Altered Amino Acid Metabolome in Patients affected by HBV cirrhosis at different stages

Altered Amino Acid Metabolome in Patients affected by HBV cirrhosis at different stages | 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 Article Altered Amino Acid Metabolome in Patients affected by HBV cirrhosis at different stages Ying Gao, Yanqun Luo, Jia Liu, Xiaoliang Deng, Junmin Chen, wu tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4186028/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 OBJECTIVE: To study the amino acid (AA) profile of serum samples from patients with compensated stage (CS) and decompensated stage (DS) of liver cirrhosis (LC). In particular, changes in AAs in different mood classes after categorizing patients with CS versus DS of LC according to mood class. METHODS: Using targeted metabolomics, serum AA levels were quantified in two populations: patients with CS (n=60) and patients with DS (n=44). We also analyzed serum AAs in 26 patients with CS and 24 patients with DS after classifying them according to mood class. RESULTS: In terms of AA levels, serum tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide levels were significantly increased in patients with DS compared to those with CS. In addition, asparagine and methionine-sulfoxide levels correlated with Child-Pugh classification in CS and DS patients; phenylalanine and tyrosine levels correlated with HBV-DNA levels. In terms of AA ratios, Fischer 's ratio, BTR, and BCAAs/AAA ratio were significantly increased in DS patients compared with CS patients. In contrast, tyrosine ratios were significantly lower. In addition, tyrosine ratio, Fischer 's ratio, BTR, and BCAAs/AAA levels were correlated with MELD score in both CS and DS patients; BCAAs/AAA ratio and Fischer 's ratio were correlated with mood score grade. CONCLUSION: The metabolic profiles of certain AAs in serum of patients with CS and DS of LC are different, which may help to detect the transition from CS to DS as early as possible and have implications for patient care and treatment decisions. In addition, the AA ratios varied with mood class, suggesting that mood factors may be influential in the progression of LC. liver cirrhosis compensated stage decompensated stage amino acids metabolomics emotional factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Hepatitis B virus (HBV) is the most common cause of acute and chronic liver disease worldwide, and approximately 4 million people worldwide are infected with HBV each year, especially in several regions of Asia and Africa. Approximately 10% of patients with HBV infection develop chronic infection, including hepatitis, liver fibrosis, and liver cirrhosis (LC). Since the introduction of the hepatitis B vaccine in 1982, people have been able to effectively prevent HBV infection 1 . However, approximately millions of people die each year from HBV-related chronic liver disease 2 . Most patients with chronic liver disease have no obvious symptoms, but as the disease progresses, it eventually leads to LC or liver cancer.LC is a long-term pathological process, and HBV is one of the main causes of LC 3 . According to the disease process, LC is divided into compensated stage (CS) and decompensated stage (DS). Patients in the CS have no obvious specific symptoms, and the liver damage increases when progressing to the DS, mainly manifesting as liver function decline and portal hypertension, which can induce a series of complications such as esophagogastric fundic variceal bleeding, hepatic encephalopathy and ascites. Patients often miss the best time for treatment because the liver reserves in the DS cannot compensate for the loss of hepatocytes and structural distortion. The diagnosis of LC is based on histological examination or a combination of clinical and imaging findings. However, the proposed method is not well suited for clinical diagnosis. First, histological examination requires puncture of liver tissue, which can cause severe pain. Second, clinical results derived from a series of laboratory tests are costly and require a long time period. Finally, imaging studies do not provide a sensitive diagnosis and are susceptible to subjective factors 4 . Therefore, establishing a simple and specific strategy to diagnose LC and differentiate between CS and DS is important for patient care and treatment decisions. Emotional factors are closely related to health, and studies 5 have shown that patients with LC have poor overall levels of mental health. The persistence of this negative mood of anxiety and depression will not only further aggravate the condition, but also seriously affect the effectiveness of treatment. Therefore, it is particularly important to further analyze the correlation between emotional factors and the CS and DS of LC. Amino acids (AAs) are generally classified as three classes including Glucogenic, ketogenic and both AAs. Lysine and leucine are the only AAs that are solely ketogenic, giving rise only to acetyl-CoA or acetoacetyl-CoA, neither of which can bring about net glucose production. A small group of AAs comprised of isoleucine, phenylalanine, threonine, tryptophan, and tyrosine give rise to both glucose and fatty acid precursors and are thus, characterized as being glucogenic and ketogenic. Abnormally elevated plasma phenylalanine and hypertyrosinemia, as well as a decreased ratio of branched chain amino acids (BCAAs, including valine, isoleucine and leucine) to aromatic amino acids (AAAs, including phenylalanine, tyrosine and tryptophan), are widely described as chronic liver disease including LC 6,7 . The Fisher ratio refers to the plasma molar ratio of BCAAs to phenylalanine and tyrosine, which serves as a marker for LC portal-systemic shunting 8 .Metcalfe conducted a systematic review and showed that BCAAs may improve portal systemic encephalopathy in LC patients, but more robust trials are needed to determine its effects by assessing its impact on encephalopathy, liver decompensation, survival, infection, length of hospital stay, and quality of life, and to review data on compliance, side effects, and cost/economic evaluation 9 .Iwasa et al. showed that BCAAs prolonged survival in LC rats, possibly as a result of reduced iron accumulation, oxidative stress and fibrosis, and improved glucose metabolism in the liver 10 . There are more studies on the metabolic profile of LC. However, little work has been done to differentiate between CS and DS cirrhotic patients by metabolomics. Not only that, there are few studies on the influence of affective factors on the metabolic profile of LC. Metabolomics measures a large number of low-molecular-weight metabolites in biological samples, including glycans, AAs, and hormones, and it is a powerful high-throughput platform for the discovery of novel biomarkers 11 . Therefore, early and accurate detection of metabolic changes in the serum of patients with LC would be very helpful in revealing the pathological transformation and progression of the disease, providing early diagnosis and timely treatment for patients. The aim of this study is to elucidate the specific metabolic profile between CS and DS using a mass spectrometry-based targeted metabolomics approach, and at the same time provide a basis for the influence of emotional factors on the metabolism of LC Methods Patients 104 patients with LC were recruited at Longhua hospital, Shanghai University of Traditional Chinse Medicine between May 2011 and July 2014, who were classified into two stages from CS and DS. All patients were hepatitis B surface antigen (HBsAg) positive.Detailed inclusion and exclusion criteria are described in our previous study 12 . Inclusion Criteria: According to the "Guideline on Prevention and Treatment of Chronic Hepatitis B in China (2010)", patients should be diagnosed with chronic HBV infection if they have a history of hepatitis B or HBsAg positive for more than 6 months, and currently have HBsAg positive and (or) hepatitis B virus DNA (HBV-DNA) positive. HBV-associated cirrhosis, which develops from chronic hepatitis B, is pathologically defined as diffuse fibrosis with pseudolobule formation. Child-Pugh Class A cirrhosis is diagnosed through imaging studies, biochemical or hematological tests showing hepatocellular dysfunction or evidence of portal hypertension (such as hypersplenism with esophageal and gastric varices), or through histologically confirmed cirrhosis. Cirrhosis is clinically classified into compensated and decompensated types. Decompensated cirrhosis, typically categorized as Child-Pugh Class B or C, includes symptoms such as esophageal and gastric variceal bleeding, hepatic encephalopathy, ascites, and/or other severe complications. In this study, the diagnosis of cirrhosis was based on ultrasonography examinations.Exclusion Criteria: Patients younger than 15 or older than 75 years, pregnant or breastfeeding, co-infected with hepatitis C virus (HCV), hepatitis D virus, or human immunodeficiency virus, or diagnosed with severe hepatitis, drug-induced liver disease, alcoholic liver disease, and autoimmune liver disease, or those who have undergone liver transplantation, or have received psychoactive medication, are excluded from the study. The study protocol was approved by the Ethics Committee of Shanghai University of Traditional Chinese Medicine. Informed consent was obtained from the participants. However, an ethical number was not obtained because it was a non-invasive examination for the collection of serum samples at that time. Clinical Data Collection The fasting blood samples were carefully obtained from each participant, and we allowed clotting for 2 h. Then, samples were centrifuged at 3000g for 10 min, and serum was separated and aliquoted. All subjects underwent conventional clinical, hematological, biochemical, and serological evaluations. The HBV-DNA viral load was detected by Shanghai Adicon Clinical laboratories Inc. The Model for End-Stage Liver Disease (MELD) Score was required based on the equation: MELD Score = 9.6 × ln[creatinine (CREA)] (mg/dl) + 3.8 × ln[total bilirubin (TBIL)] (mg/dl) + 11.2 × ln[international normalized ratio (INR)] + 6.4 × 1 13 . The Hamilton Anxiety Inventory ( Table S1 ) was used to evaluate the affective state of the 26 patients in the compensated and 24 patients in the decompensated phase, including conversation and observation, with a joint examination by two trained investigators; at the end of the examination, the raters scored independently. Patients were divided into three classes according to their scores: class I: total score < 7, no anxiety symptoms; class II: total score ≥ 7, possible anxiety; class III: total score ≥ 14, definitely anxiety. Because the number of patients in class III was small, class II and class III were combined for analysis. Serum AAs Metabolomics Analysis Ten microliter serum samples were used with a Waters Acquity UPLC instrument coupled with mass spectrometry (MS/MS) and quantified by the Absolute IDQ kit (Biocrates Life Sciences AG), and the details were described previously 12 .Two AAs, including Ac-ornithine and carnosine, in the kits cannot be well-separated with the current method, and thus we excluded these two AAs; finally, 40 AAs were detected. Statistics Analysis Data are shown as median (25–75 interquartile range, IQR) or frequency. Clinical indicators within groups were compared using the non-parametric Kruskal-Wallis test or Fisher exact test using SPSS Statistics 26 (SPSS Inc.) software. p-values were adjusted for statistical significance using the false discovery rate (FDR) method, and p < 0.05 was considered statistically significant. Multivariate full-profile prediction models were developed using SIMCA-P 14.1 (Umetrics) using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). R2X, R2Y and Q2Y values and permutation tests were performed to assess the reliability of the models. Multiple logistic regression analysis was used to assess the effects of gender, age and body mass index (BMI) on the performance of representative AA levels. The differences in the above metabolites between different Child-Pugh classification (A, B, C), MELD score (≤ 8.99, > 8.99), HBV-DNA viral load (< 1 × 1003, ≥ 1 × 1003), mood class (I, Ⅱ, Ⅲ) subgroups within the two groups were further analyzed. Results Patient characteristics The clinical characteristics of the patients are detailed in Table S2 . 104 patients were included in this study, 60 of whom were in the CS and 44 in the DS. All patients were positive for HBsAg. Fifty-six-point seven percent of the patients in the CS were male (26/34), while 61.3% of the patients in the DS were male (17/27). The median age of the CS patients was 55 years and median BMI was 23.59 kg/m 2 , while the median age of the DS patients was 57.5 years and median BMI was 23.23 kg/m 2 . From the results, serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), gamma glutamyl transferase (GGT), lactate dehydrogenase (LDH), total bile acids (TBA), triglycerides (TG), cholesterol (TC), creatinine (CREA), prothrombin time (PT), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), platelets (PLT) and HBV-DNA load were not significantly different; while alkaline phosphatase (ALP), INR, total protein (TP), albumin (ALB), cholinesterase (CHE), red blood cells (RBC), hemoglobin (HGB), glucose (GLU), and MELD scores were significantly different. Metabolomics data analysis and biomarker identification All metabolomic analyses were performed in a population of 104 cases of LC with fasting serum concentrations of 40 AAs. Detailed information is shown in Table 1 . We found significant differences in 15 of these AAs, namely asparagine, citrulline, glutamine, glycine, histidine, methionine, phenylalanine, proline, serine, tyrosine, dopamine, methionine-sulfoxide, nitro-tyrosine, spermidine, and phenylethylamine. Table 1 Serum Concentrations of amino acids in patients. Factors Compensated Stage Decompensated Stage P -value N 60 44 m001 Alanine (µM) 292.63(245.89-402.04) 348.99(285.42–570.5) 0.064 m002 Arginine(µM) 61.87(41.54–78.98) 70.97(46.8-104.42) 0.101 m003 Asparagine(µM) 48.81(38.68–59.45) 58.85(43.88–79.69) 0.004 m004 Aspartate(µM) 9.97(7.79–18.95) 9.97(7.13–21.85) 0.612 m005 Citrulline (µM) 36.36(25.07–52.02) 46.6(29.02–65.25) 0.035 m006 Glutamine (µM) 555.47(476.86-765.17) 777.8(583.45-1265.34) 0 m007 Glutamate(µM) 80.57(53.25–135.8) 72.99(53.91-142.46) 0.818 m008 Glycine (µM) 253.48(195.93-331.74) 331.93(241.16-447.22) 0.03 m009 Histidine(µM) 78.16(69.41-101.61) 86.64(76.07-130.27) 0.029 m010 Isoleucine (µM) 57.55(48.94–82.37) 59.19(49.67–87.46) 0.813 m011 Leucine(µM) 98.81(80-133.48) 95.35(76.41-122.38) 0.498 m012 Lysine(µM) 143.74(109.44-216.65) 153.55(104.89-247.81) 0.502 m013 Methionine(µM) 28.37(21.95–40.96) 41.21(26.52–61.08) 0.003 m014 Ornithine(µM) 149.08(100.6-243.37) 160.82(108.42-249.61) 0.808 m015 Phenylalanine (µM) 66.42(49.98–97.81) 93.44(66.86-140.64) 0.001 m016 Proline (µM) 185.07(151.12-256.55) 217.21(176.86-329.52) 0.031 m017 Serine (µM) 139.81(113.54-165.93) 161.03(125.09-236.02) 0.048 m018 Threonine(µM) 115.78(92.61-172.02) 138.6(105.7-190.99) 0.113 m019 Tryptophan (µM) 46.86(34.35–68.31) 53.32(33.9-73.43) 0.382 m020 Tyrosine (µM) 82.97(68.23-126.64) 136.51(96.21-178.32) 0.001 m021 Valine(µM) 180.2(137.5-243.82) 181.54(122.18–236.7) 0.742 m022 Asymmetric dimethylarginine(µM) 0.4(0.31–0.45) 0.41(0.38–0.66) 0.137 m023 symmetric dimethylarginine(µM) 0.28(0.15–0.47) 0.36(0.22–0.47) 0.065 m024 alpha-aminoadipic acid(µM) 1.94(1.02–2.37) 2.39(0.94–2.82) 0.231 m025 Creatinine(µM) 56.95(41.13–78.88) 71.78(43.23-240.06) 0.104 m026 Dihydroxyphenylalanine(µM) 0.4(0.37–0.55) 0.54(0.4–0.62) 0.056 m027 Dopamine(µM) 0.38(0.28–0.43) 0.43(0.38–0.62) 0.008 m028 Histamine(µM) 0.64(0.26–0.64) 0.64(0.59–0.76) 0.216 m029 Kynurenine(µM) 1.73(1.21–2.6) 2.29(1.18–3.28) 0.069 m030 Methionine-Sulfoxide(µM) 0.95(0.54–1.53) 1.53(1.03–2.4) 0 m031 Nitro-tyrosine(µM) 0.43(0.4–1.02) 0.95(0.43–1.24) 0.022 m032 c4-OH-Proline (µM) 0.69(0.45–0.75) 0..69(0.56–0.75) 0.255 m033 Putrescine(µM) 0.15(0.1–0.18) 0.14(0.08–0.22) 0.762 m034 Sarcosine(µM) 15.92(11.9–21.7) 17.27(13.95–22.57) 0.202 m035 Serotonin(µM) 0.17(0.08–0.54) 0.12(0.07–0.32) 0.122 m036 Spermidine(µM) 0.27(0.23–0.35) 0.36(0.25–0.55) 0.002 m037 Spermine(µM) 0.23(0.14–0.28) 0.23(0.17–0.33) 0.367 m038 Taurine(µM) 75.17(51-117.16) 80.53(51.62-121.77) 0.737 m039 Asparagine-15N2 a 0.04(0.02–0.06) 0.02(0.02–0.07) 0.527 m040 Phenethylamine a 0.02(0.02–0.03) 0.02(0.02–0.11) 0.029 ratio1 Tryptophan ratio 0.09(0.08–0.11) 0.09(0.07–0.1) 0.617 ratio2 Tyrosine ratio 0.2(0.14–0.24) 0.26(0.21–0.32) <0.01 ratio3 BCAAs/AAA ratio 1.63(1.33–2.15) 1.25(0.93–1.56) <0.01 ratio4 BCAAs/tyrosine ratio (BTR) 3.92(2.97–5.36) 2.67(1.98–3.51) <0.01 ratio5 FISCHER'S ratio 2.21(1.71–2.94) 1.54(1.12–2.08) <0.01 ratio6 KTR 0.04(0.03–0.05) 0.04(0.03–0.06) 0.364 ratio7 STR 0.00(0.00-0.01) 0.00(0.00–0.00) 0.009 ratio8 Glutamate/Glutamine ratio 0.14(0.09–0.21) 0.1(0.06–0.18) 0.046 ratio9 Phenylalanine/Tyrosine ratio 0.73(0.65–0.87) 0.72(0.65–0.85) 0.532 Note: Values are expressed as medians (interquartile ranges, IQRs) or frequencies. P values were calculated from non-parametric Kruskal-Wallis test for continuous variables and adjusted by FDR method. *, p < 0.05; **, p < 0.01; **, p < 0.001 when compared to compensated stage. a Serum concentration of asparagine-15N2 and phenethylamine were semi-qualified for the low response in LC methods. Tryptophan ratio: Tryptophan/Phenylalanine + Tyrosine + Valine + Leucine + Isoleucine. Tyrosine ratio: Tyrosine/Phenylalanine + Tryptophan + Valine + Leucine + Isoleucine. BCAA/AAA ratio: Valine + Leucine + Isoleucine/Tyrosine + Phenylalanine + Tryptophan. BTR ratio: Valine + Leucine + Isoleucine/Tyrosine. Fischer’s ratio: Valine + Leucine + Isoleucine/Tyrosine + Phenylalanine. KTR: kynurenine to tryptophan ratio. STR: serotonin to tryptophan ratio. To further screen for more representative metabolites, we performed OPLS-DA analysis of the data. Scoring plots showed a clear separation between CS and DS of LC based on 40 metabolites: correlation coefficient R2X = 0.472, correlation coefficient R2Y = 0.293, correlation coefficient Q2 = 0.18; alignment tests showed reliability: R2 = 0.136, Q2=-0.149. Combining the results of variable influence on projection (VIP) values > 1.3, tyrosine, aspartic acid, dopamine, phenylalanine, and methionine-sulfoxide contributed to the classification: 1.451, 1.397, 1.386, 1.336, and 1.33, respectively (Table 2 , Fig. 1 ). Therefore, we finally screened the above five differential metabolites. Scatter plots showed the differences between the above 5 AAs in CS and DS (Fig. 2 ), notably, serum tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide levels were significantly increased in DS patients compared with CS patients. We further performed OPLS-DA analysis of CS versus DS based on 5 differential metabolites. Scoring plots based on the 5 differential metabolites showed a significant separation between the CS and DS of LC: correlation coefficient R2X = 0.565, correlation coefficient R2Y = 0.157, and correlation coefficient Q2 = 0.14; permutation tests showed reliability: R2 = 0.021 and Q2=-0.044 (Fig. 2 ). Table 2 VIP values of 40 amino acids and 9 amino acids in OPLS-DA model for patients with compensated and decompensated liver cirrhosis. Factors VIP-value 40 amino acids Tyrosine 1.451 Asparagine 1.397 Dopamine 1.386 Phenylalanine 1.336 Methionine-Sulfoxide 1.33 9 amino acid ratios Fischer's ratio 1.464 BCAAs/tyrosine ratio (BTR) 1.46 BCAAs/AAA ratio 1.45 Tyrosine ratio 1.347 Note: VIP value was obtained from OPLS-DA models based on serum 40 amino acids, 9 amino acid ratios, 40 amino acids + 9 amino acid ratios in patients with compensated or decompensated stage. Fischer’s ratio: Valine + Leucine + Isoleucine/Tyrosine + Phenylalanine. BTR ratio: Valine + Leucine + Isoleucine/Tyrosine. BCAA/AAA ratio: Valine + Leucine + Isoleucine/Tyrosine + Phenylalanine + Tryptophan. Tyrosine ratio: Tyrosine/Phenylalanine + Tryptophan + Valine + Leucine + Isoleucine. Abbreviations: VIP, variable influence on projection; OPLS-DA, orthogonal partial least squares discriminant analysis; BCAAs, branched chain amino acids; AAA, aromatic amino acids; BTR, BCAAs/tyrosine ratio; STR, serotonin to tryptophan ratio; KTR, kynurenine to tryptophan ratio. According to logistic regression analysis, when adjusted for gender, age and BMI, significant differences among the five AAs mentioned above still existed between groups ( Table S3 ). To further screen the effect of gender on serum AA levels, a subgroup analysis was subsequently performed. We compared the differences of 40 AAs not only between women and men in the CS and DS, but also between CS and DS in female and male patients. The results showed that the five differential AAs mentioned above were significantly different in both men and women in patients in the CS and DS. Detailed information is shown in Table S4 . And subgroup analysis was performed for different age stages. We divided the patients into three age stages, i.e., age ≤ 40 years, 40 years < age ≤ 60 years, and 60 years < age. We compared the differences of 40 AAs in CS and DS patients in different age groups. The results showed that asparagine was significantly different in all age groups (≤ 40 years, 40 years < age ≤ 60 years, 60 years < age), phenylalanine, dopamine and methionine-sulfoxide in all age groups (40 years < age ≤ 60 years), and tyrosine in all age groups (≤ 40 years, 40 years < age ≤ 60 years). Detailed information is shown in Table S5 . Asparagine and methionine-sulfoxide levels correlate with Child-Pugh classification in patients with CS and DS of LC The Child-Pugh score is the most common index to evaluate the severity of liver disease. We then determined the relationship between Child-Pugh grading and metabolites based on Child-Pugh scores A ( 5 – 6 ), B ( 7 – 9 ) and C ( 10 – 15 ). Among the five metabolites selected above, asparagine and methionine-sulfoxide levels of class C metabolites were lower than those of class A and B metabolites in CS patients; asparagine and methionine-sulfoxide levels of class C metabolites were higher than those of class A and B metabolites in DS patients ( Table S6 , Fig. 3 ). Serum AAs in patients with CS and DS of LC are not associated with different MELD score classes The MELD score is a common clinical index reflecting the degree of liver damage. Then, patients with CS and DS of LC were classified according to the MELD score (≤ 8.99 or > 8.99) in order to analyze the changes of AAs in different MELD scores. Finally, we found that among the five metabolites selected above, no AA levels were associated with the MELD classification ( Table S7 ). Correlation between phenylalanine and tyrosine levels and HBV-DNA levels in patients with CS and DS of LC Based on the HBV-DNA levels in patients with CS versus DS, we divided the patients into HBV-DNA negative group (< 1 × 1003) and HBV-DNA positive group (≥ 1 × 1003). The results showed that the elevated HBV-DNA levels were accompanied by elevated phenylalanine and tyrosine levels not only in the CS patient species, but also in the DS patients ( Table S8 , Fig. 4 ). The ratios of several AAs differ between patients with CS and DS of LC To further understand AA variations, we analyzed nine AA ratios, including tryptophan ratio (tryptophan/BCAAs + phenylalanine + tyrosine), tyrosine ratio (tyrosine/BCAAs + phenylalanine + tryptophan), BCAAs/AAA ratio, BCAAs/tyrosine ratio (BTR), Fischer 's ratio (BCAAs /tyrosine + phenylalanine), kynurenine/tryptophan ratio (KTR), 5-hydroxytryptamine/tryptophan ratio (STR), glutamate/glutamine ratio and phenylalanine/tyrosine ratio. The results showed significant differences in tyrosine ratio, BCAAs/AAA ratio, BTR, Fischer 's ratio, STR and glutamate/glutamine ratio. To further screen for more representative AA ratios, we performed OPLS-DA analysis on the data. Scoring plots showed a significant separation between patients with CS and DS of LC based on nine AA ratios: correlation coefficient R2X = 0.438, correlation coefficient R2Y = 0.181, and correlation coefficient Q2 = 0.152; permutation tests showed reliability: R2 = 0.030, Q2=-0.054. Combining the results of VIP values > 1.3, the contribution of Fischer 's ratio, BTR, BCAAs/AAA ratio and tyrosine ratio to the classification were: 1.464, 1.46, 1.45 and 1.347, respectively (Table 2 , Fig. 5 ). Therefore, we finally screened the above four differential AA ratios. The scatter plot shows the difference between the above 4 AA ratios in (Fig. 5 ), and it is noteworthy that Fischer 's ratio, BTR, and BCAAs/AAA ratio were significantly increased in DS patients compared to CS patients. In contrast, tyrosine ratios were significantly lower. Interestingly, according to logistic regression analysis, the above AA ratios still differed significantly between groups when adjusted for gender, age and BMI ( Table S3 ). Further screening for the effect of gender on serum AA ratios was performed for subgroup analysis. It was found that all four serum AA ratios mentioned above were significantly different between CS and DS patients ( Table S4 ). Further screening for the effect of age on serum AA ratios was performed in a subgroup analysis. It was found that regardless of age stage (age ≤ 40 years, 40 years ≤ 60 years, or 60 years), significant differences remained between CS and DS patients for the three serum AA ratios, except for the BCAAs/AAA ratio ( Table S5 ). We further performed the OPLS-DA model based on 40 serum AAs and 9 AA ratios and showed a clear separation between CS and DS of LC: correlation coefficient R2X = 0.445, correlation coefficient R2Y = 0.287, correlation coefficient Q2 = 0.204; permutation tests showed reliability: R2 = 0.141, Q2 = 0.161 (Fig. 1 ). The results showed that the contribution of tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide to the classification was 1.451, 1.397, 1.386, 1.336 and 1.33, respectively; the contribution of Fischer 's ratio, BTR, BCAAs/AAA ratio and tyrosine ratio to the classification was 1.464, 1.46, 1.45 and 1.347. In patients with CS versus DS, AA ratios were not associated with Child-Pugh classification ( Table S6 ). It was found that the tyrosine ratio accompanied by higher MELD score was elevated not only in CS patients but also in DS patients. In contrast, Fischer 's ratio, BTR, and BCAAs/AAA ratio decreased with higher MELD scores ( Table S7 , Fig. 6 ). And the results showed that the ratio of AAs in patients with CS versus DS was independent of the different HBV-DNA groupings ( Table S8 ). BCAAs/AAA ratios and Fischer 's ratios in patients with CS and DS of LC correlated with mood score classes We classified patients with CS and DS of LC according to mood classes (Ⅰ,Ⅱ) in order to analyze the changes of AAs in different mood classes. Finally, we found that among the five metabolites and four AA ratios selected above, the BCAAs/AAA ratio and Fischer 's ratio increased with increasing grade in CS patients; and vice versa in DS patients ( Table S9 , Fig. 7 ). Discussion Targeted metabolomics based on UPLC-MS/MS technology has been applied in many studies 14–17 . Because UPLC-MS/MS can identify signals and the method can be fully validated; therefore, absolute quantification is possible. Previous studies have shown that the transition from CS to DS is so insidious that many patients miss the best time for treatment 18 . In this study, we used a targeted metabolomics approach based on UPLC-MS/MS to analyze the changes of differential AAs in the serum of patients with CS and DS of LC. The results suggest that AA metabolism is associated with the development of LC. The liver is the primary site of AAA metabolism, and plasma concentrations of these AAs are highly dependent on liver function. Studies have shown that liver disease is associated with abnormally high plasma concentrations of phenylalanine and tyrosine 19,20 . Ishii et al. 21 examined the usefulness of the L-[1-13C] phenylalanine breath test (13C-PheBT) and L-[1-13C] tyrosine breath test (13CTyrBT) for the detection of hepatic damage in patients with LC. The parameters obtained were also compared with biochemical liver function test values. Finally, it was concluded that 13C - PheBT and 13CTyrBT may be useful in assessing the extent and progression of liver function impairment. In addition, studies have shown that progressive elevation of AAA predicts loss of LC 22 . Little is known about the metabolic status and mechanisms involved in asparagine metabolism in liver disease, and the results of Bai et al. 23 suggest that patients with liver cancer with higher levels of asparagine metabolism have a poorer prognosis. This suggests that elevated levels of asparagine may be a potential marker of liver injury. Dopamine is an intermediate product produced during the metabolism of tyrosine via dihydroxyphenylalanine. In the present study, elevated dopamine levels may be associated with elevated tyrosine levels, suggesting an exacerbation of liver injury. Methionine-sulfoxide is a methionine metabolite. In recent years, several clinical studies have confirmed the deleterious effects of hypermethioninemia. Hepatic symptoms of hypermethioninemia include LC and steatosis 24–26 . In addition, experimental animal studies have shown that high concentrations of methionine and its metabolites, such as methionine-sulfoxide, similar to those found in hypertensive patients, can cause inflammatory cell infiltration, histological changes, and oxidative damage 27–29 . These changes are likely related to the fact that the liver is the primary metabolite of the AA, and both methionine and methionine-sulfoxide are included in this group. In the present study, elevated methionine-sulfoxide levels suggested more severe hepatic impairment in DS patients than in CS patients, consistent with the deleterious effects of hypermethioninemia. In addition, serum tyrosine, phenylalanine, asparagine, and methionine-sulfoxide levels were significantly higher in DS patients as the severity of child-Pugh classification and HBV-DNA levels increased. In addition to the above-mentioned AAs, we also studied the changes in AA ratios. Patients with DS had increased levels of tyrosine ratios and decreased levels of BTR, BCAAs/AAA ratios, and Fischer 's ratios compared to patients with CS. The tyrosine ratio, the ratio of tyrosine to phenylalanine, tryptophan, leucine, valine and isoleucine, is used to measure the availability of tyrosine for the synthesis of dopamine and norepinephrine. The BCAAs/AAA ratio, considered as a good indicator of liver injury and early detection of AA metabolism disorders 22,30 . It is a key component of the etiology of hepatic encephalopathy 31 . The Fischer ratio, BCAAs/tyrosine + phenylalanine, is essential for the assessment of liver function. Ishikawa et al. 32 concluded that BTR has clinical significance in predicting the prognosis of patients with LC. BTR correlates with various liver function tests, including liver fibrosis markers, liver blood flow and hepatocyte function, and therefore can be considered to reflect the degree of liver function impairment 33 . In conclusion, the decrease in BCAA/AAA, Fischer ratio and BTR in this study indicates that patients with DS have more severe hepatic impairment than those with CS. According to Chinese medicine, emotional factors such as happiness, anger and sadness are closely related to health, and when emotional factors exceed the normal level, they will lead to disorders in the body's immune system, thus aggravating the disease 34 . Wang et al. 35 studied 94 patients with LC and showed that the prevalence of anxiety and depression was significantly higher than that of the normal population. Xiao et al. 5 also found that the number of patients with LC combined with depression was as high as 46.7%. This indicates that the overall level of mental health of patients with LC is poor. The persistence of such negative emotions of anxiety and depression will not only further aggravate the disease, but also seriously affect the effectiveness of treatment. Therefore, it is especially important to provide reasonable and effective psychological interventions for patients with LC. Currently, there are many studies on emotional care to improve the quality of life of patients with LC 36 . To further explore the influence of affective factors on LC, in this study, we also classified the patients with LC according to their emotional class to analyze the changes of AAs in the different emotional classes, and finally, we found that the BCAAs/AAA ratio and Fischer's ratio changed according to the emotional class, indicating that emotional factors may be influential in the progression of LC. However, data collection was not easy, and we only collected data on 50 cases, so we need to expand the sample size in the future to further investigate the relationship between affective factors and LC. In conclusion, from a set of 40 serum AA metabolites, a number of unique metabolite profiles of specific serum AAs were identified between patients with CS and DS of LC, which may help to predict the development of LC early. In addition, affective factors were found to influence AA ratios in patients with LC. However, there are still some limitations of this study. First, the effect of diet on serum AA levels could not be excluded. Secondly, there was no normal control group. Third, the sample size was limited. Finally, since this is a cross-sectional study, a longitudinal study approach is needed to confirm the present findings. In addition, experiments with in vivo and in vitro models are needed to investigate the mechanisms in order to better understand the relationship between serum AA levels and the development of LC. Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Shanghai University of Traditional Chinese Medicine. Informed consent was obtained from the participants. However, an ethical number was not obtained because it was a non-invasive examination for the collection of serum samples at that time. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be constructed as a potential conflict of interest. Funding This work was supported by the National Natural Science Foundation of China(81873076), Subject of Science and Technology Development, Shanghai University of Traditional Chinese Medicine (23KFL017) and the Hundred Talents Program from Shanghai University of Traditional Chinese Medicine. Author Contributions Ying Gao contributed to acquisition, analysis, interpretation and drafted the manuscript. Yanqun Luo, Jia Liu and Xiaoliang Deng contributed to acquisition and analysis. Junming Chen and Tao Wu contributed to conception and design and critically revised the manuscript. All authors gave final approval. References Zhang, Z.; Zhang, J.-Y.; Wang, L.-F.; Wang, F.-S. Immunopathogenesis and Prognostic Immune Markers of Chronic Hepatitis B Virus Infection. J Gastroenterol Hepatol 2012 , 27 (2), 223–230. https://doi.org/10.1111/j.1440-1746.2011.06940.x. GBD 2013 Mortality and Causes of Death Collaborators. Global, Regional, and National Age-Sex Specific All-Cause and Cause-Specific Mortality for 240 Causes of Death, 1990-2013: A Systematic Analysis for the Global Burden of Disease Study 2013. Lancet 2015 , 385 (9963), 117–171. https://doi.org/10.1016/S0140-6736(14)61682-2. Chang, M.-H. Hepatitis B Virus Infection. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4186028","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288400565,"identity":"c2488c91-4e2b-4d69-a104-9f4e56189fbd","order_by":0,"name":"Ying Gao","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Gao","suffix":""},{"id":288400566,"identity":"e0808b85-6070-4c1e-a8f9-a94271448d28","order_by":1,"name":"Yanqun Luo","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanqun","middleName":"","lastName":"Luo","suffix":""},{"id":288400567,"identity":"adea6343-cc35-4261-b089-21e02fa2ec6e","order_by":2,"name":"Jia Liu","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":288400568,"identity":"493cea88-af54-455d-8fa6-13094a84b1dd","order_by":3,"name":"Xiaoliang Deng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoliang","middleName":"","lastName":"Deng","suffix":""},{"id":288400569,"identity":"88f95a51-c388-499d-96e8-d14bfcb2f6f1","order_by":4,"name":"Junmin Chen","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Junmin","middleName":"","lastName":"Chen","suffix":""},{"id":288400570,"identity":"04090b27-7a2a-402c-ae5d-e06bd1acc451","order_by":5,"name":"wu tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYNACAwkefvb+jw8SKmqIUc4MxBU2MpI9B4wNHpw5RqyWM2k2BjcSzCQftjAT4aQb+Qc/fGw7zCPZkJBWkdjAxsDf3p1AQEsys+RMoBZ+hgPHbiTukGGQOHN2A14tZjeSGaR5QbY0NrbdSDzDBgyKXIJamH+DtBgcZmYrSGxjJkoLmzTPmTQeg2NsbAxEabE/89jMckaFDY9kDw+zRMKZYzwE/SLZnvj4xgcDCXt++TeMH39U1Mjxt/fi14IBeEhTPgpGwSgYBaMAKwAAHOJJodmBvMIAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"wu","middleName":"","lastName":"tao","suffix":""}],"badges":[],"createdAt":"2024-03-29 06:27:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4186028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4186028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54446389,"identity":"7785eb9a-30a3-4dbd-b157-a2948e15b1a3","added_by":"auto","created_at":"2024-04-10 16:21:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":517286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOPLS-DA score plots show clear separation between compensated and decompensated stage patients based on 40 amino acids and 40 amino acids plus 9 amino acid ratios. \u003c/strong\u003e(A)-(C) based on 40 amino acids; (D)-(F) based on 40 amino acids plus 9 amino acid ratios.\u003cstrong\u003e \u003c/strong\u003e(A) 2D OPLS-DA scores plot: R2X = 0.472, R2Y = 0.293, Q2 = 0.18. (B) Permutation analysis: R2=0.136, Q2=-0.149 (C) VIP value.\u003cstrong\u003e \u003c/strong\u003e(D) 2D OPLS-DA scores plot: R2X = 0.445, R2Y = 0.287, Q2 = 0.204. (E) Permutation analysis: R2=0.141, Q2=0.161 (F) VIP value.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/e99a7618551c26b37d899065.jpg"},{"id":54446390,"identity":"4b4da5dd-660f-44d7-8dce-f5c7efa9ecda","added_by":"auto","created_at":"2024-04-10 16:21:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData analysis of patients with compensated and decompensated liver cirrhosis based on 5 different amino acids. \u003c/strong\u003e(A) Asparagine. (B) Phenylalanine. (C) Tyrosine. (D) Dopamine. (E) Methionine-Sulfoxide. (F) 3D OPLS-DA scores plot: R2X = 0.565, R2Y = 0.157, Q2 = 0.14. (G) Permutation analysis: R2=0.021, Q2=-0.044. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; **, p \u0026lt; 0.001 when compared to compensated stage.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/51f524f84fd5e1602db884c6.jpg"},{"id":54447075,"identity":"825eb336-0205-4570-b539-20d85fd6360c","added_by":"auto","created_at":"2024-04-10 16:29:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":266732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAsparagine and methionine-sulfoxide levels correlate with Child-Pugh classification in patients with compensated and decompensated liver cirrhosis.\u003c/strong\u003e (A) Asparagine. (B) Phenylalanine. (C) Tyrosine. (D) Dopamine. (E) Methionine-Sulfoxide. P values were calculated from a nonparametric Kruskal−Wallis test and adjusted by the FDR method. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001 when compared to Class A. #, p \u0026lt; 0.05; ##, p \u0026lt; 0.01; ###, p \u0026lt; 0.01when compared to Class B. Child-Pugh Class, A = 5−6, B = 7−9, C=10−15, based on hepatic encephalopathy, ascites, bilirubin, albumin, and prothrombin time (seconds).\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/333442091c775a4fe023789e.jpg"},{"id":54446397,"identity":"eb3766fc-6d3c-490f-8f8a-6a24b9cb731b","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenylalanine and tyrosine levels correlate with HBV-DNA levels in patients with compensated and decompensated liver cirrhosis.\u003c/strong\u003e (A) Asparagine. (B) Phenylalanine. (C) Tyrosine. (D) Dopamine. (E) Methionine-Sulfoxide. P values were calculated from a nonparametric Kruskal−Wallis test and adjusted by the FDR method. *, p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001 when compared to HBV-DNA negative. HBV DNA (Log IU/mL). HBV-DNA Class, Negative=HBV-DNA \u0026lt;1×1003, Positive=HBV-DNA\u0026gt;=1×1003.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/f979824dc944d07f08fcabca.jpg"},{"id":54446396,"identity":"a0b36909-3ac6-4088-b23b-9677ec452592","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData analysis of patients with compensated and decompensated liver cirrhosis based on 9 amino acid ratios. \u003c/strong\u003e(A) Tyrosine ratio. (B) BCAAs/AAA ratio. (C) BTR. (D) Fischer 's ratio.\u003cstrong\u003e \u003c/strong\u003e(E) 3D OPLS-DA scores plot: R2X = 0.438, R2Y = 0.181, Q2 = 0.152. (F) Permutation analysis: R2=0.03, Q2=-0.054. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001 when compared to compensated stage. Fischer’s ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine.\u003c/p\u003e\n\u003cp\u003eBTR ratio: Valine+ Leucine+ Isoleucine/Tyrosine.\u003c/p\u003e\n\u003cp\u003eBCAA/AAA ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine+ Tryptophan.\u003c/p\u003e\n\u003cp\u003eTyrosine ratio: Tyrosine/Phenylalanine+ Tryptophan+ Valine+ Leucine+ Isoleucine.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; BCAAs, branched chain amino acids; AAA, aromatic amino acids; BTR, BCAAs/tyrosine ratio; STR, serotonin to tryptophan ratio; KTR, kynurenine to tryptophan ratio.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/1a25db51c70697bad8fd82b1.jpg"},{"id":54446392,"identity":"ab28310b-503e-4c2f-9ab9-75df1d6fc036","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":318321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum amino acid ratios are associated with different MELD Score class in patients with compensated and decompensated liver cirrhosis.\u003c/strong\u003e (A) Tyrosine ratio. (B) BCAAs/AAA ratio. (C) BTR. (D) Fischer 's ratio. The figure represents the individual data for each patient to show the distribution of values in each group. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001 when compared to MELD ≤ 8.99. Fischer’s ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine.\u003c/p\u003e\n\u003cp\u003eBTR ratio: Valine+ Leucine+ Isoleucine/Tyrosine.\u003c/p\u003e\n\u003cp\u003eBCAA/AAA ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine+ Tryptophan.\u003c/p\u003e\n\u003cp\u003eTyrosine ratio: Tyrosine/Phenylalanine+ Tryptophan+ Valine+ Leucine+ Isoleucine.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; BCAAs, branched chain amino acids; AAA, aromatic amino acids; BTR, BCAAs/tyrosine ratio; STR, serotonin to tryptophan ratio; KTR, kynurenine to tryptophan ratio.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/151824476c5cf59ced2d209b.jpg"},{"id":54446395,"identity":"b2209865-9fd7-4a29-8761-23ab436818dc","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":361636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBCAAs/AAA ratios and Fischer 's ratios in patients with compensated and decompensated liver cirrhosis correlated with mood score classes. \u003c/strong\u003e(A) Tyrosine ratio. (B) BCAAs/AAA ratio. (C) BTR. (D) Fischer 's ratio. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001 when compared to ClassⅠ. Fischer’s ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine.\u003c/p\u003e\n\u003cp\u003eBTR ratio: Valine+ Leucine+ Isoleucine/Tyrosine.\u003c/p\u003e\n\u003cp\u003eBCAA/AAA ratio: Valine+ Leucine+ Isoleucine/Tyrosine+ Phenylalanine+ Tryptophan.\u003c/p\u003e\n\u003cp\u003eTyrosine ratio: Tyrosine/Phenylalanine+ Tryptophan+ Valine+ Leucine+ Isoleucine.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; BCAAs, branched chain amino acids; AAA, aromatic amino acids; BTR, BCAAs/tyrosine ratio; STR, serotonin to tryptophan ratio; KTR, kynurenine to tryptophan ratio.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/adbdf8a01012d4ce2617fb2b.jpg"},{"id":60866508,"identity":"b12d91af-6828-4209-acb2-938c4de48bbe","added_by":"auto","created_at":"2024-07-23 03:31:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3588828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/1c6dc26c-2172-4ec0-8bbe-77c67c989a4b.pdf"},{"id":54446391,"identity":"9b8c7d86-359a-407d-bf6f-378ab47d8196","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":97198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic Abstract；\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/7bd20121c73a479a0075df9e.jpg"},{"id":54446394,"identity":"5661a780-95db-4958-916e-ff0099b2e7fc","added_by":"auto","created_at":"2024-04-10 16:21:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":53732,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4186028/v1/1fd1c08fdc463ca825f06f44.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered Amino Acid Metabolome in Patients affected by HBV cirrhosis at different stages","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis B virus (HBV) is the most common cause of acute and chronic liver disease worldwide, and approximately 4\u0026nbsp;million people worldwide are infected with HBV each year, especially in several regions of Asia and Africa. Approximately 10% of patients with HBV infection develop chronic infection, including hepatitis, liver fibrosis, and liver cirrhosis (LC). Since the introduction of the hepatitis B vaccine in 1982, people have been able to effectively prevent HBV infection\u003csup\u003e1\u003c/sup\u003e. However, approximately millions of people die each year from HBV-related chronic liver disease \u003csup\u003e2\u003c/sup\u003e. Most patients with chronic liver disease have no obvious symptoms, but as the disease progresses, it eventually leads to LC or liver cancer.LC is a long-term pathological process, and HBV is one of the main causes of LC \u003csup\u003e3\u003c/sup\u003e. According to the disease process, LC is divided into compensated stage (CS) and decompensated stage (DS). Patients in the CS have no obvious specific symptoms, and the liver damage increases when progressing to the DS, mainly manifesting as liver function decline and portal hypertension, which can induce a series of complications such as esophagogastric fundic variceal bleeding, hepatic encephalopathy and ascites. Patients often miss the best time for treatment because the liver reserves in the DS cannot compensate for the loss of hepatocytes and structural distortion. The diagnosis of LC is based on histological examination or a combination of clinical and imaging findings. However, the proposed method is not well suited for clinical diagnosis. First, histological examination requires puncture of liver tissue, which can cause severe pain. Second, clinical results derived from a series of laboratory tests are costly and require a long time period. Finally, imaging studies do not provide a sensitive diagnosis and are susceptible to subjective factors\u003csup\u003e4\u003c/sup\u003e. Therefore, establishing a simple and specific strategy to diagnose LC and differentiate between CS and DS is important for patient care and treatment decisions. Emotional factors are closely related to health, and studies\u003csup\u003e5\u003c/sup\u003e have shown that patients with LC have poor overall levels of mental health. The persistence of this negative mood of anxiety and depression will not only further aggravate the condition, but also seriously affect the effectiveness of treatment. Therefore, it is particularly important to further analyze the correlation between emotional factors and the CS and DS of LC.\u003c/p\u003e \u003cp\u003eAmino acids (AAs) are generally classified as three classes including Glucogenic, ketogenic and both AAs. Lysine and leucine are the only AAs that are solely ketogenic, giving rise only to acetyl-CoA or acetoacetyl-CoA, neither of which can bring about net glucose production. A small group of AAs comprised of isoleucine, phenylalanine, threonine, tryptophan, and tyrosine give rise to both glucose and fatty acid precursors and are thus, characterized as being glucogenic and ketogenic.\u003c/p\u003e \u003cp\u003eAbnormally elevated plasma phenylalanine and hypertyrosinemia, as well as a decreased ratio of branched chain amino acids (BCAAs, including valine, isoleucine and leucine) to aromatic amino acids (AAAs, including phenylalanine, tyrosine and tryptophan), are widely described as chronic liver disease including LC\u003csup\u003e6,7\u003c/sup\u003e. The Fisher ratio refers to the plasma molar ratio of BCAAs to phenylalanine and tyrosine, which serves as a marker for LC portal-systemic shunting\u003csup\u003e8\u003c/sup\u003e.Metcalfe conducted a systematic review and showed that BCAAs may improve portal systemic encephalopathy in LC patients, but more robust trials are needed to determine its effects by assessing its impact on encephalopathy, liver decompensation, survival, infection, length of hospital stay, and quality of life, and to review data on compliance, side effects, and cost/economic evaluation\u003csup\u003e9\u003c/sup\u003e.Iwasa et al. showed that BCAAs prolonged survival in LC rats, possibly as a result of reduced iron accumulation, oxidative stress and fibrosis, and improved glucose metabolism in the liver\u003csup\u003e10\u003c/sup\u003e. There are more studies on the metabolic profile of LC. However, little work has been done to differentiate between CS and DS cirrhotic patients by metabolomics. Not only that, there are few studies on the influence of affective factors on the metabolic profile of LC.\u003c/p\u003e \u003cp\u003eMetabolomics measures a large number of low-molecular-weight metabolites in biological samples, including glycans, AAs, and hormones, and it is a powerful high-throughput platform for the discovery of novel biomarkers\u003csup\u003e11\u003c/sup\u003e. Therefore, early and accurate detection of metabolic changes in the serum of patients with LC would be very helpful in revealing the pathological transformation and progression of the disease, providing early diagnosis and timely treatment for patients. The aim of this study is to elucidate the specific metabolic profile between CS and DS using a mass spectrometry-based targeted metabolomics approach, and at the same time provide a basis for the influence of emotional factors on the metabolism of LC\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e104 patients with LC were recruited at Longhua hospital, Shanghai University of Traditional Chinse Medicine between May 2011 and July 2014, who were classified into two stages from CS and DS. All patients were hepatitis B surface antigen (HBsAg) positive.Detailed inclusion and exclusion criteria are described in our previous study\u003csup\u003e12\u003c/sup\u003e. Inclusion Criteria: According to the \"Guideline on Prevention and Treatment of Chronic Hepatitis B in China (2010)\", patients should be diagnosed with chronic HBV infection if they have a history of hepatitis B or HBsAg positive for more than 6 months, and currently have HBsAg positive and (or) hepatitis B virus DNA (HBV-DNA) positive. HBV-associated cirrhosis, which develops from chronic hepatitis B, is pathologically defined as diffuse fibrosis with pseudolobule formation. Child-Pugh Class A cirrhosis is diagnosed through imaging studies, biochemical or hematological tests showing hepatocellular dysfunction or evidence of portal hypertension (such as hypersplenism with esophageal and gastric varices), or through histologically confirmed cirrhosis. Cirrhosis is clinically classified into compensated and decompensated types. Decompensated cirrhosis, typically categorized as Child-Pugh Class B or C, includes symptoms such as esophageal and gastric variceal bleeding, hepatic encephalopathy, ascites, and/or other severe complications. In this study, the diagnosis of cirrhosis was based on ultrasonography examinations.Exclusion Criteria: Patients younger than 15 or older than 75 years, pregnant or breastfeeding, co-infected with hepatitis C virus (HCV), hepatitis D virus, or human immunodeficiency virus, or diagnosed with severe hepatitis, drug-induced liver disease, alcoholic liver disease, and autoimmune liver disease, or those who have undergone liver transplantation, or have received psychoactive medication, are excluded from the study. The study protocol was approved by the Ethics Committee of Shanghai University of Traditional Chinese Medicine. Informed consent was obtained from the participants. However, an ethical number was not obtained because it was a non-invasive examination for the collection of serum samples at that time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data Collection\u003c/h2\u003e \u003cp\u003eThe fasting blood samples were carefully obtained from each participant, and we allowed clotting for 2 h. Then, samples were centrifuged at 3000g for 10 min, and serum was separated and aliquoted. All subjects underwent conventional clinical, hematological, biochemical, and serological evaluations. The HBV-DNA viral load was detected by Shanghai Adicon Clinical laboratories Inc. The Model for End-Stage Liver Disease (MELD) Score was required based on the equation: MELD Score\u0026thinsp;=\u0026thinsp;9.6 \u0026times; ln[creatinine (CREA)] (mg/dl)\u0026thinsp;+\u0026thinsp;3.8 \u0026times; ln[total bilirubin (TBIL)] (mg/dl)\u0026thinsp;+\u0026thinsp;11.2 \u0026times; ln[international normalized ratio (INR)]\u0026thinsp;+\u0026thinsp;6.4 \u0026times; 1\u003csup\u003e13\u003c/sup\u003e. The Hamilton Anxiety Inventory (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) was used to evaluate the affective state of the 26 patients in the compensated and 24 patients in the decompensated phase, including conversation and observation, with a joint examination by two trained investigators; at the end of the examination, the raters scored independently. Patients were divided into three classes according to their scores: class I: total score\u0026thinsp;\u0026lt;\u0026thinsp;7, no anxiety symptoms; class II: total score\u0026thinsp;\u0026ge;\u0026thinsp;7, possible anxiety; class III: total score\u0026thinsp;\u0026ge;\u0026thinsp;14, definitely anxiety. Because the number of patients in class III was small, class II and class III were combined for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSerum AAs Metabolomics Analysis\u003c/h2\u003e \u003cp\u003eTen microliter serum samples were used with a Waters Acquity UPLC instrument coupled with mass spectrometry (MS/MS) and quantified by the Absolute IDQ kit (Biocrates Life Sciences AG), and the details were described previously\u003csup\u003e12\u003c/sup\u003e.Two AAs, including Ac-ornithine and carnosine, in the kits cannot be well-separated with the current method, and thus we excluded these two AAs; finally, 40 AAs were detected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistics Analysis\u003c/h2\u003e \u003cp\u003eData are shown as median (25\u0026ndash;75 interquartile range, IQR) or frequency. Clinical indicators within groups were compared using the non-parametric Kruskal-Wallis test or Fisher exact test using SPSS Statistics 26 (SPSS Inc.) software. p-values were adjusted for statistical significance using the false discovery rate (FDR) method, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Multivariate full-profile prediction models were developed using SIMCA-P 14.1 (Umetrics) using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). R2X, R2Y and Q2Y values and permutation tests were performed to assess the reliability of the models. Multiple logistic regression analysis was used to assess the effects of gender, age and body mass index (BMI) on the performance of representative AA levels. The differences in the above metabolites between different Child-Pugh classification (A, B, C), MELD score (\u0026le;\u0026thinsp;8.99, \u0026gt;\u0026thinsp;8.99), HBV-DNA viral load (\u0026lt;\u0026thinsp;1 \u0026times; 1003, \u0026ge;\u0026thinsp;1 \u0026times; 1003), mood class (I, Ⅱ, Ⅲ) subgroups within the two groups were further analyzed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThe clinical characteristics of the patients are detailed in \u003cb\u003eTable S2\u003c/b\u003e. 104 patients were included in this study, 60 of whom were in the CS and 44 in the DS. All patients were positive for HBsAg. Fifty-six-point seven percent of the patients in the CS were male (26/34), while 61.3% of the patients in the DS were male (17/27). The median age of the CS patients was 55 years and median BMI was 23.59 kg/m\u003csup\u003e2\u003c/sup\u003e, while the median age of the DS patients was 57.5 years and median BMI was 23.23 kg/m\u003csup\u003e2\u003c/sup\u003e. From the results, serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), gamma glutamyl transferase (GGT), lactate dehydrogenase (LDH), total bile acids (TBA), triglycerides (TG), cholesterol (TC), creatinine (CREA), prothrombin time (PT), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), platelets (PLT) and HBV-DNA load were not significantly different; while alkaline phosphatase (ALP), INR, total protein (TP), albumin (ALB), cholinesterase (CHE), red blood cells (RBC), hemoglobin (HGB), glucose (GLU), and MELD scores were significantly different.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics data analysis and biomarker identification\u003c/h2\u003e \u003cp\u003eAll metabolomic analyses were performed in a population of 104 cases of LC with fasting serum concentrations of 40 AAs. Detailed information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We found significant differences in 15 of these AAs, namely asparagine, citrulline, glutamine, glycine, histidine, methionine, phenylalanine, proline, serine, tyrosine, dopamine, methionine-sulfoxide, nitro-tyrosine, spermidine, and phenylethylamine.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSerum Concentrations of amino acids in patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompensated Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecompensated Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlanine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292.63(245.89-402.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348.99(285.42\u0026ndash;570.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArginine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.87(41.54\u0026ndash;78.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.97(46.8-104.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsparagine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.81(38.68\u0026ndash;59.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.85(43.88\u0026ndash;79.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspartate(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.97(7.79\u0026ndash;18.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.97(7.13\u0026ndash;21.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitrulline (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.36(25.07\u0026ndash;52.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.6(29.02\u0026ndash;65.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlutamine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e555.47(476.86-765.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e777.8(583.45-1265.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlutamate(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.57(53.25\u0026ndash;135.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.99(53.91-142.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253.48(195.93-331.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331.93(241.16-447.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistidine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.16(69.41-101.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.64(76.07-130.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsoleucine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.55(48.94\u0026ndash;82.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.19(49.67\u0026ndash;87.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeucine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.81(80-133.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.35(76.41-122.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLysine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.74(109.44-216.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153.55(104.89-247.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethionine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.37(21.95\u0026ndash;40.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.21(26.52\u0026ndash;61.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrnithine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.08(100.6-243.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.82(108.42-249.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenylalanine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.42(49.98\u0026ndash;97.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.44(66.86-140.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.07(151.12-256.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217.21(176.86-329.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.81(113.54-165.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.03(125.09-236.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreonine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.78(92.61-172.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.6(105.7-190.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTryptophan\u0026nbsp;(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.86(34.35\u0026ndash;68.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.32(33.9-73.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.97(68.23-126.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.51(96.21-178.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.2(137.5-243.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181.54(122.18\u0026ndash;236.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymmetric dimethylarginine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4(0.31\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41(0.38\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esymmetric dimethylarginine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28(0.15\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36(0.22\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha-aminoadipic acid(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94(1.02\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39(0.94\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.95(41.13\u0026ndash;78.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.78(43.23-240.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDihydroxyphenylalanine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4(0.37\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54(0.4\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38(0.28\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43(0.38\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistamine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64(0.26\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64(0.59\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKynurenine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73(1.21\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.29(1.18\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethionine-Sulfoxide(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.54\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53(1.03\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitro-tyrosine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43(0.4\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.43\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec4-OH-Proline (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.45\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0..69(0.56\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutrescine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15(0.1\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14(0.08\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcosine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.92(11.9\u0026ndash;21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.27(13.95\u0026ndash;22.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerotonin(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.08\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12(0.07\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpermidine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27(0.23\u0026ndash;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36(0.25\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpermine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23(0.14\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23(0.17\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaurine(\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.17(51-117.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.53(51.62-121.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsparagine-15N2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04(0.02\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenethylamine \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.02\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTryptophan ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09(0.08\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09(0.07\u0026ndash;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2(0.14\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26(0.21\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAAs/AAA ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63(1.33\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25(0.93\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAAs/tyrosine\u003c/p\u003e \u003cp\u003eratio (BTR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.92(2.97\u0026ndash;5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67(1.98\u0026ndash;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFISCHER'S ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21(1.71\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54(1.12\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04(0.03\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04(0.03\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlutamate/Glutamine\u003c/p\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14(0.09\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1(0.06\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eratio9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenylalanine/Tyrosine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73(0.65\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72(0.65\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Values are expressed as medians (interquartile ranges, IQRs) or frequencies. \u003cem\u003eP\u003c/em\u003e values were calculated from non-parametric Kruskal-Wallis test for continuous variables and adjusted by FDR method. *, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 when compared to compensated stage. \u003csup\u003ea\u003c/sup\u003e Serum concentration of asparagine-15N2 and phenethylamine were semi-qualified for the low response in LC methods.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTryptophan ratio: Tryptophan/Phenylalanine\u0026thinsp;+\u0026thinsp;Tyrosine\u0026thinsp;+\u0026thinsp;Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTyrosine ratio: Tyrosine/Phenylalanine\u0026thinsp;+\u0026thinsp;Tryptophan\u0026thinsp;+\u0026thinsp;Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBCAA/AAA ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine\u0026thinsp;+\u0026thinsp;Phenylalanine\u0026thinsp;+\u0026thinsp;Tryptophan.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBTR ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFischer\u0026rsquo;s ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine\u0026thinsp;+\u0026thinsp;Phenylalanine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eKTR: kynurenine to tryptophan ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSTR: serotonin to tryptophan ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further screen for more representative metabolites, we performed OPLS-DA analysis of the data. Scoring plots showed a clear separation between CS and DS of LC based on 40 metabolites: correlation coefficient R2X\u0026thinsp;=\u0026thinsp;0.472, correlation coefficient R2Y\u0026thinsp;=\u0026thinsp;0.293, correlation coefficient Q2\u0026thinsp;=\u0026thinsp;0.18; alignment tests showed reliability: R2\u0026thinsp;=\u0026thinsp;0.136, Q2=-0.149. Combining the results of variable influence on projection (VIP) values\u0026thinsp;\u0026gt;\u0026thinsp;1.3, tyrosine, aspartic acid, dopamine, phenylalanine, and methionine-sulfoxide contributed to the classification: 1.451, 1.397, 1.386, 1.336, and 1.33, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, we finally screened the above five differential metabolites. Scatter plots showed the differences between the above 5 AAs in CS and DS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e), notably, serum tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide levels were significantly increased in DS patients compared with CS patients. We further performed OPLS-DA analysis of CS versus DS based on 5 differential metabolites. Scoring plots based on the 5 differential metabolites showed a significant separation between the CS and DS of LC: correlation coefficient R2X\u0026thinsp;=\u0026thinsp;0.565, correlation coefficient R2Y\u0026thinsp;=\u0026thinsp;0.157, and correlation coefficient Q2\u0026thinsp;=\u0026thinsp;0.14; permutation tests showed reliability: R2\u0026thinsp;=\u0026thinsp;0.021 and Q2=-0.044 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVIP values of 40 amino acids and 9 amino acids in OPLS-DA model for patients with compensated and decompensated liver cirrhosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40 amino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsparagine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethionine-Sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e9 amino acid ratios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFischer's ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAAs/tyrosine ratio (BTR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAAs/AAA ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: VIP value was obtained from OPLS-DA models based on serum 40 amino acids, 9 amino acid ratios, 40 amino acids\u0026thinsp;+\u0026thinsp;9 amino acid ratios in patients with compensated or decompensated stage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFischer\u0026rsquo;s ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine\u0026thinsp;+\u0026thinsp;Phenylalanine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBTR ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBCAA/AAA ratio: Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine/Tyrosine\u0026thinsp;+\u0026thinsp;Phenylalanine\u0026thinsp;+\u0026thinsp;Tryptophan.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eTyrosine ratio: Tyrosine/Phenylalanine\u0026thinsp;+\u0026thinsp;Tryptophan\u0026thinsp;+\u0026thinsp;Valine\u0026thinsp;+\u0026thinsp;Leucine\u0026thinsp;+\u0026thinsp;Isoleucine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: VIP, variable influence on projection; OPLS-DA, orthogonal partial least squares discriminant analysis; BCAAs, branched chain amino acids; AAA, aromatic amino acids; BTR, BCAAs/tyrosine ratio; STR, serotonin to tryptophan ratio; KTR, kynurenine to tryptophan ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to logistic regression analysis, when adjusted for gender, age and BMI, significant differences among the five AAs mentioned above still existed between groups (\u003cb\u003eTable S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo further screen the effect of gender on serum AA levels, a subgroup analysis was subsequently performed. We compared the differences of 40 AAs not only between women and men in the CS and DS, but also between CS and DS in female and male patients. The results showed that the five differential AAs mentioned above were significantly different in both men and women in patients in the CS and DS. Detailed information is shown in \u003cb\u003eTable S4\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAnd subgroup analysis was performed for different age stages. We divided the patients into three age stages, i.e., age\u0026thinsp;\u0026le;\u0026thinsp;40 years, 40 years\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;60 years, and 60 years\u0026thinsp;\u0026lt;\u0026thinsp;age. We compared the differences of 40 AAs in CS and DS patients in different age groups. The results showed that asparagine was significantly different in all age groups (\u0026le;\u0026thinsp;40 years, 40 years\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;60 years, 60 years\u0026thinsp;\u0026lt;\u0026thinsp;age), phenylalanine, dopamine and methionine-sulfoxide in all age groups (40 years\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;60 years), and tyrosine in all age groups (\u0026le;\u0026thinsp;40 years, 40 years\u0026thinsp;\u0026lt;\u0026thinsp;age\u0026thinsp;\u0026le;\u0026thinsp;60 years). Detailed information is shown in \u003cb\u003eTable S5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAsparagine and methionine-sulfoxide levels correlate with Child-Pugh classification in patients with CS and DS of LC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Child-Pugh score is the most common index to evaluate the severity of liver disease. We then determined the relationship between Child-Pugh grading and metabolites based on Child-Pugh scores A (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), B (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and C (\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Among the five metabolites selected above, asparagine and methionine-sulfoxide levels of class C metabolites were lower than those of class A and B metabolites in CS patients; asparagine and methionine-sulfoxide levels of class C metabolites were higher than those of class A and B metabolites in DS patients (\u003cb\u003eTable S6\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSerum AAs in patients with CS and DS of LC are not associated with different MELD score classes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe MELD score is a common clinical index reflecting the degree of liver damage. Then, patients with CS and DS of LC were classified according to the MELD score (\u0026le;\u0026thinsp;8.99 or \u0026gt;\u0026thinsp;8.99) in order to analyze the changes of AAs in different MELD scores. Finally, we found that among the five metabolites selected above, no AA levels were associated with the MELD classification (\u003cb\u003eTable S7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between phenylalanine and tyrosine levels and HBV-DNA levels in patients with CS and DS of LC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the HBV-DNA levels in patients with CS versus DS, we divided the patients into HBV-DNA negative group (\u0026lt;\u0026thinsp;1 \u0026times; 1003) and HBV-DNA positive group (\u0026ge;\u0026thinsp;1 \u0026times; 1003). The results showed that the elevated HBV-DNA levels were accompanied by elevated phenylalanine and tyrosine levels not only in the CS patient species, but also in the DS patients (\u003cb\u003eTable S8\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe ratios of several AAs differ between patients with CS and DS of LC\u003c/h2\u003e \u003cp\u003eTo further understand AA variations, we analyzed nine AA ratios, including tryptophan ratio (tryptophan/BCAAs\u0026thinsp;+\u0026thinsp;phenylalanine\u0026thinsp;+\u0026thinsp;tyrosine), tyrosine ratio (tyrosine/BCAAs\u0026thinsp;+\u0026thinsp;phenylalanine\u0026thinsp;+\u0026thinsp;tryptophan), BCAAs/AAA ratio, BCAAs/tyrosine ratio (BTR), Fischer 's ratio (BCAAs /tyrosine\u0026thinsp;+\u0026thinsp;phenylalanine), kynurenine/tryptophan ratio (KTR), 5-hydroxytryptamine/tryptophan ratio (STR), glutamate/glutamine ratio and phenylalanine/tyrosine ratio. The results showed significant differences in tyrosine ratio, BCAAs/AAA ratio, BTR, Fischer 's ratio, STR and glutamate/glutamine ratio. To further screen for more representative AA ratios, we performed OPLS-DA analysis on the data. Scoring plots showed a significant separation between patients with CS and DS of LC based on nine AA ratios: correlation coefficient R2X\u0026thinsp;=\u0026thinsp;0.438, correlation coefficient R2Y\u0026thinsp;=\u0026thinsp;0.181, and correlation coefficient Q2\u0026thinsp;=\u0026thinsp;0.152; permutation tests showed reliability: R2\u0026thinsp;=\u0026thinsp;0.030, Q2=-0.054. Combining the results of VIP values\u0026thinsp;\u0026gt;\u0026thinsp;1.3, the contribution of Fischer 's ratio, BTR, BCAAs/AAA ratio and tyrosine ratio to the classification were: 1.464, 1.46, 1.45 and 1.347, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, we finally screened the above four differential AA ratios. The scatter plot shows the difference between the above 4 AA ratios in (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and it is noteworthy that Fischer 's ratio, BTR, and BCAAs/AAA ratio were significantly increased in DS patients compared to CS patients. In contrast, tyrosine ratios were significantly lower.\u003c/p\u003e \u003cp\u003eInterestingly, according to logistic regression analysis, the above AA ratios still differed significantly between groups when adjusted for gender, age and BMI (\u003cb\u003eTable S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurther screening for the effect of gender on serum AA ratios was performed for subgroup analysis. It was found that all four serum AA ratios mentioned above were significantly different between CS and DS patients (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurther screening for the effect of age on serum AA ratios was performed in a subgroup analysis. It was found that regardless of age stage (age\u0026thinsp;\u0026le;\u0026thinsp;40 years, 40 years\u0026thinsp;\u0026le;\u0026thinsp;60 years, or 60 years), significant differences remained between CS and DS patients for the three serum AA ratios, except for the BCAAs/AAA ratio (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe further performed the OPLS-DA model based on 40 serum AAs and 9 AA ratios and showed a clear separation between CS and DS of LC: correlation coefficient R2X\u0026thinsp;=\u0026thinsp;0.445, correlation coefficient R2Y\u0026thinsp;=\u0026thinsp;0.287, correlation coefficient Q2\u0026thinsp;=\u0026thinsp;0.204; permutation tests showed reliability: R2\u0026thinsp;=\u0026thinsp;0.141, Q2\u0026thinsp;=\u0026thinsp;0.161 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results showed that the contribution of tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide to the classification was 1.451, 1.397, 1.386, 1.336 and 1.33, respectively; the contribution of Fischer 's ratio, BTR, BCAAs/AAA ratio and tyrosine ratio to the classification was 1.464, 1.46, 1.45 and 1.347.\u003c/p\u003e \u003cp\u003eIn patients with CS versus DS, AA ratios were not associated with Child-Pugh classification (\u003cb\u003eTable S6\u003c/b\u003e). It was found that the tyrosine ratio accompanied by higher MELD score was elevated not only in CS patients but also in DS patients. In contrast, Fischer 's ratio, BTR, and BCAAs/AAA ratio decreased with higher MELD scores (\u003cb\u003eTable S7\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). And the results showed that the ratio of AAs in patients with CS versus DS was independent of the different HBV-DNA groupings (\u003cb\u003eTable S8\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBCAAs/AAA ratios and Fischer 's ratios in patients with CS and DS of LC correlated with mood score classes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e We classified patients with CS and DS of LC according to mood classes (Ⅰ,Ⅱ) in order to analyze the changes of AAs in different mood classes. Finally, we found that among the five metabolites and four AA ratios selected above, the BCAAs/AAA ratio and Fischer 's ratio increased with increasing grade in CS patients; and vice versa in DS patients (\u003cb\u003eTable S9\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTargeted metabolomics based on UPLC-MS/MS technology has been applied in many studies\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. Because UPLC-MS/MS can identify signals and the method can be fully validated; therefore, absolute quantification is possible. Previous studies have shown that the transition from CS to DS is so insidious that many patients miss the best time for treatment\u003csup\u003e18\u003c/sup\u003e. In this study, we used a targeted metabolomics approach based on UPLC-MS/MS to analyze the changes of differential AAs in the serum of patients with CS and DS of LC. The results suggest that AA metabolism is associated with the development of LC.\u003c/p\u003e \u003cp\u003eThe liver is the primary site of AAA metabolism, and plasma concentrations of these AAs are highly dependent on liver function. Studies have shown that liver disease is associated with abnormally high plasma concentrations of phenylalanine and tyrosine\u003csup\u003e19,20\u003c/sup\u003e. Ishii et al. \u003csup\u003e21\u003c/sup\u003eexamined the usefulness of the L-[1-13C] phenylalanine breath test (13C-PheBT) and L-[1-13C] tyrosine breath test (13CTyrBT) for the detection of hepatic damage in patients with LC. The parameters obtained were also compared with biochemical liver function test values. Finally, it was concluded that 13C - PheBT and 13CTyrBT may be useful in assessing the extent and progression of liver function impairment. In addition, studies have shown that progressive elevation of AAA predicts loss of LC\u003csup\u003e22\u003c/sup\u003e. Little is known about the metabolic status and mechanisms involved in asparagine metabolism in liver disease, and the results of Bai et al. \u003csup\u003e23\u003c/sup\u003esuggest that patients with liver cancer with higher levels of asparagine metabolism have a poorer prognosis. This suggests that elevated levels of asparagine may be a potential marker of liver injury. Dopamine is an intermediate product produced during the metabolism of tyrosine via dihydroxyphenylalanine. In the present study, elevated dopamine levels may be associated with elevated tyrosine levels, suggesting an exacerbation of liver injury. Methionine-sulfoxide is a methionine metabolite. In recent years, several clinical studies have confirmed the deleterious effects of hypermethioninemia. Hepatic symptoms of hypermethioninemia include LC and steatosis\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e. In addition, experimental animal studies have shown that high concentrations of methionine and its metabolites, such as methionine-sulfoxide, similar to those found in hypertensive patients, can cause inflammatory cell infiltration, histological changes, and oxidative damage \u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. These changes are likely related to the fact that the liver is the primary metabolite of the AA, and both methionine and methionine-sulfoxide are included in this group. In the present study, elevated methionine-sulfoxide levels suggested more severe hepatic impairment in DS patients than in CS patients, consistent with the deleterious effects of hypermethioninemia. In addition, serum tyrosine, phenylalanine, asparagine, and methionine-sulfoxide levels were significantly higher in DS patients as the severity of child-Pugh classification and HBV-DNA levels increased.\u003c/p\u003e \u003cp\u003eIn addition to the above-mentioned AAs, we also studied the changes in AA ratios. Patients with DS had increased levels of tyrosine ratios and decreased levels of BTR, BCAAs/AAA ratios, and Fischer 's ratios compared to patients with CS. The tyrosine ratio, the ratio of tyrosine to phenylalanine, tryptophan, leucine, valine and isoleucine, is used to measure the availability of tyrosine for the synthesis of dopamine and norepinephrine. The BCAAs/AAA ratio, considered as a good indicator of liver injury and early detection of AA metabolism disorders \u003csup\u003e22,30\u003c/sup\u003e. It is a key component of the etiology of hepatic encephalopathy \u003csup\u003e31\u003c/sup\u003e. The Fischer ratio, BCAAs/tyrosine\u0026thinsp;+\u0026thinsp;phenylalanine, is essential for the assessment of liver function. Ishikawa et al. \u003csup\u003e32\u003c/sup\u003econcluded that BTR has clinical significance in predicting the prognosis of patients with LC. BTR correlates with various liver function tests, including liver fibrosis markers, liver blood flow and hepatocyte function, and therefore can be considered to reflect the degree of liver function impairment \u003csup\u003e33\u003c/sup\u003e. In conclusion, the decrease in BCAA/AAA, Fischer ratio and BTR in this study indicates that patients with DS have more severe hepatic impairment than those with CS.\u003c/p\u003e \u003cp\u003eAccording to Chinese medicine, emotional factors such as happiness, anger and sadness are closely related to health, and when emotional factors exceed the normal level, they will lead to disorders in the body's immune system, thus aggravating the disease \u003csup\u003e34\u003c/sup\u003e. Wang et al. \u003csup\u003e35\u003c/sup\u003estudied 94 patients with LC and showed that the prevalence of anxiety and depression was significantly higher than that of the normal population. Xiao et al. \u003csup\u003e5\u003c/sup\u003ealso found that the number of patients with LC combined with depression was as high as 46.7%. This indicates that the overall level of mental health of patients with LC is poor. The persistence of such negative emotions of anxiety and depression will not only further aggravate the disease, but also seriously affect the effectiveness of treatment. Therefore, it is especially important to provide reasonable and effective psychological interventions for patients with LC. Currently, there are many studies on emotional care to improve the quality of life of patients with LC \u003csup\u003e36\u003c/sup\u003e. To further explore the influence of affective factors on LC, in this study, we also classified the patients with LC according to their emotional class to analyze the changes of AAs in the different emotional classes, and finally, we found that the BCAAs/AAA ratio and Fischer's ratio changed according to the emotional class, indicating that emotional factors may be influential in the progression of LC. However, data collection was not easy, and we only collected data on 50 cases, so we need to expand the sample size in the future to further investigate the relationship between affective factors and LC.\u003c/p\u003e \u003cp\u003eIn conclusion, from a set of 40 serum AA metabolites, a number of unique metabolite profiles of specific serum AAs were identified between patients with CS and DS of LC, which may help to predict the development of LC early. In addition, affective factors were found to influence AA ratios in patients with LC. However, there are still some limitations of this study. First, the effect of diet on serum AA levels could not be excluded. Secondly, there was no normal control group. Third, the sample size was limited. Finally, since this is a cross-sectional study, a longitudinal study approach is needed to confirm the present findings. In addition, experiments with in vivo and in vitro models are needed to investigate the mechanisms in order to better understand the relationship between serum AA levels and the development of LC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Shanghai University of Traditional Chinese Medicine. Informed consent was obtained from the participants. However, an ethical number was not obtained because it was a non-invasive examination for the collection of serum samples at that time.\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\u003eAvailability of data and materials\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 that the research was conducted in the absence of any commercial or financial relationship that could be constructed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China(81873076), Subject of Science and Technology Development,\u0026nbsp;Shanghai University of Traditional Chinese Medicine (23KFL017) and the Hundred Talents Program from Shanghai University of Traditional Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYing\u003c/strong\u003e \u003cstrong\u003eGao\u0026nbsp;\u003c/strong\u003econtributed to acquisition, analysis, interpretation and drafted the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYanqun Luo, Jia Liu\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eXiaoliang Deng\u003c/strong\u003e contributed to acquisition and analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJunming Chen\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Tao Wu\u0026nbsp;\u003c/strong\u003econtributed to conception and design and critically revised the manuscript. All authors gave final approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang, Z.; Zhang, J.-Y.; Wang, L.-F.; Wang, F.-S. Immunopathogenesis and Prognostic Immune Markers of Chronic Hepatitis B Virus Infection. \u003cem\u003eJ Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e27\u003c/em\u003e (2), 223\u0026ndash;230. https://doi.org/10.1111/j.1440-1746.2011.06940.x.\u003c/li\u003e\n\u003cli\u003eGBD 2013 Mortality and Causes of Death Collaborators. Global, Regional, and National Age-Sex Specific All-Cause and Cause-Specific Mortality for 240 Causes of Death, 1990-2013: A Systematic Analysis for the Global Burden of Disease Study 2013. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e385\u003c/em\u003e (9963), 117\u0026ndash;171. https://doi.org/10.1016/S0140-6736(14)61682-2.\u003c/li\u003e\n\u003cli\u003eChang, M.-H. 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Clinical Medicine Practice \u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e18\u003c/em\u003e (07), 170\u0026ndash;172.\u003c/li\u003e\n\u003cli\u003eCuixia Ning; Gang Zhang; Yang Song; Xiaojun Tan. A study on the effect of affective nursing on the psychological state of anxiety and depression in patients with liver cirrhosis. Journal of Naval Medicine \u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e35\u003c/em\u003e (05), 368\u0026ndash;372.\u003c/li\u003e\n\u003c/ol\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":"liver cirrhosis, compensated stage, decompensated stage, amino acids, metabolomics, emotional factor","lastPublishedDoi":"10.21203/rs.3.rs-4186028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4186028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eOBJECTIVE: \u003c/strong\u003eTo study the amino acid (AA) profile of serum samples from patients with compensated stage (CS) and decompensated stage (DS) of liver cirrhosis (LC). In particular, changes in AAs in different mood classes after categorizing patients with CS versus DS of LC according to mood class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS:\u003c/strong\u003e Using targeted metabolomics, serum AA levels were quantified in two populations: patients with CS (n=60) and patients with DS (n=44). We also analyzed serum AAs in 26 patients with CS and 24 patients with DS after classifying them according to mood class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS:\u003c/strong\u003e In terms of AA levels, serum tyrosine, asparagine, dopamine, phenylalanine and methionine-sulfoxide levels were significantly increased in patients with DS compared to those with CS. In addition, asparagine and methionine-sulfoxide levels correlated with Child-Pugh classification in CS and DS patients; phenylalanine and tyrosine levels correlated with HBV-DNA levels. In terms of AA ratios, Fischer 's ratio, BTR, and BCAAs/AAA ratio were significantly increased in DS patients compared with CS patients. In contrast, tyrosine ratios were significantly lower. In addition, tyrosine ratio, Fischer 's ratio, BTR, and BCAAs/AAA levels were correlated with MELD score in both CS and DS patients; BCAAs/AAA ratio and Fischer 's ratio were correlated with mood score grade.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION: \u003c/strong\u003eThe metabolic profiles of certain AAs in serum of patients with CS and DS of LC are different, which may help to detect the transition from CS to DS as early as possible and have implications for patient care and treatment decisions. In addition, the AA ratios varied with mood class, suggesting that mood factors may be influential in the progression of LC.\u003c/p\u003e","manuscriptTitle":"Altered Amino Acid Metabolome in Patients affected by HBV cirrhosis at different stages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 16:21:20","doi":"10.21203/rs.3.rs-4186028/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":"8cc72b46-26dc-4eb1-b1db-b405eb1e7e69","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T03:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 16:21:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4186028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4186028","identity":"rs-4186028","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}