Circulating Dickkopf1 Parallels Metabolic Adaptations and Predicts Disease Trajectories in Patients With COVID-19.

CONTEXT AND AIMS

Coronavirus disease 19 (COVID-19) trajectories show high interindividual variability, ranging from asymptomatic manifestations to fatal outcomes, the latter of which may be fueled by immunometabolic maladaptation of the host. Reliable identification of patients who are at risk of severe disease remains challenging. We hypothesized that serum concentrations of Dickkopf1 (DKK1) indicate disease outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals.

METHODS

We recruited hospitalized patients with PCR-confirmed SARS-CoV-2 infection and included 80 individuals for whom blood samples from 2 independent time points were available. DKK1 serum concentrations were measured by ELISA in paired samples. Clinical data were extracted from patient charts and correlated with DKK1 levels. Publicly available datasets were screened for changes in cellular DKK1 expression on SARS-CoV-2 infection. Plasma metabolites were profiled by nuclear magnetic resonance spectroscopy in an unbiased fashion and correlated with DKK1 data. Kaplan-Meier and Cox regression analysis were used to investigate the prognostic value of DKK1 levels in the context of COVID-19.

RESULTS

We report that serum levels of DKK1 predict disease outcomes in patients with COVID-19. Circulating DKK1 concentrations are characterized by high interindividual variability and change as a function of time during SARS-CoV-2 infection, which is linked to platelet counts. We further find that the metabolic signature associated with SARS-CoV-2 infection resembles fasting metabolism and is mirrored by circulating DKK1 abundance. Patients with low DKK1 levels are twice as likely to die from COVID-19 than those with high levels, and DKK1 predicts mortality independent of markers of inflammation, renal function, and platelet numbers.

CONCLUSION

Our study suggests a potential clinical use of circulating DKK1 as a predictor of disease outcomes in patients with COVID-19. These results require validation in additional cohorts.