The study, “Analysis of Metabolites in Gout: A Systematic Review and Meta-Analysis,” by Li et al. (2023) explores gout pathophysiology by pulling together evidence from 33 human metabolomics studies. These studies compared people with gout to healthy volunteers to identify metabolic changes linked to disease development. Using metabolomics, the large-scale analysis of small molecules in the body, the authors examined which metabolites consistently rise or fall in gout and used meta-analysis to confirm robust patterns. Their goal was to clarify how metabolic and immune-related pathways contribute to gout pathophysiology beyond uric acid levels alone.

In this scientific paper, gout is described as a “metabolic immune disease” that stems from problems in purine and uric acid (UA) metabolism. When UA builds up and becomes supersaturated in the body, it can crystallize in the joints. Those crystals can trigger sudden episodes of intense joint pain, swelling, warmth, and redness. Over years, repeated flares may lead to joint damage, deformity, long-term pain, visible tophi under the skin, and kidney problems. The authors also point out that gout often appears together with metabolic syndrome, cardiovascular disease, cerebrovascular disease, and renal damage.

The paper notes that the prevalence of gout has risen sharply and “has doubled in less than 30 years.” Along with this rise comes a heavy financial burden from emergency visits, hospital stays, reduced productivity, and disability. Current clinical diagnosis leans on serum UA levels, analysis of synovial fluid from joints, and imaging. These tests are helpful but not perfect. They may not always catch disease early, and not everyone with high UA (hyperuricemia) will develop gout.

Because gout has a long metabolic phase before symptoms appear, the authors argue that it makes sense to look beyond UA. Metabolomics, using tools such as liquid chromatography mass spectrometry (LC-MS) and gas chromatography mass spectrometry (GC-MS), allows scientists to measure hundreds of metabolites at once. This broader view can reveal patterns linked to gout and may eventually help predict who is at higher risk, how the disease is progressing, or how strongly inflammation and kidney involvement are present

This scientific paper is a systematic review and meta-analysis carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors also registered their protocol in the International Prospective Register of Systematic Reviews (PROSPERO).

They searched several databases, including EMBASE, the Cochrane Library, PubMed, Web of Science, VIP, Wanfang, and CNKI, up to 31 July 2022. The search combined terms related to “gout,” “metabolites,” and “metabolomics.” Studies were included if they:

• Involved patients with gout,
• Used metabolomics to study samples such as blood, urine, feces, or saliva from gout patients and healthy controls, and
• Reported metabolite profiles in cohort, case-control, or randomized controlled designs.

They excluded studies if participants were taking gout or urate-lowering drugs, if the paper was a duplicate, or if key data or full text were missing.

Two researchers independently screened titles and abstracts, then reviewed full texts. They extracted data and evaluated the quality of each study with the Newcastle Ottawa Scale (NOS), which rates observational studies on selection, comparability, and exposure. A senior researcher supervised the process and helped resolve any disagreements.

In the end, 33 studies were included, covering 3422 participants, with 2149 people with gout and 1273 healthy controls. Across these studies, 701 metabolites were identified. For qualitative synthesis, the authors focused on 104 metabolites reported at least twice. For the meta-analysis, they used 46 blood metabolites that had clear concentration data and units.

When studies reported medians and quartiles instead of means and standard deviations, the authors used formulas from the Cochrane Handbook to estimate means and standard deviations. They then calculated standardized mean differences (SMDs) with 95 percent confidence intervals and used random effects models because they expected the studies to differ in populations and methods.

Across the 33 studies, many metabolites were consistently higher in people with gout than in healthy controls. In the meta-analysis, several blood metabolites showed statistically significant increases in gout:

• Uric acid (UA)
• Hypoxanthine
• Xanthine
• Kynurenic acid (KYNA)
• Leukotriene B4 (LB4)
• Guanosine
• 2 Deoxyadenosine
• Creatinine
• 13(S)-HODE and 9(S)-HODE
• 5-oxo-ETE

The meta-analysis found a strong positive SMD for UA. Hypoxanthine and xanthine, both precursors in purine metabolism that lead to UA, were also elevated. The authors note that “hypoxanthine and xanthine…have been found to be biomarkers of gout, regardless of whether the patient has hyperuricemia.” This suggests that shifts in purine metabolism show up even when UA levels alone do not clearly point to gout.

Kynurenic acid, a product of tryptophan metabolism, was higher in gout as well. The scientific paper explains that KYNA participates in immune regulation and reports that “chronic stress or mild inflammation can promote the production of KYNA and promote various immunomodulatory actions.”

Creatinine, which reflects kidney function, was significantly higher in gout patients. The authors connect this finding to earlier work showing that creatinine clearance is linked to early subcutaneous urate deposits, hinting at a shared pathway between kidney changes and urate handling in the body.

The meta-analysis also identified several metabolites that were significantly lower in gout patients than in healthy controls:

• 12-HETE
• 20-carboxy-arachidonic acid (20-carboxy-ARA)
• 19,20-DHDPA
• 11,12-DHET
• DL-2-Aminoadipic acid
• Adenosine

DL-2-Aminoadipic acid is described as an intermediate in lysine metabolism that plays a role in glucose and lipid metabolism. The authors discuss links between DL-2-aminodipic acid and kidney function in gout and highlight it as one of the specific metabolites associated with gout.

Adenosine, another purine metabolite, also differed between groups. The scientific paper notes that adenosine acts mainly through G protein coupled receptors and has an anti inflammatory role. It can regulate interleukin-1β (IL-1β) secretion, a key cytokine in gout flares. Changes in adenosine levels may therefore reflect shifts in how the body controls inflammation during gout attacks and remission.

The authors interpret this overall metabolite pattern as strong evidence that gout is driven by both metabolic and immune factors. They discuss the concept of “meta-inflammation,” which they define as chronic low-grade inflammation triggered by excess energy and nutrient metabolism. According to the study, “metabolic excesses of energy and lipids lead to hyperuricemia and activate chronic metabolic inflammation.”

The scientific paper describes how UA can injure blood vessels in the kidney, reduce blood flow to certain regions, and encourage macrophage infiltration, which in turn contributes to tubulointerstitial damage and fibrosis. At the same time, sodium urate crystals in joints can trigger classic acute inflammation. LB4, which was elevated in gout, is described as “an important chemical mediator in acute gout attacks” that can increase and prolong tissue inflammation.

Taken together, the metabolite profile suggests that purine metabolism, lipid derived mediators, kidney function markers, and immune related molecules all interact in the development and progression of gout. This pattern links metabolic overload, kidney stress, and joint inflammation into one connected picture.

In their conclusion, the authors state that “metabolites are associated with gout” and single out UA, hypoxanthine, xanthine, KYNA, guanosine, adenosine, creatinine, LB4, and DL-2-aminoadipic acid as key metabolites in gout development. They suggest that these molecules, especially when viewed together, could form a characteristic metabolite profile that might serve as a predictive biomarker panel for gout.

Right now, diagnosis and monitoring still rely heavily on UA levels and clinical signs. The authors argue that a wider panel of metabolites could improve early identification of people at higher risk, help track disease course, and deepen our understanding of disease mechanisms. The study also points to shared signaling pathways between chronic metabolic inflammation and inflammation triggered by monosodium urate crystals. Mapping those pathways through metabolomics may clarify why gout is so closely linked to kidney disease and to other conditions such as metabolic syndrome.

At the same time, the authors are careful about the limits of their work. They stress that their findings “require further investigation and validation in larger prospective cohort studies.” They also acknowledge that there was significant heterogeneity across studies, likely due to differences in study populations, health status, sample types, and metabolomics platforms.

This 2023 scientific paper gives a detailed view of how metabolites differ between people with gout and healthy controls by combining data from 33 studies in several countries. By looking at purine metabolites such as UA, hypoxanthine, and xanthine, lipid mediators including HODEs and eicosanoids, immune related molecules like KYNA and LB4, and kidney markers such as creatinine and DL-2-aminoadipic acid, the authors describe a metabolite pattern that appears to be linked to gout.

According to the study, this pattern reflects both long-term metabolic imbalance and ongoing inflammation, with the kidneys playing a central role in the process. The authors see promise in using these metabolites as possible biomarkers to predict gout risk and better understand disease pathways, but they also call for larger, well-designed prospective studies before these markers can be used in practice. For now, the scientific paper offers a clear, evidence-based framework for future research on metabolite-based prediction and the biology of gout.