Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis

Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis | 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 Research Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis Shaobing Xie, Hua Zhang, Zhihai Xie, Yongzhen Liu, Kelei Gao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-40854/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Allergic rhinitis (AR) is a global healthcare problem with obscure pathogenesis, and few studies have evaluated the association between AR and metabolomics. The aim of this study was to identify differences in serum metabolomics profiling of AR patients compared to healthy controls and to explore novel biomarkers reflecting disease severity. Methods: Serum samples were collected form 29 healthy controls and 72 AR patients, including 30 mild AR patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomic profiling was performed by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in serum samples. Orthogonal partial least square-discriminate analysis (OPLS-DA) was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. Results: These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from health controls. In addition, MSAR patients also could be discriminated from MAR patients basing on their metabolic fingerprints. Most observed metabolite changes were related to pathways associated with glycine, serine and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism and fatty acid metabolism. Among these metabolites from dysregulated metabolic pathways, levels of sarcosine, sphingosine-1-phosphate (S1P), cytidine and linoleic acid significantly correlated with AR total nasal symptom score (TNSS) and visual analogue scale (VAS). Conclusions: MSAR patients have a distinctive serum metabolomics profile compared to MAR and health controls. These results suggest that metabolomic profiling may provide novel insights into pathophysiological mechanisms of AR and contribute to its evaluation of disease severity. Infectious Diseases Allergic rhinitis Metabolomics Metabolites Severity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Allergic rhinitis (AR) is an IgE-mediated immunologic disease characterized by mucus hypersecretion and airway hyperresponsiveness with characteristic nasal symptoms such as sneezing, rhinorrhea and nasal blockage [1-2]. Epidemiological studies have shown that AR affects at least half a billion people worldwide, and more than half of them suffer from moderate to severe AR [3]. In China, the prevalence of AR even rises to 34.3% of the general population, and rates still continue to increase [4-5]. Although without life threatening, AR exhibits negative influence on people's quality of life and their work production, and brings about a high medical cost on both the individual and society. Currently, AR is subdivided into intermittent AR and persistent AR according to the allergic rhinitis and its impact on asthma (ARIA) classification [6], and persistent AR is further grouped into mild AR (MAR) and moderate to severe AR (MSAR) based on the severity scale. The immunological underpinnings and their associations with disease severity, have been the hot topic of significant research, but remain poorly clarified. Previous publications have reported that AR is very heterogeneous with a wide degree of severity; meanwhile, there is no novel objective indication or biological marker that is specific for its disease activity [7-8]. Current monitoring the severity of this clinical disorder relies primarily on subjective clinical symptom score which is relatively insensitive, particularly in children [9-10]. In most AR patients, they often do not recognize how severe their symptoms are because of progressive tolerance to these symptoms. Furthermore, self-reported symptoms used by physicians to guide treatment and evaluate the therapeutic effect are likely imprecise. Potentially, this also can lead to growing costs of healthcare and wasted resources [7]. In addition, clinical research in AR is hampered because of a lack of sensitive biological measures of disease severity. Therefore, identification of biomarkers of disease severity or response to therapy is urgently needed to improve patient management and the accelerate drug development in AR. Metabolomics, a branch of omics science that systematically analyzes the concentration profiles of low molecular weight endogenous metabolites generated by living systems, is a promising approach to identify new biomarkers and novel metabolic pathways for several diseases, simultaneously provide new insights into underlying pathophysiological mechanisms [11-12]. Recently, several studies have employed metabolomics technologies to explore the metabolic changes in asthma, pneumonia and chronic obstructive pulmonary disease, and successfully identified some potential biomarkers and key metabolic pathways [13-15]. However, no previous study has focused on metabolite profiles and metabolic pathway changes in the serum of AR patients, especially regarding the disease severity. Therefore, the primary aim of this study was to explore metabolic profiling of AR patients and determine the relationship between the metabolite changes and clinical severity of AR, thus to provide new insights into the complex pathophysiological mechanisms and monitor disease activity and therapeutic effect. In this study, ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was performed to investigate metabolomic profiling in serum samples of MSAR, in comparison with MAR and healthy control. In addition, linear regression analysis was performed to evaluate the correlation between metabolites and disease severity. Methods Participants and settings This is a prospective study with 101 participants recruited between June 2018 and January 2019. All participants were divided into three groups: MAR (n=30), MSAR (n=42) and health control (n=29). The diagnosis of AR was done on the basis of the medical history and allergic symptoms (sneezing, rhinorrhea, nasal congestion and nasal itching) for at least 2 years, positive skin test results (a mean wheal diameter >3 mm) and positive specific IgE (>0.35 IU/ml). Patients with persistent AR were categorized into MAR and MSAR according to the allergic rhinitis and its impact on asthma (ARIA) criteria [6]. Exclusion criteria included current smoking, other allergic diseases, systemic steroid treatment, inflammatory or septic diseases, autoimmune diseases, age<18 years, pregnant condition, immunotherapy, and use of anti-allergic drugs during the 1 month that preceded the study. The total IgE levels, special IgE for house dust mite (HDM) levels, blood eosinophil count and demographic information of the study subjects were collected, including gender, age, body mass index (BMI), the duration of disease duration. All participants scored their symptoms by using the widely accepted Total Nasal Symptom Score (TNSS) and visual analogue scale (VAS) which were described by previous study [7, 16]. The TNSS is the sum of 4 individual symptom scores for sneezing, rhinorrhea, nasal congestion, and nasal itching , and each symptom score was regarded on a 4-point scale from 0 to 3 (0=no symptom, 1=minimal, well-tolerated symptoms, 2=bothersome but tolerated symptoms, 3=severe and hard to tolerate symptoms). In addition, the global disease severity over the last week was evaluated by a VAS (0-100 mm: where 0 is no symptom and 100 is the maximum severity). The detailed clinical information of the recruited participants in the three groups was described in Table 1. Serum sample collection and preparation Serum samples from AR patients and healthy volunteers were collected with serum separator tubes without anticoagulation or coagulant before breakfast, and serum separator tubes were stored for 1 hour at room temperature. All blood samples were centrifuged at 4 °C (3000 rpm for 10 min), then, we collected the supernatants and stored them at -80 °C in equal aliquots for subsequent detection and analysis. The serum samples were prepared for UHPLC-MS analysis by mixing 100 μL of serum sample with 300 uL methanol containing internal standard (L-2-Chlorophenylalanine, 2 μg/mL). After 30 s vortex, the samples were sonicated for 10 min in ice-water bath. Then the samples were incubated at -40 ℃ for 1 h and centrifuged at 12000 rpm for 15 min at 4 ℃. 100 μL of supernatant was transferred to a fresh glass vial for UHPLC-MS analysis [16]. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples, and used to evaluate the reproducibility and reliability of the UHPLC-MS analytical system as described by previous study [17]. UHPLC-MS analysis The UHPLC separation was carried out using a 1290 Infinity series UHPLC System (Waters Corporation, Milford, MA, USA), equipped with a UPLC BEH Amide column (2.1mm×100 mm, 1.7 μm). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water（pH = 9.75）(A) and acetonitrile (B). The analysis was conducted with elution gradient as follows: 0~0.5 min, 95% B; 0.5~7.0 min, 95%~65% B; 7.0~8.0 min, 65%~40% B; 8.0~9.0 min, 40% B; 9.0~9.1 min, 40%~95% B; 9.1~12.0 min, 95% B. The column temperature was 25℃. The auto-sampler temperature was 4 ℃, and the injection volume was 2 μL (positive) or 2 μL (negative), respectively. The Triple TOF 6600 mass spectrometry (AB Sciex, Boston, MA, USA) was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB Sciex, Framingham, MA, USA) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, the most intensive 12 precursor ions with intensity above 100 were chosen for MS/MS at collision energy (CE) of 30 eV. The cycle time was 0.56 s. Electrospray ionization (ESI) source conditions were set as following: Gas 1 as 60 psi, Gas 2 as 60 psi, Curtain Gas as 35 psi, Source Temperature as 600 ℃, Declustering potential as 60 V, Ion Spray Voltage Floating (ISVF) as 5000 V or -4000 V in positive or negative modes, respectively. Data processing and analysis MS raw data (.wiff) files were converted to the mzXML format by Proteo Wizard, and processed by R package XCMS V3.2. The process includes peak deconvolution, alignment and integration. Minfrac and cut off are set as 0.5 and 0.3 respectively as before [18]. In-house MS2 database was applied for metabolites identification [19]. The resultant data was exported to SIMCA (Version 14.1, Umetrics, Umea, Sweden) for multivariate analysis. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to find potential biomarkers that contributed to the metabolic difference between the groups [20]. The quality of models was validated by R2Ycum (goodness of fit) and Q2cum (goodness of prediction). Meanwhile, the 7-fold cross-validation and 200 permutation tests were conducted to reduce the risk of over-fitting and possibilities of false-positive findings. Metabolites contributing were selected according to the variable importance for project (VIP) values (VIP >1.0) and P-values (P<0.05) [21]. To determine the performance of the identified combination, receiver operating characteristics (ROC) analysis was conducted, and area under the curve (AUC) was calculated to assess the sensitivity and specificity. In order to gain insight into the underlying metabolic mechanisms associated with AR and its severity, the metabolic pathway was analyzed in both ion modes using MetaboAnalyst 3.0. Statistical analysis Continuous and categorical variables are described as mean ± Standard deviation (SD) and number (%) respectively. Kolmogorov-Smirnov test was performed to assess the normality of the distribution in continuous variables. One-way analysis of variance (ANOVA) or Student's t test was used, and non-parametric Mann-Whitney test was performed when the variables distributed non-normally. Categorical variables were compared between three groups using Chisquare test. To explore the correlation between levels of metabolites and severity of AR, Spearman's correlation analysis was conducted. Differences were considered as significant when P<0.05. All above statistical analyses were carried out using SPSS statistics software version19.0 (IBM, Chicago, IL, USA). Results Baseline characteristics of all participants The main characteristics and clinical information of the participants were shown in the Table 1. No statistically significant difference was observed in gender, age, BMI, and disease duration among three groups. In comparison with control group and MAR group, MSAR group showed higher levels of serum total IgE, special IgE for HDM, blood eosinophil counts, TNSS and VAS (all P < 0.001). Metabolomic profiling of MAR vs health controls OPLS-DA models showed a clear and distinctive clustering between MAR group and control group in both ESI+ and ESI- mode (P<0.05, Figure 1A and C). These models were then assessed by permutation analysis, and all permuted R2s were below or around 0.6 and all permuted Q2s were below 0, which means that all R2s and Q2s are lower than the original on the right (Figure 1B and D). Thus, this suggests these models fitting were valid and predictive. Finally, a total of 35 metabolites including 15 in the ESI+ mode and 20 in the ESI- mode responsible for distinguishing MAR patients from health controls were detected by UHPLC-MS analysis. Contribution plot ranks metabolites by their contribution to the model which is shown as a VIP. The top 10 metabolites with the highest VIP scores were identified as most potential discriminant metabolites and 6 related metabolic pathways were listed in the Table 2. According to metabolic pathway analyses, the most important pathways were arginine and proline metabolismand, glycerophospholipid metabolism, sphingolipid metabolism and fatty acid metabolism (Figure 4). Metabolomic profiling of MSAR vs health controls MSAR patients had different serum metabolic profiles in comparison with health controls by UHPLC-MS analysis in both ESI+ and ESI- mode (P<0.05, Figure 2A and C). The permutation analysis results showed that the models fitting were valid and predictive (Figure 2B and D). Compared with the control group, 59 metabolites including 29 in the ESI+ mode and 30 in the ESI- mode were expressed at significantly different levels in the MAR group. Results of top 10 potential discriminant metabolites and 9 related metabolic pathways were listed in the Table 3. The most important pathways including sphingolipid metabolism, pyrimidine metabolism and arginine and proline metabolism were revealed in the Figure 5. Metabolomic profiling of MSAR vs MAR In this study, AR patients were grouped into MSAR patients and MAR patients according to ARIA criteria, and the metabolic difference of these patients were further analyze. As shown in the Figure 3, serum metabolomics profiles of MSAR patients and MAR patients were significant different form each other in both ion mode (P<0.05, Figure 3A and C). The permutation analysis results exhibited good validation and predictability (Figure 3B and D). Compared with the MAR group, 30 metabolites including 17 in the ESI+ mode and 13 in the ESI- mode were detected at significantly different concentrations in the MSAR group. Results of top 10 potential discriminant metabolites and 8 related metabolic pathways were displayed in the Table 4. The most important pathways including fatty acids metabolism and sphingolipid metabolism were revealed in the Figure 6. Metabolomic profiling and severity of AR The distinctive metabolites among the three groups with good predictabilities (AUC>0.7) were included in Spearman's correlation analysis to evaluate their correlation with the severity of AR. As presented in the Table 5, sarcosine, sphingosine-1-phosphate (S1P), and cytidine levels were positively correlated with TNSS and VAS in the AR patients (P<0.05). However, linoleic acid levels were negatively correlated with TNSS and VAS (P<0.05). Discussion In the current prospective cohort study, we described a novel application of metabolomics in identifying the serum metabolic signatures and assessing the severity of AR. The OPLS-DA models showed that there were obvious discriminators between patients with different severity of AR and health controls. 35 and 59 metabolites responsible for differentiating MAR and MSAR patients from health controls, respectively, were identified. And 30 metabolites were found to be responsible for discriminating MSAR patients from MAR patients. After analyzing the relationships between the major discriminative metabolites with clinical parameters of AR patients, we observed that sarcosine, sphingosine-1-phosphate, cytidine and linoleic acid levels were associated with the severity of AR. These results showed that the identified potential serum biomarkers might be useful for diagnosing AR and developing objective indication for evaluating the severity of AR. We will next discuss the most significant metabolites and related metabolic pathways which may help us to better understand the underlying pathogenesis of AR and monitor the disease severity. Most importantly, arginine and proline metabolism pathway was significantly perturbed among the most affected pathways in AR patients. Arginase and proline metabolism is of particular importance in the nitric oxide synthesis and integrally links to cellular respiration, metabolism, and inflammation [22-23]. A recent publication detected arginase and proline metabolism significant perturbations in the serum of commuters following traffic pollution exposures, and the researchers considered that arginase and proline metabolism dysfunction strongly associated with oxidative stress and inflammation in the air pollution toxicity [24]. Yang et al [25] found that the levels of arginine and its downstream products, such as ornithine, citrulline, creatine, creatinine, hydroxyproline and sarcosine were higher in the serum of asthma patients than in health controls, and they held that arginine and proline metabolism was the most important pathway in the development of asthma. Consistent with the previous report, we also observed that the levels of sarcosine and creatinine were higher in the serum of AR patients than in the health controls, and the levels of sarcosine correlated positively with TNSS and VAS. Arginine is an essential amino acid related to endothelial function, inflammation, and airway hyperresponsiveness, and higher levels of arginine and its downstream products can regulate T cell function and promote its activity, and act a critical role in several inflammatory diseases, including asthma and AR [22-23, 26]. Therefore, we speculated that arginine and proline metabolism might be involved in the development of AR and sarcosine could roughly be related to the severity of AR. Our results also provide evidence for the sphingolipids metabolism alteration is involved in occurrence and progression of AR. Sphingolipids are ubiquitous components of the cell membrane and play an important role in cell growth, inflammation and tissue remodeling [27-28]. Among the numerous sphingolipids, S1P has received the greatest attention in allergic diseases and autoimmune diseases, as it has been implicated in the modulation of a variety of cell responses such as immune cell proliferation, differentiation and regulation [27, 29]. A previous study reported that S1P up-regulated the cytokine production, such as IL-12, IL-23 and IL-27, in activated murine bone marrow derived dendritic cells, and it might serve as a novel therapeutic target in the treatment of several inflammatory diseases [30]. In another study, researchers found that the plasma levels of SIP were elevated in cystic fibrosis patients, and S1P levels correlated with routine laboratory parameters, lung function and clinical symptoms [31]. Kowal et al [29] analyzed targeted metabolites in the serum from 22 allergic asthma patients and 11 allergic rhinitis patients, and found that the sphingolipids metabolism was altered and the biosynthesis of S1P was augmented. In the present study, we observed that sphingolipids metabolism was disturbed and the S1P levels elevated in the AR patients, and the S1P levels were correlated positively with the disease severity of AR, which was in accordance with the results in the previous publications [29-30]. Our results support the hypothesis that alterations in the serum metabolites reflect the chronic activation of immune system in AR patients and that the disease severity is consistent with greater activation of the immune system. However, the mechanism underlying these manipulations has not been well clarified. We firstly found that cytidine, identified form UHPLC-MS analysis, was associated with AR and it might be a novel marker and potential therapeutic target for AR. In our study, we observed that the levels of cytidine were elevated in the MAR and MSAR group, and the concentrations of cytidine were positively correlated with TNSS and VAS. Cytidine, a pyrimidine molecule, is considered as the precursor of the cytidine triphosphate (CTP), which is vital in the synthesis, interconversion and degradation of DNA, RNA and lipids [32-33]. Previous studies have found that abnormality of pyrimidine metabolism could influence the cell growth, development and differentiation, such as T cell and B cell [34]. A recent study demonstrated that interference of pyrimidine metabolism affected murine lymphocyte proliferation in vitro and attenuated the severity of experimental autoimmune arthritis [35]. Another study observed that the concentrations of 5,6-dihydorthymine were higher in the serum of current asthma patients compared with health controls, and the researchers believed that the alteration of pyrimidine metabolism might have relevance for asthma pathophysiology [36]. These events suggested that pyrimidine metabolism may play a role in the autoimmune diseases and allergic diseases. Therefore, we ultimately believed that cytidine was associated with AR, and that it might serve as a promising metablic biomarker for assessing the disease severity and evaluation of treatment. Interestingly, we also found that fatty acids metabolism was dysregulated in all OPLS-DA models. In recent years, growing evidence suggests that fatty acids metabolism plays important roles in the modulation of immune responses in health and disease [37]. Most researchers hold that unsaturation fatty acids, especially polyunsaturated fatty acids exhibited potential protective effects on allergic inflammation, while saturation fatty acids promoted the inflammatory response [38]. In a recent animal experimental study, Lee et al [39] observed that oleic acid had anti-asthmatic effects such as the down-regulation of inflammatory cells and eosinophil in bronchial alveolar lavage fluid, IgE in serum. Several in vitro studies also demonstrated that unsaturation fatty acids could exert immunosuppressive effects on T cells, such as reducing its proliferation and activation in a dose-dependent manner [37, 40]. However, saturation fatty acids, such as palmitic acid, have been described as essential factors promoting T cell activation and cytokine secretion [41]. In addition, considerable evidence has shown that polyunsaturated fatty acids can modify mast cell functions and suppress its activation then reduce the production of cytokine or chemokine through receptors [42]. Therefore, we suppose that fatty acids metabolism may act an important role in the development of AR. In the current study, the concentrations of several unsaturated fatty acids (linoleic acid, arachidic acid and trans-vaccenic acid) were lower in the serum of MAR or MSAR patients in comparison with health controls, while the concentrations of palmitic acid were elevated. Moreover, the levels of linoleic acid were correlated negatively with TNSS and VAS. Our results were in line with most previous studies. However, further studies should be conducted to confirm these results and to clarify the underlying mechanism of AR subtypes. There are also several limitations in our study which may affect the reported findings. First, the total sample sizes were relatively small, and a validation cohort study was needed to confirm the conclusions. Second, all the recruited participants were from single-center with the same ethnicity and region, which might limit the applicability of our findings. Third, only one biological sample (serum) was used in the present study, future studies should collect other biological samples, such as urine and nasal lavage fluid, to further verify whether the identified differential metabolites were associated with AR. Last, we did not compare serum metabolites between MAR and MSAR patients, but it does not mean that there are no differential metabolites. Future studies with larger sample sizes using targeted approaches will be important to support and extend our present findings. Conclusion Our results suggest that serum metabolomics approaches can be successfully used to discriminate MSAR patients form MAR patients and health controls and establish a metabolite signature associated with the severity of AR. These results will be useful for diagnosing AR and developing objective indication for evaluating the severity of AR. Abbreviations AR: Allergic rhinitis; MAR: Mild allergic rhinitis; MSAR: Moderate to severe allergic rhinitis; UHPLC-MS: Ultra-high performance liquid chromatography-mass spectrometry; OPLS-DA: Orthogonal partial least square-discriminate analysis; S1P: Sphingosine-1-phosphate; TNSS: Total nasal symptom score; VAS: Visual analogue scale; ARIA: Allergic rhinitis and its impact on asthma; IgE: Immunoglobulin E; HDM: House dust mite; BMI: Body mass index; ESI: Electrospray ionization; VIP: Variable importance for project; AUC: Area Under the Curve. Declarations Acknowledgments The authors would like to thank the patients, investigators and staff at participating centers in this study. Authors’ contributions All authors contributed equally to the design of the study, data analysis and drafting, and revising the manuscript. All authors read and approved the final manuscript. Funding This work was supported by National Natural Science Foundation of China (No. 81770985, No. 81770985 and No. 81800917) and Natural Science Foundation of Hunan Province (No.2020JJ4910 and No. 2018JJ2662). Availability of data and materials The datasets generated and/or analyzed during the current study are not available for public disclosure. Ethics approval and consent to participate All participants gave informed consent and the study was approved by the ethical committee of Xiangya Hospital of Central South University (protocol no. 2019050725). Consent for publication Not applicable. 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Tables Table 1 Clinical characteristics of participants Variable Control (n=29) MAR (n=30) MSAR (n=42) P value Gender, male/female 14/15 17/13 22/20 0.914 Age, years 28.5±8.5 28.2±9.6 30.4±8.4 0.518 BMI, kg/m 2 22.2±1.8 22.8±1.8 22.1±1.6 0.224 Disease duration, years NA 5.5±2.8 4.9±2.2 0.300 Total IgE, IU/mL 79.2±29.1 212.4±83.6 430.4±155.7 <0.001 Special IgE for HDM, IU/mL 0.2±0.1 0.7±0.2 5.8±3.9 <0.001 Blood eosinophil counts, cells/uL 81.2±24.0 176.1±97.8 451.2±192.4 <0.001 TNSS 1.2±0.7 3.9±1.0 9.5±1.7 <0.001 VAS 1.3±0.6 3.1±0.9 6.9±1.5 <0.001 MAR mild allergic rhinitis, MSAR moderate-severe allergic rhinitis, BMI body mass index, HDM house dust mite, TNSS total nasal symptom score, NA not applicable. Table 2 Top ten metabolites with different variations discriminating MAR patients from health controls Metabolites Ion mode VIP P-value Regulation AUC Pathway Sarcosine Positive 3.24 0.005 up 0.77 Arginine and proline metabolism Palmitic acid Positive 2.74 0.000 up 0.87 Fatty acid metabolism 5'-Methylthioadenosine Positive 2.58 0.002 up 0.60 Cysteine and methionine metabolism Triethanolamine Negative 2.16 0.047 up 0.79 Glycerophospholipid metabolism 5-Methoxyindoleacetate Negative 1.86 0.004 up 0.75 Tryptophan metabolism Trans-Vaccenic acid Positive 1.79 0.004 down 0.93 Fatty acid metabolism Creatinine Negative 1.73 0.000 up 0.78 Arginine and proline metabolism S1P Negative 1.70 0.019 up 0.91 Sphingolipid metabolism Arachidic acid Positive 1.62 0.014 down 0.54 Fatty acids metabolism L-Methionine Negative 1.61 0.022 down 0.49 Cysteine and methionine metabolism MAR mild allergic rhinitis, VIP variable importance for project, AUC area under the curve, S1P sphingosine-1-phosphate. Table 3 Top ten metabolites with different variations discriminating MSAR patients from health controls Metabolites Ion mode VIP P-value Regulation AUC Pathway S1P Positive 1.97 0.005 up 0.89 Sphingolipid metabolism 2-Oxoadipic acid Negative 2.36 0.002 up 0.68 Tryptophan metabolism Phosphorylcholine Negative 2.19 0.002 up 0.86 Glycerophospholipid metabolism Cytidine Negative 2.01 0.000 up 0.83 Pyrimidine metabolism Betaine Positive 1.97 0.000 up 0.84 Glycine, serine and threonine metabolism Sarcosine Negative 1.95 0.002 up 0.92 Arginine and proline metabolism 1,3-Diaminopropane Positive 1.85 0.007 up 0.61 beta-Alanine metabolism Taurocholic acid Positive 1.82 0.006 down 0.74 Taurine and hypotaurine metabolism Linoleic acid Negative 1.78 0.026 down 0.79 Fatty acid metabolism Cis-9-Palmitoleic acid Positive 1.75 0.005 up 0.64 Fatty acid metabolism MSAR moderate-severe allergic rhinitis, VIP variable importance for project, AUC area under the curve, S1P sphingosine-1-phosphate Table 4 Top ten metabolites with different variations discriminating MSAR from MAR Metabolites Ion mode VIP P-value Regulation AUC Pathway Linoleic acid Positive 2.79 0.049 down 0.77 Fatty acids metabolism Betaine Positive 2.24 0.034 up 0.60 Glycine, serine and threonine metabolism Coumarin Positive 2.11 0.012 down 0.63 Phenylpropanoid biosynthesis S1P Negative 2.06 0.009 up 0.72 Sphingolipid metabolism Palmitoleic acid Positive 1.97 0.016 down 0.75 Fatty acids metabolism Trans-Vaccenic acid Positive 1.89 0.003 down 0.65 Fatty acids metabolism D-Glucurono-6,3-lactone Negative 1.88 0.000 up 0.59 Ascorbate and aldarate metabolism Sarcosine Negative 1.83 0.007 up 0.91 Arginine and proline metabolism Cytidine Positive 1.79 0.017 up 0.74 Pyrimidine metabolism Pyroglutamic acid Negative 1.72 0.014 down 0.81 Glutathione metabolism MAR mild allergic rhinitis, MSAR moderate-severe allergic rhinitis, VIP variable importance for project, AUC area under the curve, S1P sphingosine-1-phosphate Table 5 Correlation of serum metabolites with severity of AR Metabolites TNSS VAS r P value r P value Sarcosine 0.551 0.012 0.376 0.040 Palmitic acid 0.489 0.137 0.413 0.107 Triethanolamine -0.212 0.170 0.301 0.019 Betaine 0.431 0.049 0.298 0.202 5-Methoxyindoleacetate -0.204 0.765 0.376 0.046 Trans-Vaccenic acid -0.312 0.031 -0.178 0.099 Creatinine 0.702 0.129 0.561 0.049 S1P 0.821 0.004 0.673 0.030 Phosphorylcholine 0.378 0.418 0.277 0.031 Cytidine 0.598 0.028 0.312 0.017 Diethanolamine 0.242 0.782 0.134 0.458 Cis-9,10-Epoxystearic acid 0.366 0.232 -0.221 0.022 Taurocholic acid -0.207 0.651 -0.319 0.562 Linoleic acid -0.792 0.031 -0.493 0.041 Palmitoleic acid -0.377 0.052 -0.274 0.093 Pyroglutamic acid -0.134 0.202 -0.307 0.089 AR allergic rhinitis, TNSS total nasal symptom score, VAS visual analogue scale, HDM house dust mite, S1P sphingosine-1-phosphate Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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control","description":"","filename":"OPLSDAMARvscontrol.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40854/v1/OPLSDAMARvscontrol.jpg"},{"id":1532545,"identity":"da0237e5-e894-4fc0-b9a0-5640aa981465","added_by":"auto","created_at":"2020-07-10 15:24:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":679177,"visible":true,"origin":"","legend":"MAR vs control","description":"","filename":"MARvscontrol.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40854/v1/MARvscontrol.jpg"},{"id":1532546,"identity":"7d8e5e89-acb0-4a46-9179-52fed49b5da7","added_by":"auto","created_at":"2020-07-10 15:24:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":699382,"visible":true,"origin":"","legend":"MSAR vs control","description":"","filename":"MSARvscontrol.jpg","url":"https://assets-eu.researchsquare.com/files/rs-40854/v1/MSARvscontrol.jpg"},{"id":13550842,"identity":"7bba008d-4261-4a4c-8943-32938abe1a14","added_by":"auto","created_at":"2021-09-17 02:26:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-40854/v1/ac552c37-b847-4760-a517-62fc576ea958.pdf"}],"financialInterests":"","formattedTitle":"Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis","fulltext":[{"header":"Background","content":"\u003cp\u003eAllergic rhinitis (AR) is an IgE-mediated immunologic disease characterized by mucus hypersecretion and airway hyperresponsiveness with characteristic nasal symptoms such as sneezing, rhinorrhea and nasal blockage [1-2]. Epidemiological studies have shown that AR affects at least half a billion people worldwide, and more than half of them suffer from moderate to severe AR [3]. In China, the prevalence of AR even rises to 34.3% of the general population, and rates still continue to increase [4-5]. Although without life threatening, AR exhibits negative influence on people's quality of life and their work production, and brings about a high medical cost on both the individual and society. Currently, AR is subdivided into intermittent AR and persistent AR according to the allergic rhinitis and its impact on asthma (ARIA) classification [6], and persistent AR is further grouped into mild AR (MAR) and moderate to severe AR (MSAR) based on the severity scale. The immunological underpinnings and their associations with disease severity, have been the hot topic of significant research, but remain poorly clarified.\u003c/p\u003e\n\u003cp\u003ePrevious publications have reported that AR is very heterogeneous with a wide degree of severity; meanwhile, there is no novel objective indication or biological marker that is specific for its disease activity [7-8]. Current monitoring the severity of this clinical disorder relies primarily on subjective clinical symptom score which is relatively insensitive, particularly in children [9-10]. In most AR patients, they often do not recognize how severe their symptoms are because of progressive tolerance to these symptoms. Furthermore, self-reported symptoms used by physicians to guide treatment and evaluate the therapeutic effect are likely imprecise. Potentially, this also can lead to growing costs of healthcare and wasted resources [7]. In addition, clinical research in AR is hampered because of a lack of sensitive biological measures of disease severity. Therefore, identification of biomarkers of disease severity or response to therapy is urgently needed to improve patient management and the accelerate drug development in AR.\u003c/p\u003e\n\u003cp\u003eMetabolomics, a branch of omics science that systematically analyzes the concentration profiles of low molecular weight endogenous metabolites generated by living systems, is a promising approach to identify new biomarkers and novel metabolic pathways for several diseases, simultaneously provide new insights into underlying pathophysiological mechanisms [11-12]. Recently, several studies have employed metabolomics technologies to explore the metabolic changes in asthma, pneumonia and chronic obstructive pulmonary disease, and successfully identified some potential biomarkers and key metabolic pathways [13-15]. However, no previous study has focused on metabolite profiles and metabolic pathway changes in the serum of AR patients, especially regarding the disease severity.\u003c/p\u003e\n\u003cp\u003eTherefore, the primary aim of this study was to explore metabolic profiling of AR patients and determine the relationship between the metabolite changes and clinical severity of AR, thus to provide new insights into the complex pathophysiological mechanisms and monitor disease activity and therapeutic effect. In this study, ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was performed to investigate metabolomic profiling in serum samples of MSAR, in comparison with MAR and healthy control. In addition, linear regression analysis was performed to evaluate the correlation between metabolites and disease severity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and settings \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective study with 101 participants recruited between June 2018 and January 2019. All participants were divided into three groups: MAR (n=30), MSAR (n=42) and health control (n=29). The diagnosis of AR was done on the basis of the medical history and allergic symptoms (sneezing, rhinorrhea, nasal congestion and nasal itching) for at least 2 years, positive skin test results (a mean wheal diameter \u0026gt;3 mm) and positive specific IgE (\u0026gt;0.35 IU/ml). Patients with persistent AR were categorized into MAR and MSAR according to the allergic rhinitis and its impact on asthma (ARIA) criteria [6]. Exclusion criteria included current smoking, other allergic diseases, systemic steroid treatment, inflammatory or septic diseases, autoimmune diseases, age\u0026lt;18 years, pregnant condition, immunotherapy, and use of anti-allergic drugs during the 1 month that preceded the study. The total IgE levels, special IgE for house dust mite (HDM) levels, blood eosinophil count and demographic information of the study subjects were collected, including gender, age, body mass index (BMI), the duration of disease duration. All participants scored their symptoms by using the widely accepted Total Nasal Symptom Score (TNSS) and visual analogue scale (VAS) which were described by previous study [7, 16]. The TNSS is the sum of 4 individual symptom scores for sneezing, rhinorrhea, nasal congestion, and nasal itching , and each symptom score was regarded on a 4-point scale from 0 to 3 (0=no symptom, 1=minimal, well-tolerated symptoms, 2=bothersome but tolerated symptoms, 3=severe and hard to tolerate symptoms). In addition, the global disease severity over the last week was evaluated by a VAS (0-100 mm: where 0 is no symptom and 100 is the maximum severity). The detailed clinical information of the recruited participants in the three groups was described in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerum sample collection and preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum samples from AR patients and healthy volunteers were collected with serum separator tubes without anticoagulation or coagulant before breakfast, and serum separator tubes were stored for 1 hour at room temperature. All blood samples were centrifuged at 4 \u0026deg;C (3000 rpm for 10 min), then, we collected the supernatants and stored them at -80 \u0026deg;C in equal aliquots for subsequent detection and analysis. The serum samples were prepared for UHPLC-MS analysis by mixing 100 \u0026mu;L of serum sample with 300 uL methanol containing internal standard (L-2-Chlorophenylalanine, 2 \u0026mu;g/mL). After 30 s vortex, the samples were sonicated for 10 min in ice-water bath. Then the samples were incubated at -40 ℃ for 1 h and centrifuged at 12000 rpm for 15 min at 4 ℃. 100 \u0026mu;L of supernatant was transferred to a fresh glass vial for UHPLC-MS analysis [16]. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples, and used to evaluate the reproducibility and reliability of the UHPLC-MS analytical system as described by previous study [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUHPLC-MS analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UHPLC separation was carried out using a 1290 Infinity series UHPLC System (Waters Corporation, Milford, MA, USA), equipped with a UPLC BEH Amide column (2.1mm\u0026times;100 mm, 1.7 \u0026mu;m). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water（pH = 9.75）(A) and acetonitrile (B). The analysis was conducted with elution gradient as follows: 0~0.5 min, 95% B; 0.5~7.0 min, 95%~65% B; 7.0~8.0 min, 65%~40% B; 8.0~9.0 min, 40% B; 9.0~9.1 min, 40%~95% B; 9.1~12.0 min, 95% B. The column temperature was 25℃. The auto-sampler temperature was 4 ℃, and the injection volume was 2 \u0026mu;L (positive) or 2 \u0026mu;L (negative), respectively. The Triple TOF 6600 mass spectrometry (AB Sciex, Boston, MA, USA) was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB Sciex, Framingham, MA, USA) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, the most intensive 12 precursor ions with intensity above 100 were chosen for MS/MS at collision energy (CE) of 30 eV. The cycle time was 0.56 s. Electrospray ionization (ESI) source conditions were set as following: Gas 1 as 60 psi, Gas 2 as 60 psi, Curtain Gas as 35 psi, Source Temperature as 600 ℃, Declustering potential as 60 V, Ion Spray Voltage Floating (ISVF) as 5000 V or -4000 V in positive or negative modes, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMS raw data (.wiff) files were converted to the mzXML format by Proteo Wizard, and processed by R package XCMS V3.2. The process includes peak deconvolution, alignment and integration. Minfrac and cut off are set as 0.5 and 0.3 respectively as before [18]. In-house MS2 database was applied for metabolites identification [19]. The resultant data was exported to SIMCA (Version 14.1, Umetrics, Umea, Sweden) for multivariate analysis. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to find potential biomarkers that contributed to the metabolic difference between the groups [20]. The quality of models was validated by R2Ycum (goodness of fit) and Q2cum (goodness of prediction). Meanwhile, the 7-fold cross-validation and 200 permutation tests were conducted to reduce the risk of over-fitting and possibilities of false-positive findings. Metabolites contributing were selected according to the variable importance for project (VIP) values (VIP \u0026gt;1.0) and P-values (P\u0026lt;0.05) [21]. To determine the performance of the identified combination, receiver operating characteristics (ROC) analysis was conducted, and area under the curve (AUC) was calculated to assess the sensitivity and specificity. In order to gain insight into the underlying metabolic mechanisms associated with AR and its severity, the metabolic pathway was analyzed in both ion modes using MetaboAnalyst 3.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous and categorical variables are described as mean \u0026plusmn; Standard deviation (SD) and number (%) respectively. Kolmogorov-Smirnov test was performed to assess the normality of the distribution in continuous variables. One-way analysis of variance (ANOVA) or Student's t test was used, and non-parametric Mann-Whitney test was performed when the variables distributed non-normally. Categorical variables were compared between three groups using Chisquare test. To explore the correlation between levels of metabolites and severity of AR, Spearman's correlation analysis was conducted. Differences were considered as significant when P\u0026lt;0.05. All above statistical analyses were carried out using SPSS statistics software version19.0 (IBM, Chicago, IL, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of all participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main characteristics and clinical information of the participants were shown in the Table 1. No statistically significant difference was observed in gender, age, BMI, and disease duration among three groups. In comparison with control group and MAR group, MSAR group showed higher levels of serum total IgE, special IgE for HDM, blood eosinophil counts, TNSS and VAS (all P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic profiling of MAR vs health controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOPLS-DA models showed a clear and distinctive clustering between MAR group and control group in both ESI+ and ESI- mode (P\u0026lt;0.05, Figure 1A and C). These models were then assessed by permutation analysis, and all permuted R2s were below or around 0.6 and all permuted Q2s were below 0, which means that all R2s and Q2s are lower than the original on the right (Figure 1B and D). Thus, this suggests these models fitting were valid and predictive. Finally, a total of 35 metabolites including 15 in the ESI+ mode and 20 in the ESI- mode responsible for distinguishing MAR patients from health controls were detected by UHPLC-MS analysis. Contribution plot ranks metabolites by their contribution to the model which is shown as a VIP. The top 10 metabolites with the highest VIP scores were identified as most potential discriminant metabolites and 6 related metabolic pathways were listed in the Table 2. According to metabolic pathway analyses, the most important pathways were arginine and proline metabolismand, glycerophospholipid metabolism, sphingolipid metabolism and fatty acid metabolism (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic profiling of MSAR vs health controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMSAR patients had different serum metabolic profiles in comparison with health controls by UHPLC-MS analysis in both ESI+ and ESI- mode (P\u0026lt;0.05, Figure 2A and C). The permutation analysis results showed that the models fitting were valid and predictive (Figure 2B and D). Compared with the control group, 59 metabolites including 29 in the ESI+ mode and 30 in the ESI- mode were expressed at significantly different levels in the MAR group. Results of top 10 potential discriminant metabolites and 9 related metabolic pathways were listed in the Table 3. The most important pathways including sphingolipid metabolism, pyrimidine metabolism and arginine and proline metabolism were revealed in the Figure 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic profiling of MSAR vs MAR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, AR patients were grouped into MSAR patients and MAR patients according to ARIA criteria, and the metabolic difference of these patients were further analyze. As shown in the Figure 3, serum metabolomics profiles of MSAR patients and MAR patients were significant different form each other in both ion mode (P\u0026lt;0.05, Figure 3A and C). The permutation analysis results exhibited good validation and predictability (Figure 3B and D). Compared with the MAR group, 30 metabolites including 17 in the ESI+ mode and 13 in the ESI- mode were detected at significantly different concentrations in the MSAR group. Results of top 10 potential discriminant metabolites and 8 related metabolic pathways were displayed in the Table 4. The most important pathways including fatty acids metabolism and sphingolipid metabolism were revealed in the Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic profiling and severity of AR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distinctive metabolites among the three groups with good predictabilities (AUC\u0026gt;0.7) were included in Spearman's correlation analysis to evaluate their correlation with the severity of AR. As presented in the Table 5, sarcosine, sphingosine-1-phosphate (S1P), and cytidine levels were positively correlated with TNSS and VAS in the AR patients (P\u0026lt;0.05). However, linoleic acid levels were negatively correlated with TNSS and VAS (P\u0026lt;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current prospective cohort study, we described a novel application of metabolomics in identifying the serum metabolic signatures and assessing the severity of AR. The OPLS-DA models showed that there were obvious discriminators between patients with different severity of AR and health controls. 35 and 59 metabolites responsible for differentiating MAR and MSAR patients from health controls, respectively, were identified. And 30 metabolites were found to be responsible for discriminating MSAR patients from MAR patients. After analyzing the relationships between the major discriminative metabolites with clinical parameters of AR patients, we observed that sarcosine, sphingosine-1-phosphate, cytidine and linoleic acid levels were associated with the severity of AR. These results showed that the identified potential serum biomarkers might be useful for diagnosing AR and developing objective indication for evaluating the severity of AR. We will next discuss the most significant metabolites and related metabolic pathways which may help us to better understand the underlying pathogenesis of AR and monitor the disease severity.\u003c/p\u003e\n\u003cp\u003eMost importantly, arginine and proline metabolism pathway was significantly perturbed among the most affected pathways in AR patients. Arginase and proline metabolism is of particular importance in the nitric oxide synthesis and integrally links to cellular respiration, metabolism, and inflammation [22-23]. A recent publication detected arginase and proline metabolism significant perturbations in the serum of commuters following traffic pollution exposures, and the researchers considered that arginase and proline metabolism dysfunction strongly associated with oxidative stress and inflammation in the air pollution toxicity [24]. Yang et al [25] found that the levels of arginine and its downstream products, such as ornithine, citrulline, creatine, creatinine, hydroxyproline and sarcosine were higher in the serum of asthma patients than in health controls, and they held that arginine and proline metabolism was the most important pathway in the development of asthma. Consistent with the previous report, we also observed that the levels of sarcosine and creatinine were higher in the serum of AR patients than in the health controls, and the levels of sarcosine correlated positively with TNSS and VAS. Arginine is an essential amino acid related to endothelial function, inflammation, and airway hyperresponsiveness, and higher levels of arginine and its downstream products can regulate T cell function and promote its activity, and act a critical role in several inflammatory diseases, including asthma and AR [22-23, 26]. Therefore, we speculated that arginine and proline metabolism might be involved in the development of AR and sarcosine could roughly be related to the severity of AR.\u003c/p\u003e\n\u003cp\u003eOur results also provide evidence for the sphingolipids metabolism alteration is involved in occurrence and progression of AR. Sphingolipids are ubiquitous components of the cell membrane and play an important role in cell growth, inflammation and tissue remodeling [27-28]. Among the numerous sphingolipids, S1P has received the greatest attention in allergic diseases and autoimmune diseases, as it has been implicated in the modulation of a variety of cell responses such as immune cell proliferation, differentiation and regulation [27, 29]. A previous study reported that S1P up-regulated the cytokine production, such as IL-12, IL-23 and IL-27, in activated murine bone marrow derived dendritic cells, and it might serve as a novel therapeutic target in the treatment of several inflammatory diseases [30]. In another study, researchers found that the plasma levels of SIP were elevated in cystic fibrosis patients, and S1P levels correlated with routine laboratory parameters, lung function and clinical symptoms [31]. Kowal et al [29] analyzed targeted metabolites in the serum from 22 allergic asthma patients and 11 allergic rhinitis patients, and found that the sphingolipids metabolism was altered and the biosynthesis of S1P was augmented. In the present study, we observed that sphingolipids metabolism was disturbed and the S1P levels elevated in the AR patients, and the S1P levels were correlated positively with the disease severity of AR, which was in accordance with the results in the previous publications [29-30]. Our results support the hypothesis that alterations in the serum metabolites reflect the chronic activation of immune system in AR patients and that the disease severity is consistent with greater activation of the immune system. However, the mechanism underlying these manipulations has not been well clarified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe firstly found that cytidine, identified form UHPLC-MS analysis, was associated with AR and it might be a novel marker and potential therapeutic target for AR. In our study, we observed that the levels of cytidine were elevated in the MAR and MSAR group, and the concentrations of cytidine were positively correlated with TNSS and VAS. Cytidine, a pyrimidine molecule, is considered as the precursor of the cytidine triphosphate (CTP), which is vital in the synthesis, interconversion and degradation of DNA, RNA and lipids [32-33]. Previous studies have found that abnormality of pyrimidine metabolism could influence the cell growth, development and differentiation, such as T cell and B cell [34]. A recent study demonstrated that interference of pyrimidine metabolism affected murine lymphocyte proliferation in vitro and attenuated the severity of experimental autoimmune arthritis [35]. Another study observed that the concentrations of 5,6-dihydorthymine were higher in the serum of current asthma patients compared with health controls, and the researchers believed that the alteration of pyrimidine metabolism might have relevance for asthma pathophysiology [36]. These events suggested that pyrimidine metabolism may play a role in the autoimmune diseases and allergic diseases. Therefore, we ultimately believed that cytidine was associated with AR, and that it might serve as a promising metablic biomarker for assessing the disease severity and evaluation of treatment.\u003c/p\u003e\n\u003cp\u003eInterestingly, we also found that fatty acids metabolism was dysregulated in all OPLS-DA models. In recent years, growing evidence suggests that fatty acids metabolism plays important roles in the modulation of immune responses in health and disease [37]. Most researchers hold that unsaturation fatty acids, especially polyunsaturated fatty acids exhibited potential protective effects on allergic inflammation, while saturation fatty acids promoted the inflammatory response [38]. In a recent animal experimental study, Lee et al [39] observed that oleic acid had anti-asthmatic effects such as the down-regulation of inflammatory cells and eosinophil in bronchial alveolar lavage fluid, IgE in serum. Several in vitro studies also demonstrated that unsaturation fatty acids could exert immunosuppressive effects on T cells, such as reducing its proliferation and activation in a dose-dependent manner [37, 40]. However, saturation fatty acids, such as palmitic acid, have been described as essential factors promoting T cell activation and cytokine secretion [41]. In addition, considerable evidence has shown that polyunsaturated fatty acids can modify mast cell functions and suppress its activation then reduce the production of cytokine or chemokine through receptors [42]. Therefore, we suppose that fatty acids metabolism may act an important role in the development of AR. In the current study, the concentrations of several unsaturated fatty acids (linoleic acid, arachidic acid and trans-vaccenic acid) were lower in the serum of MAR or MSAR patients in comparison with health controls, while the concentrations of palmitic acid were elevated. Moreover, the levels of linoleic acid were correlated negatively with TNSS and VAS. Our results were in line with most previous studies. However, further studies should be conducted to confirm these results and to clarify the underlying mechanism of AR subtypes.\u003c/p\u003e\n\u003cp\u003eThere are also several limitations in our study which may affect the reported findings. First, the total sample sizes were relatively small, and a validation cohort study was needed to confirm the conclusions. Second, all the recruited participants were from single-center with the same ethnicity and region, which might limit the applicability of our findings. Third, only one biological sample (serum) was used in the present study, future studies should collect other biological samples, such as urine and nasal lavage fluid, to further verify whether the identified differential metabolites were associated with AR. Last, we did not compare serum metabolites between MAR and MSAR patients, but it does not mean that there are no differential metabolites. Future studies with larger sample sizes using targeted approaches will be important to support and extend our present findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results suggest that serum metabolomics approaches can be successfully used to discriminate MSAR patients form MAR patients and health controls and establish a metabolite signature associated with the severity of AR. These results will be useful for diagnosing AR and developing objective indication for evaluating the severity of AR.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAR: Allergic rhinitis; MAR: Mild allergic rhinitis; MSAR: Moderate to severe allergic rhinitis; UHPLC-MS: Ultra-high performance liquid chromatography-mass spectrometry; OPLS-DA: Orthogonal partial least square-discriminate analysis; S1P: Sphingosine-1-phosphate; TNSS: Total nasal symptom score; VAS: Visual analogue scale; ARIA: Allergic rhinitis and its impact on asthma; IgE: Immunoglobulin E; HDM: House dust mite; BMI: Body mass index; ESI: Electrospray ionization; VIP: Variable importance for project; AUC: Area Under the Curve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the patients, investigators and staff at participating centers in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the design of the study, data analysis and drafting, and revising the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (No. 81770985, No. 81770985 and No. 81800917) and Natural Science Foundation of Hunan Province (No.2020JJ4910 and No. 2018JJ2662).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not available for public disclosure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave informed consent and the study was approved by the ethical committee of Xiangya Hospital of Central South University (protocol no. 2019050725).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or other conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAi J, Xie Z, Qing X, Li W, Liu H, Wang T, et al. 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Pediatr Allergy Immunol. 1998; 9:133-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Clinical characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eControl (n=29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eMAR (n=30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eMSAR (n=42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cem\u003eP \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eGender, male/female\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp; 14/15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e17/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e22/20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e28.5\u0026plusmn;8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e28.2\u0026plusmn;9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e30.4\u0026plusmn;8.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.518\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e22.2\u0026plusmn;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e22.8\u0026plusmn;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e22.1\u0026plusmn;1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.224\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eDisease duration, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; NA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e5.5\u0026plusmn;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e4.9\u0026plusmn;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eTotal IgE, IU/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e79.2\u0026plusmn;29.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e212.4\u0026plusmn;83.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e430.4\u0026plusmn;155.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eSpecial IgE for HDM, IU/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.2\u0026plusmn;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7\u0026plusmn;0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e5.8\u0026plusmn;3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eBlood eosinophil counts, cells/uL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e81.2\u0026plusmn;24.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e176.1\u0026plusmn;97.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e451.2\u0026plusmn;192.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eTNSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.2\u0026plusmn;0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e3.9\u0026plusmn;1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e9.5\u0026plusmn;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eVAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.3\u0026plusmn;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e3.1\u0026plusmn;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e6.9\u0026plusmn;1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMAR\u003c/em\u003e mild allergic rhinitis, \u003cem\u003eMSAR\u003c/em\u003e moderate-severe allergic rhinitis, \u003cem\u003eBMI\u003c/em\u003e body mass index,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHDM\u003c/em\u003e house dust mite, \u003cem\u003eTNSS\u003c/em\u003e total nasal symptom score, \u003cem\u003eNA\u003c/em\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Top ten metabolites with different variations discriminating MAR patients from health controls\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"739\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eMetabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eIon mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eVIP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eRegulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePathway\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eSarcosine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e3.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eArginine and proline metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003ePalmitic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e5'-Methylthioadenosine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eTriethanolamine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e5-Methoxyindoleacetate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp; up\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eTryptophan metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eTrans-Vaccenic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eCreatinine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eArginine and proline metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eS1P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eSphingolipid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eArachidic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acids metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eL-Methionine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;down\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMAR\u003c/em\u003e mild allergic rhinitis, \u003cem\u003eVIP\u003c/em\u003e variable importance for project, \u003cem\u003eAUC\u003c/em\u003e area under the curve, \u003cem\u003eS1P\u003c/em\u003e sphingosine-1-phosphate.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Top ten metabolites with different variations discriminating MSAR patients from health controls\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"728\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eMetabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eIon mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eVIP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eRegulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePathway\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eS1P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;up\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eSphingolipid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e2-Oxoadipic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eTryptophan metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003ePhosphorylcholine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eCytidine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePyrimidine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eBetaine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eSarcosine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp; up\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eArginine and proline metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1,3-Diaminopropane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ebeta-Alanine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eTaurocholic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eTaurine and hypotaurine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eLinoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eCis-9-Palmitoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp; up\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMSAR\u003c/em\u003e moderate-severe allergic rhinitis, \u003cem\u003eVIP\u003c/em\u003e variable importance for project, \u003cem\u003eAUC\u003c/em\u003e area under the curve, \u003cem\u003eS1P\u003c/em\u003e sphingosine-1-phosphate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Top ten metabolites with different variations discriminating MSAR from MAR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"754\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eMetabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eIon mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eVIP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eRegulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePathway\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eLinoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acids metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eBetaine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eCoumarin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePhenylpropanoid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eS1P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp; up\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eSphingolipid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003ePalmitoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acids metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eTrans-Vaccenic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;down\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eFatty acids metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eD-Glucurono-6,3-lactone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eAscorbate and aldarate metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eSarcosine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eArginine and proline metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eCytidine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ePositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003ePyrimidine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003ePyroglutamic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003e1.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"255\"\u003e\n\u003cp\u003eGlutathione metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMAR\u003c/em\u003e mild allergic rhinitis, \u003cem\u003eMSAR\u003c/em\u003e moderate-severe allergic rhinitis, \u003cem\u003eVIP\u003c/em\u003e variable importance for project, \u003cem\u003eAUC\u003c/em\u003e area under the curve, \u003cem\u003eS1P\u003c/em\u003e sphingosine-1-phosphate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Correlation of serum metabolites with severity of AR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"576\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"225\"\u003e\n\u003cp\u003eMetabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"165\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; TNSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp; VAS\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003er\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003er\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eSarcosine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.551\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003ePalmitic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eTriethanolamine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.301\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eBetaine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e5-Methoxyindoleacetate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eTrans-Vaccenic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eCreatinine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eS1P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003ePhosphorylcholine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.378\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eCytidine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eDiethanolamine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.458\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eCis-9,10-Epoxystearic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eTaurocholic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003eLinoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003ePalmitoleic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.274\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003ePyroglutamic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"45\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-0.307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAR\u003c/em\u003e allergic rhinitis, \u003cem\u003eTNSS\u003c/em\u003e total nasal symptom score, \u003cem\u003eVAS\u003c/em\u003e visual analogue scale, \u003cem\u003eHDM \u003c/em\u003ehouse dust mite, \u003cem\u003eS1P\u003c/em\u003e sphingosine-1-phosphate\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Allergic rhinitis, Metabolomics, Metabolites, Severity","lastPublishedDoi":"10.21203/rs.3.rs-40854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-40854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Allergic rhinitis (AR) is a global healthcare problem with obscure pathogenesis, and few studies have evaluated the association between AR and metabolomics. The aim of this study was to identify differences in serum metabolomics profiling of AR patients compared to healthy controls and to explore novel biomarkers reflecting disease severity.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Serum samples were collected form 29 healthy controls and 72 AR patients, including 30 mild AR patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomic profiling was performed by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in serum samples. Orthogonal partial least square-discriminate analysis (OPLS-DA) was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from health controls. In addition, MSAR patients also could be discriminated from MAR patients basing on their metabolic fingerprints. Most observed metabolite changes were related to pathways associated with glycine, serine and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism and fatty acid metabolism. Among these metabolites from dysregulated metabolic pathways, levels of sarcosine, sphingosine-1-phosphate (S1P), cytidine and linoleic acid significantly correlated with AR total nasal symptom score (TNSS) and visual analogue scale (VAS).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMSAR patients have a distinctive serum metabolomics profile compared to MAR and health controls. These results suggest that metabolomic profiling may provide novel insights into pathophysiological mechanisms of AR and contribute to its evaluation of disease severity.\u003c/p\u003e","manuscriptTitle":"Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-07-10 15:24:48","doi":"10.21203/rs.3.rs-40854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36f80299-e642-4786-8908-57959a427f62","owner":[],"postedDate":"July 10th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":148769,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2020-09-23T13:16:18+00:00","versionOfRecord":[],"versionCreatedAt":"2020-07-10 15:24:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-40854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-40854","identity":"rs-40854","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}