Decreased serum levels of the inflammaging marker miR-146a are associated with clinical non-response to tocilizumab in COVID-19 patients

Abstract

Current COVID-19 pandemic poses an unprecedented threat to global health and healthcare systems. The most amount of the death toll is accounted by old people affected by age-related diseases that develop a hyper-inflammatory syndrome. In this regard, we hypothesized that COVID-19 severity may be linked to inflammaging. Here, we examined 30 serum samples from patients enrolled in the clinical trial NCT04315480 assessing the clinical response to a single-dose intravenous infusion of the anti-IL-6 receptor drug Tocilizumab (TCZ) in COVID-19 patients with multifocal interstitial pneumonia. In these serum samples, as well as in 29 age- and gender-matched healthy control subjects, we assessed a set of microRNAs that regulate inflammaging, i.e. miR-146a-5p, miR-21-5p, and miR-126-3p, which were quantified by RT-PCR and Droplet Digital PCR. We showed that COVID-19 patients who did not respond to TCZ have lower serum levels of miR-146a-5p after the treatment (p = 0.007). Among non-responders, those with the lowest serum levels of miR-146a-5p experienced the most adverse outcome (p = 0.008). Our data show that a blood-based biomarker, such as miR-146a-5p, can provide clues about the molecular link between inflammaging and COVID-19 clinical course, thus allowing to better understand the use of biologic drug armory against this worldwide health threat.

Keywords: COVID-19; Inflammaging; Tocilizumab; interleukin-6; microRNA.

Conflict of interest statement

The authors report no declarations of interest.

Copyright © 2020 Elsevier B.V. All rights reserved.

Figures

Fig. 1

(A) Serum levels of miR-146a-5p, -21-5p, and -126-3p in 29 COVID-19 patients at baseline (T0) and after 72 h from treatment with tocilizumab (T1), divided into responders (R) and non-responders (NR), and in 29 age-matched healthy control subjects (CTR). Data are expressed as Z-scores of log2(relative expression) and presented as mean ± SD. *, p < 0.05; ***, p < 0.001 for unpaired t test (CTR vs. COVID-19). °°°, p < 0.001 for simple main effects of time (T0 vs. T1) and responder status (R vs. NR). (B) Bland-Altman plot for inter-method agreement between Droplet Digital PCR (ddPCR) and RT-PCR in the quantification of circulating miR-146a-5p. The blue line represents the mean bias between the two methods, the dashed lines indicate the limits of agreement. (C) Correlation plot showing partial correlations, controlling for age, between inflamma-miR levels and selected variables at both time points. Bold squares indicate correlations between variables assessed at the same time point. The color and the size of the circles depend on the magnitude of the correlation. Blue, positive correlation; red, negative correlation. Significant correlations are marked with * (p < 0.05), ** (p < 0.01), or *** (p < 0.001) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Fig. 2

(A) Summary of exploratory factor analysis and subsequent logistic regressions on COVID-19 patients. Baseline variables are reported into each circle according to the factor loading. The areas of the circles are proportional to the amount of variance (reported in brackets) explained by each factor. Overlapping circles include variables loading onto two factors. The green arrow points out the significant association between factor 1 and survival, while the gray lines indicate non-significant associations. (B) Age-adjusted baseline miR-21-5p, -126-3p, and -146-5p levels in dead vs. survivor NR patients. Data are expressed as Z-scores of log2(relative expression) and presented as estimated marginal mean ± SEM. *, p < 0.05; **, p < 0.01 for one-way ANCOVA (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).