The study, “Plasma and Urinary Metabolomic Analysis of Gout and Asymptomatic Hyperuricemia and Profiling of Potential Biomarkers: A Pilot Study,” by Yuki Ohashi et al. (2024), investigates metabolic differences between hyperuricemia and gout to better understand why some individuals with elevated serum urate develop gout while others remain asymptomatic.

Using plasma and urinary metabolomics combined with genetic analysis of key urate transporters, the researchers compared men with gout to men with asymptomatic hyperuricemia. Their goal was to identify metabolic patterns, particularly in carbohydrate metabolism and renal urate handling, that may be associated with the progression from hyperuricemia to clinically apparent gout.

The study starts with a clinical reality that many patients and clinicians recognize. Gout occurs when monosodium urate (MSU) crystals form and deposit in joints in the setting of hyperuricemia, which the authors define as serum urate above 7.0 mg/dL. They cite long-term data showing that the five-year cumulative incidence of gout increases sharply as serum urate rises, reaching about 30.5 percent when serum urate is at least 10.0 mg/dL.

Yet high uric acid does not guarantee gout. The authors highlight one study in which 78 percent of men with serum urate of at least 9.0 mg/dL did not develop a gout flare over five years. They also note that MSU deposition can begin during the asymptomatic phase of hyperuricemia. Taken together, these findings suggest that serum urate is necessary but not sufficient. Other biological factors must be involved in moving from asymptomatic hyperuricemia to overt gout.

Previous work has used metabolomics and genome-wide association studies to identify genes for renal urate transporters, such as ATP-binding cassette subfamily G member 2 (ABCG2) and solute carrier family 2 member 9 (SLC2A9), and to map pathways that regulate serum urate. Most earlier metabolomics studies, however, focused on plasma alone. According to the authors, no prior work had directly compared both plasma and urinary metabolomic profiles between asymptomatic hyperuricemia and gout. This gap motivated the design of this pilot study.

The study was a cross-sectional observational trial that enrolled 42 adult men treated at a single clinic in Tokyo between September 2019 and April 2020. All participants had hyperuricemia with serum urate above 7.0 mg/dL. Among them, 33 men had gout diagnosed according to the Japanese Guideline on the Management of Hyperuricemia and Gout, third edition, and 9 men had asymptomatic hyperuricemia without gout flares.

For certain analyses, the authors divided the gout group into two subgroups based on serum urate:

• High-risk gout: serum urate at least 9.0 mg/dL (16 men)
• Low-risk gout: serum urate less than 9.0 mg/dL (17 men)

Clinical data extracted from the medical records included age, blood pressure, body mass index, a full panel of blood tests, and urinary urate and creatinine. The researchers calculated the urinary urate to creatinine ratio and the fractional excretion of uric acid as indicators of renal urate excretion. Questionnaires provided information on duration of hyperuricemia, use of uric acid-lowering drugs such as xanthine oxidase inhibitors, and family history of gout.

For metabolomic profiling, they applied gas chromatography tandem mass spectrometry (GC MS/MS) to both plasma and urine. In total, they detected 149 metabolites in plasma and 211 in urine, with 135 metabolites present in both sample types.

For the genetic work, genomic DNA was extracted from leukocytes. The team examined two non-synonymous single-nucleotide polymorphisms (SNPs) in ABCG2, rs72552713 and rs2231142. They also sequenced 13 exons of SLC2A9 and evaluated several variants, including the non-synonymous rs3733591, which had been linked to serum urate in earlier studies.

To find candidate metabolites that might distinguish gout from asymptomatic hyperuricemia, the authors used partial least squares discriminant analysis (PLS DA), a multivariate method that separates groups based on patterns across many variables. From each comparison, they selected the 30 metabolites with the highest variable importance in projection (VIP) scores.

They then applied univariate tests, specifically the Mann-Whitney U test and Steel’s test, to these candidate metabolites and adjusted p-values using the Benjamini-Hochberg method. They chose a relatively inclusive false discovery rate threshold of 0.15 to avoid missing possible biomarkers in this small pilot study. Finally, they ran pathway analysis in MetaboAnalyst using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Homo sapiens pathway library to identify which metabolic pathways were perturbed.

The authors report that dysfunctional ABCG2 variants were common in the group but, importantly, did not differ between gout and asymptomatic hyperuricemia. They write that “there were no differences in the frequencies of ABCG2 variants associated with SUA elevation between the gout and asymptomatic hyperuricemia groups.” The SLC2A9 variant rs3733591, which had been associated with serum urate in prior work, also showed no meaningful difference in frequency between groups.

Based on these results, the authors suggest that while ABCG2 and SLC2A9 play central roles in raising serum urate, they do not appear to explain who progresses from asymptomatic hyperuricemia to gout in this small cohort. They propose that other genetic factors, possibly along with environmental influences, likely shape that progression.

One of the most notable biochemical findings involved the tricarboxylic acid (TCA) cycle, also known as the citric acid cycle. Plasma 2-ketoglutarate was higher in gout than in asymptomatic hyperuricemia, with a reported fold difference of 1.415 and an adjusted p-value of 0.039. Isocitrate and malate also tended to be higher in gout, especially among men in the high-risk gout group.

In pathway analysis, the TCA cycle came out as a significantly perturbed pathway when comparing all gout cases to asymptomatic hyperuricemia, with an impact value of 0.200 and an adjusted p-value of 0.003. When the authors focused on high-risk gout, the signal was even stronger. The TCA cycle had an impact value of 0.512 and an adjusted p-value below 0.001 in that subgroup.

The authors describe an imbalance in the pattern of TCA intermediates. Compounds that require nicotinamide adenine dinucleotide (NAD+) reduction, such as 2-ketoglutarate, isocitrate, and malate, tended to accumulate in gout, while other intermediates like citrate, succinate, and fumarate did not show large differences between groups. They interpret this pattern as a possible sign that gout onset is linked to carbohydrate metabolism and mitochondrial respiration, while also noting that NAD+ itself was not directly measured.

The study also found differences in markers of glycolysis and broader carbohydrate metabolism. Plasma glucose, pyruvate, and lactate tended to be higher in gout than in asymptomatic hyperuricemia. The authors describe lactate as a marker of “typical anaerobic glycolysis,” and they explain that its accumulation generally reflects high energy demand in biological systems.

In the context of gout, they suggest that elevated lactate may reflect low utilization or increased intake of glucose and could contribute to lower pH in tissues. This environment may promote conditions that favor gout flares, although the study design cannot prove causality.

Pathway analysis in the high-risk gout group also highlighted three related pathways:

• Glyoxylate and dicarboxylate metabolism
• Alanine, aspartate, and glutamate metabolism
• Pyruvate metabolism

All three are closely connected to the processing of carbohydrates and energy generation, which fits with the observed shifts in TCA cycle intermediates and glycolytic products.

Among the urinary metabolites, nicotinate stood out as a particularly strong discriminator between gout and asymptomatic hyperuricemia. In the overall gout group, urinary nicotinate showed a fold difference of 5.475 compared with asymptomatic hyperuricemia and an adjusted p-value of 0.025. In the high-risk gout group, the fold difference rose to 6.515 with an adjusted p-value of 0.020.

The authors connect this finding with the role of monocarboxylates in renal urate reabsorption. Transporters such as SLC22A12 and SLC5A8 exchange monocarboxylates, including lactate and nicotinate, with urate in the proximal tubule. The paper cites earlier work showing that excess blood lactate can be excreted in exchange for reabsorbed urate.

Based on this framework, the authors propose that elevated urinary nicotinate may reflect “the association between gout and the accumulation of monocarboxylates,” whose renal excretion is coupled to urate reabsorption. They are careful to note that this study is observational and cannot establish cause and effect, but they present urinary nicotinate as a promising candidate biomarker.

From a clinical standpoint, serum urate levels were high in all groups by design. Participants with gout tended to be older than those with asymptomatic hyperuricemia. Measures of urinary uric acid excretion and fractional excretion of uric acid tended to be lower in the gout group, although these differences did not reach statistical significance in this small sample.

The study also notes that six participants with gout were receiving xanthine oxidase inhibitors within two weeks of enrollment. These drugs reduce uric acid production and have been reported to lower the incidence of cardiovascular events as well as gout flares. The authors acknowledge that this treatment may have influenced some inflammatory or cardiovascular markers, which is one of several potential confounders they list.

In their discussion, the authors conclude that this pilot study supports a role for carbohydrate metabolism and energy-related pathways in the transition from asymptomatic hyperuricemia to gout. They emphasize that urate transporters such as ABCG2 and SLC2A9 are important for elevating serum urate, but the lack of clear differences in these genes between gout and asymptomatic hyperuricemia suggests that other pathways influence who actually develops gout flares.

They highlight plasma 2-ketoglutarate, other TCA cycle intermediates, and urinary nicotinate as potential biomarkers that may help distinguish gout from asymptomatic hyperuricemia. In their words, the study “suggests that glycolysis compounds, TCA cycle intermediates, and urinary nicotinate, which are related to urinary urate excretion, are potential biomarkers distinguishing gout from asymptomatic hyperuricemia.”

At the same time, they are explicit about the limitations of their work. The sample size is small, particularly for the asymptomatic hyperuricemia group. The cross-sectional design cannot establish cause and effect. The analyses did not fully adjust for potential confounders such as age. All participants were men, which limits how widely the findings can be applied. Finally, the presence of urate-lowering therapy in some gout patients may have influenced certain measurements.

The authors recommend larger, more diverse metabolomics studies that include women, allow adjustment for several effect modifiers, consider the duration of hyperuricemia in more detail, and extend genetic analysis to genes involved in glucose and energy metabolism.

Taken together, this scientific paper offers an early but detailed look at how metabolomic profiles in plasma and urine may differ between gout and asymptomatic hyperuricemia, even when serum urate levels are similarly high. By combining GC MS/MS metabolomics, targeted genetic testing of key urate transporters, and pathway analysis, the authors describe a pattern that involves glycolysis, TCA cycle intermediates, and monocarboxylate-linked urate handling in the kidney. Plasma 2-ketoglutarate and urinary nicotinate emerge as especially notable signals in this pattern.

Because this is a pilot study with several acknowledged limitations, its main value lies in providing a framework and a set of candidate biomarkers for future work rather than final answers. The authors present their findings as a starting point for larger studies that can test whether these metabolomic signatures reliably help distinguish people who will develop gout from those who will remain asymptomatic despite long-term hyperuricemia.