Metabolomic Profiling of COVID-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts

Metabolomic Profiling of COVID-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolomic Profiling of COVID-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts Ana Isabel Tristán, Cristina Jiménez-Luna, Ana Cristina Abreu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4504195/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analyzing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. We propose a three-metabolite diagnostic panel—comprising isoleucine, TMAO, and glucose—that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Recognizing that serum profiles are more reliable but invasive compared to urine samples, we propose reconstructing serum profiles using urine 1 H NMR data. Our robust multi-output regression model demonstrates high accuracy in this reconstruction, and in classifying the converted serum spectroscopic profile. This suggests the feasibility of determining COVID-19 infection and predicting its severity using a non-invasive sample such as urine. Physical sciences/Chemistry/Analytical chemistry Physical sciences/Chemistry/Chemical biology Physical sciences/Chemistry/Cheminformatics Biological sciences/Computational biology and bioinformatics Biological sciences/Molecular biology Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Molecular medicine Health sciences/Signs and symptoms COVID-19 metabolomics NMR serum urine biomarkers Figures Figure 1 Figure 2 Figure 3 Introduction Coronavirus disease 2019 (COVID-19), a serious respiratory disease caused by the coronavirus strain SARS-CoV-2, has caused a global pandemic causing the infection of more than 523.3 million people and more than 6.2 million deaths to date. 1 , 2 This infection mainly affects the airways and lungs and presents a wide range of clinical manifestations, ranging from mild or even asymptomatic cases to severe ones where it could also could affect other organs and cause neurological symptoms or vascular damage, among others. Several COVID-19 patients still experience severe cases where the infection can be fatal, primarily affecting older individuals and those with underlying health conditions like diabetes, lung diseases, or cardiovascular disease. 3 – 5 The reason behind these differences in symptoms is still not fully understood, but it seems to be linked to various altered pathophysiological pathways during this infection, so it becomes imperative to comprehend which mechanisms are dysregulated to enhance treatment strategies, ultimately impacting the metabolic profiles. 5 Metabolomics is a relatively recent omics science that employs analytical platforms [mostly mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy] 1 , 6 and statistical methods to characterize complex biochemical mixtures, analyzing both metabolites and metabolism, so this combined strategy probably affords the best representation of phenotype, and it enables the identification of disease biomarkers. 7 , 8 Among its benefits, NMR shows a high reproducibility, robustness, and needs a minimal sample preparation with almost no dilution factors, especially in biofluids. 6 , 8 In the field of pathology diagnosis, NMR metabolomics has been key for the determination of biomarkers of many diseases using several biofluids, such as saliva for Type 1 Diabetes and Parkinson’s disease, 9 , 10 cerebrospinal fluid for inflammatory diseases, 11 serum for pancreatic and colorectal cancer 12 , 13 and urine for thyroid and inflammatory bowel diseases. 14 , 15 Due to the tremendous impact that the COVID-19 pandemic has had in recent years, there has been a considerable amount of research concerning this topic, most of them employing serum or plasma samples and preferably MS as analytical technique. 5 , 16 – 19 Among the metabolomic studies applying NMR, we can highlight the following ones. Bruzzone et al. 20 found elevated levels of ketone bodies (acetoacetic acid, 3-hydroxybutyric acid, acetone, and 2-hydroxybutyric acid) in serum profiles of 398 hospitalized SARS-CoV-2 patients comparing to 280 healthy controls, indicating both hepatic glutathione synthesis, that could potentially cause liver damage, dyslipidemia, and oxidative stress. Luporini et al. 21 analyzed 166 COVID patients ranging from mild to severe symptoms and found an independent and positive association between phenylalanine levels (that may be associated with an increased inflammation) and disease severity. Meoni et al. 4 found that 11 metabolites in plasma-EDTA showed significant alterations for 30 COVID-19 patients compared with age - and sex matched controls, as well as higher levels of VLDL, and lower levels of Apo A1, Apo A2, cholesterol and free-cholesterol HDL and LDL subfractions. They also found that tocilizumab administration resulted in at least partial reversion of the metabolic alterations caused by SARS-CoV-2 infection. 4 Baranovicova et al. 22 analyzed plasma-EDTA samples from control (n = 55) and COVID-19 patients with positive and negative outcomes (n = 34, n = 19, respectively) and found discrimination on metabolites associated to energy metabolism and inflammatory processes. Schmelter et al. 3 also revealed distinctive differences in the metabolic and lipid profiles of serum samples from COVID-19 patients (n = 30) compared to both healthy controls (n = 58) and patients with cardiogenic shock (n = 18) and suggest that COVID-19 is associated with dyslipidemia, as already pointed out by Wu et al. 19 and Bruzzone et al. 20 Furthermore, Bizkarguenaga et al. 23 compared plasma samples from COVID-19 patients (n = 69) with varying degrees of symptom severity over several months, alongside uninfected control individuals (n = 71), and indicated that half of the patient population examined exhibited abnormal metabolism, characterized by altered porphyrin levels and lipoprotein profiles several months after the infection, whereas the other half showed minimal metabolic changes resulting from the disease. Correia et al. 24 sought to evaluate the plasma metabolomes of 57 control patients and 53 positive cases of COVID-19 with mild to severe symptoms and identified six metabolites involved in lipid and energy metabolism (glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate) as potential prognostic metabolite panels for the disease and to predict symptoms severity. Regarding urine samples, there are few recent studies, mainly comparing COVID-19 patients and healthy controls, or studying the recovery from severe coronavirus infection. 25 , 26 To the best of our knowledge, no research has yet covered the analysis of COVID-19 severity alterations using urine, either alone or in combination with serum samples. Thus, in this study, we propose a novel NMR-based metabolomics investigation using two biofluids, namely serum and urine samples, collected from COVID-19 patients, including subgroups from both medical ward (MW) and intensive care units (ICU), as well as healthy controls of both genders, aged between 31 to 89 years. Our primary objective is to identify potential biomarkers that specifically differentiate between mild and severe COVID-19 symptoms. Additionally, we aim to investigate clinical correlations with the metabolome panel to better understand the influence of specific clinical features on the disease, and to examine the feasibility of reconstructing serum profiles through the analysis of urine samples to successfully predict disease severity, enhancing the utility of non-invasive biofluids. Through this comprehensive approach, we aspire to develop essential diagnostic tools capable of assessing disease severity. These predictive models, based on the analysis of urine as the most accessible biofluid, will enable the implementation of appropriate medical treatments in advance, enhancing the potential for timely and targeted interventions. Results Overview of study population The demographic data and clinical features of the COVID-19 patients admitted to the medical ward (MW) and intensive care unit (ICU) are described in Table 1 and Table S1 . No significant differences regarding age or sex were observed between both groups of patients and healthy controls. Most patients in both groups, MW and ICU, presented previous pathologies (80.43% and 88.24%, respectively) and also some cardiovascular risk factor (71.74% and 73.53%, respectively). Of these, overweight or obesity and dyslipidemia were more prevalent among patients in ICU (13.04% vs. 29.41% and 19.57% vs. 44.12%, respectively), although this increase was only significant for dyslipidemia ( p = 0.018). As expected, certain clinical parameters were significantly more frequent in the ICU patients at the time of hospital admission, such as dyspnoea (71.11% vs. 91.18%, p = 0.028), pneumonia (64.44% vs. 88.24%, p = 0.016) and decreased oxygen saturation ( p = 0.002). Laboratory findings also tended to reflect the severity of the disease between both patient groups, showing non-normal levels of the blood parameters measured in the ICU group. Particularly, we observed a statistically significant increase for lactate dehydrogenase (LDH, 48.89% vs. 76.47, p = 0.013) and fibrinogen (42.22% vs. 67.65%, p = 0.025), and a significant decrease in the lymphocyte count (37.78% vs. 79.41%, p < 0.001). The average length of hospitalization was also significantly higher in ICU patients (27.59 vs. 42.85 days, p < 0.001), as well as the mortality rate due to the disease (15.91% vs. 44.12%, p = 0.006). Table 1 Demographic and clinical characteristics of patients with COVID-19 admitted to medical ward and ICU, and healthy controls. Variable Medical ward (n = 46) ICU (n = 34) Total (n = 80) Healthy controls (n = 32) p -value Age (years) mean, (range) 64.24 (31–88) 61.53 (36–83) 62.14 (31–89) 59.71 (35–89) 0.281 Gender 0.287 Male, n (%) 29 (63.04) 27 (79.41) 78 (69.64) 22 (68.75) Female, n (%) 17 (36.96) 7 (20.59) 34 (30.36) 10 (31.25) Time in hospital (days) mean, (SD) 22.72 (27.59) 43.79 (25.12) 32.03 (28.38) < 0.001*** Death, n (%) 7 (15.91) 15 (44.12) 22 (28.21) 0.006** Prognosis a < 0.001*** Good, n (%) 37 (84.09) 0 (0.00) 37 (47.44) Poor, n (%) 7 (15.91) 34 (100.00) 41 (52.56) Previous diseases 0.350 No, n (%) 9 (19.57) 4 (11.76) 13 (16.25) Yes, n (%) 37 (80.43) 30 (88.24) 67 (83.75) Cardiovascular risk comorbidities Hypertension 0.519 No, n (%) 21 (45.65) 18 (52.94) 39 (48.75) Yes, n (%) 25 (54.35) 16 (47.06) 41 (51.25) Obesity or overweight 0.070 No, n (%) 40 (86.96) 24 (70.59) 64 (80.00) Yes, n (%) 6 (13.04) 10 (29.41) 16 (20.00) Dyslipidemia 0.018* No, n (%) 37 (80.43) 19 (55.88) 56 (70.00) Yes, n (%) 9 (19.57) 15 (44.12) 24 (30.00) Diabetes 0.968 No, n (%) 35 (76.09) 26 (76.47) 61 (76.25) Yes, n (%) 11 (23.91) 8 (23.53) 19 (23.75) Presence ≥ 1 cardiovascular risk factors b 0.859 No, n (%) 13 (28.26) 9 (26.47) 22 (27.50) Yes, n (%) 33 (71.74) 25 (73.53) 58 (72.50) a Poor prognosis was considered when admission to ICU was required or death due to COVID-19 disease occurred in the hospital. b Cardiovascular risk factors including hypertension, obesity/overweight, dyslipidemia, and diabetes. SD : standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001. Clinical signs and symptoms on admission, and initial laboratory findings, including biochemistry, blood count and clotting, can be found in Table S2. MW and ICU times associated to clinical variables A bivariate analysis was performed in order to determine whether certain clinical characteristics (cardiovascular risk factors and laboratory results) were associated with length of hospital stay (Table S2). We observed that patients with high ferritin or C-reactive protein levels required more time in ICU than those with normal values, being the mean number of days 32.50 vs. 16.17 for ferritin measurements ( p = 0.021) and 30.78 vs. 11 days for C-reactive protein levels ( p = 0.048). Likewise, high ferritin concentration was significantly associated with longer hospitalization time in patients admitted to the medical ward (27.55 vs. 10.25 days, p = 0.023), those admitted to the ICU (47.64 vs. 25.83 days, p = 0.03), as well as in both groups together (37.08 vs. 15.44 days, p = 0.002). Furthermore, considering all COVID-19 patients, we observed that a lower number of lymphocytes in the blood was significantly associated with a longer hospital stay compared to those patients with a normal lymphocyte count (36.42 vs. 26.47 days, p = 0.015) and the same trend was observed in patients admitted to the ICU (47.67 vs. 28.86 days), although this difference did not reach statistical significance ( p = 0.064). These observations are consistent with previous studies in which elevated ferritin and C-reactive protein, and decreased absolute lymphocyte counts were associated with critical disease, unfavorable evolution and increased mortality due to COVID-19. 27 , 28 Similarly, it has been demonstrated that the cardiometabolic status has an important impact on the clinical outcome of these patients. 29 Disorders as hypertension, dyslipidemia, obesity and diabetes mellitus have been shown to increase the risk of severe COVID-19 and mortality. 30 Accordingly, in our study, having one or more cardiovascular risk factors (including hypertension, obesity/overweight, dyslipidemia, and/or diabetes mellitus ) was associated with longer hospital stay in the group of patients admitted to the ward (26.00 vs. 14.25 days, p = 0.022). In fact, patients with hypertension in this group spent a mean of 27.38 days in hospital compared to those with normal values, whose stay was 16.84 days ( p = 0.015). In addition, when we analyzed all COVID-19 patients, we found that having dyslipidemia was also associated with a longer time in hospital (45.5 vs. 25.92 days, p = 0.009). These results reflect the importance of these variables in the clinical setting. Metabolic differences between COVID-19 patients and healthy controls To investigate the possible differences between COVID-19 patients and healthy controls, an NMR metabolomic approach was applied first and used over a set of serum samples and then over their corresponding urine aliquots, collected in both cases under the same conditions. All the metabolite assignments of both serum and urine samples are shown in representative 1 H NMR spectra of each matrix on Figure S1 A and Figure S1 B and are described in Tables S3 and S4, respectively. It was possible the identification of 32 metabolites and metabolites classes in serum samples, and 55 in the case of urine, falling into various classes of compounds, including amino acids, ketone bodies, organic acids, sugars, fatty acids, among others. First, an unbiased statistical analysis for the comparison of COVID-19 patients and healthy controls was performed by employing Principal Component Analysis (PCA) to 1 H NMR data of each matrix. In Fig. 1A and Fig. 2 A appear both PCA scores plots from serum and urine data, respectively. A slight clustering trend between the COVID-19 and control samples was observed. Therefore, to emphasize the differentiation between the groups and unveil potential biomarkers, supervised analysis using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was applied to each 1 H NMR data set. OPLS-DA model correlates the information of the samples, in this case, the COVID-19 or Control groups, with the spectral information obtained through NMR. The OPLS-DA scores plot (Figs. 1B and 2 B for serum and urine, respectively) displayed a remarkable discrimination between samples from different groups in both matrices. Moreover, the Important Features Analysis, which reveals the discriminating molecules responsible for the separation of the spectroscopic profiles through Variable Importance in the Projection (VIP) score analysis (VIP > 1), identified specific metabolites such as trimethylamine- N -oxide (TMAO), phenylalanine, N -acetylglycoproteins (NAG), tyrosine, lysine, acetone, mannose, citrate, glycerol, and fatty acids, particularly unsaturated fatty acids (UFA), as the key molecules driving the separation between COVID-19 patients and healthy controls in the serum metabolome (Fig. 1C). Otherwise, Fig. 2 C shows the Important Features Analysis with VIP > 1 for urine metabolites, that displays methionine, formate, fucose, arabinose, N -phenylacetylglycine, lysine, 3-methylhistidine, trigonelline, hippurate, 2-phenylpropionate, glutamine, creatinine, 3-indoxylsulphate and pseudouridine, as metabolites responsible for urine metabolome differentiation into COVID-19 patients and healthy controls. In addition, evaluation tools from univariate analysis were also taken into consideration for both matrices, including fold change (FC) analysis, p -values and ROC analysis (see below). To investigate more deeply the functional implications of these metabolites and their interactions in the context of COVID-19, a metabolic pathway analysis (MetPA) was performed. MetPA provides valuable information on the perturbations and alterations that occur in metabolic pathways, shedding light on their potential roles in the pathogenesis and clinical manifestations of the disease. The metabolic pathways associated with the discriminant metabolites in serum for the comparison between COVID-19 and healthy controls are shown in Fig. 1D. The most affected metabolic routes by COVID-19 disease with a higher impact index are “Phenylalanine, tyrosine and tryptophan biosynthesis”, “Phenylalanine metabolism”, “Glycerolipid metabolism” and “Arginine and proline metabolism”. In the cases of “Phenylalanine, tyrosine and tryptophan biosynthesis” ( p , p -Holm and p -FDR < 0.05) and “Phenylalanine metabolism” ( p and p -FDR < 0.05), these two routes involve two common metabolites: tyrosine and phenylalanine. With respect to “Glycerolipid metabolism” ( p < 0.05), disturbed metabolites were glycerol and fatty acids (including acyl-, diacyl- and triacylglycerols, phosphatidic and lysophosphatidic acids) and, for “Arginine and proline metabolism” ( p < 0.05), metabolites that hit this pathway were creatine and proline. Evaluation of potential biomarkers for COVID-19 infection and severity One of the main questions in this past pandemic is why some people experienced severe symptoms of coronavirus while others remained asymptomatic. In this context, we conducted comparisons to deepen our understanding of the disease and its varying impacts. We analyzed metabolic differences between: i) healthy individuals and COVID patients, ii) patients admitted to the MW and those admitted to the ICU, and iii) between individuals with good or poor prognoses. These comparisons aimed to shed light on the severity of symptoms, and for the first time investigate if COVID-19 patients who required ICU admission or experienced fatal outcomes during their hospital stay would fit in the model as having poor prognosis. In this instance, multivariate data analysis did not yield valid models with satisfactory goodness-of-prediction (Q 2 ) cumulative values. Consequently, we resorted to performing univariate data analysis instead. Under the criteria of fold change ≥ 1.1 or ≤ 0.9 ( p < 0.05), several potential metabolite biomarkers in serum and urine in COVID-19 patients were observed, as shown in Table 2 . Furthermore, in order to evaluate the diagnostic performance of metabolic biomarkers, ROC analysis was conducted and the AUC was calculated with AUC ≥ 0.6, ranging from poor (AUC = 0.6 to 0.7), fair (AUC = 0.7 to 0.8), good (AUC = 0.8 to 0.9) to excellent (AUC = 0.9 to 1) diagnostic ability. 31 This ROC analysis was performed for both matrices, serum and urine, and for the three aforementioned comparisons. Table 2 summarizes all the comparisons studied in serum and urine, detailing the FC, p -values, AUC, cutoff, sensitivity and specificity values. Table 2 Potential biomarkers in serum and urine for different comparisons: COVID-19 patients vs healthy controls (HC), intensive care unit (ICU) vs medical ward (MW), and poor vs good prognosis (PvsG). SERUM Group Metabolite δ (ppm) FC p -value AUC a Cut-off Sensitivity (%) Specificity (%) COVID/ HC TMAO 3.31#* 0.48 8.48×10 − 29 0.98 -0.14 92.5 93.8 Phenylalanine 7.33#* 1.20 2.07×10 − 7 0.74 0.01 63.7 81.2 Proline 2.09#* 1.24 9.93×10 − 18 0.89 0.01 76.2 92.2 2.44#* 1.14 2.92×10 − 3 0.66 -0.22 58.8 70.3 Lysine 1.41#* 1.11 5.79×10 − 5 0.69 -0.04 66.2 64.1 1.72* 1.13 1.32×10 − 5 0.70 -0.01 61.3 78.1 Acetone 2.25#* 1.37 4.85×10 − 11 0.91 -0.17 81.2 93.8 Glycerol 3.68#* 1.44 6.17×10 − 6 0.88 -0.72 86.3 89.1 UFA 2.01#* 1.18 8.82×10 − 6 0.71 0.25 60.0 76.6 5.31* 1.21 2.05×10 − 3 0.63 -0.56 66.2 54.7 FA 1.28#* 1.35 2.86×10 − 5 0.70 -0.66 66.2 67.2 1.57#* 1.27 1.24×10 − 5 0.70 -0.20 65.0 68.8 2.23#* 1.37 1.06×10 − 8 0.75 -0.12 67.5 75.0 NAG 2.06#* 1.32 8.61×10 − 22 0.93 -0.32 87.5 89.1 Mannose 5.20#* 1.53 1.76×10 − 10 0.82 -0.11 77.5 81.2 Citrate 2.54#* 0.89 3.81×10 − 9 0.78 -0.43 67.5 82.8 2.68#* 0.90 1.48×10 − 8 0.77 -0.35 67.5 81.2 Creatine 3.95* 1.32 1.13x10 − 2 0.92 -0.31 90.0 90.6 TMA 2.84* 1.16 1.39x10 − 2 0.60 0.14 43.8 78.1 Tyrosine 6.90* 1.22 4.88×10 − 4 0.73 -0.13 71.3 67.2 ICU/MW TMAO 3.31 0.86 2.33×10 − 2 0.64 -0.45 54.5 70.2 Betaine 3.29 0.76 1.47×10 − 2 0.67 -0.30 66.7 63.8 Ethanol 1.18 0.89 3.90×10 − 3 0.66 -0.19 69.7 57.4 PvsG Betaine 3.29 0.76 2.08×10 − 2 0.65 -0.30 65.0 62.5 TMAO 3.31 0.89 4.86×10 − 2 0.63 -0.43 70.0 52.5 URINE Group Metabolite δ (ppm) FC p -value AUC a Cut-off Sensitivity (%) Specificity (%) COVID/ HC Methionine 2.22#* 2.26 3.37×10 − 7 0.69 -0.34 59.3 75.4 3.86# 1.35 2.91×10 − 2 0.67 -0.42 66.7 72.3 Formate 8.46#* 0.66 2.36×10 − 7 0.79 -0.05 76.5 81.5 Fucose 1.22#* 1.48 1.20×10 − 2 0.54 -0.33 40.7 67.7 3.66#* 1.38 1.57×10 − 7 0.76 0.06 63.0 80.0 Lysine 1.90# 1.23 3.09×10 − 2 0.65 0.01 51.9 69.2 3.70#* 1.89 7.35×10 − 6 0.68 -0.26 51.9 78.5 3-methyl-histidine 7.10#* 0.72 2.52×10 − 3 0.66 0.16 74.1 58.5 Trigonelline 8.94#* 0.83 4.68×10 − 3 0.66 -0.01 60.5 70.8 9.30#* 0.82 4.36×10 − 3 0.67 0.04 66.7 69.2 Hippurate 3.94# 1.21 2.78×10 − 2 0.54 -0.22 56.8 53.8 7.62#* 2.82 5.12×10 − 4 0.66 -0.30 59.3 64.6 2-phenyl-propionate 7.30#* 1.52 3.75×10 − 4 0.63 -0.14 49.4 69.2 7.38#* 1.43 2.97×10 − 3 0.60 -0.32 56.8 55.4 Glutamine 2.06# 1.18 3.05×10 − 2 0.61 0.26 46.9 80.0 2.46# 1.21 2.59×10 − 2 0.59 -0.15 60.5 58.5 Creatinine 4.10#* 0.80 4.99×10 − 3 0.65 0.23 71.6 63.1 3-indoxyl-sulphate 7.50#* 3.73 3.50×10 − 3 0.72 -0.27 71.6 56.9 Pseudouridine 4.26* 1.45 4.88×10 − 3 0.72 -0.16 60.5 76.9 ICU/MW TMAO 3.30 0.52 3.64×10 − 3 0.71 -0.13 85.3 57.4 Hippurate 3.94 1.34 3.93×10 − 2 0.60 -0.30 58.8 68.1 7.62 2.18 4.73×10 − 2 0.68 -0.27 61.8 78.7 7.78 2.11 1.20×10 − 2 0.71 -0.20 61.8 72.3 Urea 5.74 0.78 2.10×10 − 2 0.67 0.56 76.5 55.3 5.78 0.75 1.69×10 − 2 0.66 0.21 73.5 61.7 DMA 2.74 0.79 1.72×10 − 2 0.64 -0.31 67.6 68.1 3HB 1.18 2.40 1.88×10 − 2 0.68 -0.16 50.0 87.2 2.30 1.39 4.23×10 − 2 0.59 -0.31 64.7 51.1 Fucose 1.18 2.40 1.88×10 − 2 0.68 -0.16 50.0 87.2 3.46 1.32 1.69×10 − 2 0.67 0.16 55.9 78.7 4.58 1.24 3.97×10 − 2 0.63 0.07 55.9 70.2 4-hydroxyphenyl acetate 3.46 1.32 1.69×10 − 2 0.67 0.16 55.9 78.7 7.18 1.21 4.52×10 − 2 0.62 -0.03 55.9 70.2 3-methyl-2-oxovalerate 1.10 2.04 2.55×10 − 2 0.63 -0.21 52.9 63.8 3-indoxylsulphate 7.50 0.32 2.59×10 − 2 0.60 -0.24 70.6 51.1 Tryptophan 7.26 1.40 3.35×10 − 2 0.59 -0.03 50.0 66.0 7.74 1.42 2.24×10 − 2 0.65 -0.30 70.6 57.4 Formate 8.46 0.79 3.69×10 − 2 0.63 -0.12 76.5 57.4 PvsG cis -aconitate 3.10 0.69 1.21×10 − 2 0.65 -0.23 65.0 63.4 Urea 5.74 0.77 1.32×10 − 2 0.68 0.56 57.5 73.2 5.78 0.78 3.19×10 − 2 0.64 0.21 62.5 68.3 Formate 8.46 0.75 1.62×10 − 2 0.67 -0.12 65.0 78.0 Creatine 3.06 1.53 2.83×10 − 2 0.65 -0.23 70.0 63.4 3.94 1.34 2.96×10 − 2 0.62 -0.30 72.5 58.5 Creatinine 4.10 0.77 1.75×10 − 2 0.66 -0.00 57.5 70.7 Methanol 3.34 0.73 2.32×10 − 2 0.61 -0.26 72.5 53.7 DMA 2.74 0.80 2.38×10 − 2 0.66 -0.31 72.5 65.9 Hippurate 3.94 1.34 2.96×10 − 2 0.62 -0.30 72.5 58.5 7.78 1.80 2.76×10 − 2 0.65 -0.30 62.5 65.9 TMAO 3.30 0.63 3.58×10 − 2 0.74 -0.40 75.0 68.3 a (95% cl). # VIP > 1 in OPLS-DA models. * p (FDR) < 0.05. The biomarker ability, evaluated through AUC value, of those biomarkers obtained from the OPLS-DA models was, in most cases, higher than 0.7 in serum, and higher than 0.6 in urine. In general, highest values of AUC were obtained for the COVID-19 vs controls comparison in serum samples. Furthermore, we evaluated the sensitivity and specificity for each metabolite, and both were found to be higher than 50% in each case. Regarding the metabolic changes between COVID-19 and control groups, higher levels of phenylalanine, tyrosine, lysine, creatine, proline, trimethylamine (TMA), mannose, acetone, NAG and fatty acids, especially UFA, were found in serum of COVID-19 group accompanied by decrease levels of TMAO and citrate. In urine, higher levels of methionine, fucose, lysine, hippurate, 2-phenylpropionate, glutamine, 3-indoxylsulphate and pseudouridine were observed for COVID-19 group in comparison to control group, that in turn revealed higher content of formate, 3-methylhistidine, trigonelline and creatinine. Concerning disease severity (ICU vs MW), a decrease on TMAO, betaine and ethanol were found in serum samples from ICU patients, while metabolic changes on the contents of TMAO, hippurate, urea, dimethylamine (DMA), 3-hydroxybutyrate (3HB), fucose, 4-hydroxyphenyl acetate, 3-methyl-2-oxovalerate, 3-indoxylsulphate, tryptophan and formate were detected in urine. About prognosis, we found that a decrease on betaine and TMAO were associated to a negative outcome (poor prognosis) in serum, and a decrease in cis -aconitate, urea, formate, and creatinine, methanol, DMA, and TMAO in urine, together with an increase in creatine and hippurate also in urine. Relation between serum metabolites and clinical parameters Bivariate analysis was performed to study the association between serum metabolite levels and fatal outcome in COVID-19 patients. Of total metabolites analyzed, TMAO levels were found to be significantly related with death due to the disease ( p = 0.015). This is in accordance with our previous findings, in which TMAO was also found to be correlated with poor prognosis (ICU and death). We did not found metabolites with significant correlation with time in ICU, but in contrast, serum glucose levels were positively correlated with time spent in hospital in COVID-19 patients ( p = 0.043, Fig. 3 A). This reinforces what has been seen in other studies, in which high glucose levels have been associated with severe disease, probably derived from sustained inflammation resulting from infection. 32 However, this data should be taken with caution since the time spent in hospital is a variable with a multifactorial effect component and it would not be expected that a single metabolite would be a good indicator of this response variable on its own. In addition, numerous metabolites were significantly correlated to clinical features in COVID-19 patients, especially those related to cardiovascular risk factors and blood parameters of tissue/cell damage and clotting factors (Fig. 3 B and Table S6). When we analyzed individually the group of patients admitted to medical ward, we observed some metabolites significantly correlated with having arterial hypertension or being overweight/obese (Fig. 3 C and Table S7). Among them, ethanol, NAG, TMA, creatine and glycerol ranked among the top positive correlates to arterial hypertension, and in the same line, fatty acids n-3, acetoacetate, proline, glycerol, tryptophan, tyrosine and triacylglycerol levels were the top to obesity/overweight. These metabolites are involved in numerous metabolic pathways, such us arginine and proline, galactose, and glycerolipid metabolisms, which were shared between these two conditions in the correlation studies. Similarly, ICU patients also showed metabolites positively correlated with cardiovascular risk factors (Fig. 3 D and Table S8). In particular, the most significant metabolites were 3HB and choline for hypertension, creatine, tyrosine and acetate for overweight/obesity and TMAO for dyslipidemia, which are mainly involved in pathways of amino acid, carbohydrate and lipid metabolism. Some authors have suggested an association between the deregulation of these metabolic routes and high risk of severe COVID-19. 5 , 33 – 35 Remarkably, we also detected that certain clinical variables were associated with the same metabolite in both groups of patients. Thus, the presence of one or more cardiovascular risk comorbidities was significantly associated with increased ethanol and glycerol. In particular, increased 3HB levels were related to hypertension, and elevated n-3 fatty acids, creatine, tyrosine, phenylalanine and acetate with obesity/overweight. Biomarker panels based on serum metabolites and clinical variables The use of biomarker panels has the advantage of better capturing inter-patient variability than individual metabolites, which is more feasible when they are transferred to large cohorts. Therefore, in order to determine the ability of metabolites as biomarkers to classify COVID-19 patients, a multivariate logistic regression models were constructed. For the model comparing COVID-19 patients with healthy subjects, eight metabolites were initially identified and following the analysis, the final model included isoleucine, TMAO and glucose. The ROC curve for this panel showed outstanding discrimination (AUC = 0.91) reporting 86.08% of sensitivity and 83.87% of specificity (Figure S4A). In addition, we investigated a model to discriminate patients admitted to the MW from ICU patients. In this case, nine metabolites were identified to build the model, and after subsequent analyses, the final panel consisted of ethanol, TMAO, tyrosine and betaine. However, this panel did not provide sufficient discriminatory capacity (AUC = 0.697, sensitivity 45.45% and specificity 91.11%, Figure S4B). Interestingly, the models providing biomarker panels that included additional metabolites to those shown to be more discriminating at the individual level. So, we constructed a multivariate logistic regression model including both metabolite levels and clinical variables (analytical results or cardiovascular risks) as independent variables, with the aim of finding a predictor model to determine the risk of severe disease in COVID-19 patients. For this purpose, a previous selection of metabolites and variables to be included in the model was made by means of LASSO regression, due to the problems of adjustment by a high degree of correlation. Firstly, six metabolites and five clinical variables were identified (obesity/overweight, dyslipidemia, C-reactive protein, neutrophiles, lymphocytes, ethanol, lactate, TMAO, tyrosine, betaine, and acetate) and after subsequent analysis, the final reduced model included: obesity/overweight, dyslipidemia, lymphocytes, ethanol, TMAO, tyrosine and betaine. According to the ROC curves performed to differentiate between ICU and MW patients, this classifier model showed a good discriminatory performance (AUC = 0.825), providing a sensitivity of 81.82% and a specificity of 71.11%, for a cut-off point of 0.41 (Figure S4C). These results suggest the contribution of conditions as low lymphocytes counts, and comorbidities as obesity/overweight and dyslipidemia to COVID-19 outcomes. Furthermore, the panel included only a few variables that would facilitate its clinical implementation in order to predict which of the hospital MW patients may require admission to the ICU due to potential severity. Reconstruction of serum profiles by using urine 1 H NMR spectra As established, serum samples exhibit greater stability and lower variability compared to urine samples, that are influenced by numerous external factors, including diet, medication, and lifestyle. Consequently, we have introduced a novel method for converting urine-to-serum profiles, aiming to endorse the utilization of less invasive techniques in clinical practice. Such reconstruction is a challenge itself due to its inherent complexity and high dimensionality. We performed backward elimination as an effective variable selection strategy in order to have the best possible performance in the model. The robustness and predictive accuracy of the urine-to-serum conversion is underscored by a robust set of compelling statistical metrics. Developing a robust multi-output regression model involved careful consideration of dataset partitioning. Our approach led to an 80–20% partitioning between training and test sets, respectively. Table S9 summarizes the metrics of the multi-output regression model based on the evaluation of the test dataset. Compared with models reported in existing metabolomics literature and other scientific fields, the performance of the developed multi-output regression model is remarkable. The R 2 value of 0.997 and Adjusted-R 2 of 0.949 of the model overpass previous studies in the metabolomic field where values above 0.9 are generally considered exceptional. 36 The F-statistic of 38.25 and its associated p-value of 0.0025 indicate statistical significance, confirming the robustness of the model's predictors. In addition, the calculated AIC (-183.86) and BIC (88.76) values, which incorporate a penalty for model complexity, further emphasize the model's parsimony and predictive capability. 37 Moreover, the Log-Likelihood value of 193.72, which serves as a measure of model fit, also stands out as remarkably high, supporting the model's reliability. 38 Following the successful development of a multi-output regression model, which reliably converts urine metabolomic profiles to their serum counterpart, an ensuing verification was undertaken to assess the utility of the converted data in classification tasks. Specifically, the converted urine dataset was subjected to multiple classification algorithms and were conducted as a proof of concept in the model comparing COVID-19 patients with healthy subjects. The purpose of this verification was to validate that the reconstructed data preserves the discriminative features necessary for accurate classification, like that achieved by using the original serum dataset. To validate our obtained results, Naïve Bayes, Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees, Random Forest, and XGBoost were applied as classification models. Each one was trained on the original serum dataset and subsequently tested on the reconstructed urine dataset. The primary metric of interest was the classification accuracy, as it directly reflects the efficacy of our original conversion model in retaining the relevant features of the serum metabolomic profiles for the task of classification. The accuracy results can be visualized in Table 3 . Table 3 Accuracy results applied to various classification models used commonly in machine learning problems. Accuracy AUC Accuracy Rate Logistic Regression 0.955 0.95 0.89 Naïve Bayes 0.903 0.93 0.86 K-NN 0.910 0.98 0.97 SVM 0.963 0.97 0.98 Decision Tree 0.895 0.90 0.97 Random Forest 0.895 0.98 0.96 XGBoost 0.918 0.97 0.94 Among the various classification algorithms applied to the converted urine dataset, Support Vector Machine (SVM) demonstrated remarkable performance, achieving a classification accuracy of 0.963. This high level of accuracy is particularly noteworthy as it suggests that the SVM model is highly adept at discriminating COVID-19 detection based on the converted urine metabolomic profiles. The AUC of 0.97 denotes excellent discrimination between positive and negative classes (Figure S5A). Furthermore, an accuracy rate of 0.98, highlights the model's robustness in correctly identifying class labels (Figure S5B). These metrics show that the false positive rate is extremely low while maintaining a high true positive rate, and that the classifier is not only accurate but also reliable in classifying new unseen data. In this context, the superior performance of SVM can be attributed to its ability to maximize the margin between different classes while minimizing classification error. This characteristic makes it particularly effective when dealing with high-dimensional data or data that is not linearly separable, common scenarios in metabolomics. In the case of high-dimensional data, the risk of model overfitting is significant, but linear SVM kernels are less severe than that of nonlinear counterparts, and therefore the use of a linear kernel could provide the model with a higher generalization capability, thus improving its predictive accuracy on unobserved data. 39 , 40 SVM has been very useful in for example classifying climate zones successfully applied in the harvest of mung beans, 41 in protein classification, gene expression data analysis, 42 and has also shown promise results in disease prediction tasks such as cancer diagnosis based on genetic markers or imaging data. 43 , 44 To provide a comprehensive evaluation of our SVM classification model, a confusion matrix was calculated. The high accuracy score of 0.963 for the SVM model is corroborated by the significantly larger counts along the diagonal of the confusion matrix as compared to the off-diagonal elements (Table S10). The results give 70 true positives, 59 true negatives, 4 false positives and, finally, 1 false negative. The model has a precision of 98.3% and 94.6% in identifying positive and negative cases, respectively. Discussion This study demonstrates how NMR-based metabolomics can identify metabolites associated with COVID infection and its severity. Our research has highlighted some biomarkers that are able to differentiate, not only between COVID patients and healthy controls but also regarding good or poor prognosis of patients, with AUC higher than 0.6 in case of urine and 0.7 in case of serum samples. Regarding serum samples, they exhibited higher levels of phenylalanine, tyrosine, lysine, creatine, proline, TMA, mannose, acetone, NAG and fatty acids, especially UFA, for COVID-19 group compared to control group, that in turn, exhibited higher levels of TMAO and citrate. These findings align with previous studies, 4 , 20 , 45 – 47 which reported higher concentrations of phenylalanine, mannose, and glycoproteins in the COVID-19 group, while citrate showed higher concentration in the control group. However, the results for tyrosine and creatine have been reported in opposite direction to ours only in one case. 4 Acetone, tyrosine, citrate, and glycerol have been found to be discriminant metabolites between the control and COVID-19 groups, but not all studies agree on whether their content increases or decreases in the COVID-19 group. 20 , 24 , 47 , 48 Our finding in terms of greater amounts of creatine and mannose in the COVID-19 group are also in accordance with some other reports. 22 , 47 These found biomarkers implicated in COVID-19 disease play a role in specific pathways, mainly “Phenylalanine, tyrosine and tryptophan biosynthesis”, “Phenylalanine metabolism”, “Glycerolipid metabolism” and “Arginine and proline metabolism”, either activating or dysregulating them. The study of metabolic pathways is of utmost importance in understanding diseases, as abnormalities in specific pathways often underlie the development and progression of various medical conditions and is also critical for the development of targeted and effective medical treatments. Our findings agree well with previous studies, such as Schmelter et al. 3 that proposed changes in amino acid and lipoprotein metabolism, Blasco et al. 17 that unraveled the tryptophan‑nicotinamide pathway clearly linked to inflammatory signals and microbiota, or Correia et al. 24 and Lorente et al. 48 that found altered phenylalanine, tyrosine and tryptophan biosynthesis, together with the glycerolipid metabolism. In urine samples, we reveal higher levels of methionine, fucose, lysine, hippurate, 2-phenylpropionate, glutamine, 3-indoxylsulphate and pseudouridine for COVID-19 group in comparison to control group, that in turn revealed higher content of formate, 3-methylhistidine, trigonelline and creatinine. Marhuenda-Egea et al. 25 identified metabolic differences between the COVID-19 and healthy control groups, primarily related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate), as well as processes associated with bacterial flora (TMAO and formate) and detoxification (hippurate). Additionally, they observed higher levels of formate in COVID-19 urine samples, while the control group exhibited elevated creatinine concentrations, which were linked to sarcopenia—a medical condition characterized by the progressive loss of muscle mass, strength, and function, resulting in a decrease in creatine/creatinine ratio. In contrast to our results, Marhuenda-Egea et al. 25 have indicated lower hippurate content in COVID-19 patients, which was linked to possible impairments in the detoxification process. Concerning disease severity (ICU vs MW), it was detected a decrease on TMAO, betaine and ethanol in serum samples from ICU patients. On the other hand, some metabolic changes were observed on the contents of TMAO, hippurate, urea, DMA, 3HB, fucose, 4-hydroxyphenyl acetate, 3-methyl-2-oxovalerate, 3-indoxylsulphate, tryptophan and formate in urine. Regarding prognosis, we found a decrease on betaine and TMAO concentrations, that were associated to a negative outcome (poor prognosis) in serum. Furthermore, it was found a decrease in cis -aconitate, urea, formate, and creatinine, methanol, DMA, and TMAO in urine, together with an increase in creatine and hippurate. Terruzzi et al. 49 noticed a relationship between microbial metabolites (TMAO and lipopolysaccharide) that generate inflammatory microenvironment and a risk of severe illness from COVID-19. Furthermore, Israr et al. 50 conducted a study in which they measured gut-related metabolites, including betaine and TMAO, in plasma samples from patients with COVID-19, healthy individuals, and patients with non-COVID-19 respiratory symptoms. The researchers aimed to explore these metabolites since they have previously been associated with respiratory diseases, and they postulated that the connection between the gut and lungs might influence gut health in COVID-19 cases. Their findings revealed that metabolites from the choline-TMAO and carnitine-TMAO pathways are linked to COVID-19 symptoms and severity, effectively distinguishing between COVID-19 and acute asthma. Of note, they identified betaine as a potential biomarker of gut microbiome health and hypothesized that dietary interventions targeting the gut microbiome could lead to improved outcomes and enhanced immunity. Regarding disease severity in urine, Rosolanka et al. 26 reported elevated levels of ketone bodies in patients with severe COVID-19 during the first week after hospital admission, which is consistent with our findings of higher 3HB content in ICU patients compared to those in the MW. As in the previous work, they found that hippurate levels were lower in COVID-19 patients, which they associated with the administration of strong antibiotic treatments. This disparity in hippurate content with our results might be explained by the timing of urine sample collection in our study, which occurred after hospital admission when patients were not yet under medication. Regarding the clinical context, it should be noted that certain cardiovascular comorbidities may increase the risk of severe COVID-19. Specifically, the prevalence of obesity/overweight and dyslipidemia tended to be higher in ICU patients, although it was statistically significant only for the latter. Similarly, LDH and fibrinogen were increased in ICU patients, who also showed a decrease in lymphocyte count compared to the ward group. In addition, those patients with elevated ferritin spent more time hospitalized and also in ICU, reflecting the association of this parameter with the course of disease. Similarly, patients with decreased lymphocyte counts also had longer hospital stays, as well as patients with high C-reactive protein, who spent more time in the ICU. Another relevant fact was that certain metabolites were related to clinical variables in both study groups. Thus, 3HB was associated with hypertension, and n-3 fatty acids, creatine, tyrosine, phenylalanine and acetate with obesity/overweight. Phenylalanine is an essential amino acid that is converted to tyrosine and is related to inflammation. Both metabolites have been observed markedly increased in patients with moderate to severe COVID-19 disease. 21 Shi et al., reported a biomarker panel including 3HB that could predict COVID-19 patients who progressed from mild to severe. 51 Through our study, we were able to construct different panels for the management of COVID-19 patients. We propose a diagnostic panel based on three metabolites, consisting of isoleucine, TMAO and glucose, which was able to discriminate COVID-19 patients from healthy individuals providing very high efficiency (AUC = 0.91, sensitivity 86.08%, specificity 83.87%, respectively). In line with our results, other authors have also reported the perturbation of amino acids, glucose and energy metabolisms as a result of COVID-19 infection, and some of these metabolites, such as glucose, has previously been related to disease severity. 52 , 53 An optimal biomarker panel capable of efficiently classify disease severity was only obtained when considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine) (AUC = 0.825, sensitivity 84.85%, specificity 72.09%). Supporting our results, other authors have shown the benefit of including clinical variables in metabolite-based biomarker panels to classify COVID-19 patients. López‑Hernández et al. 54 reported several metabolite panels which increased their discriminatory power after the addition of clinical and/or demographic characteristics: e.g. the panel to discriminate COVID-19 positive individuals from non-COVID-19 including Kynurenine-tryptophan ratio, lysoPC a C26:0 and pyruvic acid incremented its AUC value from 0.947 to 0.971 after adding sex and neutrophile percentage; moreover, the panel obtained to distinguish hospitalized from intubated COVID-19 patients by adding hypertension and neutrophil-lymphocyte ratio to LysoPC a C28:0 increased its AUC value from 0.770 to 0.829. Despite the good performance provided by our panels, the lack of validation studies represents one of the limitations of this investigation, so further studies with an independent cohort of patients will be necessary to corroborate our results. Moreover, translation of these panels to the clinic will require large-scale studies to provide the actual accuracy of these panels. Finally, we were able to correlate the serum matrix with the urine matrix, which is a less invasive and easily obtainable sample. The exceptional classification accuracy obtained by the SVM demonstrates the usefulness and reliability of the urine to serum reconstruction dataset for downstream biomedical applications. It reaffirms the robustness of our conversion method and emphasizes that SVM is a particularly effective tool for classification tasks involving converted metabolome data. Methods Methods Study design and patients’ characteristics The clinical parameters of COVID-19 patients who were finally included in the study (n = 80) and the healthy controls (n = 32) are summarized in Table 1 . At the time of sampling, all patients had a positive polymerase chain reaction (PCR) in COVID-19. Of the 80 patients, 56 (70%) were males and 24 (30%) females, and the range of ages was 31 to 88 years. Furthermore, 46 patients were admitted in medical ward (57.5%) and 34 were derived to ICU (42.5%), among them 58 patients survived to the disease (72.5%) and 22 patients dead (27.5%). Other pathologies were also presented in some patients, which also appear in Table 1 . The length of hospital stay was also studied in the analyses indicated, which was defined as the time elapsed between the date of admission to the hospital and the date of discharge or death. Regarding the control group (n = 32), 22 individuals (68.75%) were males and 10 (31.25%) were females with a range of ages of 35 to 89 years. Sample preparation and NMR experiments Serum and urine samples from the patient groups were inactivated by adding 1.0 mL of viral lysis buffer (guanidine thiocyanate) and incubated at room temperature for 10 min, followed by centrifugation at 2500 rpm for 5 min. Supernatants were aliquoted and stored in − 80 o C following standardized biobank protocols. Serum samples were thawed at room temperature. NMR samples were then prepared mixing 200 µL of each serum sample with 400 µL of a saline solution (0.9% NaCl and 0.1% TSP in D 2 O). Urine samples, previously stored in -80 o C freezer too, were also thawed at room temperature. NMR samples were then prepared mixing 200 µL of each urine sample with 300 µL of a phosphate buffer solution (0.5 M KH 2 PO 4 in D 2 O and 0.05% TSP). Both were centrifuged during 5 min at 13500 rpm and then 500 µL of supernatants for serum samples and 490 µL for urine samples were transferred to a 5 mm NMR tube. Samples were then measured in a 600 MHz Bruker Avance III NMR spectrometer, equipped with a quadrupole cryoprobe and a thermostated automatic SampleJet. All 1 H-NMR spectra were measured at 300 ± 0.1 K and referenced to the TSP signal (0 ppm). Two different 1 H NMR experiments were recorded: a one-dimensional 1 H Carr-Purcell-Meiboom-Gill (CPMG) experiment (cpmgpr1d) for serum samples, with water presaturation pulse to supress water signal and implementing a T 2 filter to suppress the broad signals of proteins and other macromolecules, and a one-dimensional 1 H NOESY spectrum (noesygppr1d) with water presaturation pulse as previously described. 55 The identification of metabolites was carried out using 2D NMR homo- and heteronuclear experiments, as 1 H− 1 H total correlation spectroscopy (TOCSY) and 1 H− 13 C heteronuclear multiple bonds coherence (HMBC), recorded using standard Bruker sequences, together with the use of some NMR databases, as Chenomx NMR Suite 8.6 software (Chenomx, Edmonton, Canada) and public NMR databases such as Human Metabolome Database (HMDB) 56 and literature data. 14 , 57 , 58 Statistical analysis For the processing step of the data, each NMR spectrum was divided into 0.04 ppm chemical shift regions or buckets from δ H 0.5 to 10.0 ppm using AMIX 3.9.15 (Bruker BioSpin GmbH, Rheinstetten, Germany), and the corresponding spectral areas were integrated. Region containing residual signals of water suppression (δ H 4.64 − 4.80 ppm) was excluded from the bucket table employed in the analysis. The following step prior to statistical analysis, the normalization, was carried out by scaling the intensity of each individual peak to the total intensity recorded in the region mentioned above. Some statistical analyses were performed on the data matrix resulting, such as univariate and multivariate data analysis, including exploratory or non-supervised models as Principal Component Analysis (PCA), and supervised models as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Scores plots were generated for both models, and they were scaled to Pareto. Goodness-of-fit (R 2 ) and of goodness-of-prediction (Q 2 ) parameters were given for OPLS-DA models as well as CV-ANOVA parameter validation with a level of significance of p < 0.05 to prove the model’s predictive accuracy. A bivariate analysis was performed to determine the association between clinical variables, hospital and ICU times, and metabolites between the different study groups. In the case of qualitative variables, the association was determined by using the chi-squared test or Fisher's exact test. For the treatment of quantitative variables, the Student's t-test or Wilcoxon rank sum test (Mann-Whitney U test) were used, according to compliance with the assumptions of normality. To compare metabolites in the three groups we used the ANOVA test, when the normality assumptions were met, or the Kruskal-Wallis test, otherwise. For those variables that were statistically significant, two-by-two comparisons were performed. During the preprocessing of the data for the correlation studies, we applied the Shapiro-Wilk test to assess normality, which revealed that the assumptions for the use of Pearson's correlation coefficient were not met, so the correlation between variables was studied using Spearman's correlation coefficient, which assesses monotonic relationships and is a particularly resilient method for normality deviations and the presence of outliers. In addition, we performed a multivariate analysis, fitting multivariate logistic regression models. LASSO regression was used for variable selection based on Spearman's correlation, and subsequently the variance inflation factor (VIF) was calculated to re-evaluate correlations and eliminate variables in the final model (variables with VIF > 2.5 were removed). Finally, receiver operating characteristic (ROC) curves were plotted to investigate the classification performance of individual metabolites and proposed panels. The cut-off point providing the highest sensitivity and specificity was identified according to the Youden index. The discriminatory capacity was evaluated by the area under the curve (AUC) value of the ROC curve (95% confidence intervals). The R program, version 4.2.1 (R Core Team 2022) and p < 0.05 were used for analysis. Metabolomics pathway analysis To detect altered metabolic routes, a pathway analysis, consisting of enrichment analysis and pathway topological analysis, was performed with the Metabolomics Pathway Analysis (MetPA) function within the MetaboAnalyst online tool ( https://www.metaboanalyst.ca/ ). It was used a global test algorithm for pathway enrichment and the library of the metabolic pathways of Homo Sapiens was employed. Pathways were considered significantly enriched if they fulfilled to the following criteria: number of metabolites hits in relation to the total number of the pathway > 1, p -value of Fischer’s Exact test < 0.05, Holm p -value < 0.05 (adjusted by the Holm-Bonferroni method), adjusted p -value of the false discovery rate (FDR) 0. The pathway impact value was calculated as the sum of importance measures of the metabolites, normalized by the sum of importance measures of all metabolites in each pathway. Reconstruction of serum spectra In order to perform an effective variable selection strategy and the conversion from urine 1 H NMR data to serum 1 H NMR data we employed the statistical technique of backward elimination within the functionalities of scikit-learn and Statsmodels, two well-regarded Python libraries in the domain of machine learning and statistical modeling. 59 , 60 Backward elimination iteratively refines the model by excluding variables that exhibit the least statistical impact based on a p -value threshold. By reducing the feature set to only those variables that are statistically significant, the model becomes both computationally efficient and theoretically justifiable, optimizing its performance in predicting complex biological conversions. Thus, data were split into training and test sets at an 80–20% ratio, respectively. To ensure the robustness of our findings, the classification models employed for this task were varied, covering a broad spectrum of machine learning algorithms. These models included Naive Bayes, Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees, Random Forest, and XGBoost. These models were applied with the same Python libraries mentioned above. Declarations Data Avialability The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. Acknowledgements This research has been funded by the State Research Agency of the Spanish Ministry of Science and Innovation (PID2021-126445OB-I00), and by the Gobierno de España MCIN/AEI/10.13039/501100011033 and Unión Europea “Next Generation EU”/PRTR (PDC2021-121248-I00, PLEC2021-007774 and CPP2022-009967). A. I. Tristán thanks to Junta de Andalucía for a predoctoral grant (PREDOC_01024). C. Jiménez-Luna was supported by the María Zambrano program funded by the Ministry of Universities with EU Next Generation funds. Author contributions The following authors were involved in study design (A.I.T., C.J.L., A.C.A., F.M.A.C., A.M.S., F.I.R., M.A.R.M., A.B.G., C.M., J.C.P., I.F.), data acquisition (A.I.T., C.J.L., A.C.A., A.M.S., F.I.R., M.A.R.M.), data processing and analysis (A.I.T., C.J.L., A.C.A., F.M.A.C.), table and figure generation (A.I.T., C.J.L), writing of the manuscript (A.I.T., C.J.L., F.M.A.C.,), critical review of the manuscript (A.C.A., C.M., J.C.P., I.F.), decision to submit (all authors). Data availability and patient consent This study was approved by the Ethics Committee of Torrecardenas Hospital and informed consent was obtained from all patients for being included in the study. All data was anonymised prior to data analysis and no patient-identifiable features are included within the manuscript in accordance with applicable guidelines and regulations. 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Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004). Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995). Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995). Vapnik, V. N. The nature of statistical learning theory (Ed. New York: Springer) (New York, 2010). He, L. et al. Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods. Food Control 153, 109927; https://doi.org/10.1016/j.foodcont.2023.109927 (2023). Brown, M. P. S. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA 97, 262–267 (2000). Yu, W., Liu, T., Valdez, R., Gwinn, M. & Khoury, M. J. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inf. Decis. 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Care 25, 390; https://doi.org/10.1186/s13054-021-03810-3 (2021). Terruzzi, I. & Senesi, P. Does intestinal dysbiosis contribute to an aberrant inflammatory response to severe acute respiratory syndrome coronavirus 2 in frail patients? Nutrition 79–80, 110996; https://doi.org/10.1016/j.nut.2020.110996 (2020). Israr, M. Z. et al. Association of gut-related metabolites with respiratory symptoms in COVID-19: A proof-of-concept study. Nutrition 96, 111585; https://doi.org/10.1016/j.nut.2021.111585 (2022). Shi, D. et al. The serum metabolome of COVID-19 patients is distinctive and predictive. Metabolism 118, 154739; https://doi.org/10.1016/j.metabol.2021.154739 (2021). Jia, H. et al. Metabolomic analyses reveal new stage-specific features of COVID-19. Eur. Respir. J. 59, 2100284; https://doi.org/10.1183/13993003.00284-2021 (2022). Codo, A. C. et al. Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through a HIF-1α/Glycolysis-dependent axis. Cell Metab. 32, 437–446.e5; https://doi.org/10.1016/j.cmet.2020.07.007 (2020). López-Hernández, Y. et al. Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19. Sci. Rep. 11, 14732; https://doi.org/10.1038/s41598-021-94171-y (2021). Tristán, A. I. et al. Serum nuclear magnetic resonance metabolomics analysis of human metastatic colorectal cancer: Biomarkers and pathway analysis. NMR Biomed. 36, e4935; https://doi.org/10.1002/nbm.4935 (2023). Wishart, D. S. et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 50, D622-D631; https://doi.org/10.1093/nar/gkab1062 (2022). Laíns, I. et al. Urine nuclear magnetic resonance (NMR) metabolomics in age-related macular degeneration. J. Proteome Res. 18, 1278–1288 (2019). Dubey, D. et al. NMR-Based serum metabolomics revealed distinctive metabolic patterns in reactive arthritis compared with rheumatoid arthritis. J. Proteome Res. 18, 130–146 (2019). Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn Res. 12, 2825–2830 (2012). Seabold, S. & Perktold, J. Statsmodels: Econometric and Statistical Modeling with Python. Procedures of the Python in science conference 92–96 https://doi.org/10.25080/Majora-92bf1922-011 (2010). Additional Declarations No competing interests reported. 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(IBS)","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Jiménez-Luna","suffix":""},{"id":313665884,"identity":"cb62dfbf-948e-4858-bab8-94b47f0eab51","order_by":2,"name":"Ana Cristina Abreu","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Cristina","lastName":"Abreu","suffix":""},{"id":313665885,"identity":"f57a7218-33d9-4e8b-8fd1-6ccd5678ddde","order_by":3,"name":"Francisco Manuel Arrabal-Campos","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Manuel","lastName":"Arrabal-Campos","suffix":""},{"id":313665886,"identity":"e145c33c-7905-40fd-b439-c155baf39a95","order_by":4,"name":"Ana del Mar Salmerón","email":"","orcid":"","institution":"University of Almería","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"del 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Fernández","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACNuYDYFqOgYG5gUgtbAlg2piBgZFILQxQLYkNRGvhY2M+9rmg5k76huMHGxg+/CHKYWzJs2cce5a74UxiA+PMNmK0yPcYM/OwHc7dcCCxgZmXGLexsfF/Zub5dzjd4PzDBuY/xDmMh5mZt+1wgsENoC0MbERpYTNm5u07bDjzxsOGg73E+EW+jfkxM8+3w/J855MPPvhBjMNQwAFSNYyCUTAKRsEowAEAWaI0KDiGuRoAAAAASUVORK5CYII=","orcid":"","institution":"University of Almería","correspondingAuthor":true,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Fernández","suffix":""}],"badges":[],"createdAt":"2024-05-30 15:58:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4504195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4504195/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-74641-9","type":"published","date":"2024-10-10T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58317179,"identity":"5c31e351-43ea-4c76-9919-ac76e14ddc75","added_by":"auto","created_at":"2024-06-13 21:17:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130936,"visible":true,"origin":"","legend":"\u003cp\u003eA) PCA scores plot, B) OPLS-DA scores plot and C) important features (VIP-plot from OPLS-DA) distinguishing healthy controls and COVID-19 serum samples. Scaling was done to Pareto. R\u003csup\u003e2\u003c/sup\u003eX = 0.79, R\u003csup\u003e2\u003c/sup\u003eY = 0.90, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.83, CV-ANOVA = 2.12 x 10\u003csup\u003e-32\u003c/sup\u003e for OPLS-DA, which was validated through Permutation Test (Figure S2). Important features (VIP \u0026gt; 1) plot shows those buckets that contain metabolites that increase (in red) or decrease (in blue) its content for each group. D) Significantly enriched metabolic routes by COVID-19 built with serum metabolites with VIP \u0026gt; 1 from OPLS-DA model: 1) Phenylalanine, tyrosine and tryptophan biosynthesis, 2) Phenylalanine metabolism, 3) Glycerolipid metabolism and 4) Arginine and proline metabolism. The more affected metabolic pathways appear with a color gradient, from yellow (less significant) to red (most significant) and satisfy the following criteria: number of matching metabolites in the pathway \u0026gt; 1, FDR adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and impact index \u0026gt; 0 (see Table S5).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4504195/v1/cb625518156052d149db9511.png"},{"id":58316739,"identity":"de8b42a8-63ce-4636-9c10-8cd1e1dec59e","added_by":"auto","created_at":"2024-06-13 21:09:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160255,"visible":true,"origin":"","legend":"\u003cp\u003eA) PCA scores plot, B) OPLS-DA scores plot, and C) important features (VIP-plot) distinguishing healthy controls and COVID-19 urine samples. Scaling was done to Pareto. R\u003csup\u003e2\u003c/sup\u003eX = 0.72, R\u003csup\u003e2\u003c/sup\u003eY = 0.85, Q\u003csup\u003e2\u003c/sup\u003e (cum) = 0.73, CV-ANOVA = 1.01 x 10\u003csup\u003e-21 \u003c/sup\u003efor OPLS-DA model which was validated through Permutation Test (Figure S3). Important features (VIP \u0026gt; 1) plot shows those buckets that contain metabolites that increase (in red) or decrease (in blue) its content for each group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4504195/v1/6d9ca9954ad3693be84f420b.png"},{"id":58317181,"identity":"17936d00-3886-43d0-8a5a-153729ba43df","added_by":"auto","created_at":"2024-06-13 21:17:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1285987,"visible":true,"origin":"","legend":"\u003cp\u003eRelations between clinical features and serum metabolite levels. A) Scatterplot on correlation between serum glucose levels in COVID-19 patients and hospital time (R-squared= 0.055, correlation coefficient= 0.235, \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05). Correlation analysis between clinical features and serum metabolite levels in COVID-19 patients B) Considering MW and ICU groups together. C) COVID-19 patients admitted to the MW. D) COVID-19 patients admitted to the ICU. The color scale shows whether the relationship between two variables is positive (blue) or negative (violet). Spearman's correlation coefficient was used for the analysis. Abbreviations: TAG: triacylglycerol, PC: phosphocholine, GPC: glycerophosphocholine, FA: fatty acids, PUFA: polyunsaturated FA, TMAO: trimethylamine, NAG: \u003cem\u003eN\u003c/em\u003e-acetylglutamate, UFA: unsaturated FA, LDH: lactate dehydrogenase, IL-6: interleukin 6.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4504195/v1/0ee92b17b86c50c9c8889bf7.png"},{"id":66597132,"identity":"cd416fad-1164-420d-9846-86120d61c5d9","added_by":"auto","created_at":"2024-10-14 16:07:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3346659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4504195/v1/59912677-8cc1-49ad-97cc-c8a2f083cf75.pdf"},{"id":58316741,"identity":"80bacc84-af09-4eb3-8cbc-00c3d8ecc19c","added_by":"auto","created_at":"2024-06-13 21:09:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1141361,"visible":true,"origin":"","legend":"","description":"","filename":"28052024SupportSciRep.docx","url":"https://assets-eu.researchsquare.com/files/rs-4504195/v1/0f19ab0c0039528e38fd0300.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomic Profiling of COVID-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19), a serious respiratory disease caused by the coronavirus strain SARS-CoV-2, has caused a global pandemic causing the infection of more than 523.3\u0026nbsp;million people and more than 6.2\u0026nbsp;million deaths to date.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This infection mainly affects the airways and lungs and presents a wide range of clinical manifestations, ranging from mild or even asymptomatic cases to severe ones where it could also could affect other organs and cause neurological symptoms or vascular damage, among others. Several COVID-19 patients still experience severe cases where the infection can be fatal, primarily affecting older individuals and those with underlying health conditions like diabetes, lung diseases, or cardiovascular disease.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The reason behind these differences in symptoms is still not fully understood, but it seems to be linked to various altered pathophysiological pathways during this infection, so it becomes imperative to comprehend which mechanisms are dysregulated to enhance treatment strategies, ultimately impacting the metabolic profiles.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMetabolomics is a relatively recent omics science that employs analytical platforms [mostly mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy]\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and statistical methods to characterize complex biochemical mixtures, analyzing both metabolites and metabolism, so this combined strategy probably affords the best representation of phenotype, and it enables the identification of disease biomarkers.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Among its benefits, NMR shows a high reproducibility, robustness, and needs a minimal sample preparation with almost no dilution factors, especially in biofluids.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In the field of pathology diagnosis, NMR metabolomics has been key for the determination of biomarkers of many diseases using several biofluids, such as saliva for Type 1 Diabetes and Parkinson\u0026rsquo;s disease,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e cerebrospinal fluid for inflammatory diseases,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e serum for pancreatic and colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and urine for thyroid and inflammatory bowel diseases.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDue to the tremendous impact that the COVID-19 pandemic has had in recent years, there has been a considerable amount of research concerning this topic, most of them employing serum or plasma samples and preferably MS as analytical technique.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Among the metabolomic studies applying NMR, we can highlight the following ones. Bruzzone et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e found elevated levels of ketone bodies (acetoacetic acid, 3-hydroxybutyric acid, acetone, and 2-hydroxybutyric acid) in serum profiles of 398 hospitalized SARS-CoV-2 patients comparing to 280 healthy controls, indicating both hepatic glutathione synthesis, that could potentially cause liver damage, dyslipidemia, and oxidative stress. Luporini et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e analyzed 166 COVID patients ranging from mild to severe symptoms and found an independent and positive association between phenylalanine levels (that may be associated with an increased inflammation) and disease severity. Meoni et al.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e found that 11 metabolites in plasma-EDTA showed significant alterations for 30 COVID-19 patients compared with age - and sex matched controls, as well as higher levels of VLDL, and lower levels of Apo A1, Apo A2, cholesterol and free-cholesterol HDL and LDL subfractions. They also found that tocilizumab administration resulted in at least partial reversion of the metabolic alterations caused by SARS-CoV-2 infection.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Baranovicova et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e analyzed plasma-EDTA samples from control (n\u0026thinsp;=\u0026thinsp;55) and COVID-19 patients with positive and negative outcomes (n\u0026thinsp;=\u0026thinsp;34, n\u0026thinsp;=\u0026thinsp;19, respectively) and found discrimination on metabolites associated to energy metabolism and inflammatory processes. Schmelter et al.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e also revealed distinctive differences in the metabolic and lipid profiles of serum samples from COVID-19 patients (n\u0026thinsp;=\u0026thinsp;30) compared to both healthy controls (n\u0026thinsp;=\u0026thinsp;58) and patients with cardiogenic shock (n\u0026thinsp;=\u0026thinsp;18) and suggest that COVID-19 is associated with dyslipidemia, as already pointed out by Wu et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and Bruzzone et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Furthermore, Bizkarguenaga et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e compared plasma samples from COVID-19 patients (n\u0026thinsp;=\u0026thinsp;69) with varying degrees of symptom severity over several months, alongside uninfected control individuals (n\u0026thinsp;=\u0026thinsp;71), and indicated that half of the patient population examined exhibited abnormal metabolism, characterized by altered porphyrin levels and lipoprotein profiles several months after the infection, whereas the other half showed minimal metabolic changes resulting from the disease. Correia et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e sought to evaluate the plasma metabolomes of 57 control patients and 53 positive cases of COVID-19 with mild to severe symptoms and identified six metabolites involved in lipid and energy metabolism (glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate) as potential prognostic metabolite panels for the disease and to predict symptoms severity.\u003c/p\u003e \u003cp\u003eRegarding urine samples, there are few recent studies, mainly comparing COVID-19 patients and healthy controls, or studying the recovery from severe coronavirus infection.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e To the best of our knowledge, no research has yet covered the analysis of COVID-19 severity alterations using urine, either alone or in combination with serum samples. Thus, in this study, we propose a novel NMR-based metabolomics investigation using two biofluids, namely serum and urine samples, collected from COVID-19 patients, including subgroups from both medical ward (MW) and intensive care units (ICU), as well as healthy controls of both genders, aged between 31 to 89 years. Our primary objective is to identify potential biomarkers that specifically differentiate between mild and severe COVID-19 symptoms. Additionally, we aim to investigate clinical correlations with the metabolome panel to better understand the influence of specific clinical features on the disease, and to examine the feasibility of reconstructing serum profiles through the analysis of urine samples to successfully predict disease severity, enhancing the utility of non-invasive biofluids. Through this comprehensive approach, we aspire to develop essential diagnostic tools capable of assessing disease severity. These predictive models, based on the analysis of urine as the most accessible biofluid, will enable the implementation of appropriate medical treatments in advance, enhancing the potential for timely and targeted interventions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of study population\u003c/h2\u003e \u003cp\u003eThe demographic data and clinical features of the COVID-19 patients admitted to the medical ward (MW) and intensive care unit (ICU) are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. No significant differences regarding age or sex were observed between both groups of patients and healthy controls. Most patients in both groups, MW and ICU, presented previous pathologies (80.43% and 88.24%, respectively) and also some cardiovascular risk factor (71.74% and 73.53%, respectively). Of these, overweight or obesity and dyslipidemia were more prevalent among patients in ICU (13.04% vs. 29.41% and 19.57% vs. 44.12%, respectively), although this increase was only significant for dyslipidemia (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). As expected, certain clinical parameters were significantly more frequent in the ICU patients at the time of hospital admission, such as dyspnoea (71.11% vs. 91.18%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), pneumonia (64.44% vs. 88.24%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and decreased oxygen saturation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Laboratory findings also tended to reflect the severity of the disease between both patient groups, showing non-normal levels of the blood parameters measured in the ICU group. Particularly, we observed a statistically significant increase for lactate dehydrogenase (LDH, 48.89% vs. 76.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and fibrinogen (42.22% vs. 67.65%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), and a significant decrease in the lymphocyte count (37.78% vs. 79.41%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average length of hospitalization was also significantly higher in ICU patients (27.59 vs. 42.85 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as the mortality rate due to the disease (15.91% vs. 44.12%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients with COVID-19 admitted to medical ward and ICU, and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical ward (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICU (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealthy controls (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) mean, (range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.24\u003c/p\u003e \u003cp\u003e(31\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.53\u003c/p\u003e \u003cp\u003e(36\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.14\u003c/p\u003e \u003cp\u003e(31\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.71\u003c/p\u003e \u003cp\u003e(35\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (63.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (79.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (69.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (36.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (20.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (30.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime in hospital (days) mean, (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.72\u003c/p\u003e \u003cp\u003e(27.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.79 (25.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.03 (28.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (44.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (28.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrognosis\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGood, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (84.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (47.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoor, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (52.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (16.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (80.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (88.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (83.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular risk comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (45.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (52.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (48.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (54.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (47.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (51.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity or overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (86.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (70.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (29.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (80.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (55.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (44.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (76.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (76.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (76.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (23.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (23.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (23.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u0026thinsp;\u0026ge;\u0026thinsp;1 cardiovascular risk factors\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes, n (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (71.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (73.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (72.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Poor prognosis was considered when admission to ICU was required or death due to COVID-19 disease occurred in the hospital. \u003csup\u003eb\u003c/sup\u003e Cardiovascular risk factors including hypertension, obesity/overweight, dyslipidemia, and diabetes. \u003cem\u003eSD\u003c/em\u003e: standard deviation. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Clinical signs and symptoms on admission, and initial laboratory findings, including biochemistry, blood count and clotting, can be found in Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMW and ICU times associated to clinical variables\u003c/h2\u003e \u003cp\u003eA bivariate analysis was performed in order to determine whether certain clinical characteristics (cardiovascular risk factors and laboratory results) were associated with length of hospital stay (Table S2). We observed that patients with high ferritin or C-reactive protein levels required more time in ICU than those with normal values, being the mean number of days 32.50 \u003cem\u003evs.\u003c/em\u003e 16.17 for ferritin measurements (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and 30.78 \u003cem\u003evs.\u003c/em\u003e 11 days for C-reactive protein levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). Likewise, high ferritin concentration was significantly associated with longer hospitalization time in patients admitted to the medical ward (27.55 \u003cem\u003evs.\u003c/em\u003e 10.25 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), those admitted to the ICU (47.64 \u003cem\u003evs.\u003c/em\u003e 25.83 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), as well as in both groups together (37.08 vs. 15.44 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Furthermore, considering all COVID-19 patients, we observed that a lower number of lymphocytes in the blood was significantly associated with a longer hospital stay compared to those patients with a normal lymphocyte count (36.42 \u003cem\u003evs.\u003c/em\u003e 26.47 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and the same trend was observed in patients admitted to the ICU (47.67 \u003cem\u003evs.\u003c/em\u003e 28.86 days), although this difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064). These observations are consistent with previous studies in which elevated ferritin and C-reactive protein, and decreased absolute lymphocyte counts were associated with critical disease, unfavorable evolution and increased mortality due to COVID-19.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSimilarly, it has been demonstrated that the cardiometabolic status has an important impact on the clinical outcome of these patients.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Disorders as hypertension, dyslipidemia, obesity and diabetes \u003cem\u003emellitus\u003c/em\u003e have been shown to increase the risk of severe COVID-19 and mortality.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Accordingly, in our study, having one or more cardiovascular risk factors (including hypertension, obesity/overweight, dyslipidemia, and/or diabetes \u003cem\u003emellitus\u003c/em\u003e) was associated with longer hospital stay in the group of patients admitted to the ward (26.00 \u003cem\u003evs.\u003c/em\u003e 14.25 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). In fact, patients with hypertension in this group spent a mean of 27.38 days in hospital compared to those with normal values, whose stay was 16.84 days (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). In addition, when we analyzed all COVID-19 patients, we found that having dyslipidemia was also associated with a longer time in hospital (45.5 \u003cem\u003evs.\u003c/em\u003e 25.92 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). These results reflect the importance of these variables in the clinical setting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic differences between COVID-19 patients and healthy controls\u003c/h2\u003e \u003cp\u003eTo investigate the possible differences between COVID-19 patients and healthy controls, an NMR metabolomic approach was applied first and used over a set of serum samples and then over their corresponding urine aliquots, collected in both cases under the same conditions. All the metabolite assignments of both serum and urine samples are shown in representative \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR spectra of each matrix on Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB and are described in Tables S3 and S4, respectively. It was possible the identification of 32 metabolites and metabolites classes in serum samples, and 55 in the case of urine, falling into various classes of compounds, including amino acids, ketone bodies, organic acids, sugars, fatty acids, among others.\u003c/p\u003e \u003cp\u003eFirst, an unbiased statistical analysis for the comparison of COVID-19 patients and healthy controls was performed by employing Principal Component Analysis (PCA) to \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR data of each matrix. In Fig.\u0026nbsp;1A and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA appear both PCA scores plots from serum and urine data, respectively. A slight clustering trend between the COVID-19 and control samples was observed. Therefore, to emphasize the differentiation between the groups and unveil potential biomarkers, supervised analysis using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was applied to each \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR data set. OPLS-DA model correlates the information of the samples, in this case, the COVID-19 or Control groups, with the spectral information obtained through NMR. The OPLS-DA scores plot (Figs.\u0026nbsp;1B and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB for serum and urine, respectively) displayed a remarkable discrimination between samples from different groups in both matrices. Moreover, the Important Features Analysis, which reveals the discriminating molecules responsible for the separation of the spectroscopic profiles through Variable Importance in the Projection (VIP) score analysis (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1), identified specific metabolites such as trimethylamine-\u003cem\u003eN\u003c/em\u003e-oxide (TMAO), phenylalanine, \u003cem\u003eN\u003c/em\u003e-acetylglycoproteins (NAG), tyrosine, lysine, acetone, mannose, citrate, glycerol, and fatty acids, particularly unsaturated fatty acids (UFA), as the key molecules driving the separation between COVID-19 patients and healthy controls in the serum metabolome (Fig.\u0026nbsp;1C). Otherwise, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC shows the Important Features Analysis with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 for urine metabolites, that displays methionine, formate, fucose, arabinose, \u003cem\u003eN\u003c/em\u003e-phenylacetylglycine, lysine, 3-methylhistidine, trigonelline, hippurate, 2-phenylpropionate, glutamine, creatinine, 3-indoxylsulphate and pseudouridine, as metabolites responsible for urine metabolome differentiation into COVID-19 patients and healthy controls. In addition, evaluation tools from univariate analysis were also taken into consideration for both matrices, including fold change (FC) analysis, \u003cem\u003ep\u003c/em\u003e-values and ROC analysis (see below).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate more deeply the functional implications of these metabolites and their interactions in the context of COVID-19, a metabolic pathway analysis (MetPA) was performed. MetPA provides valuable information on the perturbations and alterations that occur in metabolic pathways, shedding light on their potential roles in the pathogenesis and clinical manifestations of the disease. The metabolic pathways associated with the discriminant metabolites in serum for the comparison between COVID-19 and healthy controls are shown in Fig.\u0026nbsp;1D. The most affected metabolic routes by COVID-19 disease with a higher impact index are \u0026ldquo;Phenylalanine, tyrosine and tryptophan biosynthesis\u0026rdquo;, \u0026ldquo;Phenylalanine metabolism\u0026rdquo;, \u0026ldquo;Glycerolipid metabolism\u0026rdquo; and \u0026ldquo;Arginine and proline metabolism\u0026rdquo;. In the cases of \u0026ldquo;Phenylalanine, tyrosine and tryptophan biosynthesis\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e-Holm and \u003cem\u003ep\u003c/em\u003e-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u0026ldquo;Phenylalanine metabolism\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), these two routes involve two common metabolites: tyrosine and phenylalanine. With respect to \u0026ldquo;Glycerolipid metabolism\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), disturbed metabolites were glycerol and fatty acids (including acyl-, diacyl- and triacylglycerols, phosphatidic and lysophosphatidic acids) and, for \u0026ldquo;Arginine and proline metabolism\u0026rdquo; (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), metabolites that hit this pathway were creatine and proline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of potential biomarkers for COVID-19 infection and severity\u003c/h2\u003e \u003cp\u003eOne of the main questions in this past pandemic is why some people experienced severe symptoms of coronavirus while others remained asymptomatic. In this context, we conducted comparisons to deepen our understanding of the disease and its varying impacts. We analyzed metabolic differences between: i) healthy individuals and COVID patients, ii) patients admitted to the MW and those admitted to the ICU, and iii) between individuals with good or poor prognoses. These comparisons aimed to shed light on the severity of symptoms, and for the first time investigate if COVID-19 patients who required ICU admission or experienced fatal outcomes during their hospital stay would fit in the model as having poor prognosis. In this instance, multivariate data analysis did not yield valid models with satisfactory goodness-of-prediction (Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) cumulative values. Consequently, we resorted to performing univariate data analysis instead. Under the criteria of fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.1 or \u0026le;\u0026thinsp;0.9 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), several potential metabolite biomarkers in serum and urine in COVID-19 patients were observed, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Furthermore, in order to evaluate the diagnostic performance of metabolic biomarkers, ROC analysis was conducted and the AUC was calculated with AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.6, ranging from poor (AUC\u0026thinsp;=\u0026thinsp;0.6 to 0.7), fair (AUC\u0026thinsp;=\u0026thinsp;0.7 to 0.8), good (AUC\u0026thinsp;=\u0026thinsp;0.8 to 0.9) to excellent (AUC\u0026thinsp;=\u0026thinsp;0.9 to 1) diagnostic ability.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e This ROC analysis was performed for both matrices, serum and urine, and for the three aforementioned comparisons. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes all the comparisons studied in serum and urine, detailing the FC, \u003cem\u003ep\u003c/em\u003e-values, AUC, cutoff, sensitivity and specificity values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential biomarkers in serum and urine for different comparisons: COVID-19 patients vs healthy controls (HC), intensive care unit (ICU) vs medical ward (MW), and poor vs good prognosis (PvsG).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSERUM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMetabolite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eδ (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eCut-off\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSensitivity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpecificity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID/\u003c/p\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.48\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;29\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.33#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.93\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.92\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcetone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycerol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.61\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMannose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13x10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39x10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.90*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU/MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePvsG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eURINE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMetabolite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eδ (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eCut-off\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSensitivity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSpecificity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID/\u003c/p\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.37\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.35\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-methyl-histidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.10#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrigonelline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.94#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.68\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.30#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHippurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.94#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.62#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-phenyl-propionate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.30#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.75\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.05\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.59\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.10#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.99\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-indoxyl-sulphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50#*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudouridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.26*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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colname=\"c9\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3HB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.23\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.97\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-hydroxyphenyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-methyl-2-oxovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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\u003cp\u003e3.35\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.69\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePvsG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ecis\u003c/em\u003e-aconitate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHippurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e (95% cl). # VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 in OPLS-DA models. * p (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe biomarker ability, evaluated through AUC value, of those biomarkers obtained from the OPLS-DA models was, in most cases, higher than 0.7 in serum, and higher than 0.6 in urine. In general, highest values of AUC were obtained for the COVID-19 vs controls comparison in serum samples. Furthermore, we evaluated the sensitivity and specificity for each metabolite, and both were found to be higher than 50% in each case.\u003c/p\u003e \u003cp\u003eRegarding the metabolic changes between COVID-19 and control groups, higher levels of phenylalanine, tyrosine, lysine, creatine, proline, trimethylamine (TMA), mannose, acetone, NAG and fatty acids, especially UFA, were found in serum of COVID-19 group accompanied by decrease levels of TMAO and citrate. In urine, higher levels of methionine, fucose, lysine, hippurate, 2-phenylpropionate, glutamine, 3-indoxylsulphate and pseudouridine were observed for COVID-19 group in comparison to control group, that in turn revealed higher content of formate, 3-methylhistidine, trigonelline and creatinine.\u003c/p\u003e \u003cp\u003eConcerning disease severity (ICU vs MW), a decrease on TMAO, betaine and ethanol were found in serum samples from ICU patients, while metabolic changes on the contents of TMAO, hippurate, urea, dimethylamine (DMA), 3-hydroxybutyrate (3HB), fucose, 4-hydroxyphenyl acetate, 3-methyl-2-oxovalerate, 3-indoxylsulphate, tryptophan and formate were detected in urine.\u003c/p\u003e \u003cp\u003eAbout prognosis, we found that a decrease on betaine and TMAO were associated to a negative outcome (poor prognosis) in serum, and a decrease in \u003cem\u003ecis\u003c/em\u003e-aconitate, urea, formate, and creatinine, methanol, DMA, and TMAO in urine, together with an increase in creatine and hippurate also in urine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRelation between serum metabolites and clinical parameters\u003c/h2\u003e \u003cp\u003eBivariate analysis was performed to study the association between serum metabolite levels and fatal outcome in COVID-19 patients. Of total metabolites analyzed, TMAO levels were found to be significantly related with death due to the disease (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). This is in accordance with our previous findings, in which TMAO was also found to be correlated with poor prognosis (ICU and death).\u003c/p\u003e \u003cp\u003eWe did not found metabolites with significant correlation with time in ICU, but in contrast, serum glucose levels were positively correlated with time spent in hospital in COVID-19 patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This reinforces what has been seen in other studies, in which high glucose levels have been associated with severe disease, probably derived from sustained inflammation resulting from infection.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e However, this data should be taken with caution since the time spent in hospital is a variable with a multifactorial effect component and it would not be expected that a single metabolite would be a good indicator of this response variable on its own.\u003c/p\u003e \u003cp\u003eIn addition, numerous metabolites were significantly correlated to clinical features in COVID-19 patients, especially those related to cardiovascular risk factors and blood parameters of tissue/cell damage and clotting factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Table S6). When we analyzed individually the group of patients admitted to medical ward, we observed some metabolites significantly correlated with having arterial hypertension or being overweight/obese (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and Table S7). Among them, ethanol, NAG, TMA, creatine and glycerol ranked among the top positive correlates to arterial hypertension, and in the same line, fatty acids n-3, acetoacetate, proline, glycerol, tryptophan, tyrosine and triacylglycerol levels were the top to obesity/overweight. These metabolites are involved in numerous metabolic pathways, such us arginine and proline, galactose, and glycerolipid metabolisms, which were shared between these two conditions in the correlation studies. Similarly, ICU patients also showed metabolites positively correlated with cardiovascular risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Table S8). In particular, the most significant metabolites were 3HB and choline for hypertension, creatine, tyrosine and acetate for overweight/obesity and TMAO for dyslipidemia, which are mainly involved in pathways of amino acid, carbohydrate and lipid metabolism. Some authors have suggested an association between the deregulation of these metabolic routes and high risk of severe COVID-19.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRemarkably, we also detected that certain clinical variables were associated with the same metabolite in both groups of patients. Thus, the presence of one or more cardiovascular risk comorbidities was significantly associated with increased ethanol and glycerol. In particular, increased 3HB levels were related to hypertension, and elevated n-3 fatty acids, creatine, tyrosine, phenylalanine and acetate with obesity/overweight.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker panels based on serum metabolites and clinical variables\u003c/h2\u003e \u003cp\u003eThe use of biomarker panels has the advantage of better capturing inter-patient variability than individual metabolites, which is more feasible when they are transferred to large cohorts. Therefore, in order to determine the ability of metabolites as biomarkers to classify COVID-19 patients, a multivariate logistic regression models were constructed.\u003c/p\u003e \u003cp\u003eFor the model comparing COVID-19 patients with healthy subjects, eight metabolites were initially identified and following the analysis, the final model included isoleucine, TMAO and glucose. The ROC curve for this panel showed outstanding discrimination (AUC\u0026thinsp;=\u0026thinsp;0.91) reporting 86.08% of sensitivity and 83.87% of specificity (Figure S4A). In addition, we investigated a model to discriminate patients admitted to the MW from ICU patients. In this case, nine metabolites were identified to build the model, and after subsequent analyses, the final panel consisted of ethanol, TMAO, tyrosine and betaine. However, this panel did not provide sufficient discriminatory capacity (AUC\u0026thinsp;=\u0026thinsp;0.697, sensitivity 45.45% and specificity 91.11%, Figure S4B). Interestingly, the models providing biomarker panels that included additional metabolites to those shown to be more discriminating at the individual level. So, we constructed a multivariate logistic regression model including both metabolite levels and clinical variables (analytical results or cardiovascular risks) as independent variables, with the aim of finding a predictor model to determine the risk of severe disease in COVID-19 patients. For this purpose, a previous selection of metabolites and variables to be included in the model was made by means of LASSO regression, due to the problems of adjustment by a high degree of correlation. Firstly, six metabolites and five clinical variables were identified (obesity/overweight, dyslipidemia, C-reactive protein, neutrophiles, lymphocytes, ethanol, lactate, TMAO, tyrosine, betaine, and acetate) and after subsequent analysis, the final reduced model included: obesity/overweight, dyslipidemia, lymphocytes, ethanol, TMAO, tyrosine and betaine.\u003c/p\u003e \u003cp\u003eAccording to the ROC curves performed to differentiate between ICU and MW patients, this classifier model showed a good discriminatory performance (AUC\u0026thinsp;=\u0026thinsp;0.825), providing a sensitivity of 81.82% and a specificity of 71.11%, for a cut-off point of 0.41 (Figure S4C). These results suggest the contribution of conditions as low lymphocytes counts, and comorbidities as obesity/overweight and dyslipidemia to COVID-19 outcomes. Furthermore, the panel included only a few variables that would facilitate its clinical implementation in order to predict which of the hospital MW patients may require admission to the ICU due to potential severity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eReconstruction of serum profiles by using urine\u003c/b\u003e \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH NMR spectra\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAs established, serum samples exhibit greater stability and lower variability compared to urine samples, that are influenced by numerous external factors, including diet, medication, and lifestyle. Consequently, we have introduced a novel method for converting urine-to-serum profiles, aiming to endorse the utilization of less invasive techniques in clinical practice. Such reconstruction is a challenge itself due to its inherent complexity and high dimensionality. We performed backward elimination as an effective variable selection strategy in order to have the best possible performance in the model.\u003c/p\u003e \u003cp\u003eThe robustness and predictive accuracy of the urine-to-serum conversion is underscored by a robust set of compelling statistical metrics. Developing a robust multi-output regression model involved careful consideration of dataset partitioning. Our approach led to an 80\u0026ndash;20% partitioning between training and test sets, respectively. Table S9 summarizes the metrics of the multi-output regression model based on the evaluation of the test dataset. Compared with models reported in existing metabolomics literature and other scientific fields, the performance of the developed multi-output regression model is remarkable. The R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.997 and Adjusted-R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.949 of the model overpass previous studies in the metabolomic field where values above 0.9 are generally considered exceptional.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The F-statistic of 38.25 and its associated p-value of 0.0025 indicate statistical significance, confirming the robustness of the model's predictors. In addition, the calculated AIC (-183.86) and BIC (88.76) values, which incorporate a penalty for model complexity, further emphasize the model's parsimony and predictive capability.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Moreover, the Log-Likelihood value of 193.72, which serves as a measure of model fit, also stands out as remarkably high, supporting the model's reliability.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFollowing the successful development of a multi-output regression model, which reliably converts urine metabolomic profiles to their serum counterpart, an ensuing verification was undertaken to assess the utility of the converted data in classification tasks. Specifically, the converted urine dataset was subjected to multiple classification algorithms and were conducted as a proof of concept in the model comparing COVID-19 patients with healthy subjects. The purpose of this verification was to validate that the reconstructed data preserves the discriminative features necessary for accurate classification, like that achieved by using the original serum dataset.\u003c/p\u003e \u003cp\u003eTo validate our obtained results, Na\u0026iuml;ve Bayes, Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees, Random Forest, and XGBoost were applied as classification models. Each one was trained on the original serum dataset and subsequently tested on the reconstructed urine dataset. The primary metric of interest was the classification accuracy, as it directly reflects the efficacy of our original conversion model in retaining the relevant features of the serum metabolomic profiles for the task of classification. The accuracy results can be visualized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy results applied to various classification models used commonly in machine learning problems.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u0026iuml;ve Bayes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK-NN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the various classification algorithms applied to the converted urine dataset, Support Vector Machine (SVM) demonstrated remarkable performance, achieving a classification accuracy of 0.963. This high level of accuracy is particularly noteworthy as it suggests that the SVM model is highly adept at discriminating COVID-19 detection based on the converted urine metabolomic profiles. The AUC of 0.97 denotes excellent discrimination between positive and negative classes (Figure S5A). Furthermore, an accuracy rate of 0.98, highlights the model's robustness in correctly identifying class labels (Figure S5B). These metrics show that the false positive rate is extremely low while maintaining a high true positive rate, and that the classifier is not only accurate but also reliable in classifying new unseen data.\u003c/p\u003e \u003cp\u003eIn this context, the superior performance of SVM can be attributed to its ability to maximize the margin between different classes while minimizing classification error. This characteristic makes it particularly effective when dealing with high-dimensional data or data that is not linearly separable, common scenarios in metabolomics. In the case of high-dimensional data, the risk of model overfitting is significant, but linear SVM kernels are less severe than that of nonlinear counterparts, and therefore the use of a linear kernel could provide the model with a higher generalization capability, thus improving its predictive accuracy on unobserved data.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e SVM has been very useful in for example classifying climate zones successfully applied in the harvest of mung beans,\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e in protein classification, gene expression data analysis,\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and has also shown promise results in disease prediction tasks such as cancer diagnosis based on genetic markers or imaging data.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo provide a comprehensive evaluation of our SVM classification model, a confusion matrix was calculated. The high accuracy score of 0.963 for the SVM model is corroborated by the significantly larger counts along the diagonal of the confusion matrix as compared to the off-diagonal elements (Table S10). The results give 70 true positives, 59 true negatives, 4 false positives and, finally, 1 false negative. The model has a precision of 98.3% and 94.6% in identifying positive and negative cases, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates how NMR-based metabolomics can identify metabolites associated with COVID infection and its severity. Our research has highlighted some biomarkers that are able to differentiate, not only between COVID patients and healthy controls but also regarding good or poor prognosis of patients, with AUC higher than 0.6 in case of urine and 0.7 in case of serum samples. Regarding serum samples, they exhibited higher levels of phenylalanine, tyrosine, lysine, creatine, proline, TMA, mannose, acetone, NAG and fatty acids, especially UFA, for COVID-19 group compared to control group, that in turn, exhibited higher levels of TMAO and citrate. These findings align with previous studies,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e which reported higher concentrations of phenylalanine, mannose, and glycoproteins in the COVID-19 group, while citrate showed higher concentration in the control group. However, the results for tyrosine and creatine have been reported in opposite direction to ours only in one case.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Acetone, tyrosine, citrate, and glycerol have been found to be discriminant metabolites between the control and COVID-19 groups, but not all studies agree on whether their content increases or decreases in the COVID-19 group.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Our finding in terms of greater amounts of creatine and mannose in the COVID-19 group are also in accordance with some other reports.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e These found biomarkers implicated in COVID-19 disease play a role in specific pathways, mainly \u0026ldquo;Phenylalanine, tyrosine and tryptophan biosynthesis\u0026rdquo;, \u0026ldquo;Phenylalanine metabolism\u0026rdquo;, \u0026ldquo;Glycerolipid metabolism\u0026rdquo; and \u0026ldquo;Arginine and proline metabolism\u0026rdquo;, either activating or dysregulating them. The study of metabolic pathways is of utmost importance in understanding diseases, as abnormalities in specific pathways often underlie the development and progression of various medical conditions and is also critical for the development of targeted and effective medical treatments. Our findings agree well with previous studies, such as Schmelter et al.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e that proposed changes in amino acid and lipoprotein metabolism, Blasco et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e that unraveled the tryptophan‑nicotinamide pathway clearly linked to inflammatory signals and microbiota, or Correia et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and Lorente et al.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e that found altered phenylalanine, tyrosine and tryptophan biosynthesis, together with the glycerolipid metabolism.\u003c/p\u003e \u003cp\u003eIn urine samples, we reveal higher levels of methionine, fucose, lysine, hippurate, 2-phenylpropionate, glutamine, 3-indoxylsulphate and pseudouridine for COVID-19 group in comparison to control group, that in turn revealed higher content of formate, 3-methylhistidine, trigonelline and creatinine. Marhuenda-Egea et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e identified metabolic differences between the COVID-19 and healthy control groups, primarily related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate), as well as processes associated with bacterial flora (TMAO and formate) and detoxification (hippurate). Additionally, they observed higher levels of formate in COVID-19 urine samples, while the control group exhibited elevated creatinine concentrations, which were linked to sarcopenia\u0026mdash;a medical condition characterized by the progressive loss of muscle mass, strength, and function, resulting in a decrease in creatine/creatinine ratio. In contrast to our results, Marhuenda-Egea et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e have indicated lower hippurate content in COVID-19 patients, which was linked to possible impairments in the detoxification process.\u003c/p\u003e \u003cp\u003eConcerning disease severity (ICU vs MW), it was detected a decrease on TMAO, betaine and ethanol in serum samples from ICU patients. On the other hand, some metabolic changes were observed on the contents of TMAO, hippurate, urea, DMA, 3HB, fucose, 4-hydroxyphenyl acetate, 3-methyl-2-oxovalerate, 3-indoxylsulphate, tryptophan and formate in urine.\u003c/p\u003e \u003cp\u003eRegarding prognosis, we found a decrease on betaine and TMAO concentrations, that were associated to a negative outcome (poor prognosis) in serum. Furthermore, it was found a decrease in \u003cem\u003ecis\u003c/em\u003e-aconitate, urea, formate, and creatinine, methanol, DMA, and TMAO in urine, together with an increase in creatine and hippurate. Terruzzi et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e noticed a relationship between microbial metabolites (TMAO and lipopolysaccharide) that generate inflammatory microenvironment and a risk of severe illness from COVID-19. Furthermore, Israr et al.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e conducted a study in which they measured gut-related metabolites, including betaine and TMAO, in plasma samples from patients with COVID-19, healthy individuals, and patients with non-COVID-19 respiratory symptoms. The researchers aimed to explore these metabolites since they have previously been associated with respiratory diseases, and they postulated that the connection between the gut and lungs might influence gut health in COVID-19 cases. Their findings revealed that metabolites from the choline-TMAO and carnitine-TMAO pathways are linked to COVID-19 symptoms and severity, effectively distinguishing between COVID-19 and acute asthma. Of note, they identified betaine as a potential biomarker of gut microbiome health and hypothesized that dietary interventions targeting the gut microbiome could lead to improved outcomes and enhanced immunity. Regarding disease severity in urine, Rosolanka et al.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e reported elevated levels of ketone bodies in patients with severe COVID-19 during the first week after hospital admission, which is consistent with our findings of higher 3HB content in ICU patients compared to those in the MW. As in the previous work, they found that hippurate levels were lower in COVID-19 patients, which they associated with the administration of strong antibiotic treatments. This disparity in hippurate content with our results might be explained by the timing of urine sample collection in our study, which occurred after hospital admission when patients were not yet under medication.\u003c/p\u003e \u003cp\u003eRegarding the clinical context, it should be noted that certain cardiovascular comorbidities may increase the risk of severe COVID-19. Specifically, the prevalence of obesity/overweight and dyslipidemia tended to be higher in ICU patients, although it was statistically significant only for the latter. Similarly, LDH and fibrinogen were increased in ICU patients, who also showed a decrease in lymphocyte count compared to the ward group. In addition, those patients with elevated ferritin spent more time hospitalized and also in ICU, reflecting the association of this parameter with the course of disease. Similarly, patients with decreased lymphocyte counts also had longer hospital stays, as well as patients with high C-reactive protein, who spent more time in the ICU.\u003c/p\u003e \u003cp\u003eAnother relevant fact was that certain metabolites were related to clinical variables in both study groups. Thus, 3HB was associated with hypertension, and n-3 fatty acids, creatine, tyrosine, phenylalanine and acetate with obesity/overweight. Phenylalanine is an essential amino acid that is converted to tyrosine and is related to inflammation. Both metabolites have been observed markedly increased in patients with moderate to severe COVID-19 disease.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Shi et al., reported a biomarker panel including 3HB that could predict COVID-19 patients who progressed from mild to severe.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThrough our study, we were able to construct different panels for the management of COVID-19 patients. We propose a diagnostic panel based on three metabolites, consisting of isoleucine, TMAO and glucose, which was able to discriminate COVID-19 patients from healthy individuals providing very high efficiency (AUC\u0026thinsp;=\u0026thinsp;0.91, sensitivity 86.08%, specificity 83.87%, respectively). In line with our results, other authors have also reported the perturbation of amino acids, glucose and energy metabolisms as a result of COVID-19 infection, and some of these metabolites, such as glucose, has previously been related to disease severity.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e An optimal biomarker panel capable of efficiently classify disease severity was only obtained when considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine) (AUC\u0026thinsp;=\u0026thinsp;0.825, sensitivity 84.85%, specificity 72.09%). Supporting our results, other authors have shown the benefit of including clinical variables in metabolite-based biomarker panels to classify COVID-19 patients. L\u0026oacute;pez‑Hern\u0026aacute;ndez et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e reported several metabolite panels which increased their discriminatory power after the addition of clinical and/or demographic characteristics: e.g. the panel to discriminate COVID-19 positive individuals from non-COVID-19 including Kynurenine-tryptophan ratio, lysoPC a C26:0 and pyruvic acid incremented its AUC value from 0.947 to 0.971 after adding sex and neutrophile percentage; moreover, the panel obtained to distinguish hospitalized from intubated COVID-19 patients by adding hypertension and neutrophil-lymphocyte ratio to LysoPC a C28:0 increased its AUC value from 0.770 to 0.829. Despite the good performance provided by our panels, the lack of validation studies represents one of the limitations of this investigation, so further studies with an independent cohort of patients will be necessary to corroborate our results. Moreover, translation of these panels to the clinic will require large-scale studies to provide the actual accuracy of these panels.\u003c/p\u003e \u003cp\u003eFinally, we were able to correlate the serum matrix with the urine matrix, which is a less invasive and easily obtainable sample. The exceptional classification accuracy obtained by the SVM demonstrates the usefulness and reliability of the urine to serum reconstruction dataset for downstream biomedical applications. It reaffirms the robustness of our conversion method and emphasizes that SVM is a particularly effective tool for classification tasks involving converted metabolome data.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eStudy design and patients\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eThe clinical parameters of COVID-19 patients who were finally included in the study (n\u0026thinsp;=\u0026thinsp;80) and the healthy controls (n\u0026thinsp;=\u0026thinsp;32) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At the time of sampling, all patients had a positive polymerase chain reaction (PCR) in COVID-19. Of the 80 patients, 56 (70%) were males and 24 (30%) females, and the range of ages was 31 to 88 years. Furthermore, 46 patients were admitted in medical ward (57.5%) and 34 were derived to ICU (42.5%), among them 58 patients survived to the disease (72.5%) and 22 patients dead (27.5%). Other pathologies were also presented in some patients, which also appear in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The length of hospital stay was also studied in the analyses indicated, which was defined as the time elapsed between the date of admission to the hospital and the date of discharge or death. Regarding the control group (n\u0026thinsp;=\u0026thinsp;32), 22 individuals (68.75%) were males and 10 (31.25%) were females with a range of ages of 35 to 89 years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation and NMR experiments\u003c/h2\u003e \u003cp\u003eSerum and urine samples from the patient groups were inactivated by adding 1.0 mL of viral lysis buffer (guanidine thiocyanate) and incubated at room temperature for 10 min, followed by centrifugation at 2500 rpm for 5 min. Supernatants were aliquoted and stored in \u0026minus;\u0026thinsp;80\u003csup\u003eo\u003c/sup\u003eC following standardized biobank protocols. Serum samples were thawed at room temperature. NMR samples were then prepared mixing 200 \u0026micro;L of each serum sample with 400 \u0026micro;L of a saline solution (0.9% NaCl and 0.1% TSP in D\u003csub\u003e2\u003c/sub\u003eO). Urine samples, previously stored in -80\u003csup\u003eo\u003c/sup\u003eC freezer too, were also thawed at room temperature. NMR samples were then prepared mixing 200 \u0026micro;L of each urine sample with 300 \u0026micro;L of a phosphate buffer solution (0.5 M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e in D\u003csub\u003e2\u003c/sub\u003eO and 0.05% TSP). Both were centrifuged during 5 min at 13500 rpm and then 500 \u0026micro;L of supernatants for serum samples and 490 \u0026micro;L for urine samples were transferred to a 5 mm NMR tube. Samples were then measured in a 600 MHz Bruker Avance III NMR spectrometer, equipped with a quadrupole cryoprobe and a thermostated automatic SampleJet. All \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH-NMR spectra were measured at 300\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 K and referenced to the TSP signal (0 ppm). Two different \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR experiments were recorded: a one-dimensional \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH Carr-Purcell-Meiboom-Gill (CPMG) experiment (cpmgpr1d) for serum samples, with water presaturation pulse to supress water signal and implementing a T\u003csub\u003e2\u003c/sub\u003e filter to suppress the broad signals of proteins and other macromolecules, and a one-dimensional \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NOESY spectrum (noesygppr1d) with water presaturation pulse as previously described.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e The identification of metabolites was carried out using 2D NMR homo- and heteronuclear experiments, as \u003csup\u003e1\u003c/sup\u003eH\u0026minus;\u003csup\u003e1\u003c/sup\u003eH total correlation spectroscopy (TOCSY) and \u003csup\u003e1\u003c/sup\u003eH\u0026minus;\u003csup\u003e13\u003c/sup\u003eC heteronuclear multiple bonds coherence (HMBC), recorded using standard Bruker sequences, together with the use of some NMR databases, as Chenomx NMR Suite 8.6 software (Chenomx, Edmonton, Canada) and public NMR databases such as Human Metabolome Database (HMDB)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and literature data.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor the processing step of the data, each NMR spectrum was divided into 0.04 ppm chemical shift regions or buckets from δ\u003csub\u003eH\u003c/sub\u003e 0.5 to 10.0 ppm using AMIX 3.9.15 (Bruker BioSpin GmbH, Rheinstetten, Germany), and the corresponding spectral areas were integrated. Region containing residual signals of water suppression (δ\u003csub\u003eH\u003c/sub\u003e 4.64\u0026thinsp;\u0026minus;\u0026thinsp;4.80 ppm) was excluded from the bucket table employed in the analysis. The following step prior to statistical analysis, the normalization, was carried out by scaling the intensity of each individual peak to the total intensity recorded in the region mentioned above. Some statistical analyses were performed on the data matrix resulting, such as univariate and multivariate data analysis, including exploratory or non-supervised models as Principal Component Analysis (PCA), and supervised models as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Scores plots were generated for both models, and they were scaled to Pareto. Goodness-of-fit (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and of goodness-of-prediction (Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) parameters were given for OPLS-DA models as well as CV-ANOVA parameter validation with a level of significance of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to prove the model\u0026rsquo;s predictive accuracy. A bivariate analysis was performed to determine the association between clinical variables, hospital and ICU times, and metabolites between the different study groups. In the case of qualitative variables, the association was determined by using the chi-squared test or Fisher's exact test. For the treatment of quantitative variables, the Student's t-test or Wilcoxon rank sum test (Mann-Whitney U test) were used, according to compliance with the assumptions of normality. To compare metabolites in the three groups we used the ANOVA test, when the normality assumptions were met, or the Kruskal-Wallis test, otherwise. For those variables that were statistically significant, two-by-two comparisons were performed.\u003c/p\u003e \u003cp\u003eDuring the preprocessing of the data for the correlation studies, we applied the Shapiro-Wilk test to assess normality, which revealed that the assumptions for the use of Pearson's correlation coefficient were not met, so the correlation between variables was studied using Spearman's correlation coefficient, which assesses monotonic relationships and is a particularly resilient method for normality deviations and the presence of outliers.\u003c/p\u003e \u003cp\u003eIn addition, we performed a multivariate analysis, fitting multivariate logistic regression models. LASSO regression was used for variable selection based on Spearman's correlation, and subsequently the variance inflation factor (VIF) was calculated to re-evaluate correlations and eliminate variables in the final model (variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;2.5 were removed).\u003c/p\u003e \u003cp\u003eFinally, receiver operating characteristic (ROC) curves were plotted to investigate the classification performance of individual metabolites and proposed panels. The cut-off point providing the highest sensitivity and specificity was identified according to the Youden index. The discriminatory capacity was evaluated by the area under the curve (AUC) value of the ROC curve (95% confidence intervals). The R program, version 4.2.1 (R Core Team 2022) and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics pathway analysis\u003c/h2\u003e \u003cp\u003eTo detect altered metabolic routes, a pathway analysis, consisting of enrichment analysis and pathway topological analysis, was performed with the Metabolomics Pathway Analysis (MetPA) function within the MetaboAnalyst online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It was used a global test algorithm for pathway enrichment and the library of the metabolic pathways of Homo Sapiens was employed. Pathways were considered significantly enriched if they fulfilled to the following criteria: number of metabolites hits in relation to the total number of the pathway\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e-value of Fischer\u0026rsquo;s Exact test\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Holm \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (adjusted by the Holm-Bonferroni method), adjusted \u003cem\u003ep\u003c/em\u003e-value of the false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and impact\u0026thinsp;\u0026gt;\u0026thinsp;0. The pathway impact value was calculated as the sum of importance measures of the metabolites, normalized by the sum of importance measures of all metabolites in each pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eReconstruction of serum spectra\u003c/h2\u003e \u003cp\u003eIn order to perform an effective variable selection strategy and the conversion from urine \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR data to serum \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR data we employed the statistical technique of backward elimination within the functionalities of scikit-learn and Statsmodels, two well-regarded Python libraries in the domain of machine learning and statistical modeling.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e Backward elimination iteratively refines the model by excluding variables that exhibit the least statistical impact based on a \u003cem\u003ep\u003c/em\u003e-value threshold. By reducing the feature set to only those variables that are statistically significant, the model becomes both computationally efficient and theoretically justifiable, optimizing its performance in predicting complex biological conversions. Thus, data were split into training and test sets at an 80\u0026ndash;20% ratio, respectively. To ensure the robustness of our findings, the classification models employed for this task were varied, covering a broad spectrum of machine learning algorithms. These models included Naive Bayes, Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees, Random Forest, and XGBoost. These models were applied with the same Python libraries mentioned above.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eData Avialability\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been funded by the State Research Agency of the Spanish Ministry of Science and Innovation (PID2021-126445OB-I00), and by the Gobierno de Espa\u0026ntilde;a MCIN/AEI/10.13039/501100011033 and Uni\u0026oacute;n Europea \u0026ldquo;Next Generation EU\u0026rdquo;/PRTR (PDC2021-121248-I00, PLEC2021-007774 and CPP2022-009967). A. I. Trist\u0026aacute;n thanks to Junta de Andaluc\u0026iacute;a for a predoctoral grant (PREDOC_01024). C. Jim\u0026eacute;nez-Luna was supported by the Mar\u0026iacute;a Zambrano program funded by the Ministry of Universities with EU Next Generation funds.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eThe following authors were involved in study design (A.I.T., C.J.L., A.C.A., F.M.A.C., A.M.S., F.I.R., M.A.R.M., A.B.G., C.M., J.C.P., I.F.), data acquisition (A.I.T., C.J.L., A.C.A., A.M.S., F.I.R., M.A.R.M.), data processing and analysis (A.I.T., C.J.L., A.C.A., F.M.A.C.), table and figure generation (A.I.T., C.J.L), writing of the manuscript (A.I.T., C.J.L., F.M.A.C.,), critical review of the manuscript (A.C.A., C.M., J.C.P., I.F.), decision to submit (all authors).\u003c/p\u003e\n\u003cp\u003eData availability and patient consent\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Torrecardenas Hospital and informed consent was obtained from all patients for being included in the study. All data\u003c/p\u003e\n\u003cp\u003ewas anonymised prior to data analysis and no patient-identifiable features are included within the manuscript in accordance with applicable guidelines and regulations. To comply with data privacy regulations, data from this study, including individual participant data, is not available for sharing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCosta Dos Santos Junior, G., Pereira, C. M., Kelly da Silva Fidalgo, T. \u0026amp; Valente, A. P. Saliva NMR-based metabolomics in the war against COVID-19. Anal. Chem. 92, 15688\u0026ndash;15692 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorldometer [COVID Live - Coronavirus Statistics]. 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Procedures of the Python in science conference 92\u0026ndash;96 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25080/Majora-92bf1922-011\u003c/span\u003e\u003cspan address=\"10.25080/Majora-92bf1922-011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, metabolomics, NMR, serum, urine, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4504195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4504195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analyzing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. We propose a three-metabolite diagnostic panel\u0026mdash;comprising isoleucine, TMAO, and glucose\u0026mdash;that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Recognizing that serum profiles are more reliable but invasive compared to urine samples, we propose reconstructing serum profiles using urine \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR data. Our robust multi-output regression model demonstrates high accuracy in this reconstruction, and in classifying the converted serum spectroscopic profile. This suggests the feasibility of determining COVID-19 infection and predicting its severity using a non-invasive sample such as urine.\u003c/p\u003e","manuscriptTitle":"Metabolomic Profiling of COVID-19 Using Serum and Urine Samples in Intensive Care and Medical Ward Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 21:09:13","doi":"10.21203/rs.3.rs-4504195/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-25T05:32:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T15:41:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304277100203691451090476413598256492093","date":"2024-07-08T08:12:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T07:51:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266998599891005396553932822737815830282","date":"2024-06-17T08:12:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-16T17:50:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-13T10:49:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-04T17:37:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-31T10:05:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-30T15:57:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12b942a9-da75-48e8-80a4-8dbac4574214","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33166656,"name":"Physical sciences/Chemistry/Analytical chemistry"},{"id":33166657,"name":"Physical sciences/Chemistry/Chemical biology"},{"id":33166658,"name":"Physical sciences/Chemistry/Cheminformatics"},{"id":33166659,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":33166660,"name":"Biological sciences/Molecular biology"},{"id":33166661,"name":"Health sciences/Biomarkers"},{"id":33166662,"name":"Health sciences/Diseases"},{"id":33166663,"name":"Health sciences/Medical research"},{"id":33166664,"name":"Health sciences/Molecular medicine"},{"id":33166665,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2024-10-14T16:00:55+00:00","versionOfRecord":{"articleIdentity":"rs-4504195","link":"https://doi.org/10.1038/s41598-024-74641-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-10 15:57:23","publishedOnDateReadable":"October 10th, 2024"},"versionCreatedAt":"2024-06-13 21:09:13","video":"","vorDoi":"10.1038/s41598-024-74641-9","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74641-9","workflowStages":[]},"version":"v1","identity":"rs-4504195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4504195","identity":"rs-4504195","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}