The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

Unraveling the effects of uric acid on endothelial cells: A global proteomic study

This work aims to understand how normouricemic levels of uric acid can induce endothelial dysfunction seeking global proteomic alterations in Human Umbilical Vein cells (HUVEC). It reveals significant alterations in redox-sensitive and antioxidant proteins, chaperones, and proteins associated with cell migration and adhesion in response to uric acid exposure. Monitoring cellular oxidation with the roGFP2-Grx1 probe proved increased oxidation levels induced by uric acid, which can be attenuated by peroxidasin (PXDN) inhibition, suggesting a regulatory role for PXDN in mitigating oxidative stress induced by uric acid. As a consequence of uric acid oxidation and the formation of reactive intermediate, we identified adducts in proteins (+140 kDa) in a novel post-translation modification named uratylation. Increased misfolded protein levels and p62 aggregation were also found, indicating disturbances in cellular proteostasis. Furthermore, uric acid promoted monocyte adhesion and upregulated ICAM and VCAM protein levels, implicating a pro-inflammatory response in endothelial cells. These findings provide critical insights into the molecular mechanisms underlying vascular damage associated with uric acid.

Integration of metabolomics and genomics implicates a causality between 1398 blood metabolites and gout

INTRODUCTION

Metabolic disorders represent a hallmark feature of gout. However, evidence on the causality of blood metabolites and gout remains lacking. We performed a Mendelian randomization (MR) analysis to systematically evaluate the causality from genetically proxied 1398 blood metabolites to gout.

METHOD

Genome-wide association study (GWAS) data for 1398 blood metabolites were extracted from 8299 European subjects. The discovery stage was performed using gout data from FinnGen consortium R9 to initially explore causal associations between metabolites and gout. The significant associations identified in the discovery stage were validated in the replication stage employing gout GWAS data from the IEU database. Random-effect inverse variance weighted was chosen as the main method for causality analysis, with MR-Egger, weighted median, robust adjusted profile score, and maximum likelihood as complementary analysis methods. Then, a series of sensitivity analyses were performed.

RESULTS

Results showed that there was a significant causal relationship between eight metabolites and gout, including 21-hydroxypregnenolone disulfate levels, carnitine levels, ethyl beta-glucopyranoside levels, gamma-glutamylglycine levels, glycine levels, glycine-to-alanine ratio, glycolithocholate sulfate levels, and propionylglycine levels. Colocalization analysis evidence strongly supported a causal relationship between 21-hydroxypregnenolone disulfate levels and carnitine levels and gout. In addition, four metabolic pathways were involved in the biological process of gout (carnitine synthesis, beta oxidation of very long-chain fatty acids, alanine metabolism, glutathione metabolism).

CONCLUSIONS

The current study provides evidentiary support for the causal relationship between eight blood metabolites and gout and identifies four significant metabolic pathways. These findings hold the potential to inform future research, clinical interventions, and therapeutic strategies for gout.

Gut microbiota Lactobacillus johnsonii alleviates hyperuricemia by modulating intestinal urate and gut microbiota-derived butyrate

BACKGROUND

Gut microbiota are important for uric acid (UA) metabolism within hyperuricemia (HUA); however, the underlying mechanisms of how the gut microbiota regulate intestinal UA metabolism remain unclear. This study aimed to explore the function of the intestine in HUA and to further reveal the possible mechanism.

METHODS

We conducted gut microbiota depletion to validate the role of gut microbiota in UA metabolism. A mouse model of HUA was established, and the gut microbiota and microbiome-derived metabolites were analyzed via 16S RNA gene sequencing and metabolomics analysis. The mechanism of the gut microbiota in HUA was elucidated by in vivo and in vitro experiments.

RESULTS

Antibiotic treatment elevated serum UA, disturbed purine metabolism, and decreased the relative abundance of Lactobacillus. HUA mice had a lower relative abundance of Lactobacillus johnsonii (L. johnsonii) and decreased gut butyrate concentration. Supplementation of L. johnsonii significantly reduces serum UA in hyperuricemia mice by preventing UA synthesis and promoting the excretion of gut purine metabolites. In addition, L. johnsonii enhanced intestinal UA excretion by heightening the urate transporter ABCG2 (adenosine triphosphate-binding cassette transporter, subfamily G, member 2) expression, and increasing the levels of butyrate, which upregulated ABCG2 expression via the Wnt5a/b/β-catenin signaling pathway.

CONCLUSION

Our results suggest that gut microbiota and microbiota-derived metabolites directly regulate gut UA metabolism, highlighting potential applications in the treatment of diet-induced HUA by targeting gut microbiota and its metabolites.

A Review on Optical Biosensors for Monitoring of Uric Acid and Blood Glucose Using Portable POCT Devices: Status, Challenges, and Future Horizons

The growing demand for real-time, non-invasive, and cost-effective health monitoring has driven significant advancements in portable point-of-care testing (POCT) devices. Among these, optical biosensors have emerged as promising tools for the detection of critical biomarkers such as uric acid (UA) and blood glucose. Different optical transduction methods, like fluorescence, surface plasmon resonance (SPR), and colorimetric approaches, are talked about, with a focus on how sensitive, specific, and portable they are. Despite considerable advancements, several challenges persist, including sensor stability, miniaturization, interference effects, and the need for calibration-free operation. This review also explores issues related to cost-effectiveness, data integration, and wireless connectivity for remote monitoring. The review further examines regulatory considerations and commercialization aspects of optical biosensors, addressing the gap between research developments and clinical implementation. Future perspectives emphasize the integration of artificial intelligence (AI) and healthcare for improved diagnostics, alongside the development of wearable and implantable biosensors for continuous monitoring. Innovative optical biosensors have the potential to change the way people manage their health by quickly and accurately measuring uric acid and glucose levels. This is especially true as the need for decentralized healthcare solutions grows. By critically evaluating existing work and exploring the limitations and opportunities in the field, this review will help guide the development of more efficient, accessible, and reliable POCT devices that can improve patient outcomes and quality of life.

Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans

Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of four KO in a larger sample by qRT-PCR. A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange > 23, p-adj ≤ 3.56 × 10- 9), the pentose pathway (Log2foldchange > 24, p-adj < 1.10 × 10-12) and purine metabolism (Log2foldchange > 22, p-adj < 1.25 × 10- 7). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p-adj < 1.72 × 10-6) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p-adj < 3.31 × 10- 16) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated. There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.

The Diagnostic Value of Serum MCP-1 Combined with OPN Detection for Early Renal Injury in Gout Patients

Objective

To explore the changes in serum Monocyte chemoattractant protein-1 (MCP-1) and Osteopontin (OPN) in gout patients and their diagnostic value for early renal injury.

Methods

In this research, 174 gout patients (January 2022-October 2024) were divided into the early renal injury group (50 cases) and non-early renal injury group (124 cases). Additionally, 169 healthy individuals were included as controls. Clinical indicators such as serum creatinine, cystatin C (CysC), and GFR were recorded. MCP-1 and OPN levels were measured using ELISA. Pearson's correlation was used to analyze relationships; Logit regression was applied to identify influencing factors, and ROC curves assessed diagnostic value, with AUC comparisons via Z-test.

Results

Serum MCP-1 and OPN levels were significantly higher in the gout group compared to controls (P<0.05) and further elevated in the early renal injury group (P<0.05). MCP-1 and OPN correlated positively with creatinine and CysC and negatively with GFR (P<0.05). Logit regression identified MCP-1 (OR: 2.765, 95% CI: 1.308-5.846) and OPN (OR: 3.019, 95% CI: 1.468-6.210) as independent risk factors (P<0.05). The AUC for diagnosing early renal injury was 0.775 (MCP-1), 0.827 (OPN), and 0.938 (combined), with the combination significantly outperforming either marker alone (Z=3.075, 2.273, P<0.05).

Conclusion

The combination of serum MCP-1 and OPN in gout patients has a higher diagnostic value for early renal injury, it is obviously higher than the individual diagnosis of each indicator, and demonstrates significant clinical implications.

Association between RC/HDL-C and hyperuricemia in adults: evidence from NHANES 2005-2018

Background

The incidence of hyperuricemia is growing in the world, with a significant influence on the survival and healthy condition of the patient. The connection between serum residual cholesterol (RC) to high-density lipoprotein cholesterol (HDL-C) ratio and hyperuricemia is uncertain. Consequently, we tried to elucidate the connection between the hyperuricemia and RC/HDL-C ratio.

Methods

Based on the National Health and Nutrition Examination Survey (NHANES) database, data from 2005 to 2018 were utilized in this cross-sectional research. RC/HDL-C index was calculated by (TC - HDL-C - LDL-C)/HDL-C. Participants were diagnosed with hyperuricemia when the serum uric acid concentration reached 6 mg/dL in women and 7 mg/dL in men. Our researcher utilized smoothed curve fitting and multivariate logistic regression analysis to examine between RC/HDL-C and hyperuricemia among adults. The consistency of these results was examined in various population subgroups.

Results

2376 individuals (19.1%) were stratified into the hyperuricemia group. We observed statistically significant differences (P values < 0.05) in the hyperuricemia population for remaining variables, except for economic level and alcohol drinking. After correcting for potential confounders, our researchers discovered the strong positive connection between the RC/HDL-C and the possibility of incurring hyperuricemia. The incidence of RC/HDL-C elevated by 98% with each additional unit of the RC/HDL-C. Subgroup analyses showed correlations for the majority of subgroups remained stable. However, gender and several diseases may modify this association.

Conclusions

Higher RC/HDL-C is correlated with higher prevalence rate of developing hyperuricemia. However, further research is still required to confirm the causal association.

Specific blood metabolite associations with Gout: a Mendelian randomization study

OBJECTIVE

Gout, common metabolic disorders, have poorly understood links with blood metabolites. Exploring these relationships could enhance clinical prevention and treatment strategies.

METHODS

We applied bidirectional two-sample Mendelian randomization (MR) analysis, using data from a genome-wide association (GWAS) study of 486 blood metabolites. Gout data was obtained from FinnGen R8 (7461 gout and 221,323 control cases). We implemented the inverse variance-weighted (IVW) method for main analytical approach. Extensive heterogeneity, pleiotropy tests, leave-one-out analysis, and reverse MR were conducted to validate the robustness of our findings. Both Bonferroni and False Discovery Rate (FDR) corrections were used to adjust for multiple comparisons, ensuring stringent validation of our results.

RESULTS

Initial MR identified 31 candidate metabolites with potential genetic associations to gout. Following rigorous sensitivity analysis, 23 metabolites as potential statistical significance after final confirmation. These included metabolites enhancing gout risk such as X-11529 (OR = 1.225, 95% CI 1.112-1.350, P < 0.001), as well as others like piperine and stachydrine, which appeared to confer protective effects. The analysis was strengthened by reverse MR analysis. Additionally, an enrichment analysis was conducted, suggesting that 1-methylxanthine may be involved in the metabolic process of gout through the caffeine metabolism pathway.

CONCLUSION

Identifying causal metabolites offers new insights into the mechanisms influencing gout, suggesting pathways for future research and potential therapeutic targets.

Genetic insights into therapeutic targets for gout: evidence from a multi-omics mendelian randomization study

BACKGROUND

Considering that the treatment of gout is poor, we performed a Mendelian randomization (MR) study to identify candidate biomarkers and therapeutic targets for gout.

METHODS

A drug-targeted MR study was performed for gout by integrating the gout genome-wide association studies (GWAS) summary data and cis expression quantitative trait loci of 2,633 druggable genes from multiple cohorts. Summary data-based Mendelian randomization (SMR) analyses based on transcript and protein levels were further implemented to validate the reliability of the identified potential therapeutic targets for gout. Phenome-wide MR (Phe-MR) analysis was conducted in 1403 diseases to investigate incidental side effects of potential therapeutic targets for gout.

RESULTS

Eight potential therapeutic targets (ALDH3B1, FCGR2B, IL2RB, NRBP1, RCE1, SLC7A7, SUMF1, THBS3) for gout were identified in the discovery cohort using MR analysis. Replication analysis and meta-analysis implemented in the replication cohort validated the robustness of the MR findings (P < 0.05). Evidence from the SMR analysis (P < 0.05) further strengthened the reliability of the 8 potential therapeutic targets for gout also revealed that high levels of ALDH3B1 reduced the gout risk possibly modified by the methylation site cg25402137. SMR analysis (P < 0.05) at the protein level added emphasis on the impact of the risk genes NRBP1 and SUMF1 on gout. Phe-MR analysis indicated significant causality between 7 gout causal genes and 45 diseases.

CONCLUSION

This study identified several biomarkers associated with gout risk, providing new insights into the etiology of gout and promising targets for the development of therapeutic agents.

Research progress on the mechanism of hyperuricemic nephropathy based on multi-omics technique: A review

Hyperuricemic nephropathy is a metabolic disease in which renal uric acid deposition and excretion are impaired due to elevated levels of uric acid in the blood, leading to impaired renal tubule function and chronic renal disease. Hyperuricemic nephropathy is one of the important complications of hyperuricemia, which seriously affects the quality of life and prognosis of patients. The pathogenesis of hyperuricemic nephropathy involves a variety of factors, including: amino acid metabolism disorder, energy metabolism abnormality, increased nucleotide metabolism, lipid metabolism disorder and bile acid metabolism imbalance, REDOX process disorder, cell cycle and apoptosis imbalance, signal transduction and inflammatory response enhancement, and intestinal flora imbalance. In recent years, omics techniques such as metabolomics, transcriptomics and intestinal microecology have been used to reveal the metabolic, gene and microflora characteristics of hyperuricemic nephropathy from different levels, as well as their interactions and regulatory mechanisms. This paper reviews these studies, analyzes the existing problems and challenges, and puts forward future research directions and suggestions, aiming at providing new theoretical basis and practical guidance for the prevention and treatment of hyperuricemic nephropathy.

Alterations in the gut microbiome and metabolism profiles reveal the possible molecular mechanism of renal injury induced by hyperuricemia in a mouse model of renal insufficiency

Objectives: To investigate the role of the intestinal flora and metabolites in the development of hyperuricemic renal injury in chronic kidney disease (CKD).Methods: Unilaterally nephrectomized mice were fed with adenine and potassium oxonate for 9 weeks. HE staining combined with plasma biochemical indicators was used to evaluate renal pathological and functional changes. We conducted 16S rRNA sequencing and untargeted metabolomics on feces and plasma samples to reveale changes in intestinal microbiota and metabolites.Result: Our analysis revealed significant differences in 15 bacterial genera, with 7 being upregulated and 8 being downregulated. Furthermore, metabolomic analysis revealed changes in the distribution of amino acid and biotin metabolites in basic metabolic pathways in both feces and serum. Specifically, differentially abundant metabolites in feces were associated primarily with histidine metabolism; the biosynthesis of phenylalanine, tyrosine, and tryptophan; and tyrosine metabolism. In plasma, the differentially abundant metabolites were involved in multiple metabolic pathways, including aminoacyl-tRNA biosynthesis; glycine, serine, and threonine amino acid metabolism; valine, leucine, and isoleucine biosynthesis; tyrosine biosynthesis and metabolism; biotin metabolism; and taurine and hypotaurine metabolism. Furthermore, correlation analysis revealed that Akkermansia, UCG-005, Lachnospiraceae_NK4A136_group, Lactococcus, and Butymonas were associated with various differentially abundant metabolites as well as renal function, oxidative stress, and mitophagy. The changes in the intestinal flora observed in hyperuricemia may lead to imbalances in amino acid and biotin metabolism in both the intestine and host, ultimately affecting oxidative stress and mitophagy in mice and accelerating the progression of CKD.Conclusion: Our findings provide insights into a potential pathogenic mechanism by which hyperuricemia exacerbates renal injury in mice with renal insufficiency. Understanding these pathways may offer new therapeutic strategies for managing hyperuricemic renal injury in CKD patients.

Comorbidities confound metabolomics studies of human disease

The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

Hyperuricemia and its related diseases: mechanisms and advances in therapy

Hyperuricemia, characterized by elevated levels of serum uric acid (SUA), is linked to a spectrum of commodities such as gout, cardiovascular diseases, renal disorders, metabolic syndrome, and diabetes, etc. Significantly impairing the quality of life for those affected, the prevalence of hyperuricemia is an upward trend globally, especially in most developed countries. UA possesses a multifaceted role, such as antioxidant, pro-oxidative, pro-inflammatory, nitric oxide modulating, anti-aging, and immune effects, which are significant in both physiological and pathological contexts. The equilibrium of circulating urate levels hinges on the interplay between production and excretion, a delicate balance orchestrated by urate transporter functions across various epithelial tissues and cell types. While existing research has identified hyperuricemia involvement in numerous biological processes and signaling pathways, the precise mechanisms connecting elevated UA levels to disease etiology remain to be fully elucidated. In addition, the influence of genetic susceptibilities and environmental determinants on hyperuricemia calls for a detailed and nuanced examination. This review compiles data from global epidemiological studies and clinical practices, exploring the physiological processes and the genetic foundations of urate transporters in depth. Furthermore, we uncover the complex mechanisms by which the UA induced inflammation influences metabolic processes in individuals with hyperuricemia and the association with its relative disease, offering a foundation for innovative therapeutic approaches and advanced pharmacological strategies.

A metabolomics perspective reveals the mechanism of the uric acid-lowering effect of Prunus salicina Lindl. cv. "furong" polyphenols in hypoxanthine and potassium oxybate-induced hyperuricemic mice

The incidence of hyperuricemia (HUA) shows a gradually increasing trend towards affecting younger individuals, and it can significantly harm the overall health status of the body. Based on a metabolomics perspective, this study reveals the mechanism of the uric acid-lowering action of Prunus salicina Lindl. cv. "furong" polyphenols (PSLP) on a hyperuricemia mouse model induced by hypoxanthine and potassium oxybutyrate. The results demonstrate that PSLP comprise an effective treatment strategy for reducing the levels of serum uric acid (SUA), serum creatinine (SCr) and blood urea nitrogen (BUN) in HUA mice (p < 0.05), wherein the maximum decrease rates are up to 44.50%, 29.46%, and 32.95%, respectively. PSLP are observed to exert a pronounced inhibitory effect on the activities of xanthine oxidase (XOD) and adenosine deaminase (ADA) in the livers of HUA mice, with reductions of up to 16.36% and 20.13%, respectively. These findings illustrate that PSLP exert a significant uric acid-lowering effect. Subsequent metabolomic analysis of mouse serum identified 28 potential biomarkers for hyperuricemia, whose levels were markedly diminished by PSLP. This process involved alterations in purine, glycine, the pentose phosphate pathway, and galactose metabolism. Twenty-eight potential biomarkers were identified for hyperuricemia by subsequent metabolomic analysis of mouse serum, whose levels were markedly reversed by PSLP intervention. The regulation of HUA by PSLP involved alterations in purine metabolism, glycerolipid metabolism, the pentose phosphate pathway, and galactose metabolism. The mechanism of PSLP ameliorated hyperuricemia might be attributed to reduction of the level of the uric acid precursor ribose-5-phosphate in the pentose phosphate pathway, the inhibition of the activities of uric acid synthase XOD and ADA in purine metabolism, and reduction of the synthesis of the end product uric acid. This study provides a theoretical basis for the development of functional foods based on PSLP, which can potentially reduce uric acid levels.

Nonlinear association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and hyperuricemia in cancer patients: evidence from NHANES 2007-2018

BACKGROUND

Evidence shows that cancer patients are more likely to have hyperuricemia (HUA) compared to the general population, with lipid metabolism playing a significant role. However, it is still unclear whether there is a non-linear relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and HUA in these patients. This study aims to explore the association between NHHR and HUA in cancer patients.

METHODS

This study included participants from the NHANES database from 2007 to 2018. We used multivariable logistic regression, restricted cubic splines (RCS) analysis, and subgroup analysis to examine the association between NHHR and HUA and gout in cancer patients, as well as to investigate differences in this association among specific subgroups.

RESULTS

A total of 2826 participants were included, with a HUA prevalence of 24.30%. Weighted multivariable logistic regression showed that for each unit increase in NHHR, the odds of HUA in cancer patients increased by 16% (95% confidence interval [CI]: 1.06, 1.29, P = 0.002). When NHHR was divided into tertiles, those in the highest tertile (Q3) had a 1.84 times higher odds of developing HUA compared to those in the lowest tertile (Q1) (95% CI: 1.32, 2.58, P < 0.001). However, there was no significant association with gout. RCS analysis further revealed a significant non-linear positive association, particularly among males. Subgroup analysis and interaction tests indicated a stronger association in cancer patients who did not have a history of stroke.

CONCLUSION

There is a non-linear association between NHHR and HUA in cancer patients.

Association between combined exposure to ambient air pollutants, genetic risk, and incident gout risk: A prospective cohort study in the UK Biobank

BACKGROUND

Limited research has been conducted on the association between long-term exposure to air pollutants and the incidence of gout.

OBJECTIVES

This study aims to assess the individual and combined effects of prolonged exposure to five air pollutants (NO2, NOx, PM10, PMcoarse and PM2.52) on the incidence of gout among 458,884 initially gout-free participants enrolled in the UK Biobank.

METHODS

Employing a land use regression model, we utilized an estimation method to ascertain the annual concentrations of the five air pollutants. Subsequently, we devised a weighted air pollution score to facilitate a comprehensive evaluation of exposure. The Cox proportional hazards model was utilized to investigate the association between ambient air pollution and gout risk. Interaction and stratification analyses were conducted to evaluate age, sex, BMI, and genetic predisposition as potential effect modifiers in the air pollution-gout relationship. Furthermore, mediation analyses were conducted to explore the potential involvement of biomarkers in mediating the association between air pollution and gout.

RESULTS

Over a median follow-up time of 12.0 years, 7,927 cases of gout were diagnosed. Significant associations were observed between the risk of gout and a per IQR increase in NO2 (HR3: 1.05, 95 % CI4: 1.02-1.08, p = 0.003), NOx (HR: 1.04, 95 % CI: 1.01-1.06, p = 0.003), and PM2.5 (HR: 1.03, 95 % CI: 1.00-1.06, p = 0.030). Per IQR increase in the air pollution score was associated with an elevated risk of gout (p = 0.005). Stratified analysis revealed a significant correlation between the air pollution score and gout risk in participants ≥60 years (HR: 1.05, 95 % CI: 1.02-1.09, p = 0.005), but not in those <60 years (p = 0.793), indicating a significant interaction effect with age (p-interaction=0.009). Mediation analyses identified five serum biomarkers (SUA:15.87 %, VITD: 5.04 %, LDLD: 3.34 %, GGT: 1.90 %, AST: 1.56 %5) with potential mediation effects on this association.

CONCLUSIONS

Long-term exposure to air pollutants, particularly among the elderly population, is associated with an increased risk of gout. The underlying mechanisms of these associations may involve the participation of five serum biomarkers.

Reassessing Gout Management through the Lens of Gut Microbiota

Gout, recognized as the most common form of inflammatory arthritis, arises from the accumulation of uric acid crystals, leading to intense pain, particularly in the big toe. This condition has traditionally been associated with the overproduction or reduced clearance of uric acid. Recent studies, however, have underscored the significant role of the gut microbiota in uric acid metabolism, impacting both its production and elimination. This emerging understanding suggests that maintaining gut health could offer innovative approaches to treating gout, complementing traditional dietary and pharmacological interventions. It highlights the potential of probiotics or microbiome-based therapies, indicating a future where treatments are tailored to an individual’s microbiome. This offers a fresh perspective on gout management and underscores the broader influence of the microbiota on health and disease.

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

BACKGROUND

Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout.

METHODS

Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model.

RESULTS

An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model.

CONCLUSION

The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

Assessing the causal association between human blood metabolites and the risk of gout

BACKGROUND

The occurrence of gout is closely related to metabolism, but there is still a lack of evidence on the causal role of metabolites in promoting or preventing gout.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and gout using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. We also performed a metabolic pathway analysis to identify potential metabolic pathways.

RESULTS

After screening, 486 metabolites were retained for MR analysis. After screening by IVW and sensitivity analysis, 14 metabolites were identified with causal effect on gout (P < 0.05), among which hexadecanedioate was the most significant candidate metabolite associated with a lower risk of gout (IVW OR = 0.50; 95% CI = 0.38-0.67; P = 1.65 × 10-6 ). Metabolic pathway analysis identified one pathway that may be associated with the disease.

CONCLUSION

This MR study combining genomics with metabolomics provides a novel insight into the causal role of blood metabolites in the risk of gout, which implies that examination of certain blood metabolites would be a feasible strategy for screening populations with a higher risk of gout.

Design, synthesis and evaluation of a myricetin and nobiletin hybrid compound for alleviating hyperuricemia based on metabolomics and gut microbiota

Hyperuricemia (HUA) is the fourth most common basic metabolic disease that can cause damage to multiple organs throughout the body. In this study, a hybrid compound consisting of myricetin and nobiletin was synthesized and its biological activity was evaluated. We named the hybrid compound MNH, and its structure was confirmed by spectroscopy. This study used serum metabolomics profiling with LC/MS and 16S rRNA gene sequencing analysis to explore the anti-HUA efficacy of MNH on a yeast paste-induced mouse model. The results showed that serum uric acid (UA), creatinine (CRE) and urea nitrogen (BUN) levels were significantly decreased after the intervention of MNH. The efficacy of MNH in lowering UA was somewhat greater than that of myricetin and nobiletin. In addition, MNH could repair the renal histopathological damage. Moreover, serum metabolomics demonstrated that MNH regulated the metabolic pathways involved in glycerophospholipid metabolism, arachidonic acid metabolism and alanine etc. Furthermore, MNH supplementation restored the composition of gut microbiota with remarkable reductions in Lactobacillus and Limosilactobacillus and significant elevations in norank_f_Muribaculaceae and Bacteroides at the genus level. Taken together, these results indicated that MNH might represent a protective effect against HUA via modulating gut microbiota and metabolomics.