Study Design and Population
This study was an across-sectional observational study. From October 2018 to May 2019, 60 participants and 30 volunteers were enrolled from the outpatient department or hospitalized at the Center for Endocrine Metabolism and Immune Disease Center at Beijing Lu He Hospital, Capital Medical University. Inclusion criteria for gout and hyperuricemia group: ①18–75 years old; ②definite diagnosis of hyperuricemia (serum uric acid ≥ 420µmo1/L (7 mg/dl), ≥ 2 times in the last month; ③willing to participate in this study. The gout group also met the diagnostic criteria of ACR-EULAR gout arthritis. Healthy control group: Normal blood uric acid levels. Exclusion criteria: malignant tumors/hematological tumors; pregnant/lactating women or women planning to become pregnant; chronic renal failure (CKD stage II and above, eGFR < 90 m1/min/1.73m²); abnormal liver function (ALT、AST、GGT、T-Bil、D-Bil > upper limit of normal value); hyperlipidemia (TG > 1.7 mmol/l, CHO > 5.17 mmol/l, LDL > 3.36 mmol/l); diabetes, hypertension, cardiovascular and cerebrovascular diseases; rheumatic diseases such as rheumatoid arthritis, osteoarthritis, spondyloarthropathy, etc.; have taken the following drugs in the last 6 months: glucocorticoids, immunosuppressants (mercaptopurine, 6-mercaptopurine, cyclosporine, cyclophosphamide), chemotherapy drugs; those who are allergic to drugs such as non-steroidal anti-inflammatory drugs, colchicine, febuxostat, allopurinol, or hypersensitivity; those with severe infections, active tuberculosis; have accepted urate-lowering therapy in the past three months; persons with mental illness who cannot cooperate; combined with other diseases affecting the efficacy or poor compliance.
The healthy control group was age-matched healthy volunteers registered in the physical examination center of Beijing Lu He Hospital. This study was approved by the ethics committee of the Beijing Lu He 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, hyperuricemia patients and HCs after an overnight fast for at least 8 h and cooled down in freezer (4 °C) at once. They were then centrifuged at 3000 rpm for 10 min at 4 °C within 2 h. Supernatants (plasma) were separated and transferred into new vials, and immediately stored frozen (80 °C) until sample preparation.
Prior to LC-MS analysis,100µL of sample was transferred to an EP tube, and 400µL extract solution (acetonitrile: methanol = 1: 1) containing internal standard (L-2-Chlorophenylalanine, 2 µg/mL) was added. After 30 s vortex, the samples were sonicated for 10 min in an ice-water bath. Then the samples were incubated at -40 °C for 1 h and centrifuged at 10000 rpm for 15 min 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 10 min. The constitution was then centrifuged at 13000 rpm for 15 min 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
UPLC-MS/MS metabolomics profiling
The UHPLC separation was carried out using a 1290 Infinity series UHPLC System (Agilent Technologies), equipped with a UPLC BEH Amide column (2.1 * 100 mm, 1.7 µm, Waters). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 ammonia hydroxide in water(pH = 9.75)(A) and acetonitrile (B). The analysis was carried with elution gradient as follows: 0 ~ 0.5 min, 95%B; 0.5 ~ 7.0 min, 95%~65% B; 7.0 ~ 8.0 min, 65%~40% B; 8.0 ~ 9.0 min, 40% B; 9.0 ~ 9.1 min, 40%~95% B; 9.1 ~ 12.0 min, 95% B. The column temperature was 25 °C. The auto-sampler temperature was 4 °C, and the injection volume was 1µL (pos) or 1µL (neg), respectively.
The TripleTOF 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 0.56 s. 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 pre-processing and statistical analysis
MS raw data (.wiff) files 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 identification.
In this study, the original data were preprocessed firstly. Then, the missing values were filled up by half of the minimum value. Also, internal standard normalization method was employed in this data analysis. The final 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 was 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% confidence interval in the PCA score plot was used as the threshold to identify potential outliers in the dataset. In order to visualize group separation and find significantly changed metabolites, supervised orthogonal projections to OPLS-DA was applied. Then, a 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 was further conducted. Afterward, the R2 and Q2 intercept values were obtained. Here, the intercept value of Q2 represents the robustness of the model, the risk of overfitting and the reliability of the model, which will be the smaller the better. Furthermore, the value of variable importance in the projection (VIP) of the first principal component in OPLS-DA analysis was obtained. It summarizes the contribution of each variable to the model. The metabolites with VIP > 1 and p < 0.05(student t test) were considered as significantly changed metabolites. In addition, commercial databases including KEGG(http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/) were used for pathway enrichment analysis.