Elevated serum SDMA and ADMA at hospital admission predict in-hospital mortality of COVID-19 patients

COVID-19 is a disease with a variable clinical course ranging from mild symptoms to critical illness, organ failure, and death. Prospective biomarkers may help to predict the severity of an individual’s clinical course and mortality risk. We analyzed asymmetric (ADMA) and symmetric dimethylarginine (SDMA) in blood samples from 31 patients hospitalized for COVID-19. We calculated associations of ADMA and SDMA with mortality and organ failure, and we developed a predictive algorithm based upon these biomarkers to predict mortality risk. Nine patients (29%) experienced in-hospital death. SDMA and ADMA serum concentrations were significantly higher at admission in COVID-19 patients who died than in survivors. Cut-offs of 0.90 µmol/L for SDMA (AUC, 0.904, p = 0.0005) and 0.66 µmol/L for ADMA (AUC, 0.874, p = 0.0013) were found in ROC analyses to best discriminate both subgroups of patients. Hazard ratio for in-hospital mortality was 12.2 (95% CI: 2.2–31.2) for SDMA and 6.3 (1.1–14.7) for ADMA above cut-off. Sequential analysis of both biomarkers allowed discriminating a high-risk group (87.5% mortality) from an intermediate-risk group (25% mortality) and a low-risk group (0% mortality). Elevated circulating concentrations of SDMA and ADMA may help to better identify COVID-19 patients with a high risk of in-hospital mortality.


Results
Baseline characteristics and clinical course of the patients. We included 31 patients (11 women, 20 men) with a mean age of 63.3 ± 17.8 years. 15 patients were primary admissions; 16 patients were referred from other hospitals (amongst them 8 with ongoing mechanical ventilation). With the exception of one, all patients had pre-existing co-morbidities. The mean duration from first symptoms to hospital admission was 6.8 ± 6.3 days. The mean duration of treatment in our medical center was 30.6 ± 27.0 days; need for oxygen insufflation varied from 0 to 97 days with a mean of 24.8 ± 29.1 days. 19 patients were treated on ICU for a mean of 34.7 ± 31.5 days. Detailed patient characteristics of this cohort are given in Table 1.
Nine patients (29%) died in-hospital. Causes of death were multi-organ failure in five patients (16%), respiratory failure in two (6.5%), and hemorrhagic complications in two patients with respiratory failure (6.5%). Patients who survived had a mean SOFA score at admission of 3.0 ± 3.5, whereas patients who died during in-patient treatment had a mean initial SOFA score of 7.0 ± 3.8 (p < 0.001). 14 patients had ARDS (seven of them died), five patients with ARDS were treated by ECMO (all of them died), 10 patients developed a high thromboembolic burden (six of them died), 14 patients had cardiac injury (nine of them died), 11 patients had liver injury (five of them died), eight patients developed acute kidney injury (three of them died), and ten patients developed circulatory insufficiency (three of them died). Patients who died in-hospital had significantly higher leukocyte count, higher CRP, and higher PCT concentrations at admission (Table 1). D-dimer concentrations and eGFR were not significantly different between survivors and non-survivors.
Serum concentrations of ADMA and SDMA. Mean ADMA serum concentration was significantly higher in patients referred to our hospital either with ongoing mechanical ventilation or without (0.84 ± 0.15 µmol/L and 0.66 ± 0.29 µmol/L, respectively; p = n.s.); it was lowest in primary admissions (0.58 ± 0.13 µmol/L; p = 0.010 for difference between groups in ANOVA). By contrast, there was no significant difference in mean SDMA serum concentration between patients referred to our ICU with ongoing mechanical ventilation (0.99 ± 0.40 µmol/L), patients referred without mechanical ventilation (0.79 ± 0.24 µmol/L; p = n.s. vs. patients with mechanical ventilation), and primary admissions (0.85 ± 0.46 µmol/L; p = 0.598 for difference between groups in ANOVA). ADMA concentration correlated significantly with CRP and leukocyte count, but not with PCT and eGFR (supplementary Fig. S1). SDMA concentration correlated inversely with eGFR, positively with CRP, but not with PCT nor with leukocyte count (supplementary Fig. S2).
The serum concentrations of ADMA and SDMA at hospital admission were significantly higher in patients who experienced in-hospital death versus those who survived (ADMA, 0.86 ± 0.07 µmol/L vs. 0.59 ± 0.03 µmol/L, p = 0.0004; SDMA, 1.15 ± 0.09 µmol/L vs. 0.78 ± 0.08 µmol/L, p = 0.017; Fig. 1). The ADMA serum concentration further increased significantly over time in patients who died, but not in survivors (Fig. 2a). The difference of SDMA levels between both groups remained significant during the course of hospitalization (Fig. 2b). The differences in ADMA and SDMA between survivors and non-survivors were stable when six patients with preexisting chronic kidney disease were excluded (supplementary Fig. S3); the same was true for the comparison of eight patients admitted with ongoing mechanical ventilation versus those who were not (supplementary Fig. S4). 0.58 ± 0.03 µmol/L, p = 0.01) and tended to be higher in 11 patients with liver injury, but this trend did not reach statistical significance. SDMA was significantly higher in patients with liver injury (1.07 ± 0.16 vs. 0.79 ± 0.07 µmol/L, p = 0.017) and showed a non-significant trend towards higher levels in ARDS. None of the two biomarkers was different in patients with (N = 8) or without acute kidney injury. The differences in the two biomarkers in these diseases are graphically shown in supplementary Fig. S5. We performed ROC analyses with mortality as outcome for SDMA, ADMA, SOFA score, and other blood biomarkers that were significantly elevated at admission in non-survivors. The SOFA score showed an AUC of 0.819 (95% CI, 0.659-0.980; cut-off, 5.5 points; p = 0.007); CRP had an AUC of 0.843 (95% CI, 0.702-0.985; cutoff, 94.1; p = 0.003), and PCT had an AUC of 0.876 (95% CI, 0.745-1.000; cut-off, 0.32; p = 0.001). Leukocyte cell count had an AUC of 0.833 (95% CI, 0.687-0.980; cut-off, 10.7; p = 0.004). The ROC curves for these inflammatory parameters are given in supplementary Fig. S6.
We performed multivariable-adjusted logistic regression analyses with SDMA and ADMA above and below the cut-off concentrations as categorical variables. Both markers were significantly associated with in-hospital mortality in models adjusted for age and sex and for age, sex, and eGFR (Table 3). In a fully adjusted model including inflammatory markers, SDMA remained highly significantly associated with survival, whilst this association lost significance for ADMA (Table 3).
We next tested whether SDMA and ADMA combined in a single variable improved predictive power. However, neither (SDMA + ADMA) nor (SDMAxADMA) showed significant improvement over SDMA used alone (supplementary Fig. S8). In addition, we tested three previously published COVID-19 mortality risk scores 5,7,8 ; none of these scores showed a significant prediction of mortality in our patient cohort (supplementary Fig. S9).
By contrast, we observed that sequential measurements of SDMA and ADMA significantly enhanced discrimination of mortality risk. Patients with high SDMA and high ADMA concentrations had a HR of in-hospital mortality of 9.30 (95% CI, 2.09-41.37), p = 0.0034, as compared to those with both biomarker levels low; individuals with only one biomarker level elevated had an intermediate risk (p = n.s. vs. both biomarkers low; Fig. 4c). Using a decision tree algorithm, we were able to discriminate high-risk patients (SDMA ≥ 0.90 µmol/L and ADMA ≥ 0.66 µmol/L) with an in-hospital mortality of 87.5%, intermediate-risk patients (either SDMA or ADMA elevated) with an in-hospital mortality of 25%, and low-risk patients (SDMA < 0.90 µmol/L and ADMA < 0.66 µmol/L), whose in-hospital mortality was 0% (Fig. 5). Sequential measurement of SDMA and www.nature.com/scientificreports/ ADMA therefore provided the best predictive power for in-hospital death when compared to traditional risk markers or their combination with either SDMA or ADMA.

Discussion
The present study is the first to report two novel biomarkers beyond currently established clinical chemistry and blood hematology parameters to identify hospitalized COVID-19 patients at high risk of in-hospital mortality. These biomarkers, SDMA and ADMA, have a high sensitivity and specificity to predict mortality amongst hospitalized COVID-19 patients.
In the general population, pre-existing conditions like advanced age, obesity, type 2 diabetes, and hypertension are risk factors for a severe course of COVID-19 requiring hospitalization 30,31 . In our cohort of hospitalized COVID-19 patients, however, none of these conditions was significantly associated with in-hospital mortality or organ dysfunction. This is not astonishing, as 30 out of the 31 patients had pre-existing conditions and the mean age of our cohort was above 60 years. Although severe COVID-19 is an inflammation-driven disease, leukocyte cell count was the only traditional inflammatory marker that was weakly associated with mortality, whilst neither the SOFA score nor other commonly used laboratory parameters like CRP and PCT were able to significantly identify high-risk patients. In addition, three previously published risk scores that are based on a variety of different traditional diagnostic parameters 5,7,8 failed to significantly predict mortality in our COVID-19 cohort.   www.nature.com/scientificreports/ Current data suggest that proper function of endothelial NO synthase (eNOS) may be an important mechanism of defense after infection with SARS-CoV-2 32 . Endothelial cell tropism of the SARS-CoV-2 virus causes endothelial inflammation 14 , which possibly accelerates endothelial dysfunction and NO deficiency 33 . Endothelial dysfunction as marked by dysfunctional endothelium-dependent, NO-mediated vasodilation, and the ensuing high thrombosis risk contribute to COVID-19 morbidity and mortality 34 . ADMA is an endogenous, competitive inhibitor of NO synthesis an-like its congener molecule, SDMA-an inhibitor of cellular L-arginine uptake 18 . Both dimethylarginines are formed through the action of protein arginine methyltransferases (PRMTs), enzymes that are involved in innate immune responses and in the response to hypoxia in the lung 19 . PRMTs produce mainly SDMA in the central nervous system, but ADMA in many other organs including heart, circulatory system, and lungs (for review, cf. 17 ). ADMA is enzymatically degraded by dimethylarginine dimethylaminohydrolases (DDAH), the activity of which is reduced by cysteine nitrosylation 35 , resulting in ADMA accumulation in a manner reversible by antioxidants. SDMA, by contrast, is inactivated by alanine-glyoxylate aminotransferase (AGXT2), an enzyme predominantly expressed in the kidneys and liver 36 . The underlying biochemistry may explain the associations of ADMA and SDMA with ensuing organ dysfunction typical for COVID-19 patients, i.e. acute kidney injury, cardiovascular thromboembolic events, neurological damage, and multi-organ failure. ADMA and SDMA, two biomarkers causing impaired NO production, may interfere with two essential pathophysiological steps in COVID-19-associated critical illness: vascular failure and immune response. Whilst the interaction of ADMA and SDMA with endothelial NO production has been extensively studied, there is much less information available on their interaction with inducible NO synthase 20 . Nonetheless, both mechanisms may contribute to their roles as predictive biomarkers in the present cohort. In line with this, we have previously reported that sequential measurement of SDMA and ADMA helps to predict the lethality of sepsis in ICU-treated patients 26 . Others have also reported ADMA to be associated with ICU death in a heterogeneous cohort of critically ill patients 27 . Table 3. Stepwise multivariable-adjusted regression analysis for SDMA and ADMA with in-hospital mortality. a Model 1 was adjusted for age and sex. b Model 2 was adjusted for age, sex, and eGFR. c Model 3 was adjusted for age, sex, eGFR, C-reactive protein, and pro-calcitonin. *denotes statistically significant associations with in-hospital mortality.  www.nature.com/scientificreports/ Respiratory failure and global hypoxemia were major pathophysiological problems that led to hospital admission of the patients included in our present study. We have previously shown that ADMA continuously increases in humans exposed to chronic-intermittent hypoxia 37 , and that DDAH1 -/mice that have high circulating ADMA concentration are prone to develop pulmonary hypertension and right ventricular hypertrophy upon exposure to chronic hypoxia 38 . Based upon our data showing that inhibitors of NO synthesis are predictive biomarkers for COVID-19 survival with high discriminative power, and in line with published pilot studies that successfully administered inhaled NO to treat severe respiratory failure 39,40 , therapeutic approaches aimed at restoring physiological NO function may help to better treat severe COVID-19.
Combined analysis of ADMA and SDMA was previously shown by us to predict mortality risk in sepsis patients 26 . In that study, critically ill patients during ICU treatment were included, the cut-off values used were 1.34 µmol/L for SDMA and 0.97 µmol/L for ADMA. In the present cohort of COVID-19 patients, we found lower cut-off values for both biomarkers, which is in line with the unselected character of the present cohort, including hospitalized COVID-19 patients with a moderate to severe disease course and 19 out of a total of 31 patients being treated on ICU.
This is a retrospective cohort study and therefore has certain limitations. The study was carried out at a single center and involved a relatively small number of patients, which did not allow us to perform extensive subgroup analyses. The population comprised exclusively hospitalized patients with confirmed COVID-19 in a tertiary care hospital, with a relevant portion of patients that were referred as secondary admissions from other hospitals in the region, mostly but not exclusively because of ARDS. Although we used a validated liquid chromatographytandem mass spectrometric (LC-MS/MS) method for biomarker analysis, ADMA and SDMA may be measured by ubiquitously available laboratory methods. We have previously validated an ADMA enzyme-linked immunosorbent assay (ELISA) and established reference ranges using this method 41 . However, in the absence of a widely used routine analytical method for ADMA and SDMA, reference ranges reported in the literature are broad and may vary according to the analytical method applied, and cut-off values reported in this study relate to the analytical method we used, LC-MS/MS. Nevertheless, our observations warrant follow-up studies with larger patient groups and a more formalized statistical approach to confirm the utility of SDMA and ADMA to independently predict COVID-19 outcome and severity.
In conclusion, we show here for the first time that ADMA and SDMA are biomarkers that allow us to prospectively identify COVID-19 patients with a high mortality risk beyond the diagnostic utility of the SOFA score and commonly used laboratory parameters. This may help to monitor such patients more closely, establish intensive care treatment earlier, and reduce the lethality of the COVID-19 pandemic.

Patients and methods
Study cohort and protocol. 31 consecutive patients with confirmed SARS-CoV-2 infection were primarily admitted with symptomatic COVID-19 to the University Hospital Aachen (UKA) or referred from another hospital between March and May 2020. Patients were included in this study if the main cause for hospital admission was COVID-19 disease. Patients had to have a positive SARS-CoV-2 test result in respiratory samples that was performed in our hospital or externally before admission. Patients were only included if an initial blood sample for biomarker analysis was available within 24 h after admission. All patients gave their informed consent to have their blood samples included into the RWTH centralized Biomaterial Bank (RWTH cBMB) for further scientific study. The Ethics Committee of the Medical Faculty of RWTH Aachen had consented the Covid-19 Aachen study (COVAS) according to their vote EK080/20 and the regulations of the RWTH CBMB (vote EK206/09). All investigations were performed in accordance with the Declaration of Helsinki in its latest revision. No further selection criteria were applied.
All patients were treated according to best medical practice and individual clinical needs. Patients were either isolated under standard care or treated in our intensive care unit (ICU). The decision on treatment strategies was based on clinical judgment of the severity of the disease and the presence or absence of acute respiratory distress syndrome (ARDS). Severity of ARDS was classified according to the degree of hypoxia as defined by the "Berlin definition" 42 .
Comorbidities (such as hypertension, overweight or obesity, diabetes, pre-existing respiratory or cardiovascular diseases, smoking, chronic kidney disease, malignancies, chronic liver disease), and medications prescribed at the time of admission were recorded in hospital, or taken from existing medical records. Blood samples were drawn into EDTA vacutainers on day 1 after admission, after one week, two weeks, and six weeks. Samples were immediately centrifuged and stored at -80 °C until analysis. Sample storage and logistics were managed by the team of the RWTH cBMB.

Measurement of ADMA and SDMA by LC-MS/MS. Validated protocols for liquid chromatography-
tandem mass spectrometry (LC-MS/MS) were used to quantify ADMA and SDMA in serum 43 . Briefly, 25 μl of serum were diluted in methanol to which stable isotope labelled internal standards had been added. Subsequently, the compounds were converted into their butyl ester derivatives and quantified by LC-MS/MS (Xevo TQ-S cronos, Waters GmbH, Eschborn, Germany). Compounds were separated on an Aquity UPLC BEH C18 column (2·1 × 50 mm, 1.7 µm, Waters GmbH). The coefficient of variation for the quality control samples was below 15% for both compounds.
Clinical and biochemical assessment of patient status. Patients were assessed for eligibility based on a positive RT-PCR assay for SARS-CoV-2 in a respiratory tract sample as previously described 30,44 . Vital parameters presented in this study were taken between four and 24 h following hospital admission or intubation, with the worst values being depicted. We defined multi organ failure (MOF) as a failure of at least four major organs www.nature.com/scientificreports/ (heart, lungs, liver, kidneys) because of complications of COVID 19. Severity of ARDS was defined using P/Fratio or the Horowitz index. Acute kidney injury was defined according to the AKIN criteria 45 and/or need for continuous veno-venous hemofiltration in patients with no pre-existing chronic renal failure. Cardiac injury was defined as troponin T levels > 52 ng/mL or a relative increase by ≥ twofold during in-hospital treatment. Liver injury was defined as an increase in serum total bilirubin by ≥ twofold and/or increases in serum ALT and/or AST activities by ≥ threefold. High thromboembolic burden was defined as a relative increase of D-dimer levels by ≥ twofold during in-hospital treatment. Circulatory insufficiency/shock was defined as the need for catecholamines at any time during in-hospital treatment. Febrile days were defined as the time from fever onset until the last documented value above 38.5 ℃. Patients with a body mass index (BMI) of 25 to < 30 kg/m 2 were classified as overweight and those with BMI ≥ 30 kg/m 2 as obese. Diabetes and prediabetes were defined by clinical history, medication and HbA 1c values ≥ 6.5% or ≥ 5.7 to < 6.5%, respectively. Serum and whole blood samples were obtained routinely at the time of admission. Complete blood count, coagulation tests, inflammatory markers [circulating levels of C-reactive protein (CRP), pro-calcitonin (PCT)] and creatinine levels in blood were measured among other tests. Creatinine clearance was estimated using the CKD-EPI formula 46 .
Statistical analyses. All variables were tested for normal distribution using the Kolmogorov-Smirnov test.
Data are presented as mean with standard deviation (SD). Differences between groups were tested for significance using the nonparametric Mann-Whitney U test for two groups or the Kruskal-Wallis analysis of variance for more than two groups. The Chi 2 test was used for comparison of categorical variables between groups. Time courses of ADMA and SDMA concentrations were examined using repeated measures two-way ANOVA followed by Tukey's multiple comparisons test. Spearman's rank correlation was used to assess pairwise correlations. Survival analyses were performed using Kaplan-Meier curves comparing patients with ADMA and SDMA above or below the cut-off value determined in receiver-operated curve (ROC) analyses. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by multivariable-adjusted logistic regression analyses. As we identified two biomarkers, ADMA and SDMA, as predictors of COVID-19 mortality, we analyzed additional models using (SDMA + ADMA) or (SDMAxADMA) as variables, respectively. In addition, we performed a decision tree analysis to determine risk upon sequential analysis of SDMA and ADMA. Cut-offs to separate risk groups were based on values determined in ROC analysis for both biomarkers. All statistical analyses were performed using SPSS (version 25; IBM Corporation, Armonk, NY, USA) and GraphPad Prism (version 6.01, GraphPad Software, San Diego, CA, USA). For all tests, p < 0.05 was considered statistically significant.

Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.