The study, “Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry,” by Li et al. (2018) investigated gout using a urine metabolomics approach to better understand how the disease alters small-molecule metabolites excreted in urine. Using GC-MS, the researchers compared urine samples from patients with gout and healthy volunteers to identify metabolic patterns associated with the condition.

By mapping changes in urinary amino acids, energy-related metabolites, and purine-related compounds, the study highlighted how gout affects multiple metabolic pathways reflected in urine. The authors also showed that a combined urine metabolomics model using urate and isoxanthopterin could effectively distinguish gout patients from controls, pointing to urine metabolomics as a useful tool for future gout research.

The paper describes gout as “a common type of arthritis that features by deposits of monosodium urate crystals in the articular cavity or synovial fluid,” typically accompanied by sudden joint inflammation. The authors note that the incidence of gout has increased over recent decades worldwide and that the condition is more frequent in men, especially before the age of 70.

For diagnosis, the gold standard is to examine synovial fluid for monosodium urate crystals. However, this procedure is invasive, requires technical skill, and, as the authors state, “does not always work in clinical practice.” Blood urate levels are also widely used, but they are not perfect. Many people have high urate without ever developing gout, and some patients experience gout attacks even when blood urate is in the normal range. Because of these limitations, the authors argue that there is a need for “a more accurate, rapid, and reliable diagnostic method” for gout.

Metabolomics offers a way to measure many small molecules, or metabolites, in body fluids such as blood or urine. It has already been applied in conditions like ankylosing spondylitis, rheumatoid arthritis, and systemic lupus erythematosus. According to the paper, studies of gout using gas chromatography–mass spectrometry (GC–MS) are still relatively rare. Li et al. set out to use urinary metabolomics to map metabolic pathways that differ in gout and to look for urinary metabolites that might serve as potential markers for the disease.

The study enrolled 35 patients diagnosed with gout under the 2015 American College of Rheumatology and European League Against Rheumatism criteria, along with 29 healthy volunteers matched by age and sex. Both groups were similar in age, sex distribution, and body mass index, and individuals with major health conditions or drug abuse were excluded. All participants provided informed consent, and the study was approved by the ethics committee of Zhejiang Chinese Medical University.

Morning midstream urine samples were collected, allowed to settle, and then stored at −80°C. No dietary restrictions were applied. Before analysis, the team treated each sample with urease to break down urea, removed proteins with cold acetonitrile, dried the extracts, and performed a two-step derivatization to make the metabolites volatile enough for gas chromatography–mass spectrometry.

GC–MS was carried out on an Agilent 7890/5975C system. The instrument used a DB5 MS column, helium as the carrier gas, and a temperature program that ranged from 70°C to 300°C. Mass spectra were recorded between m/z 33 and 600. Quality control samples were injected regularly to ensure stable performance.

Raw data were processed with AMDIS to identify peaks and ChemStation to integrate them. Each sample was normalized by total peak area, and the full data matrix was imported into SIMCA P. The authors used orthogonal partial least squares discriminant analysis to separate gout and control samples based on their metabolic patterns.

Metabolites with a variable importance in projection value above 1 and a p-value below 0.05 were considered significantly altered. These metabolites were then tested in binary logistic regression models, and diagnostic performance was evaluated with receiver operating characteristic curves. Final metabolite identities were confirmed by matching spectra to the NIST05 library and, when possible, commercial standards.

After peak integration, the authors detected 334 signals in the urine samples. They used the QC data to check reproducibility and reported that 84.1 percent of peaks had relative standard deviations below 30 percent in QC injections, representing more than 93.2 percent of the total peak area. On this basis, they concluded that the method showed “good reproducibility.”

OPLS DA score plots showed a clear separation between urine samples from gout patients and those from healthy volunteers. The cumulative R²Y of 0.911 and Q²Y of 0.755 suggested that the model had both strong explanatory power and good predictive capacity. From the statistical analysis, 76 variables met the criteria of VIP greater than 1 and p less than 0.05. Of these, 30 metabolites were structurally identified.

These 30 metabolites covered several chemical classes. They included amino acids such as glycine, serine, threonine, aspartate, isoleucine, phenylalanine, and tryptophan. They also included organic acids such as succinate, fumarate, gluconate, threonate, and β-lactate, along with carbohydrates and polyols like d-lyxose, ribitol, sorbitol, d-allose, and gluconate. Stearate represented fatty acid metabolism, and urate and isoxanthopterin reflected purine-related pathways.

Most metabolites were found at lower levels in the urine of gout patients than in controls, while a smaller number were higher. According to the paper, glycine, aspartate, several branched-chain amino acids, succinate, fumarate, phenylalanine, sorbitol, and stearate were decreased in gout, whereas urate, isoxanthopterin, gluconate, galacturonate, and threonate were increased.

The authors connected many of these changes to purine nucleotide synthesis and one-carbon metabolism. Urate is the end product of purine metabolism in humans, and purine nucleotides are mainly synthesized via a de novo pathway that consumes amino acids such as glycine, aspartate, serine, threonine, and tryptophan. Isoxanthopterin is described as a degradation product of folic acid, and reduced forms of folate serve as carriers of one-carbon units.

Based on these links, the authors suggest that the combination of lower amino acid levels and higher isoxanthopterin in gout patients “may indicate a disorder of the metabolism of one-carbon units and nucleic acid synthesis and appears to be associated with the acceleration of purine nucleotide synthesis in patients.” This proposed mechanism stays within the metabolic pathways they mapped and offers a possible explanation for increased urate production in gout.

Several metabolites related to energy production were also altered. The paper notes that inflammatory factors produced during gout “may stimulate the requirement of energy,” and the authors observed that intermediates of the TCA cycle, specifically succinate and fumarate, were reduced in patients’ urine. They interpret this as a sign that these intermediates were “excessively consumed to produce energy.”

Stearate, a fatty acid that can be broken down through beta oxidation, was also decreased in gout, as were branched-chain amino acids, another potential energy source. These patterns led the authors to propose that branched-chain amino acids, fatty acids, and TCA intermediates that help generate energy were downregulated in order to meet increased energy demands in gout patients.

Pyroglutamate, which can be converted into the antioxidant glutathione (GSH), was found at significantly lower levels in patients with gout. The authors refer to previous work suggesting that oxidation of urate can increase the use of GSH and state that “the excessive consumption of GSH may lead to a lower level of pyroglutamate in the urine of gout patients.”

The study also reports changes in metabolites linked to immune function and gut-related processes. Tryptophan and its downstream product 5 hydroxyindole 3 acetate were decreased in gout patients. The paper notes that tryptophan is an aromatic amino acid involved in humoral immunity and that tryptophan and its intermediates can influence T cell function. The authors suggest that these reductions “may be associated with the abnormality of immunological response of patients.”

Phenylalanine was also reduced in the urine of gout patients. The authors point out that phenyl-containing compounds are mainly degraded by intestinal flora because humans lack many enzymes for breaking down benzene ring structures. They infer that a drop in urinary phenylalanine may reflect a disturbance of gut flora and state that “an imbalance in intestinal flora is likely to occur during the development of gout,” although they present this as a possibility based on metabolite patterns, not a proven cause.

To examine potential diagnostic markers, the authors treated the 30 differential metabolites as candidates and entered them into binary logistic regression models. Using a stepwise method based on likelihood ratios, they arrived at a model that included two metabolites, urate and isoxanthopterin, both of which were elevated in the urine of gout patients.

According to the paper, this two-marker model achieved classification accuracies of 80.0 percent for gout patients and 79.3 percent for healthy controls. The ROC analysis for this combination produced an AUC of 0.879. The authors conclude that “the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls” in their study sample.

The authors conclude that a GC–MS–based urinary metabolomics approach is “an efficient tool for a better understanding of the metabolic changes of gout.” By linking the 30 altered metabolites to pathways such as purine nucleotide synthesis, amino acid metabolism, lipid metabolism, carbohydrate metabolism, and the TCA cycle, the study outlines how gout can affect several connected metabolic networks rather than just urate alone.

From a diagnostic point of view, the work suggests that a noninvasive combination of urine urate and isoxanthopterin could help differentiate gout patients from healthy individuals. At the same time, the authors are careful to note that their sample size is relatively small and that “large sample validation is still required” before any clinical tool can be developed. They frame their findings as an early step that “may support the clinical diagnosis and management of gout,” not as a replacement for current diagnostic standards.

This scientific paper shows that urinary metabolomics using GC–MS can separate gout patients from healthy controls and highlight 30 metabolites that change in the setting of gout. These shifts point toward faster purine nucleotide synthesis, increased urate production, altered one-carbon metabolism, higher energy demands, markers of oxidative stress, and possible changes in immune response and gut-related metabolism.

In addition, the combined measurement of urate and isoxanthopterin in urine provided an AUC of 0.879 for distinguishing gout from control samples in this study, suggesting that these metabolites have potential as noninvasive markers after further validation. The authors emphasize the need for larger and more diverse cohorts, but their findings give a detailed metabolic snapshot that may guide future research on gout mechanisms and support the development of improved diagnostic strategies based strictly on the data reported in this study.