Vitamin C Deficiency in Blood Samples of COVID-19 Patients

Abstract

Coronavirus disease 2019 (COVID-19) is the most notable pandemic of the modern era. A relationship between ascorbate (vitamin C) and COVID-19 severity is well known, whereas the role of other vitamins is less understood. The present study compared the blood levels of four vitamins in a cohort of COVID-19 patients with different severities and uninfected individuals. Serum concentrations of ascorbate, calcidiol, retinol, and α-tocopherol were measured in a cohort of 74 COVID-19 patients and 8 uninfected volunteers. The blood levels were statistically compared and additional co-morbidity factors were considered. COVID-19 patients had significantly lower plasma ascorbate levels than the controls (p-value < 0.001), and further stratification revealed that the controls had higher levels than fatal, critical, and severe COVID-19 cases (p-values < 0.001). However, no such trend was observed for calcidiol, retinol, or α-tocopherol (p-value ≥ 0.093). Survival analysis showed that plasma ascorbate below 11.4 µM was associated with a lengthy hospitalization and a high risk of death. The results indicated that COVID-19 cases had depleted blood ascorbate associated with poor medical conditions, confirming the role of this vitamin in the outcome of COVID-19 infection.

Keywords: COVID-19; ascorbate; calcidiol; retinol; vitamin plasma levels; α-tocopherol.

Figure 1

Measurement of plasma vitamins in COVID-19 patients and healthy controls. Plasma concentrations of selected vitamins. Take note of how two samples had ascorbate levels greater than 100 µM, setting them apart from the others; their removal did not change the reported statistical trends. Upper panel. Comparison of ascorbate, retinol, α-tocopherol, and calcidiol between healthy controls (uninfected) and COVID-19 cases (infected). Lower panel. Stratification of plasma ascorbate, retinol, α-tocopherol, and calcidiol by COVID-19 disease grade. Statistical significance: p-value < 0.001 (***), p-value < 0.0001 (****).

Figure 2

Stratification of plasma vitamins by gender and age. (A) Comparison of the natural logarithm of plasma ascorbate, calcidiol, retinol, and α-tocopherol between healthy controls (uninfected) and COVID-19 cases (infected) stratified by gender. (B) Stratification of plasma ascorbate, calcidiol, retinol, and α-tocopherol in the COVID-19 patients by age group. To reduce the impact of two samples with plasma ascorbate levels above 100 µM, the measured values are expressed as the natural logarithm (ln). This type of transformation reduces the spread of the data, while also assisting in meeting the assumptions of a statistical inference and improving their interpretation.

Figure 3

Stratification of plasma ascorbate in the COVID-19 patients by selected clinical features. To reduce the impact of two samples with plasma ascorbate levels above 100 µM, the measured values are expressed as the natural logarithm (ln). This type of transformation reduces the spread of the data, while also assisting in meeting the assumptions of a statistical inference and improving their interpretation.

Figure 4

Survival analysis based on vitamin C levels in COVID-19 patients. (A) Time until the end of either ventilation time, (B) hospitalization, or (C) a COVID-19 related death event based on the widely accepted cut-off for vitamin C deficiency (plasma ascorbate of 11.4 µM) for the 74 COVID-19 cases. p-values are calculated by the Gehan-Breslow-Wilcoxon test. (D) ROC analysis for the differentiation of healthy controls and mild COVID-19 cases based on plasma ascorbate. The ROC curve is constructed by plotting the sensitivity against the false positive rate (1—specificity) at various threshold of plasma ascorbate. ROC analysis helps selecting cut-off points to separate two populations, in this case uninfected people and mild COVID-19 cases. A cut-off of 21.8 µM achieved a sensitivity (Sens.) of 89.5%, a specificity (Spec.) of 65.0%, a PPV of 23.5%, a NPV of 17.1%, and an AUC of 0.778. AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver-operating characteristics.