Plasma Metabolomics Characteristic of Gout Patients With Regular Glucose, Blood Pressure, and Blood Lipid

Background Gout is a metabolic disease and is the most common form of inammatory arthritis affecting men. However, the pathogenesis of gout is still uncertain, and novel biomarkers are needed for early prediction and diagnosis of gout. The aim of this study is to reveal the metabolic alterations in plasma of gout patients and discover novel molecular biomarkers for early diagnosis. Metabonomics was employed to screen and identify novel biomarkers of gout based on human plasma. Ultra High-Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS) and orthogonal signal correction partial least squares discriminate analysis (OPLS-DA) were also used for metabonomics study. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were used for pathway enrichment analysis. In this study, 51 metabolites in positive ion mode and 39 metabolites in negative ion mode were selected as remarkable signicant variables between gout and healthy control group. Four unique pathways were found, namely Citrate cycle (TCA cycle), cysteine and methionine metabolism and alanine, aspartate, and glutamate metabolism. A total of 7 metabolites with AUC>0.75 were involved in the above three metabolic pathways, including L-Malic acid (cid:0) Pyruvate (cid:0) cis-Aconitate, Cysteine-S-sulfate, L-2-Aminobutyric acid, L-Methionine, Succinic semialdehyde. The present study identied the plasma metabolomics characteristic of gout through UHPLC-MS. The differential metabolites pathways of gout screened out were involved in Citrate cycle (TCA cycle), cysteine and methionine metabolism and alanine, aspartate, and glutamate metabolism. The metabolomics characteristic of gout could provide novel insights into the pathogenesis of gout. After these the of 95% condence to identify potential outliers the dataset. To group separation, and nd metabolites, supervised orthogonal projections OPLS-DA applied. Then, cross-validation As an intermediate metabolite of the TCA cycle, the content of L-Malic acid, pyruvate, and Cis-aconitate was signicantly lower in the gout group than in the healthy control group in our study. L-malic acid reduces oxidative stress and increases the mitochondrial antioxidative defenses. In Zeng's study, L-malate signicantly reduces reactive oxygen species (ROS) and malondialdehyde content levels in the liver and heart of aged rats[15]. L-malate might increase the antioxidant capacity of mitochondria by enhancing the expression of mRNAs involved in the malate synthase and the antioxidant enzymes[15]. Pyruvate is the end-product of glycolysis, is derived from additional sources in the cellular cytoplasm, and is ultimately destined for transport into mitochondria as a master fuel input undergirding citric acid cycle carbon ux. In mitochondria, Pyruvate drives ATP production by oxidative phospho-relation and multiple biosynthetic pathways intersecting the citric acid cycle. Appropriate regulation of pyruvate ux is critical for maintaining cellular function in multiple contexts.

The healthy control group was age-matched healthy volunteers registered in the physical examination center of Beijing Luhe Hospital. This study was approved by the ethics committee of the Beijing Luhe Hospital, Capital Medical University. Written informed consent was obtained from each subject.

Sample collection and preparation
Venous plasma samples were collected in K2 EDTA vacutainer tubes from gout patients and HCs after an overnight fast for at least eight hours and cooled down in freezer (4°C) at once. They were then centrifuged at 3000rpm for 10min at 4°C within 2h. Supernatants (plasma) were separated and transferred into new vials, and immediately stored frozen (-80°C) until sample preparation.
Before LC-MS analysis, 100μL of the sample was transferred to an EP tube, and 400μL extract solution (acetonitrile: methanol = 1: 1) containing internal standard (L-2-Chlorophenylalanine, 2μg/mL) was added. After 30s vortex, the samples were sonicated for 10 min in an ice-water bath. Then the samples were incubated at -40°C for 1h and centrifuged at 10000rpm for 15min at 4°C. 425μL of supernatant was transferred to a fresh tube and dried in a vacuum concentrator at 37°C. Then, the dried samples were reconstituted in 200μL of 50% acetonitrile by sonication on ice for 10min. The constitution was then centrifuged at 13000rpm for 15min at 4°C, and 75μL of supernatant was transferred to a fresh glass vial for LC/MS analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples.
The Triple TOF 6600 mass spectrometry (AB Sciex) was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB Sciex) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, the most intensive 12 precursor ions with intensity above 100 were chosen for MS/MS at collision energy (CE) of 30 eV. The cycle time was 0.56s. ESI source conditions were set as follows: Gas 1 as 60 psi, Gas 2 as 60 psi, Curtain Gas as 35 psi, Source Temperature as 600°C, Declustering potential as 60V, Ion Spray Voltage Floating (ISVF) as 5000V or -4000V in positive or negative modes, respectively.
Data preprocessing and statistical analysis MS raw data (.wiff) les were converted to the mzXML format by ProteoWizard, and processed by R package XCMS (version 3.2). The process includes peak deconvolution, alignment, and integration. Minfrac and cut-off are set as 0.5 and 0.6 respectively. In-house MS2 database was applied for metabolites identi cation.
In this study, the original data were preprocessed rstly. Then, the missing values were lled up by half of the minimum value. Also, the internal standard normalization method was employed in this data analysis. The nal dataset containing the information of peak number, sample name, and normalized peak area was imported to SIMCA15.0.2 software package (Sartorius Stedim Data Analytics AB, Umea, Sweden) for multivariate analysis. Data were scaled and logarithmically transformed to minimize the impact of both noise and high variance of the variables. After these transformations, PCA, an unsupervised analysis that reduces the dimension of the data, was carried out to visualize the distribution and the grouping of the samples. 95% con dence interval in the PCA score plot was used as the threshold to identify potential outliers in the dataset. To visualize group separation, and nd signi cantly changed metabolites, supervised orthogonal projections to OPLS-DA was applied. Then, 7-fold cross-validation was performed to calculate the value of R2 and Q2. R2 indicates how well the variation of a variable is explained, and Q2 means how well a variable could be predicted. To check the robustness and predictive ability of the OPLS-DA model, 200 times permutations were further conducted. Afterwards, the R2 and Q2 intercept values were obtained. Here, the intercept value of Q2 represents the robustness of the model, the risk of over tting and the reliability of the model, which will be the smaller the better. Furthermore, the value of VIP of the rst principal component in OPLS-DA analysis was obtained. It summarizes the contribution of each variable to the model. The metabolites with VIP>1, p<0.05(student t-test) and fold change>1.2 or<0.83 were considered as signi cantly changed metabolites. Besides, commercial databases, including KEGG (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/) were used for pathway enrichment analysis.

Results
Demographic characteristics of the study participants In this study, 30 male gout patients 30 age, gender, and body mass index (BMI)-matched healthy controls (HCs) were included. The average age of the participants was 33.78±9.05 years, and the mean BMI was 25.50±2.90 kg/m2. Gout patients had higher blood pressure(BP), uric acid (UA), glucose (GLU), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), creatinine (Cr), and blood urea nitrogen (BUN) compared with healthy controls. Demographic characteristics of gout patients and healthy controls were shown in Table 1.
Identi cation of plasma differential metabolites A total of 1505 features in positive ion mode and 1608 features in negative ion mode were detected using HUPLC-MS. After relative standard deviation denoising and lling up the missing values by half of the minimum value, 1492 features in positive ion mode and 1589 features in negative ion mode remained. analysis could outline the original distribution of metabolites. As shown in Fig.1a and b, there were no obvious outlier samples in modes. However, the scatter plot failed to show a clear separation in modes. Then, the OPLS-DA model was used to characterize the metabolic disturbances (Figure1.c and d). The cumulative values of R2Y(R2Y=0.731) and Q2Y (Q2Y=0.593) in positive ion mode and R2Y(R2Y=0.891) and Q2Y (Q2Y=0.739) in negative ion mode indicated that the models had an excellent predictive and interpretative capacity. Gout samples could be clearly distinguished from healthy control samples of positives and negative ion modes. Permutation plots of two OPLS-DA models repeated 200 times veri ed the validation of the models ( Fig. 1 e and f). All the permuted R2 (cum)and Q2 (cum) values to the left were lower than the original point to the right, and all the regression lines of the Q2 (cum) points have a negative intercept [9,10], which indicates that the models were valid with no over tting.
The standard value used in this project was that the P-value < 0.05, and Variable Importance in the Projection (VIP) value >1.0, 80 and 62 metabolites were selected as remarkable signi cant variables in the two modes between gout and control group. According to fold change>1.2 or <0.83, 56 and 45 potential metabolic biomarkers were remained in positive and negative ion mode, respectively. Among the potential metabolic biomarkers, 51 metabolites positive ion mode and 39 metabolites negative ion mode can nd identi er (ID) in the Human Metabolome Database (HMDB) ( Figure.2a,2b). Correlation analysis of metabolite differential was shown in Figure.2c,2d. The differential metabolites can be categorized as amino acids, purine derivatives, fatty acyls, carboxylic acids and derivatives. ROC analysis of each metabolite was conducted to seek novel diagnostic biomarkers. Among 90 metabolites, 40% presented high AUC values (AUC >0.75). The HMDB ID, VIP, P-values, fold change, and AUC of each metabolite was shown in detail (Table2.). Using uric acid as a diagnostic indicator of gout, compared with healthy control, the AUC was 0.948. If using thymine as a diagnostic indicator of gout, the AUC was 0.949, and 5 metabolites(Thymine, N-Acetylcadaverine, Cyclohexylamine, Monomethyl glutaric acid, Val-Thr, N-Acetyl-L-alanine) AUC>0.9.

Pathway analysis of gout
To investigate whether metabolism is affected by gout, metabolites with an AUC area greater than 0.75 were selected for metabolic pathway enrichment analysis. We performed pathway analysis using MetaboAnalyst. The P-value and the pathway impact were calculated from the Metabolite Set Enrichment Analysis(MSEA) and the pathway topology analysis, respectively. The P-value threshold was set 0.05, and the values above this threshold were filtered out as insignificant pathways. As a result, we found four unique pathways. They were TCA cycle, glyoxylate and dicarboxylate metabolism, cysteine and methionine metabolism and alanine, aspartate and glutamate metabolism ( Figure 3). Glyoxylate and dicarboxylate metabolism occur in plants and microorganisms. It has the same metabolites as TCA cycle. So there were three metabolic pathways identi ed in our study. A total of 7 metabolites with AUC>0.75 were involved in the above three metabolic pathways, including L-Malic acid Pyruvate cis-Aconitate, Cysteine-S-sulfate, L-2-Aminobutyric acid, L-Methionine, Succinic semialdehyde( Figure 4, Figure5).

Correlation analysis of clinical features and metabolites
The correlation analysis between metabolites and clinical indicators showed that L-Malic acid Pyruvate cis-Aconitate, L-2-Aminobutyric acid, L-Methionine were correlated negatively with blood pressure, glucose, uric acid, and blood lipid. Succinic semialdehyde and cysteine-S-sulfate were correlated positively with blood pressure, glucose, uric acid, and blood lipid( Figure 6).

Discussion
we enrolled the patients with gout and healthy control group, compared to the difference of plasma metabolites. Unlike previous studies, this study excluded obesity, hyperlipidemia, hypertension, which could be better re ect the difference metabolites between gout and healthy control. We still observed that blood glucose and systolic blood pressure were higher in the gout group than in the healthy controls, which indicated that uric acid was related to glucose and salt metabolism [11][12][13][14].
In this study, 51 metabolites in positive ion mode and 39 metabolites in negative ion mode were identi ed as different metabolites that involved amino acid metabolism, purine metabolism, carbohydrate metabolism, and fatty metabolism. However, many new features were still present, but there were no matches in the database of known metabolites. The identi cation of these unknown substances using HPLC-MS will be focused on in our future work. HPLC-MS -based metabolomics is a promising technique for biomarker discovery.
We found that uric acid was a perfect biomarker for the diagnosis of gout. However, using thymine as a diagnostic indicator of gout, the AUC was 0.949, which was better than uric acid. These bases are the building blocks of DNA and life forms on earth. We found ve metabolites (N-Acetylcadaverine, Cyclohexylamine, Monomethyl glutaric acid, Val-Thr, N-Acetyl-L-alanine) which AUC>0.9. However, no metabolic pathways, including the ve metabolites, were found in the analysis of metabolic pathways. The role of these ve metabolites needs further study.
In the enrichment analysis of metabolic pathways, we found three speci c pathways. The TCA cycle is an important aerobic pathway for the nal steps of the oxidation of carbohydrates. As an intermediate metabolite of the TCA cycle, the content of L-Malic acid, pyruvate, and Cis-aconitate was signi cantly lower in the gout group than in the healthy control group in our study. L-malic acid reduces oxidative stress and increases the mitochondrial antioxidative defenses. In Zeng's study, L-malate signi cantly reduces reactive oxygen species (ROS) and malondialdehyde content levels in the liver and heart of aged rats [15]. L-malate might increase the antioxidant capacity of mitochondria by enhancing the expression of mRNAs involved in the malate synthase and the antioxidant enzymes [15]. Pyruvate is the end-product of glycolysis, is derived from additional sources in the cellular cytoplasm, and is ultimately destined for transport into mitochondria as a master fuel input undergirding citric acid cycle carbon ux. In mitochondria, Pyruvate drives ATP production by oxidative phosphorelation and multiple biosynthetic pathways intersecting the citric acid cycle. Appropriate regulation of pyruvate ux is critical for maintaining cellular function in multiple contexts.
Pyruvate dysmetabolism presents in chronic, progressive diseases such as chronic obstructive pulmonary disease (COPD), obesity, diabetes, and aging [16]. The mitochondrial pyruvate carrier mediates high fat diet-induced increases in hepatic TCA cycle capacity [17]. In our study, L-Malic acid, pyruvate and Cisaconitate in the gout group were lower than those in the normal control group. It was speculated that the energy metabolism of the TCA cycle was slowed down, and mitochondrial function was affected in patients with gout. Studies have reported that excess uric acid induced oxidative stress, triglyceride accumulation, and mitochondrial dysfunction in the liver and hyperuricemia induced mitochondrial dysfunction and triglyceride accumulation in skeletal muscle. These ndings may explain why hyperuricemia is an independent predictor of diabetes [18].
In our study, in the cysteine and methionine metabolism pathway, the cysteine-S-sulfate level in the gout group was elevated. In contrast, the L-methionine and L-2-aminobutyric acid level were lower than those in the control group. Methionine is an essential amino acid in humans, as the substrate for other amino acids such as cysteine and taurine, versatile compounds such as S-Adenosyl methionine, and the critical antioxidant glutathione. Cysteine-S-sulfate is an abnormal metabolite discovered in the urine and blood of a patient with cysteine oxidase de ciency, a rare disorder of sulfur amino acid metabolism associated with brain damage and mental retardation. L-methionine is levorotatory of methionine. Methionine promotes phospholipids methylation in the liver cell membrane, boots the membrane uidity and strengthens the Na + -K + ATPase pump function. Methionine reduces bile aggradations of the liver cells, and has a protective effect on the liver. Thus it enhances the cysteine, glutathione, and taurine synthesis, reduces the bile acid accumulation in the liver, and strengthens detoxi cation. Lack of methionine can lead to a blockage in protein synthesis in the body, causing damage to the body. Methionine-and cholinede cient diets (MCDD) induce fatty liver in mice which are partly mediated by TG lipolysis in white adipose tissues (WATs) [19]. The plasma concentration of Lmethionine was reduced in the gout group, which may be associated with a predisposition to fatty liver in patients with gout. In a 6-year study, the author found that the methionine to homocysteine ratio was associated with dementia development and structural brain. It was suggesting that a higher methionine to homocysteine ratio may be important in reducing the rate of brain atrophy and decreasing the risk of dementia in older adults [20]. L-2-aminobutyric acid is a kind of non-natural chiral amino acid, which has the functions of inhibiting human nerve information transmission, enhancing the activity of glucose phosphatase and promoting the metabolism of brain cells. A study demonstrated that oral administration of L-2-aminobutyric acid -AB e ciently raised both circulating and myocardial glutathione levels and protected against doxorubicin-induced cardiomyopathy in mice [21].
In the present study, we found the plasma level of succinic semialdehyde evaluated and pyruvate reduced. The role of gout metabolism and succinic semialdehyde in gout metabolism is unclear and needs further research.
Our study was a cross-sectional observational study, which found that some metabolites might be used in the diagnosis of gout or hyperuricemia. However, the causal relationship cannot be determined. To better understand the physiological and pathological mechanisms of gout and hyperuricemia, the mechanism of the role of metabolites in the pathogenesis of gout needs further veri cation.

Conclusions
In summary, the present study identi ed the plasma metabolomics signatures of gout through LC-MS. The differential metabolites of gout screened out were involved in the TCA cycle, cysteine and methionine metabolism and alanine, aspartate and glutamate metabolism. These results can provide novel insights into the development of gout.