease.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Serum concentrations of TGF-β1 in patients with and without NAFLD depending on the COVID-19 severity. Data are presented as medians with IQRs and analyzed via a Kruskal–Wallis test with Dunn’s multiple comparisons test.
Figure 2
Figure 2
The ROC curve analysis of serum TGF-β1 concentrations for discrimination of: (a) moderate COVID-19 and severe/critical COVID-19 patients; (b) critical COVID-19 and moderate/severe COVID-19. AUCs are shown with 95%CI.
Figure 3
Figure 3
Spearman’s correlation correlogram. The strength of the correlation between two variables is represented by the color at the intersection of those variables. Colors range from dark blue (strong negative correlation; r = −1.0) to red (strong positive correlation; r = 1.0). Results were not represented if p > 0.05.
Figure 4
Figure 4
TGF-β1 serum concentrations in COVID-19 patients according to the presence of type 2 diabetes mellitus (T2DM), obesity, dyslipidemia, sex, age group and duration of symptoms. Data are presented as medians with IQR, and p-values were calculated via the Mann–Whitney test or Kruskal–Wallis test.
Figure 5
Figure 5
Association of time to recovery with serum concentrations of TGF-β1 using Kaplan–Meier curves in patients with COVID-19. Hazard ratios with 95% confidence intervals and p-values were calculated using the log-rank test.
Figure 6
Figure 6
TGF-β1 serum concentrations in COVID-19 patients according to the level of respiratory support, ICU admission, development of nosocomial infections, and pulmonary thrombosis. Data are presented as medians with IQR, and p-values were calculated using the Mann–Whitney test.
Figure 7
Figure 7
ROC curve analysis of TGF-β1 and IL-6 with the corresponding AUCs for COVID-19 mortality.

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