Prediction of survival odds in COVID-19 by zinc, age and selenoprotein P as composite biomarker

Abstract

SARS-CoV-2 infections cause the current coronavirus disease (COVID-19) pandemic and challenge the immune system with ongoing inflammation. Several redox-relevant micronutrients are known to contribute to an adequate immune response, including the essential trace elements zinc (Zn) and selenium (Se). In this study, we tested the hypothesis that COVID-19 patients are characterised by Zn deficiency and that Zn status provides prognostic information. Serum Zn was determined in serum samples (n = 171) collected consecutively from patients surviving COVID-19 (n = 29) or non-survivors (n = 6). Data from the European Prospective Investigation into Cancer and Nutrition (EPIC) study were used for comparison. Zn concentrations in patient samples were low as compared to healthy subjects (mean ± SD; 717.4 ± 246.2 vs 975.7 ± 294.0 μg/L, P < 0.0001). The majority of serum samples collected at different time points from the non-survivors (25/34, i.e., 73.5%) and almost half of the samples collected from the survivors (56/137, i.e., 40.9%) were below the threshold for Zn deficiency, i.e., below 638.7 μg/L (the 2.5th percentile in the EPIC cohort). In view that the Se status biomarker and Se transporter selenoprotein P (SELENOP) is also particularly low in COVID-19, we tested the prevalence of a combined deficit, i.e., serum Zn below 638.7 μg/L and serum SELENOP below 2.56 mg/L. This combined deficit was observed in 0.15% of samples in the EPIC cohort of healthy subjects, in 19.7% of the samples collected from the surviving COVID-19 patients and in 50.0% of samples from the non-survivors. Accordingly, the composite biomarker (SELENOP and Zn with age) proved as a reliable indicator of survival in COVID-19 by receiver operating characteristic (ROC) curve analysis, yielding an area under the curve (AUC) of 94.42%. We conclude that Zn and SELENOP status within the reference ranges indicate high survival odds in COVID-19, and assume that correcting a diagnostically proven deficit in Se and/or Zn by a personalised supplementation may support convalescence.

Keywords: Biomarker; COVID-19; Inflammation; Micronutrient; Trace element.

Fig. 1

Comparison of Zn status in the available samples from patients with COVID-19 and healthy controls. Zn concentrations were relatively low in serum samples from COVID-19 patients in contrast to healthy European adults. Average Zn concentrations were particularly depressed in the samples from non-survivors as compared to the samples collected from patients surviving the SARS-CoV-2 infection. All available data are plotted into this figure, i.e., several data points refer to different samples from the same patients in the groups of deceased (“Death”) and surviving (“Discharge”) patients, respectively. The dashed line at <642.5 μg/L indicates the threshold for Zn deficiency. Groups of data were compared by ANOVA, **** indicates p < 0.0001.

Fig. 2

Correlation analyses of serum Zn concentrations with biomarkers of Se status in the samples from COVID-19 patients. All of the available serum samples (n = 171) from COVID-19 patients (n = 35) were analysed for total serum Zn and Se concentrations, as well as for serum SELENOP levels. (A) The concentrations of Zn and SELENOP showed a positive linear correlation (R = 0.49), in agreement with a comparable correlation between (B) total serum Zn and total serum Se concentrations (R = 0.45). The analysis of the data for (C) the Se transporter SELENOP and total serum Se concentrations showed the expected tight positive correlation (R = 0.74). R: Spearman correlation coefficient (2-sided, 2-tailed); p < 0.0001 for all three analyses (indicated on top of the graphics).

Fig. 3

Dynamic changes in Zn levels in comparison to SELENOP and Se concentrations over the first weeks in hospital in the serum of COVID-19 patients in relation to survival. (A) Zn concentrations increased in the samples from COVID-19 patients irrespective of survival. (B) Serum SELENOP concentrations increased in survivors and decreased in non-survivors. (C) Serum Se concentrations increased with time in surviving patients only. Graphs B and C are slightly modified from the original publication and shown for comparison to Zn dynamics [32]. The dashed lines indicate the threshold of deficiency for the different biomarkers. Correlation analysis by Pearson, R indicates the correlation coefficient, and p indicates significance.

Fig. 4

Analysis of combined deficits in the trace element parameters analysed in samples from COVID-19 patients in comparison to a reference cohort of samples from healthy subjects. (A) Patients not surviving the Sars-CoV-2 infection showed substantial deficits in the trace element biomarkers, with many of the analysed samples displaying relevant deficits in both Zn, Se and SELENOP. (B) In comparison, combined deficiencies in Zn, Se or SELENOP or combinations thereof were less prevalent in the samples from COVID-19 survivors. (C) In the large cohort of samples from healthy European subjects, simultaneous deficiencies in Zn and Se or Zn and SELENOP or all three biomarkers were very rare. In the diagram, the numbers of subjects deficient in at least one of the biomarkers are shown only, and the circles are not drawn to scale. The colours used are indicative of non-survivors (red), survivors (blue) or control subjects (yellow), with colour densities representing the prevalence of deficiency (according to the scale indicated to the right of each diagram). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

ROC-analysis of serum trace elements to predict survival of patients with COVID-19. (A) Serum concentrations of Se (pale grey), Zn (grey) and SELENOP (black) predict survival of patients with COVID-19 with similar and moderately good precision. (B) The sums of serum Zn with serum Se or serum Zn with serum SELENOP concentrations both yield a higher area under the curve (AUC). (C) The multiple regression model based on Zn, SELENOP and the patient's age outperformed any other combination of variables via stepwise AIC selection. The final model, based on these three parameters, yielded the highest AUC of 94.42%. The corresponding cutpoint, according to the Youden Index [48] is indicated.

Fig. 6

Overview of the physiological regulation and biochemical pathways potentially underlying the observed different dynamics of serum Zn and Se in COVID-19 in relation to survival or death.