Biological and Clinical Factors contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19

The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. Exploratory studies evaluating the impact of COVID-19 infection on the plasma metabolome have been performed, often with small numbers of patients, and with or without relevant control data; however, determining the impact of biological and clinical variables remains critical to understanding potential markers of disease severity and progression. The present large study, including relevant controls, sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831), testing positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on 831 plasma samples from acutely ill patients while in the emergency department, at admission, and during hospitalization. We then performed additional lipidomics analyses of the 60 subjects with the lowest and highest body mass index, either COVID-19 positive or negative. Omics data were correlated to detailed data on patient characteristics and clinical laboratory assays measuring coagulation, hematology and chemistry analytes. Significant changes in arginine/proline/citrulline, tryptophan/indole/kynurenine, fatty acid and acyl-carnitine metabolism emerged as highly relevant markers of disease severity, progression and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half yielding ~ 78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for follow-up mechanistic studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.


Introduction
On April 28 2020 we began performing one of the earliest investigations on the impact of SARS-CoV-2 infection on the circulating metabolome. 1 At that time, we reported that ~ 3 million cases had been con rmed worldwide, a number that has dramatically risen since to ~ 140 million cases and 3 million deaths -according to the World Health Organization (https://www.who.int/emergencies/diseases/novelcoronavirus-2019). Our original study aimed to identify metabolic signatures that could help inform prognosis and guide treatment early after the onset of COVID-19, the disease caused by SARS-CoV-2 infection. Indeed, small molecule metabolites provide the building blocks that fuel viral replication, from nucleic acids to proteins and membrane lipids. SARS-CoV-2 1-5 -like other viral infections 6 -was found to promote the mobilization of free fatty acids to support the formation of viral capsid-associated membranes, a phenomenon that could be explained, at least in part, by activation of phospholipase A2, 7,8 a target amenable to pharmacological intervention.
Despite public health interventions and the advent of multiple vaccination strategies, the number of cases in the United States has plateaued, rather than continuing to decrease, and COVID-19 cases continue to rise globally. This may be explained by multiple factors, including (i) vaccination rates lagging behind the percentage required to reach herd immunity; (ii) reopening too early, while discontinuing public health mandates; and (iii) the emergence of variants with more e cient transmission, 9,10 which may also escape acquired immunity and/or vaccination. 11,12 Therefore, efforts aimed at identifying strategies to treat SARS-CoV-2, including metabolic interventions or repurposing drugs with potential metabolic targets, 13 remain important.
To this end, herein we collected a large independent data set, including patient characteristics, clinical information, and routine and specialized clinical laboratory results of acutely ill patients, who tested either positive or negative for SARS-CoV-2. This large patient group allowed for veri cation and expansion of previously published metabolomics studies in COVID-19 and other acute critical illnesses.
Our 2020 study, 1 like most subsequent studies, [2][3][4][5]14−21 evaluated small cohorts of COVID-19 patients, with varying severity of disease, and often used healthy individuals as controls. Therefore, some of the reported observations could result from in ammation or infection, in general, rather than COVID-19, in particular. For example, activation of the kynurenine pathway 1,16,20,22,23 could result from in ammatory or interferon-activated responses to viral infection, 24 and from downstream activation of indole 2,3dioxygenase. Based on these observational reports, it was correctly 25 predicted that subjects with basal activation of these pathways (e.g., Down syndrome) may be more susceptible to life-threatening COVID- 19. 1,26 The published metabolomics studies on COVID-19 were not powered to characterize the effects of other variables critical for disease severity and prognosis. As exsmples, evaluation of biological (e.g., sex, 27 age, 28 ethnicity, 29 body mass index, 30 blood group 31 ) and clinical (e.g., obesity, diabetes, cardiovascular disease, kidney disease) 32 characteristics are necessary to de ne independent and overlapping metabolic ndings in COVID-19 and other acute diseases. To this end, in some cases, we performed sub-analyses focusing on one variable at a time, like sex 3,23 or in ammation (e.g., circulating interleukin-6 (IL-6) levels). 1,4 Another limitation of previous studies, including ours, 1 was the lack of longitudinal data to evaluate progression of metabolic dysfunction in hospitalized patients with severe COVID-19 or other acute illnesses. Nonetheless, some studies tried addressing this, 4,17 and identi ed distinct omics phases corresponding to the acute response to infection (Stage 1) and then the development of antibody responses (Stage 2). 33 Herein, we expand this approach, as a proof-of-principle, by comparing three mechanically-ventilated COVID-19 patients at up to 21 longitudinal time points; these data suggest a pattern of metabolic changes with relevance to prognosis in severe COVID-19 cases.  Fig. 1). Our prior studies 1 compared the metabolome of COVID-19 patients to healthy controls; this prevented de ning a metabolic signature speci c to COVID-19 that could be differentiated from other acute illnesses or infections. Therefore, here we enrolled hospitalized patients who were COVID-19-negative (by PCR and/or serology), some of whom had non-COVID-19 respiratory tract infections. Not surprisingly, the metabolic phenotypes of these COVID-19-negative patients partially overlapped with those who were COVID-19-positive ( Fig. 1.B). Figure 1.C shows which metabolites differentiate between these two groups, using variable importance in projection (VIP) scores in the PLS-DA analysis. Volcano plot elaborations ( Fig. 1.D) also clearly showed decreased levels of almost all amino acids ( Fig. 1.E) and increased levels of purine oxidation products ( Fig. 1 37 and S1P 38 were recently tied to kidney ischemia and chronic kidney disease, respectively. In addition, moderate-severe kidney dysfunction was observed in all COVID-19 (+) patients, indicated by blood urea nitrogen (BUN) and creatinine levels ( Fig. 1.H). The positive correlation between BUN and creatinine was paralleled by similar trends for acyl-carnitines (markers of kidney dysfunction 39 ), and negative correlations between BUN and amino acids. As an internal validation of this approach, creatinine measured in the same samples by a CLIA-certi ed clinical chemistry assay and mass spectrometry correlated extremely well (p < 0.0001; r 2 = 0.871 Spearman; Fig. 1.J). Overall, these results demonstrate signi cant up-regulation of creatine metabolism, accompanied by dysregulation of arginine catabolism to proline, polyamines, and citrulline ( Fig. 1.J); also a hallmark of COVID-19-induced endotheliopathy. 40 Interestingly, other markers of endothelial coagulopathy were also signi cantly increased in COVID-19 patients (Figs. 1-8), including VWF and its collagen binding activity (p < 0.0001). However, no signi cant differences in ADAMTS-13 levels or activity were observed; thus, VWF antigen:ADAMTS13 activity ratios were increased (p < 0.0001) favoring high molecular weight VWF oligomers and increased thrombotic potential.
Male patients, both with and without COVID-19, had higher RBC counts and hemoglobin levels, lower citrulline and creatine levels, lower levels of highly-unsaturated fatty acids (e.g., eicosapentaenoic, docosapentaenoic, docosahexaenoic acid; Fig. 3.E-I, Supplementary Fig. 5); however, only COVID-positive males, but not females, had increased urate levels ( Fig. 3.J). Fig. 6) and were affected by both age and sex, we divided both cohorts into sub-groups based on RBC count ( Fig. 3.K); this highlighted a positive correlation between RBC count and kidney damage (BUN, creatinine, guanidinoacetate), total protein level, and glycemia, along with negative correlations with one-carbon metabolites choline and methionine.

Because RBC count and hemoglobin level always strictly correlated (Supplementary
In these cohorts, race was also associated with in ammation, thromboin ammatory complications, body weight/BMI, and kidney dysfunction; indeed, IL-6, D-dimer, BUN, and creatinine levels were highest in individuals of African descent ( Supplementary Fig. 7). In addition, plasma dimethylglycine, indole, and cystine levels were highest in individuals of African descent, whereas kynurenine levels increased in all COVID-19 patients independent of race. Interestingly, ABO blood group status, which is controversially associated with COVID-19 prognosis, 31 indicated that the highest kynurenine, GABA, dimethylglycine, and creatinine levels were in blood group O subjects ( Supplementary Fig. 8). Although our sample size was limited for blood group A COVID-19 patients (n = 111 samples), they had the with highest IL-6 levels ( Supplementary Fig. 8).

Markers of mortality in acutely-ill hospitalized patients
While previous studies identi ed prognostic and disease severity markers in COVID-19 patients, they studied relatively few patients and did not include hospitalized COVID-19-negative patients as controls. 1,[16][17][18]20,21,33,47,48 To visualize ranking correlates of mortality, we performed preliminary correlation analyses of both our cohorts ( Fig. 4.A), con rming strong positive correlations between mortality and markers of in ammation, coagulopathy, kidney and tissue damage and hypoxia. Because death is a noncontinuous variable, biomarker analyses were also performed to calculate ROC curves for metabolites and clinical covariates at admission that signi cantly associated with poor outcomes independent of cohort ( Fig. 3.B-F), or divided into COVID-19 patients and controls ( Supplementary Fig. 9). Several of the highest-ranking variables ( Fig. 3) included IL-6, acyl-carnitines (especially hexanoyl-carnitine), D-dimers, albumin, and tryptophan metabolites.
Because metabolomics data and clinical variables were available for 542 COVID-19 samples, we used 244 randomly-selected samples to train a machine learning model to predict mortality in these patients ( Fig. 4.G). Data on training, ROC curves from multivariate models, prediction accuracy, and the top 15 variables fed into the model are shown in Supplementary Fig. 10.A-B for elaboration with the random forest or SVM algorithm. Overall, the top 10 variables from the random forest algorithm ( Fig. 4.H) showed an AUC of 0.81 (con dence interval 0.71-0.89), resulting in the highest predictive ability with the fewest variables. Using the remaining 298 samples as a test set correctly predicted survival or death of 234 patients, with only 5 false positives (i.e., predicted to die, but survived) and 59 false negatives (i.e., predicted to survive, but died), demonstrating a 78% accuracy of the model, with high speci city (> 95%), but moderate sensitivity (< 70%). Hypertension, chronic kidney disease, lung disease, and coronary artery disease share altered tryptophan and arginine/proline/citrulline metabolism, trends exacerbated by COVID-19. Carnitine metabolism and aromatic amino acids were increased in patients with a history of kidney disease (Fig. 7.F-K), whereas cancer was accompanied by increased lactate (perhaps resulting from a Warburg phenotype; Fig. 7.Q). A history of liver disease was accompanied by increased levels of conjugated bile acids (e.g., taurochenodeoxycholate), total bilirubin, and methionine ( Fig. 7.S). Finally, a history of diabetes was associated with increased lactate and lactoyl-glutathione levels, the latter a marker of glyoxylase damage ( Supplementary Fig. 13).
Longitudinal sampling in severe COVID-19 patients Sampling at admission allowed us to collect longitudinal samples from some patients. As illustrative, thought-provoking examples, the results with three severe COVID-19 cases, only two of whom recovered, are presented here. Figure 8 (vectorial version in Supplementary Figs. 14-16, data in Supplementary  Table 1) shows hierarchical clustering of metabolites as a function of time (19 time points for 2 patients and 21 for the third patient). These three patients were female, 14-, 45-, and 52-years old, of different ethnicity and BMI. Despite similar disease severity (e.g. all mechanically ventilated, with either stroke, clotting, or DVT manifestations), only the surviving patients manifested a spike in kynurenine levels throughout their course, which was not observed in the patient who died (Fig. 8.C, F). Increased creatine/creatinine eventually resolved in the surviving patients, but not in the patient who died. The surviving patients also manifested increased free fatty acid levels at the latest time points, especially poly and highly-unsaturated fatty acids of the 18, 20, and 22C series; in contrast, the non-surviving patient exhibited late accumulation of acyl-carnitines and amino acids which did not resolve ( Supplementary  Fig. 16).

Discussion
The present study provides the most extensive metabolomics analysis of COVID-19 patients to date, including 831 samples at admission from hospitalized patients and 59 longitudinal samples from three case studies. These analyses used state-of-the-art high-throughput metabolomics approaches, 53,54 which allow not only simultaneous discovery of novel markers, but also quantitative validation of previously identi ed correlates to in ammatory states, renal dysfunction, and mortality by using stable isotopelabeled internal standards. Importantly, these mass spectrometry-based results were comparable to quantitative measurements using CLIA-certi ed clinical assays of various metabolites, including creatinine, suggesting that implementing clinical metabolomics 55 strategies in next-generation clinical chemistry laboratories may eventually become feasible. Leveraging the combination of large omics datasets from COVID-19 patients and controls with manually-curated clinical records, allowed identi cation of novel metabolic correlates to biological variables and patient characteristics; these con rmed and signi cantly enhanced previous efforts in this disease. 56 For example, despite a positive correlation with weight and BMI, aging was accompanied by decreased circulating levels of several poly-and highly-unsaturated fatty acids, consistent with reported agedependent declines in unsaturated fatty acids in healthy blood donors 57 and fatty acid desaturase activity, with functional implication in hematopoiesis. 58 Aging was also accompanied by increased markers of hypoxia (e.g., lactate, citrate, alpha-ketoglutarate, fumarate), indicative of progressive mitochondrial dysfunction. 59 Given the role of these metabolites in immunometabolism, 42 older patients also demonstrated increased in ammation, especially COVID-19 patients, accompanied by poorer outcomes. Similarly, purine catabolism and oxidation products (e.g., urate, xanthine), hallmarks of ischemic 44 and hemorrhagic 41 hypoxic organ damage, increased with age. Importantly, mitochondria activity, aging, and in ammation are all associated with hypercoagulabiity, 49 harmonizing our observational results with the known increased incidence of thromboembolic complications in COVID-19.
In contrast, aging, especially in COVID-19 patients, was accompanied by altered levels of free fatty acids and acyl-carnitines. The former may fuel viral membrane synthesis, which may be sustained by lipid mobilization from adipose tissue and other sources, similar to observations in trauma patients 60 and following the pathological vesiculation of RBC membranes. 45 Because obesity also leads to poor outcomes in COVID-19, lipidomics analyses of 60 subjects with the highest and lowest BMIs allowed identi cation of obesity-related lipid signatures in COVID-19 patients. In particular, neutral lipids (MG, DAG, TAG) and phospholipids (PC and LPE) were mobilized; the latter may result from release of methylgroups from LPCs to meet one carbon demands for viral nucleotide synthesis or repair of oxidant-induced isoaspartyl-damage 61 .
These metabolic observations were exacerbated in COVID-19 patients and were consistent with disease severity, as indicated by clinical records and clinical measurements of markers of in ammation (IL-6, CRP), coagulopathy (D-dimers, APTT, INR, FVIII, VWF:AG, VWF:collagen binding activity, VWF:ADAMTS-13 activity ratios, thrombin and plasmin generation), and renal dysfunction (BUN, creatinine). Metabolic correlates of these clinical parameters are provided in this study, as part of the efforts aimed at compiling an encyclopedic characterization of metabolism in health and disease. For example, we found strong negative correlations between kidney dysfunction and circulating amino acid levels, as possible indicators of decreased renal reabsorption 36,37,62 and hemodilution. As another example, positive correlations between pro-in ammatory conjugated bile acids and liver transaminases support prior ndings of mechanistic interactions of these metabolites with IL-1beta and hepatic stress. 52 Interestingly, these metabolites were also associated with coagulopathy in trauma/hemorrhagic shock, 63 and with microbiome dysbiosis related to iron metabolism 64 , observations informing the correlations in our study between ferritin levels, acute phase response proteins (CRP), and conjugated bile acids.
Besides aging and in ammation, other factors are also associated with poor outcomes in COVID-19. For example, the expression levels of angiotensin converting enzyme 2 (ACE2) receptor in enterocytes modulate disease severity, in that viral entry into cells is mediated by pairing of ACE2 with the viral spike protein. 65 Notably, we con rm 1,4 that arginine/proline/citrulline metabolism is an important pathway affected by COVID-19, which not only depends on kidney function, but also on enterocytes. 66 Furthermore, arginase to nitric oxide synthase activity may in uence the pro-/anti-in ammatory state of gut resident macrophages. 67 In addition, circulating levels of arginine pathway metabolites can be affected by RBC arginase activity 45 , which is in turn affected by oxidant stress and can contribute to COVID-19-induced endotheliopathy. 40 Indole metabolites of microbial origin 68 were also signi cantly decreased in COVID-19 patients, especially in those with the poorest outcomes. These decreases may result from tryptophan depletion as a function of kynurenine pathway activation in COVID-19, 1,16,22,23 especially in older males. We con rmed that kynurenine levels correlated with SARS-CoV-2 infection, disease severity, and mortality. Indeed, IL-6 levels and kynurenine/tryptophan ratios were among the top predictors of mortality in COVID-19 patients, con rming previous targeted analyses 20 of our larger, independent cohort. However, as activation of interferon responses appear necessary for eliciting adaptive immunity against COVID-19, 33 it is interesting that, in our longitudinal blood collections of the COVID-19 patient who died, plasma kynurenine levels did not increase. In contrast, because some metabolites in the kynurenine pathway are neurotoxic (e.g., picolinic acid, quinolinic acid) 69 , uncontrolled activation of this pathway may contribute to some neurological comorbidities of COVID-19 (e.g., brain fog, weakness, fatigue). Interestingly, declines in tryptophan-derived de novo nicotinamide synthesis is associated with aging and in ammation, 70 suggesting that nutritionally replenishing NAD reservoirs (e.g., nicotinamide riboside) may be therapeutic in facilitating recovery from severe COVID-19. 71 Depleting tryptophan to promote kynurenine synthesis may also lead to serotonin depletion, a key component of platelet dense granules with a role in platelet activation. 72 This is relevant given the importance of coagulopathy in COVID-19, with increased plasma levels of FVIII, D-dimers, and VWF (i.e., increased VWF:collagen binding activity, increased VWF:ADAMTS-13 activity ratio), which are among the top correlates of mortality in our cohort. In addition, in ammation negatively correlated with albumin levels, perhaps due to in ammation-induced proteolysis, agreeing with previous reports that albumin predicts all-cause and cardiovascular mortality in chronic kidney disease patients. 73 Albumin strongly correlated with total protein and hemoglobin levels, which were also among the top correlates with kidney dysfunction, thereby strengthening the evidence supporting RBC contributions to kidney physiology. 38 In contrast, no major effects of ABO blood group were noted in our cohort, except for a link to IL-6 levels (highest in blood group A, corroborating prior evidence relating to increased disease severity 31 ). Not surprisingly, ABO blood group was also linked to patient ethnicity in our cohort, which correlated with increased in ammation (IL-6), D-dimers, creatinine, and cystine (oxidant stress) in individuals of African descent.
Finally, as a proof-of-principle, we entered admission data (clinical and metabolomics) into machine learning algorithms, randomly selecting approximately half of the COVID-19 patient cohort as a training set and the other half as a test set. The resulting model exhibited high speci city (> 95%), but moderate sensitivity (~ 70%). The prediction accuracy of these models may be affected by clinical contributors to the metabolic heterogeneity of hospitalized patients, such as elements of their medical history.
Nonetheless, we report here for the rst time that metabolic phenotypes of COVID-19 patients were most extreme in patients presenting with a history of hypertension, chronic kidney disease, lung disease, cancer, coronary artery disease, or lung disease.
Taken together, the extensively detailed information in this large, prospective, observational study will support future mechanistic studies and data sharing opportunities to enhance understanding of the plasma metabolism in COVID-19 and other acute critical illnesses. comorbidities (hypertension, diabetes mellitus, coronary artery disease, renal disease, hyperlipidemia, liver disease, lung disease), intubation/ventilator requirement, continuous veno-venous hemo ltration (CVVH) requirement, radiographically-con rmed thrombotic complications (deep vein thrombosis, pulmonary embolism, stroke), clotting of CVVH, hospitalization course (admission date, date of Emergency Department presentation, discharge date), mortality and date of death) were collected manually by reviewing the electronic medical record. Data were collected retrospectively for patients treated at two New York-Presbyterian Hospital campuses (CUIMC and The Allen hospital). Residual platelet poor plasma samples were collected for subsequent analyses.

Patients
Sample processing and metabolite extraction: Plasma samples were extracted via a modi ed Folch method (chloroform/methanol/water 8:4:3), which completely inactivates other coronaviruses, such as MERS-CoV. 74 Brie y, 20 µL of plasma were diluted in 130 µl of LC-MS grade water, 600 µl of ice-cold chloroform/methanol (2:1) was added, and the samples vortexed for 10 seconds. Samples were then incubated at 4ºC for 5 minutes, quickly vortexed (5 seconds), and centrifuged at 14,000 x g for 10 minutes at 4ºC. The top (i.e., aqueous) phase was transferred to a new tube for metabolomics analysis.
Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry metabolomics and lipidomics Analyses were performed using a Vanquish UHPLC coupled online to a Q Exactive mass spectrometer (Thermo Fisher, Bremen, Germany). Samples were analyzed using a 5 and 17 min gradient as described. 53,54,75 Solvents were supplemented with 0.1% formic acid for positive mode runs and 1 mM ammonium acetate for negative mode runs. MS acquisition, data analysis and elaboration was performed as described. 53,54,75 Metabolomics: UHPLC-MS metabolomics analyses were performed as described in method 53,54,75 and application papers, 1,76 using a Vanquish UHPLC system coupled online to a high-resolution Q Exactive mass spectrometer (Thermo Fisher, Bremen, Germany). Samples were resolved over a Kinetex C18 column (2.1x150 mm, 1.7 µm; Phenomenex, Torrance, CA, USA) at 45°C. A volume of 10 ul of sample extracts was injected into the UHPLC-MS. Each sample was injected and run four times with two different chromatographic and MS conditions as follows: 1) using a 5 minute gradient at 450 µL/minute from 5-95% B (A: water/0.1% formic acid; B:acetonitrile/0.1% formic acid) and the MS was operated in positive mode and 2) using a 5 minute gradient at 450 µL/minute from 5-95% B (A: 5% acetonitrile, 95%water/1 mM ammonium acetate; B:95%acetonitrile/5% water, 1 mM ammonium acetate) and the MS was operated in negative ion mode. The UHPLC system was coupled online with a Q Exactive (Thermo, San Jose, CA, USA) scanning in Full MS mode at 70,000 resolution in the 60-900 m/z range, 4 kV spray voltage, 15 sheath gas and 5 auxiliary gas, operated in negative or positive ion mode (separate runs).
These chromatographic and MS conditions were applied for both relative and targeted quantitative metabolomics measurements, with the differences that for the latter targeted quantitative post hoc analyses were performed on the basis of the stable isotope-labeled internal standards used as a reference quantitative measurement, as detailed below.
Lipidomics: Samples were resolved as described, 4-6, 45 over an ACQUITY HSS T3 column (2.1 x 150 mm, 1.8 µm particle size (Waters, MA, USA) using an aqueous phase (A) of 25% acetonitrile and 5 mM ammonium acetate and a mobile phase (B) of 50% isopropanol, 45% acetonitrile and 5 mM ammonium acetate. Samples were eluted from the column using either the solvent gradient: 0-1 min 25% B and 0. Quality control and data processing Calibration was performed prior to analysis using the Pierce™ Positive and Negative Ion Calibration Solutions (Thermo Fisher Scienti c). Acquired data was then converted from .raw to .mzXML le format using Mass Matrix (Cleveland, OH, USA). Samples were analyzed in randomized order with a technical mixture (generated by mixing 5 ul of all samples tested in this study) injected every 10 runs to qualify instrument performance. This technical mixture was also injected three times per polarity mode and analyzed with the parameters above, except CID fragmentation was included for unknown compound identi cation (10 ppm error for both positive and negative ion mode searches for intact mass, 50 ppm error tolerance for fragments in MS2 analyses -further details about the database searched below).

Metabolite assignment and relative quantitation
Metabolite assignments, isotopologue distributions, and correction for expected natural abundances of deuterium, 13  Louis, MO, USA) and labeled standards (see below for the latter). Discovery mode analysis was performed with standard work ows using Compound Discoverer 2.1 SP1 (Thermo Fisher Scienti c, San Jose, CA). From these analyses, metabolite IDs or unique chemical formulae were determined from highresolution accurate intact mass, isotopic patterns, identi cation of eventual adducts (e.g., Na + or K+, etc.) and MS 2 fragmentation spectra against the KEGG pathway, HMDB, ChEBI, and ChEMBL databases.
Additional untargeted lipidomics analyses were performed with the software LipidSearch (Thermo Fisher, Bremen, Germany).

Simultaneous thrombin and plasmin generation assay (STPGA)
Simultaneous evaluation of thrombin and plasmin generation (TG and PG, respectively) was performed as described previously 79  VWF, FVIII and ADAMTS13 activity and antigen quantitation: The antigen and activity measurement of VWF and ADAMTS13 was performed by using commercial ELISA kits. VWF antigen and collagen binding activity levels were measured by using Human Von Willebrand Factor ELISA Kit (ab168548, Abcam, Cambridge, UK) and TECHNOZYM® vWF:CBA ELISA Kit (5450301, Technoclone, Vienna, Austria) respectively. ADAMTS13 antigen and activity levels were measured by using Human ADAMTS13 ELISA Kit (ab234559, Abcam) and TECHNOZYM® ADAMTS13 Activity ELISA (5450701, Technoclone) respectively. FVIII antigen levels were measured by using Human Factor VIII total antigen assay ELISA kit (HFVIIIKT-TOT, Molecular Innovations, Novi, MI, USA). All assays were performed following manufacturer's recommendations with additional dilution of plasma samples as required.

Statistical Analysis
Graphs and statistical analyses (either t-test or repeated measures ANOVA) were prepared with GraphPad

Disclosure of Con ict of Interest
Though unrelated to the contents of this manuscript, the authors declare that AD, and TN are founders of Omix Technologies Inc and Altis Biosciences LLC. AD and SLS are consultants for Hemanext Inc. SLS is also a consultant for Tioma, Inc. and TCIP, Inc., and the Executive Director of the Worldwide Initiative for Rh Disease Eradication (WIRhE). AD is a consultant for FORMA LLC. All the other authors disclose that no con ict of interest exist.

Declarations
Disclosure of Con ict of Interest: Though unrelated to the contents of this manuscript, the authors declare that AD, and TN are founders of Omix Technologies Inc and Altis Biosciences LLC. AD and SLS are consultants for Hemanext Inc. SLS is also a consultant for Tioma, Inc. and TCIP, Inc., and the Executive Director of the Worldwide Initiative for Rh Disease Eradication (WIRhE). AD is a consultant for FORMA LLC. All the other authors disclose that no con ict of interest exist. Metabolomics of hospitalized patients with (n=543) and without (n=288) COVID-19 (A). Partial least square-discriminant analysis of metabolomics data separated the two cohorts (B). Top 15 metabolites with the highest loading weights are indicated in the variable importance in projection (VIP) ranked list in C. In D, the volcano plot highlights signi cant effects of COVID-19 on plasma amino acid levels and purine oxidation. Violin plots (including median + ranges) are shown for amino acids (E) and purines (F) from relative quantitative analyses, and for two markers of mitochondrial dysfunction and hypoxia, alpha-ketoglutarate and sphingosine 1-phosphate (S1P) using absolute quantitative analyses against stable isotope-labeled internal standards in G. In H, blood urea nitrogen (BUN) and creatinine, markers of kidney dysfunction, were signi cantly increased in COVID-19 patients. Metabolic and clinical correlates of BUN (top positive correlate being creatinine) are in I. A signi cant positive correlation (p<0.0001; r2 = 0.871) was observed between creatinine measurements via CLIA-certi ed and mass spectrometry (MS)based approaches (J). In J, violin plots highlight metabolites in the arginine, proline, and creatine metabolism.

Figure 2
Alteration of tryptophan/kynurenine/indole metabolism as a function of in ammation, and dysregulation of lipid metabolism as a function of body mass index in hospitalized patients with and without COVID- 19. In A, violin plot of tryptophan metabolism as a function of COVID status (median + range). Metabolic and clinical correlates to interleukin 6 (IL-6) as a marker of in ammation (B) and patient age (C) indicate a strong correlation of this pathway and lipid metabolism, especially free fatty acids (D) and acyl-Page 24/30 carnitines (E), with disease state. Free fatty acids may derive from blood cell vesiculation and/or mobilization from brown adipose tissue, a process that could fuel viral membrane formation (F). In G, breakdown of free fatty acid levels as a function of patients' body mass index (BMI) and COVID-19 status. Lipidomics analyses of COVID-19-positive and -negative patients with BMI lower than 20 or higher than 38 revealed a signi cant impact of these variables on lipid class (H) and fatty acyl (I) composition.  Markers of mortality in hospitalized patients, including COVID-19 patients. In A, clinical and metabolic markers of mortality were ranked from Spearman correlation analyses (y axes indicate -log10 of pvalues). Because mortality is a non-continuous variable, additional univariate (B-F) and multivariate (G) biomarker analyses were performed to calculate ROC curves and train machine learning algorithms (random forest in this gure, supporting vector machine in the Supplement) to predict mortality in hospitalized COVID-19 patients based on the top 10 clinical and metabolic variables (H), a model that yielded 78% prediction accuracy (G-I).    The second patient, a 52-year old female with a history of obesity and lung disease, did not survive COVID-19; no activation of the kynuenine pathway was observed and creatine levels remained elevated.

Supplementary Files
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