Background: Gout is a metabolic disease and is the most common form of inflammatory 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 was to develop a systemic metabolic profile of patients with asymptomatic hyperuricemia (HUA) and gout by using a metabolomics approach, and find potential pathophysiological mechanisms of and markers of predisposition to gout. Methods: Serum samples were collected from 149 subjects, including 50 patients with HUA, 49 patients with gout and 50 healthy controls. H nuclear magnetic resonance (NMR) spectroscopy combined with principal components analysis and orthogonal partial least squares-discriminant analysis were used to distinguish between samples from patients and healthy controls. Clinical measurements and pathway analysis were also performed to contribute to understanding of the metabolic change. Results: By serum metabolic profiling, 21 metabolites including lipids and amino acids were significantly altered in patients with HUA or gout. The levels of identified biomarkers together with clinical data showed apparent alteration trends in patients with HUA or gout compared to healthy individuals. According to pathway analysis, three and five metabolic pathways were remarkably perturbed in patients with HUA or gout, respectively. These enriched pathways involve in lipid metabolism, carbohydrate metabolism, amino acids metabolism and energy metabolism. Conclusions: Taken together, we identified the biomarker signature for HUA and gout, which provides biochemical insights into the metabolic alteration, and identified a continuous progressive axis of development from HUA to gout. Keywords: Metabolomics, NMR, Hyperuricemia, Gout, Biomarkers Background the deposition of monosodium urate crystals in the Gout is a type of common inflammatory arthritis in joints and hyperuricemia (HUA) contributes to the adults that is associated with excruciating pain, and the development of gout [4, 5]. HUA has long been prevalence of gout has risen over the last few decades recognized as the key causal precursor in the deve- [1–3]. It reduces quality of life in patients, even causing lopment of gout and the prevalence of comorbidities disability due to excruciatingly painful acute attacks of tends to increase with serum uric acid (SUA) levels gouty arthritis, malformation of joints, chronic joint [6, 7]. HUA and gout are closely associated with damage and renal stone formation. Gout is triggered by components of metabolic syndrome, kidney injury and cardiovascular diseases [8–10]. However, the patho- * Correspondence: firstname.lastname@example.org; email@example.com genesis of gout seems to be complex because many Department of Sanitary Inspection, Shanghai University of Medical & Health individuals with HUA form monosodium urate Sciences, Shanghai 201318, China crystals and develop acute attacks of gouty arthritis, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China but some of them do not follow this trend . It is Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 2 of 11 well-known that HUA and gout are metabolic diseases, attacks of gout within the last 3 months, thus excluding but it is uncertain what the metabolic difference between the effects of inflammation and drug treatments. All par- them is and whether this difference promotes acute at- ticipants were male adult residents in the Harbin regio- tacks of gouty arthritis. Therefore, it is important to nand were did not have diabetes mellitus, heart disease, recognize metabolism-related indicators in gout, which liver or renal dysfunction, gastrointestinal disease, pul- may contribute to understanding of the predisposition to monary disease or cancer, and had taken no metabolic gout. Moreover, due to the possibility that the diagnosis of drugs or dietary supplements within the last 3 months. gout based on SUA as a biomedical indicator in the clinic Each participant had been given a standardized diet plan may be inaccurate, even if crystals are identified in the for 3 days before blood was drawn, and the consumption digits , the identification of novel biomarkers associ- of alcohol and caffeine products were forbidden during ated with the occurrence and development of gout is this period. Demographic data (age and gender) and an- highly desirable to prevent the acute attacks of gouty thropometric data (height, weight, diastolic blood pres- arthritis and destruction of the joints. sure (DBP) and systolic blood pressure (SBP)) were Metabolomics is an emerging and rapidly developing obtained from all participants, and current medications field that offers an efficient approach to describe and medical history were also recorded. biomarkers or characterize perturbations of diseases via Venous blood was taken from participants after over- detection, identification and quantification of low- night fasting and allowed to clot for 30 min at room molecular-weight metabolites (< 1 kDa) in biological sam- temperature. It was then centrifuged at 3000 rpm for ples such as plasma and urine [13–15]. The metabolomics 10 min and the supernatant was stored at − 80 °C until approach has been successfully applied in recent years to NMR analysis. identify early signals or biomarkers of abnormalities , biological pathway characterization  and disease diag- Clinical chemistry measurements nosis [16, 18]. H nuclear magnetic resonance (NMR) Alanine aminotransferase (ALT), aspartate aminotrans- spectroscopy is an attractive tool in metabolomics ferase (AST), total protein, albumin, globulin, fasting research because of several advantages, such as simple glucose, creatinine, urea nitrogen, triglyceride, uric acid, sample preparation, high reproducibility and fast analysis. cholesterol, high-density lipoprotein (HDL)-cholesterol Therefore, H NMR-based metabolomics is suitable for and low-density lipoprotein (LDL)-cholesterol were mea- simultaneous and systemic analysis of multiple com- sured using an automatic biochemical analyzer (AUTO- pounds of metabolite fingerprinting . LAB PM4000, Rome, Italy). The values were expressed In the current study, we carried out a serum metabolo- as mean ± SD. Student’s t test was conducted to compare mics study on a male population with normal SUA, hyper- the clinical biochemical data using SPSS 20 software uricaemia and gout by using H NMR spectroscopy (SPSS Inc., Chicago, IL, USA). A p value <0.05 was coupled with chemometric methods. The aim of this regarded as statistically significant. study was to explore the serum metabolic alteration in patients with asymptomatic hyperuricemia and patients Serum sample preparation with gout, to capture the metabolic alteration associated Serum samples were removed from − 80 °C storage and with the initiation and progression of gout. thawed at 4 °C. A volume of 200 μL serum and 350 μL of 0.9% NaCl (w/v) solution containing 20% D O were Methods mixed and then followed by centrifugation (10,000 g, 4 °C, Study subjects and sample collection 10 min). Finally 500 μL of the supernatant of each Asymptomatic patients with hyperuricemia (n = 50) and sample was transferred into individual 5-mm high- patients with gout (n = 49) were enrolled in the physical quality NMR tubes. examination center of the Second Affiliated Hospital of Harbin Medical University between January 2013 and H NMR spectroscopic analysis June 2015. An additional 50 healthy control samples NMR spectra of serum samples were recorded on a Bruker were collected from healthy donors. All the diagnosis of AVIII 500 spectrometer (Bruker Biospin, Rheinstetten, these patients was confirmed by experienced doctors ac- Germany) equipped with a 5-mm inverse broadband probe cording to serum uric acid levels and joint swelling. Hy- at 300 K. The H NMR spectra were recorded with the peruricemia in men was defined as SUA ≥ 416 mmol/L, relaxation edited Carr–Purcell–Meiboom–Gill (CPMG, which is a widely accepted diagnostic criterion [20–23]. RD-90°-(τ-180°-τ)n-acquisition) pulse sequence to detect All patients with gout fulfilled the 1977 preliminary low-molecular-weight metabolites over a spectral width American Rheumatism Association classification criteria of 20 ppm with 128 transients, 60 k data points, and for gout . They had been newly diagnosed with gout 4 s relaxation delay. In order to facilitate the identifica- within the last 2 years and had experienced no acute tion of metabolites, two dimensional (2D) J-resolved Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 3 of 11 spectroscopy (JRES) spectra were acquired as previ- Results ously reported [25, 26]. Baseline characteristics of participants The representative sample of this metabolomics study con- sisted of 149 male participants (50 controls, 50 patients NMR data processing with HUA and 49 patients with gout) whose basic charac- NMR spectra were processed using TOPSPIN software 1 teristics and clinical variables are summarized in Table 1. package (version 3.2, Bruker Biospin, Germany). For H Body mass index (BMI), DBP, SBP, ALT, AST, fasting glu- NMR spectra, an exponential window function was cose, uric acid, triglyceride, cholesterol and LDL-cholesterol employed with a line broadening factor of 0.3 Hz and were notably increased in HUA and in patients with gout zero-filled to 128 k prior to Fourier transformation. Each compared to the control group (p < 0.05). Compared to the spectrum was then manually phase-corrected and HUA group, the gout group had significant higher levels of baseline-corrected and calibrated with the anomeric DBP, SBP, fasting glucose, uric acid and HDL-cholesterol proton signal of α-glucose (δ 5.23 ppm). The spectra were and a lower level of albumin. segmented into regions with a width of 0.01 ppm (δ 0.5– 9.0 ppm) using AMIX software package (V3.9.14, Bruker H NMR spectroscopy Biospin). The regions of imperfect water saturation signals Three CPMG H NMR spectra (Fig. 1) of serum samples (δ 4.50–5.15 ppm) and urea signals (δ 5.50–6.50 ppm) obtained from control individuals (Fig. 1a), patients with were discarded. The NMR resonances were assigned HUA (Fig. 1b) and gout (Fig. 1c) show the average according to an electronic database (HMDB, http://www. signals of metabolites. In total, 41 metabolites were hmdb.ca/) and data from the literature [27, 28], and were identified in serum samples including lipids, glucose, confirmed with 2D NMR results. amino acids and organic acids, as shown in Fig. 1 and Additional file 1. Multivariate statistical analysis Multivariate data analysis was performed in order to Multivariate analysis of NMR data establish a systemic overview of the discrimination of Since SUA is an important factor in gout, and the range metabolic patterns in patients with HUA, patients with of SUA levels was large in subjects with gout in this gout and controls. At first, principal components ana- study (Table 1), the NMR spectrum data from the two lysis (PCA) was used to observe the intrinsic metabolic gout subgroups including gout with HUA (n = 32) and variation in H NMR spectra data. Next, orthogonal gout with normal SUA (n = 17) were analyzed by PCA partial least squares-discriminant analysis (OPLS-DA) and OPLS-DA, to determine whether SUA affected the was carried out to maximize the variation between metabolic profiles in patients with gout. The OPLS-DA groups and then detect significant metabolites that scores plot (Additional file 2) showed no metabolic vari- contribute to the variation. A coefficient of variation- ation trend between the two subgroups in relation to analysis of variance (CV-ANOVA) approach was SUA. This may be due to the complexity of gout patho- further applied to test the significance of intergroup genesis, which cannot be explained by levels of uric acid, differentiations (p < 0.05) in OPLS-DA models. Load- as aforementioned. Therefore, in the following multivari- ings plots of OPLS-DA models were generated using ate data analysis, the two gout subgroups were processed MATLAB 7.1 (Mathworks Inc., USA) with correlation and treated as one group. coefficients. In these loadings plots, hot-colored To observe the clustering trends of samples obtained metabolites have greater contribution in intergroup from patients with HUA or gout and control subjects, differentiations than cold-colored ones. The selection serum metabolic profiling was performed using PCA of significant metabolites was based on correlation and OPLS-DA. The PCA scores plot for the first two coefficients (|r| > 0.6) and Student’s t test (p < 0.01). 2 2 components (R X = 0.367, Q = 0.34) reflecting a To visualize the alterations of remarkable metabolites separation trend in the gout, HUA and control groups in three groups, a heat map was created using Meta- (Fig. 2a). Furthermore, three distinct clusters of samples boAnalyst 3.0 (http://www.metaboanalyst.ca/). 2 were observed in the OPLS-DA scores plot (R X = 0.484, 2 2 R Y = 0.711 and Q = 0.566; Fig. 2b). To assess the risk Pathway analysis that the current OPLS-DA model was spurious, the Pathway analysis of remarkably changed metabolites in data were analyzed using the CV-ANOVA approach; a patients with HUA and patients with gout was applied p value of 1.1E-26 showed that the OPLS-DA model within MetaboAnalyst 3.0. Among all the perturbed was valid. pathways, the ones with impact value > 0.1 and p < 0.05 OPLS-DA was further performed to identify the sig- were selected as significantly perturbed metabolic nificantly altered metabolites in the HUA and gout pathways in HUA and patients with gout. groups as shown in Fig. 3. The OPLS-DA scores plot Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 4 of 11 Table 1 Baseline characteristics (demographic, anthropometric and clinical data) for the HUA, gout, and control groups Parameter Control (n = 50) HUA (n = 50) Gout (n = 49) Basic characteristics * ## Age (years) 43.8 ± 11.5 39.08 ± 10.4 45.6 ± 7.3 Sex (female/male) 0/50 0/50 0/50 2 ** ** BMI (kg/m ) 23.4 ± 3.2 27.09 ± 3.0 26.5 ± 3.3 Smoker/non-smoker 24/26 22/28 23/26 Alcohol consumption (%) 61 67 57 Clinical variables ** **## DBP (mmHg) 78.6 ± 5.7 85.1 ± 11.1 91.8 ± 12.4 ** **## SBP (mmHg) 114.8 ± 6.5 125.8 ± 14.4 136.3 ± 19.2 ** ** ALT(U/L) 22.6 ± 11.3 43.8 ± 30.6 34.8 ± 18.9 ** ** AST(U/L) 20.5 ± 5.9 27.7 ± 14.8 26.8 ± 12.9 Total protein (g/L) 74.8 ± 4.3 75.5 ± 4.4 74.6 ± 4.5 **# Albumin (g/L) 49.4 ± 2.9 48.9 ± 2.2 47.8 ± 3.3 Globulin (g/L) 25.4 ± 4.0 26.7 ± 3.5 26.9 ± 4.0 ** **# Fasting glucose (mmol/L) 5.0 ± 0.4 5.7 ± 0.7 6.3 ± 1.9 Urea nitrogen (mmol/L) 5.6 ± 1.3 5.4 ± 1.2 5.5 ± 1.8 **## Creatinine (μmol/L) 80.6 ± 9.6 81.2 ± 13.9 91.8 ± 23.2 ** **## Uric acid (μmol/L) 325.1 ± 60.6 470.6 ± 55.0 536.2 ± 131.4 ** ** Triglyceride (mmol/L) 1.1 ± 0.4 2.3 ± 1.2 3.1 ± 2.1 ** ** Cholesterol (mmol/L) 4.7 ± 0.8 5.2 ± 0.8 5.4 ± 1.0 ** ## HDL-cholesterol (mmol/L) 1.4 ± 0.3 1.1 ± 0.2 1.3 ± 0.3 ** * LDL-cholesterol (mmol/L) 2.7 ± 0.5 3.2 ± 0.7 3.0 ± 0.6 Data are presented as mean ± SD except where stated otherwise HUA hyperuricemia, BMI body mass index, DBP diastolic blood pressure, SBP systolic blood pressure, ALT alanine aminotransferase, AST aspartate aminotransferase, HDL high-density lipoprotein, LDL low-density lipoprotein * ** # ## p < 0.05, p < 0.01, compared to control; p < 0.05, p < 0.01, compared to the HUA group 2 2 2 (R X = 0.516, Q Y = 0.931, Q = 0.538, p = 2.4E-10) metabolites are summarized in Table 2. Among the 21 showed clear separation between the HUA and control metabolites remarkably changed in patients with HUA groups (Fig. 3a). According to the corresponding loading and patients with gout, a total of 11 metabolites were dis- plot, compared with control group, the HUA group had turbed in both groups (Fig. 4a). To further understand the significantly higher levels of very low-density lipoprotein metabolic changes in patients with HUA and patients with (VLDL), isoleucine, leucine, lipid, lactate, alanine, lysine, gout, a clustering heatmap was used to visualize changes acetone, glutamate, creatinine, β-glucose, α-glucose, threo- in metabolites. The heatmap (Fig. 4b) of 21 significantly nine, triglycerides, unsaturated lipids and tyrosine. The changed metabolites in patients with HUA and patients metabolic differences between the gout and control groups with gout, showed that there was a remarkable change of 2 2 were visible in the OPLS-DA scores plot (R X=0.52, R Y= the metabolic profile in patients with HUA and a more 0.963, Q = 0.729, p = 1.5E-19; Fig. 3b). Compared with the greater difference in patients with gout. control group, VLDL, isoleucine, leucine, lipid, glutamine, methionine, acetone, citrate, aspartate, β-glucose, creatinine, Pathway analysis α-glucose, threonine, triglycerides, unsaturated lipids and According to the pathway analysis, 29 and 30 meta- phenylalanine were remarkably increased in the gout group. bolic pathways were disturbed in patients with HUA Moreover, there was a clear difference in metabolic profiles and patients with gout, respectively. In the HUA between the HUA and gout groups in the OPLS-DA scores group, three pathways were significantly perturbed in- 2 2 2 plot (R X = 0.518, R Y = 0.945, Q = 0.641, p = 5.5E-15; cluding aminoacyl-transfer RNA (tRNA) biosynthesis, Fig. 3c). Compared with the HUA group, the gout group valine, leucine and isoleucine biosynthesis, and D- had notably higher VLDL, lipid, acetone, citrate, aspartate, glutamine and D-glutamate metabolism were signifi- β-glucose and α-glucose. The significantly changed cantly perturbed. In patients with gout, five metabolic Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 5 of 11 Fig. 1 Typical 500-MHz Carr–Purcell–Meiboom–Gill (CPMG) H nuclear magnetic resonance spectra of human serum samples from controls (a), patients with hyperuricemia (b) and patients with gout (c). The dotted regions were vertically expanded 32 times. 1, high-density lipoprotien; 2, very low-density lipoprotein; 3, isoleucine; 4, leucine; 5, valine; 6, ethanol; 7, 3-hydroxybutytrate; 8, lipid; 9, lactate; 10, alanine; 11, lysine; 12, acetate; 13, glutamine; 14, methionine; 15, glycoprotein; 16, acetone; 17, glutamate; 18, citrate; 19, aspartate; 20, methylguanidine; 21, trimethylamine; 22, dimethylglycine; 23, creatine; 24, creatinine; 25, choline; 26, arginine; 27, β-glucose; 28, trimethylamine n-oxide; 29, myo-inositol; 30, proline; 31, scyllo-inositol; 32, α-glucose; 33, glycine; 34, threonine; 35, triglycerides; 36, unsaturated lipids; 37, tyrosine; 38, 1-methylhistidine; 39, phenylalanine; 40, tryptophan; 41, formate pathways were remarkably disturbed including analyze metabolites in serum from patients with asymp- aminoacyl-tRNA biosynthesis, valine, leucine and tomatic hyperuricemia and gout, hoping to help gain isoleucine biosynthesis, nitrogen metabolism, alanine, understanding of the predisposition to gout. aspartate and glutamate metabolism, D-glutamine and Our research demonstrated that the H NMR-based D-glutamate metabolism (Fig. 5 and Additional file 3). metabolomics approach is feasible to examine metabolic change in patients with asymptomatic hyperuricemia Discussion and gout. Such an approach is also helpful in selecting Gout is a worldwide public health problem. However, metabolic pathways that play vital roles in the develop- current research falls short in evaluating the metabolic ment of gout. The levels of some identified biomarkers change in gout and asymptomatic hyperuricemia. In the showed a trend of an apparent increase in patients with current study, we used H NMR-based metabolomics to HUA and patients with gout from levels in healthy Fig. 2 Principal components analysis (a) and orthogonal partial least squares-discriminant analysis (b) score plots based on H nuclear magnetic resonance data from serum samples obtained from controls, patients with hyperuricemia (HUA) and patients with gout Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 6 of 11 Fig. 3 Orthogonal partial least squares-discriminant analysis score plots of samples (left panel) and corresponding coefficient loading plots (right panel) obtained from different pairwise groups: a hyperuricemia (HUA) (blue dots) and control groups (green dots); b gout (red dots) and control groups (green dots); c gout (red dots) and HUA (blue dots). The color bar on the right corresponds to the weight of a variable in the discrimination between sets of samples, beginning from weak (blue) to strong (red) correlation for the discrimination. VLDL, very low-density lipoprotein individuals; together with clinical data this suggests and the elevated blood pressure, fasting plasma glucose an increase in metabolic disorders. In pathway ana- and BMI in patients with HUA and patients with gout lysis (Fig. 5 and Additional file 3), more metabolic were consistent with the previous study showing that pathways were notably affected in the gout groups the prevalence of metabolic syndrome among individuals (five pathways) than in the HUA groups (three path- with HUA and gout is remarkably high [29, 31]. Our re- ways), which indicated that there was a more severe sults indicate that lipid levels are highly linked with HUA metabolic disorder in patients with gout. and gout, thereby lipid-lowering therapy may provide a supplementary role to slow the development of gout. Lipid metabolism Altered lipid profiles were observed in patients with Carbohydrate metabolism HUA, including increased levels of VLDL, fatty acids, Increased α-glucose and β-glucose were observed in triglyceride (TG) and unsaturated lipids; related variables samples from both patients with HUA and patients with in clinical chemistry results (Table 1) were also found to gout, which suggests changes in carbohydrate metabol- be significantly changed including increased TG, total ism. A large number of studies have shown that serum cholesterol (TC), LDL-cholesterol and decreased HDL- uric acid is positively related to elevated blood glucose cholesterol. Similarly, elevated VLDL, fatty acids, TG due to insulin resistance [32–35]. Insulin is the only and unsaturated lipids were observed in patients with hormone in the body that promotes the uptake and gout, which was consistent with clinical chemistry data utilization of glucose in tissues and lowers blood showing that TC, TG and LDL-cholesterol increased in glucose. Although we did not measure insulin, the patients with gout. These results suggest that there was increased glucose verified the inhibition of glucose me- a lipid metabolism disorder in both patients with HUA tabolism in both patients with HUA and patients with and patients with gout. Several researchers have reported gout, and it was more severe in patients with gout that HUA and gout are associated with cardiovascular because of the higher glucose in the samples from and cerebrovascular diseases due to the correlation patients with gout than in those from patients with between serum uric acid and serum lipids [29, 30]. HUA. As the main product of glycolysis, lactate is Moreover, our findings that lipid metabolism disorder typically interpreted as a marker of anaerobic Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 7 of 11 Table 2 Summary of significantly changed metabolites in the HUA and gout group Metabolites Changes in HUA Changes in gout Changes in gout (vs control) (vs control) (vs HUA) a b a b a b Trend r p Trend r p Trend r p VLDL ↑ 0.71 0.00 ↑ 0.61 0.00 ↑ 0.61 0.00 Isoleucine ↑ 0.86 0.00 ↑ 0.72 0.00 –– – Leucine ↑ 0.83 0.00 ↑ 0.71 0.00 –– – Lipid ↑ 0.68 0.00 ↑ 0.72 0.00 ↑ 0.63 0.00 Lactate ↑ 0.70 0.00 –– – –– – Alanine ↑ 0.77 0.00 –– – –– – Lysine ↑ 0.76 0.00 –– – –– – Glutamine –– – ↑ 0.60 0.00 –– – Methionine –– – ↑ 0.67 0.00 –– – Acetone ↑ 0.64 0.00 ↑ 0.63 0.00 ↑ 0.65 0.00 Glutamate ↑ 0.69 0.00 –– – –– – Citrate –– – ↑ 0.68 0.00 ↑ 0.65 0.00 Aspartate –– – ↑ 0.73 0.00 ↑ 0.63 0.00 Creatinine ↑ 0.63 0.00 ↑ 0.65 0.00 –– – β-Glucose ↑ 0.65 0.00 ↑ 0.70 0.00 ↑ 0.65 0.00 α-Glucose ↑ 0.63 0.00 ↑ 0.68 0.00 ↑ 0.66 0.00 Threonine ↑ 0.65 0.00 ↑ 0.60 0.00 –– – Triglycerides ↑ 0.70 0.00 ↑ 0.61 0.00 –– – Unsaturated lipids ↑ 0.67 0.00 ↑ 0.63 0.00 –– – Tyrosine ↑ 0.75 0.00 –– – –– – Phenylalanine –– – ↑ 0.63 0.00 –– – Increased levels are indicated by arrows (↑) VLDL very low-density lipoprotein Correlation coefficient (r) was obtained from the orthogonal partial least squares-discriminant analysis model The p value was calculated using Student’s t test metabolism, and its accumulation usually accounts for (phenylalanine, glutamine, aspartic acid, methionine, a high energy demand in the biological system . isoleucine, leucine, threonine) groups, respectively Increased lactate was observed in HUA samples, (Additional file 3), which indicates decreased protein indicating the energy demand in patients with HUA syntheses or increased amino acid synthesis. Coinci- induced by low utilization of glucose. However, the dentally, aminoacyl-tRNA biosynthesis was signifi- trend of increased lactate was not observed in gout; cantly affected in both patients with gout and this may be due to the accelerated gluconeogenesis in patients with HUA. Aminoacyl-tRNA biosynthesis patients with gout for converting lactate to glucose to plays an important role in matching amino acids with meet the more urgent energy demand. Increased tRNAs containing the corresponding anticodon for citrate levels were seen in gout but not in HUA sam- the messenger RNA (mRNA)-guided synthesis of ples. Since citrate is an important intermediate in the proteins at the ribosome . As we all know, amino tricarboxylic acid cycle (TCA) in mitochondria, the acid metabolism is the biochemical basis in the regu- data may imply that altered mitochondrial function lation of both proteins and energy metabolisms. affected citrate handling and induced and imbalance Greater involvement of amino acids and the greater in the global energy supply in patients with gout. impact value of aminoacyl-tRNA biosynthesis in gout compared to HUA suggests that translation was sup- Aminoacyl-tRNA biosynthesis pressed following the development of gout. Six and seven amino acids were significantly increased Furthermore, aminoacyl-tRNA synthetases (AARSs) (p < 0.01) in patients the HUA (alanine, lysine, isoleu- are essential enzymes in aminoacyl-tRNA biosynthesis, cine, leucine, threonine and tyrosine) and the gout whichhaveafamilyoftwentyenzymes . It is Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 8 of 11 Fig. 4 Significantly changed metabolites in patients with hyperuricemia (HUA) and patients with gout. a Numbers of significant metabolites. b Heatmap of significantly changed metabolites. The color of each section corresponds to a concentration value of each metabolite calculated by the peak area normalization method (red, upregulated; blue, downregulated) Fig. 5 Pathway analysis of significantly changed metabolites in the hyperuricemia group (a) and gout group (b). tRNA, transfer RNA Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 9 of 11 reported that mutations in AARSs have been identi- periods of starvation, alanine is generated from muscle fied in diverse human diseases, such as musculoskel- BCAAs and transported to the liver where it is used in etal, cardiovascular, and urinary diseases . the glucose alanine cycle to make glucose for energy Therefore, AARSs maybe potential indicators for needs . Hence, the fact that increased alanine was identifying HUA and gout. only seen in HUA but not in gout may be due to its con- sumption in gluconeogenesis to meet the more urgent Valine, leucine and isoleucine biosynthesis energy demand in patients with gout. Branched chain amino acids (BCAAs), including isoleu- cine, leucine and valine, are essential amino acids and act D-Glutamine and D-glutamate metabolism as important signaling molecules and substrate in protein The D-glutamine and D-glutamate metabolism is a synthesis. On the other hand, increasing evidence shows major regulatory mechanism of glutamate and glutamine that perturbed amino acid metabolism, especially circulat- levels in organisms . Glutamate is an excitatory ing metabolites such as high levels of blood BCAAs, are neurotransmitter and glutamine is the precursor and strongly associated with insulin resistance, obesity, storage form of glutamate. In this study, compared to diabetes mellitus and cardiovascular disease [40–42]. controls, patients with HUA had a higher level of Mitochondrial branched chain aminotransferase glutamate and patients with gout had a higher level of (BCATm), one of the two BCAT isoforms and highly glutamine. Thus, our results indicate that the pertur- expressed in all tissues in the mitochondria of the cell, bation of D-glutamine and D-glutamate metabolism converts the BCAAs into their corresponding α-keto occurred in both patients with HUA and patients with acids. Thus, the increased levels of BCAAs in our study gout. There is evidence to suggest that uric acid has a can be attributed to reduced expression of BCATm. It in- remarkable antioxidant effect on neurons [51, 52]. dicates that the increase in BCAAs causes the accumula- However, the protective effect of gout on the risk of tion of its byproducts that can impaire mitochondrial neurological disease is a controversial issue [53, 54]. It is capacity, and the affected mitochondrial function is re- said that metabolic syndrome, a frequent comorbidity of lated to the development of insulin resistance [40, 43]. HUA and gout, might offset the anti-oxidative benefit Wang et al. found that BCAAs are significantly related to from the high uric acid level [55, 56]. Zheng et al. obesity and risk factors for some metabolic diseases . identified disturbance of the glutamate-glutamine cycle Another study followed 2422 normoglycemic individuals with an increased level of glutamine in the hippocampus for 12 years and found that the BCAAs may presage the of mice with diabetes-associated decline in cognition, development of type 2 diabetes mellitus by up to a decade and regarded this change as the underlying reason for or more and thus, may be among the earliest detectable diabetes-related neurological complications . metabolic derangements on the route to diabetes mellitus Although none of the patients with gout in the present . Our findings are in agreement, as both the HUA and study had diabetes mellitus, fasting glucose was signifi- gout groups had higher levels of isoleucine and leucine, cantly increased in these patients. Thus, glutamine may and elevated BMI and fasting glucose, suggesting that be an early biomarker of gout and its comorbidities. there is correlation between insulin resistance and gout development. Alanine, aspartate and glutamate metabolism and Furthermore, it is known that BCAAs can undergo nitrogen metabolism transamination to generate nitrogen for synthesis of In our study, metabolites related to alanine, aspartate non-essential amino acids such as glutamine and alanine and glutamate metabolism (aspartic acid and glutamine) . In our study, although BCAAs were increased in and nitrogen metabolism (phenylalanine, aspartic acid samples both from patients with HUA and patients with and glutamine) were increased in serum from patients gout, increased glutamine was only observed in gout but with gout: this indicates the perturbation of amino acid not in HUA and increased alanine was only seen in metabolism and energy metabolism in patients with HUA but not in gout. These may have resulted from dif- gout. Among all the significantly disturbed metabolic ferential consumption of these amino acids in HUA and pathways, alanine, aspartate and glutamate metabolism in gout. Glutamine is the most abundant free amino acid and nitrogen metabolism were disturbed in patients with in human blood; it is consumed by proliferating cells gout but were not detected in patients with HUA, which and converted to glutamate en route to producing other showed the aggravation of metabolic disorders in metabolic intermediates that contribute to cell growth patients with gout. [47, 48]. Therefore, the increased level of glutamine in gout may due to the lower cellular metabolic rate in Conclusion patients with gout. Alanine is used in protein synthesis In summary, we investigated the application of H NMR and as precursor for gluconeogenesis in the liver. Under spectroscopy-based metabolomics to detect metabolic Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 10 of 11 changes in serum from patients with HUA and patients Author details Department of Nutrition and Food Hygiene, School of Public Health, with gout. Our results indicated significant dysregulation Ningxia Medical University, Yinchuan 750004, China. Department of of metabolic pathways in patients with gout. The meta- Obstetrics and Gynecology, Tai’an Hospital of Traditional Chinese Medicine, bolic alterations were associated with the disturbance of Tai’an 271000, China. Department of Clinical Laboratory, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201301, China. lipid metabolism, carbohydrate metabolism, amino acids Department of Nutrition and Food Safety, School of Public Health, Fujian metabolism and energy metabolism. Clear metabolic 5 Medical University, Fuzhou 350122, China. Department of Sanitary differences were observed between patients with HUA, Inspection, Shanghai University of Medical & Health Sciences, Shanghai 201318, China. National Institute for Nutrition and Health, Chinese Center patients with gout and controls, indicating that the dis- for Disease Control and Prevention, Beijing 100050, China. ease has a continuous progressive development axis. The combination of these metabolic alterations may corpor- Received: 4 January 2018 Accepted: 19 April 2018 ately hold promise for early prediction and diagnosis of the progression of gout. References 1. Roddy E, Choi H. Epidemiology of gout. Rheum Dis Clin N Am. 2014;40(2): Additional files 155–75. 2. Yanyan Z, Pandya BJ, Choi HK. Prevalence of gout and hyperuricemia in the Additional file 1: Metabolite assignments of major resonances detected US general population: the National Health and Nutrition Examination in H NMR spectra from human serum samples. (DOCX 29 kb) Survey 2007-2008. Arthritis Rheum. 2011;63(10):3136–41. 3. Wallace KL, Riedel AA, Nancy JR, Robert W. Increasing prevalence of gout Additional file 2: PCA and OPLS-DA scores plots based on H NMR and hyperuricemia over 10 years among older adults in a managed care spectrum data of serum samples obtained from two gout subgroups population. J Rheumatol. 2004;31(8):1582–7. including gout with HUA (n = 32) and gout with normal SUA (n = 17). 4. Priyanka C, Edward R, Lorna C, Jane R, Hider SL, Mallen CD. Health- (PDF 337 kb) related quality of life in gout: a systematic review. Rheumatology. 2013; Additional file 3: Metabolic pathways significantly altered in patients 52(11):2031–40. with HUA and patients with gout. (DOCX 18 kb) 5. Reginato AM, Mount DB, Yang I, Choi HK. The genetics of hyperuricaemia and gout. Nat Rev Rheumatol. 2012;8(10):610–21. 6. Campion EW, Glynn RJ, Delabry LO. Asymptomatic hyperuricemia. Risks and Abbreviations consequences in the Normative Aging Study. Am J Med. 1987;82(3):421–6. AARSs: Aminoacyl-tRNA synthetases; ALT: Alanine aminotransferase; 7. Annemans L, Spaepen E, Gaskin M, Bonnemaire M, Malier V, Gilbert T, AST: Aspartate aminotransferase; BCAAs: Branched chain amino acids; Nuki G. Gout in the UK and Germany: prevalence, comorbidities and BCATm: Mitochondrial branched chain aminotransferase; BMI: Body mass management in general practice 2000-2005. Ann Rheum Dis. index; CPMG: Carr–Purcell–Meiboom–Gill; DBP: Diastolic blood pressure; 2008;67(7):960–6. HDL: High-density lipoprotein; HUA: Asymptomatic hyperuricemia; JRES: J- 8. Soltani Z, Rasheed K, Kapusta DR, Reisin E. Potential role of uric acid in resolved spectroscopy; LDL: Low-density lipoprotein; NMR: Nuclear magnetic metabolic syndrome, hypertension, kidney injury, and cardiovascular resonance; OPLS-DA: Orthogonal partial least squares-discriminant analysis; diseases: is it time for reappraisal? Curr Hypertens Rep. 2013;15(3):175–81. PCA: Principal components analysis; SBP: Systolic blood pressure; SUA: Serum 9. Puig JG, Martínez MA. Hyperuricemia, gout and the metabolic syndrome. uric acid; TC: Total cholesterol; tRNA: Transfer RNA; TG: Triglyceride; Curr Opin Rheumatol. 2008;20(2):187–91. VLDL: Very low-density lipoprotein 10. Bardin T, Richette P. Impact of comorbidities on gout and hyperuricaemia: an update on prevalence and treatment options. BMC Med. 2017;15(1):123. Funding 11. Schlesinger N, Baker DG, Schumacher HR. Serum urate during bouts of This work was supported by the Danone Institute Nutrition Research and acute gouty arthritis. J Rheumatol. 1997;24(11):2265–6. Education Fund (DIC2015–07). 12. Vincent G, Liliane C, Jean-Charles G. Contribution of digit joint aspiration to the diagnosis of rheumatic diseases. Joint Bone Spine. 2002;69(1):58–61. Availability of data and materials 13. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the The datasets used and/or analyzed during the current study are available metabolic responses of living systems to pathophysiological stimuli via from the corresponding author on reasonable request. multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181–9. 14. Nicholson JK, John C, Lindon JC, Elaine H. Metabonomics: a platform for Authors’ contributions studying drug toxicity and gene function. Nat Rev Drug Discov. 2002;1(2): Authors’ contributions were as follows: conception and design of the study 153–61. (YZ, HP, YY); collection of clinical samples (YZ, HZ, DC); metabolomics analysis 15. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and and the multivariate statistics (YZ, FG); drafting the article (YZ, HP, YY) and disease. Cell. 2008;134(5):714–7. critical revisions for important intellectual content (YZ, HZ, DC, FG, HP, YY). 16. Li Y, Ju L, Hou Z, Deng H, Zhang Z, Wang L, Yang Z, Yin J, Zhang Y. All authors read and approved the final manuscript. Screening, verification, and optimization of biomarkers for early prediction of cardiotoxicity based on metabolomics. J Proteome Res. Ethics approval and consent to participate 2015;14(6):2437–45. The experimental protocol was approved by the ethics committee of 17. Ratnasekhar C, Sonane M, Satish A, Mudiam MK. Metabolomics reveals the Shanghai University of Medical & Health Sciences, and written informed perturbations in the metabolome of Caenorhabditis elegans exposed to consent was obtained from all the participants. titanium dioxide nanoparticles. Nanotoxicology. 2015;9(8):994–1004. 18. Chan GH, Ho EN, Leung DK, Wong KS, Wan TS. Targeted metabolomics Competing interests approach to detect the misuse of steroidal aromatase inhibitors in equine The authors declare that they have no competing interests. sports by biomarkers profiling. Anal Chem. 2015;88(1):764–72. 19. Larive CK, Barding GA Jr, Dinges MM. NMR spectroscopy for metabolomics and metabolic profiling. Anal Chem. 2015;87(1):133–46. Publisher’sNote 20. Becker MA, Schumacher HR Jr, Wortmann RL, MacDonald PA, Eustace D, Springer Nature remains neutral with regard to jurisdictional claims in Palo WA, Streit J, Joseph-Ridge N. Febuxostat compared with allopurinol in published maps and institutional affiliations. patients with hyperuricemia and gout. N Engl J Med. 2005;353(23):2450–61. Zhang et al. Arthritis Research & Therapy (2018) 20:111 Page 11 of 11 21. Nagahama K, Inoue T, Iseki K, Touma T, Kinjo K, Ohya Y, Takishita S. disrupts glucose metabolism and sensitizes the heart to ischemia- Hyperuricemia as a predictor of hypertension in a screened cohort in reperfusion injury. Cell Metab. 2017;25(2):374–85. Okinawa, Japan. Hypertens Res. 2004;27(11):835–41. 43. Martos-Moreno GA, Mastrangelo A, Barrios V, Garcia A, Chowen JA, Ruperez 22. Zhang W, Doherty MT, Pascual E, Barskova V, Conaghan P, Gerster J, Jacobs FJ, Barbas C, Argente J. Metabolomics allows the discrimination of the J, Leeb B, Liote F, Mccarthy G. EULAR evidence based recommendations for pathophysiological relevance of hyperinsulinism in obese prepubertal gout. Part I: Diagnosis. Report of a task force of the standing committee for children. Int J Obes. 2017;41(10):1473–80. international clinical studies including therapeutics (ESCISIT). Ann Rheum 44. Wang SM, Yang RY, Wang M, Ji FS, Li HX, Tang YM, Chen WX, Dong J. Dis. 2006;65(10):1301–11. Identification of serum metabolites associated with obesity and traditional risk factors for metabolic disease in Chinese adults. Nutr Metab Cardiovasc 23. Cristóbal B. Uric acid and cardiovascular risk. N Engl J Med. 2009;360(17): Dis. 2018;28(2):112–8. 1811–21. 45. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox 24. Wallace SL, Robinson H, Masi HT, Decker JL, Mccarty DJ, TSF Y. Preliminary CS, Jacques PF, Fernandez C, et al. Metabolite profiles and the risk of criteria for the classification of the acute arthritis of primary gout. Arthritis developing diabetes. Nat Med. 2011;17(4):448–53. Rheumatol. 1977;20(3):895–900. 46. Darmaun D, Dechelotte P. Role of leucine as a precursor of glutamine alpha- 25. Zhang X, Wang Y, Hao F, Zhou X, Han X, Tang H, Ji L. Human serum amino nitrogen in vivo in humans. Am J Phys. 1991;260(2 Pt 1):E326–9. metabonomic analysis reveals progression axes for glucose intolerance and 47. Dang CV. Links between metabolism and cancer. Genes Dev. 2012;26(9): insulin resistance statuses. J Proteome Res. 2009;8(11):5188–95. 877–90. 26. Pechlivanis A, Kostidis S, Saraslanidis P, Petridou A, Tsalis G, Veselkov K, 48. DeBerardinis RJ, Cheng T. Q's next: the diverse functions of glutamine in Mikros E, Mougios V, Theodoridis GA. 1H NMR study on the short- and metabolism, cell biology and cancer. Oncogene. 2010;29(3):313–24. long-term impact of two training programs of sprint running on the 49. Felig P. The glucose-alanine cycle. Metabolism. 1973;22(2):179–207. metabolic fingerprint of human serum. J Proteome Res. 2013;12(1):470–80. 50. Bak LK, Schousboe A, Waagepetersen HS. The glutamate/GABA-glutamine 27. MacIntyre DA, Jimenez B, Lewintre EJ, Martin CR, Schafer H, Ballesteros CG, cycle: aspects of transport, neurotransmitter homeostasis and ammonia Mayans JR, Spraul M, Garcia-Conde J, Pineda-Lucena A. Serum metabolome transfer. J Neurochem. 2006;98(3):641–53. analysis by 1H-NMR reveals differences between chronic lymphocytic 51. Chen X, Burdett TC, Desjardins CA, Logan R, Cipriani S, Xu Y, Schwarzschild leukaemia molecular subgroups. Leukemia. 2010;24(4):788–97. MA. Disrupted and transgenic urate oxidase alter urate and dopaminergic 28. Nicholson JK, Foxall PJ, Spraul M, ., Farrant RD, Lindon JC: 750 MHz 1H and neurodegeneration. Proc Natl Acad Sci U S A. 2013;110(1):300–5. 1H-13C NMR spectroscopy of human blood plasma. Anal Chem 1995, 67(5): 52. Bakshi R, Zhang H, Logan R, Joshi I, Xu Y, Chen X, Schwarzschild M. 793–811. Neuroprotective effects of urate are mediated by augmenting astrocytic 29. Tsouli SG, Liberopoulos EN, Mikhailidis DP, Athyros VG, Elisaf MS. Elevated glutathione synthesis and release. Neurobiol Dis. 2015;82:574–9. serum uric acid levels in metabolic syndrome: an active component or an 53. De Vera M, Rahman MM, Rankin J, Kopec J, Gao X, Choi H. Gout and the risk innocent bystander? Metabolism. 2006;55(10):1293–301. of Parkinson's disease: a cohort study. Arthritis Rheum. 2008;59(11):1549–54. 30. Bansal BC, Gupta RR, Bansal MR, Prakash C. Serum lipids and uric acid 54. Ungprasert P, Srivali N, Thongprayoon C. Gout is not associated with a relationship in ischemic thrombotic cerebrovascular disease. Stroke. 1975; lower risk of Parkinson's disease: a systematic review and meta-analysis. 6(3):304–7. Parkinsonism Relat Disord. 2015;21(10):1238–42. 31. Choi HK, Ford ES, Li C, Curhan G. Prevalence of the metabolic syndrome in 55. Choi H, Ford E. Prevalence of the metabolic syndrome in individuals with patients with gout: the Third National Health and Nutrition Examination hyperuricemia. Am J Med. 2007;120(5):442–7. Survey. Arthritis Rheum. 2007;57(1):109–15. 56. Zhang P, Tian B. Metabolic syndrome: an important risk factor for 32. Cook DG, Shaper AG, Thelle DS, Whitehead TP. Serum uric acid, serum Parkinson's disease. Oxidative Med Cell Longev. 2014;2014:729194. glucose and diabetes: relationships in a population study. Postgrad Med J. 57. Zheng Y, Yang Y, Dong B, Zheng H, Lin X, Du Y, Li X, Zhao L, Gao H. 1986;62(733):1001–6. Metabonomic profiles delineate potential role of glutamate-glutamine cycle 33. Han T, Lan L, Qu R, Xu Q, Jiang R, Na L, Sun C. Temporal relationship in db/db mice with diabetes-associated cognitive decline. Mol Brain. 2016; between hyperuricemia and insulin resistance and its impact on future risk https://doi.org/10.1186/s13041-016-0223-5. of hypertension. Hypertension. 2017;70(4):703–11. 34. Shani M, Vinker S, Dinour D, Leiba M, Twig G, Holtzman E, Leiba A. High normal uric acid levels are associated with an increased risk of diabetes in lean, normoglycemic healthy women. J Clin Endocrinol Metab. 2016;101(10):3772–8. 35. Bombelli M, Quarti-Trevano F, Tadic M, Facchetti R, Cuspidi C, Mancia G, Grassi G. Uric acid and risk of new-onset metabolic syndrome, impaired fasting glucose and diabetes mellitus in a general Italian population: data from the Pressioni Arteriose Monitorate E Loro Associazioni study. J Hypertens. 2018; https://doi.org/10.1097/HJH.0000000000001721. 36. Kokushi E, Uno S, Harada T, Koyama J. (1)H NMR-based metabolomics approach to assess toxicity of bunker a heavy oil to freshwater carp, Cyprinus carpio. Environ Toxicol. 2012;27(7):404–14. 37. Ibba M, Söll D. Aminoacyl-tRNA synthesis. Annu Rev Biochem. 2000;69(1): 617–50. 38. Mirande M. The aminoacyl-tRNA synthetase complex. Subcell Biochem. 2017;83:505–22. 39. Sissler M, Gonzalez-Serrano LE, Westhof E. Recent advances in mitochondrial aminoacyl-tRNA synthetases and disease. Trends Mol Med. 2017;23(8):693–708. 40. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26. 41. Würtz P, Tiainen M, Mäkinen VP, Kangas AJ, Soininen P, Saltevo J, Keinänen- Kiukaanniemi S, Mäntyselkä P, Lehtimäki T, Laakso M. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care. 2012;35(8):1749. 42. Li T, Zhang Z, Kolwicz SC Jr, Abell L, Roe ND, Kim M, Zhou B, Cao Y, Ritterhoff J, Gu H, et al. Defective branched-chain amino acid catabolism
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