Gout is a metabolic inflammatory disease caused by monosodium urate (MSU) crystal deposition in joints and surrounding tissues, with clinical features including hyperuricemia, recurrent acute arthritis, tophi formation, and chronic joint damage. According to the Global Burden of Disease Study (GBD) 2021 data, the global prevalence of gout has reached 55.8 million, with age-standardized prevalence increasing by 22.5% since 1990, and projected to exceed 95.8 million by 2050 (1). This sustained upward trend makes gout a global public health concern requiring urgent attention.

Metabolic syndrome (MetS) is a cluster of metabolic abnormalities characterized by central obesity, insulin resistance, hypertension, dyslipidemia, and glucose metabolism disorders. Epidemiological data indicate that the global prevalence of MetS ranges from 11.9% to 49% and continues to rise (2, 3). Notably, numerous clinical studies have revealed a high degree of comorbidity between gout and MetS. A multicenter study by Jung et al. (4) showed that the prevalence of MetS in gout patients was as high as 43.6%, significantly higher than the 5.2% in the general population. Yoo et al. (5) further confirmed that gout patients had nearly three times higher rates of insulin resistance compared to healthy controls.

Although the epidemiological association between the two conditions has been widely reported, the directionality of causality and its biological mechanisms remain controversial. Traditional views suggest that MetS promotes gout development through multiple mechanisms: inflammatory factors produced by visceral adipose tissue can enhance hepatic uric acid synthesis (6); insulin resistance can increase uric acid reabsorption by upregulating renal tubular URAT1 expression (7); and hypertension affects uric acid excretion through renal microvascular damage (8). However, emerging evidence suggests that hyperuricemia itself may also serve as a precursor factor for MetS, participating in MetS development through promoting endothelial dysfunction, activating the renin-angiotensin system, and increasing oxidative stress (9). Therefore, clarifying the causal relationship between them is of important scientific significance for understanding disease mechanisms and developing targeted intervention strategies.

Previous studies investigating the association between MetS and gout have several methodological limitations. First, most observational studies have limited sample sizes from single sources, making it difficult to effectively control confounding factors, weakening the reliability of conclusions (10). Second, traditional analyses are mostly based on linear assumptions, ignoring possible non-linear dose-response relationships and threshold effects, which are crucial for clinically determining intervention cutoff points. Third, few studies have systematically explored the modifying effects of population heterogeneity (such as age and gender) on association strength. Fourth, due to the inherent defects of observational study design, it is difficult to exclude the influence of reverse causation and unmeasured confounding, making it impossible to establish definite causal relationships (11). Finally, previous studies have mostly focused on epidemiological association descriptions, lacking in-depth exploration of the molecular mechanisms underlying the comorbidity of these two diseases.

Mendelian randomization (MR), as a causal inference method using genetic variants as instrumental variables, can effectively overcome the limitations of traditional observational studies. Since genetic variants are randomly allocated during gamete formation and are already determined at birth, MR analysis can maximize the avoidance of reverse causation and confounding bias, providing more reliable evidence for causal inference (12, 13). Additionally, integrating transcriptomic data for multi-omics analysis can validate genetic findings and reveal potential molecular mechanisms at the gene expression level, providing a more comprehensive biological explanation for understanding disease comorbidity.

Based on the above considerations, this study establishes a stepwise causal evidence framework of “Observation-Validation-Mechanism,” rather than a simple superposition of multiple methods. First, we utilized a large real-world clinical cohort (n=8,853) to capture the macroscopic associations and non-linear characteristics between MetS and gout. Subsequently, addressing the limitations of observational studies regarding confounding, we applied Mendelian Randomization (MR) to confirm the causal nature of these associations and filter out spurious links caused by environmental confounders (distinguishing the true from the false). Finally, we delved into the transcriptomic level to decipher the molecular landscape behind the causality, verifying how genetic signals translate into specific immuno-metabolic disorders. This progressive strategy aims to build a coherent and complete causal evidence chain, providing a solid evidence-based foundation for early risk stratification, precision prevention, and targeted treatment of gout.

Moving beyond a parallel multi-omics presentation, this study constructs a stepwise, mutually reinforcing causal validation framework to systematically dissect the comorbidity mechanism of metabolic syndrome and gout. The logical evidence chain is as follows: First, real-world clinical data provided macroscopic phenotypic evidence, identifying hypertension and hypertriglyceridemia as the most significant comorbid features of gout. Second, MR analysis overcame the confounding bias of clinical data, providing strong genetic evidence suggesting that these metabolic factors may have a direct causal role of these metabolic factors on gout, while ruling out the direct pathogenicity of hyperglycemia. Finally, transcriptomics filled the “black box” between “genetic causality” and “clinical phenotype,” revealing that “antigen presentation defects driving Th17 immune imbalance” may represent a potential molecular bridge connecting metabolic disorders and gouty inflammation. The main findings include: (1) Clinical cohort study confirmed MetS as an independent risk factor for gout, with significant non-linear relationships and population heterogeneity; (2) MR analysis confirmed causal associations between gout and MetS, hypertension, and triglycerides at the genetic level; (3) LDSC analysis revealed significant genetic correlation between them; (4) Multi-omics integration analysis identified the molecular mechanism of comorbidity centered on “antigen presentation-immune response.”

Our real-world clinical study provided strong clinical evidence. In a large cohort containing 8,853 subjects, after PSM matching to control confounding factors, MetS remained an independent risk factor for gout (OR = 1.456, P<0.001). This finding is consistent with previous epidemiological studies. A nationwide cohort study by Eun et al. (11) based on 3.5 million young Korean men showed that MetS patients had a 2.5-fold increased risk of gout incidence. Notably, among MetS components, hypertension (OR = 2.984) and hyperlipidemia (OR = 2.719) showed the strongest associations, suggesting that blood pressure and lipid control should receive particular attention in gout risk management. RCS analysis further revealed significant non-linear dose-response relationships between multiple metabolic indicators and gout risk, with most indicators showing “J” or “U” shaped curves with clear risk thresholds. For example, LDL-C showed lowest risk around 2.7 mmol/L, a finding of important guiding significance for clinically determining intervention cutoff points. Subgroup analysis revealed significant population heterogeneity: the association between MetS and gout was much stronger in males than females (P-interaction=0.007), while hyperlipidemia had greater risk impact on younger populations (P-interaction=0.007). These findings emphasize the necessity of adopting gender- and age-stratified individualized strategies in gout prevention.

To overcome the inherent limitations of observational studies in causal inference, we performed two-sample MR analysis. Results showed that genetically predicted MetS (OR = 1.171, 95%CI: 1.069-1.283, P<0.001), hypertension (OR = 5.426, 95%CI: 1.857-15.853, P = 0.002), triglycerides (OR = 1.325, 95%CI: 1.162-1.512, P<0.001), and waist circumference (OR = 1.523, 95%CI:1.195-1.941, P<0.001) had significant causal associations with gout risk, while HDL-C showed a protective effect (OR = 0.887, 95%CI: 0.786-1.000, P = 0.049). Notably, fasting blood glucose showed no significant causal association with gout risk (OR = 0.906, P = 0.558). Intriguingly, we observed discrepancies between our observational and MR findings regarding fasting blood glucose and LDL-C. While observational data suggested a protective or inverted J-shaped association, MR analysis did not support a causal effect. This discordance likely reflects reverse causation or physiological confounding in the real-world setting. For instance, the apparent protective effect of high glucose levels may be explained by the uricosuric effect of glycosuria (the “Bellomo phenomenon”), where severe hyperglycemia promotes uric acid excretion through osmotic diuresis (20). Similarly, the inverse association with LDL-C in the clinical cohort may be confounded by the use of lipid-lowering medications in patients with metabolic comorbidities or the lipid-lowering effects of chronic systemic inflammation (21). These findings highlight the superiority of MR in filtering out environmental confounders to identify true therapeutic targets. Multiple sensitivity analyses (MR-Egger regression, weighted median method) all supported the robustness of main results, and MR-Egger intercept test and MR-PRESSO global test did not detect significant horizontal pleiotropy. It is noteworthy that the strong causal effect of hypertension shown in MR analysis (OR = 5.426) echoes the high association observed in our clinical study, suggesting that hypertension may play a central role in gout pathogenesis. Biologically, hypertension can reduce uric acid excretion through renal microvascular damage (8), while renin-angiotensin system activation can upregulate urate transporter expression (22). LDSC analysis further confirmed significant positive genetic correlation between MetS and gout (rg=0.321, P = 4.24×10^-¹⁵), indicating that the two diseases share a large number of genetic risk variants at the genome-wide level, providing a genetic basis for comorbidity.

Gene-level MR analysis identified a series of common pathogenic genes that simultaneously affect MetS and gout. Among them, PGAP3 (OR = 1.102) and FIBP (OR = 1.151) were identified as common risk genes, while PLEK2 (OR = 0.742) and USP36 (OR = 0.907) showed common protective effects. Functional enrichment analysis revealed the biological significance of these genes: KEGG pathway analysis was significantly enriched for the “ABC transporter” pathway, which is highly related to uric acid transport mechanisms. ABCG2 is known to be a key transporter for uric acid excretion in kidneys and intestines, and its dysfunction can lead to elevated serum uric acid (23). Additionally, significant enrichment of immune-related pathways such as “primary immunodeficiency” and “antigen processing and presentation” suggests that immune system dysfunction is a potential bridge for comorbidity of the two diseases. PPI network analysis identified MAD1L1, SIGLEC10, and TAP2 as core hub genes, among which SIGLEC10, as an immunosuppressive receptor, may play a key role in regulating the transition from metabolic inflammation to gout (24).

To bridge the mechanistic gap between genetic causality and transcriptomic phenotypes, we propose a specific “Genetic-Stress-Immune” pathological axis. The TAP2 gene, identified by MR, is crucial for antigen peptide transport; its variants can lead to failure in MHC-I peptide loading. Evidence suggests that these conformationally unstable “empty” MHC molecules accumulate in the endoplasmic reticulum, triggering a persistent Unfolded Protein Response (UPR) (25). Notably, UPR activation is known to induce antigen-presenting cells to secrete cytokines such as IL-6 and IL-23, which create the critical microenvironment for Th17 cell differentiation (26). This mechanism may help explain the significant enrichment of the Th17 pathway observed in our transcriptomic data. Therefore, we tentatively hypothesize that the genetic background associated with metabolic syndrome (e.g., TAP2 variants) may primarily induce intracellular ER stress, which then drives the gout-characteristic Th17 adaptive immune response through cytokine crosstalk. However, this hypothesis requires further experimental validation. We acknowledge that in classical immunology, TAP2-mediated MHC-I molecules primarily engage CD8+ T cells through antigen presentation. However, we propose that the link between MHC-I pathway defects and Th17 polarization occurs through an indirect, paracrine mechanism rather than direct antigen presentation. Specifically, the accumulation of misfolded empty MHC-I molecules triggers ER stress and activates the UPR in antigen-presenting cells, particularly dendritic cells and monocytes (27). This chronic ER stress induces these cells to secrete a distinct cytokine milieu, notably IL-6, IL-1β, and IL-23 (28). These cytokines serve as the critical “bridge” converting intracellular MHC-I defects into extracellular immune polarization signals: IL-6 combined with TGF-β drives naive CD4+ T cell differentiation toward the Th17 lineage, while IL-23 sustains Th17 cell survival and expansion (29, 30). This paracrine cytokine-mediated pathway provides a mechanistic explanation for how genetic defects in the MHC-I loading machinery can ultimately result in CD4+ Th17-dominated inflammatory responses characteristic of gout. Furthermore, hypertriglyceridemia may synergize with TAP2 genetic variants to amplify ER stress in APCs. Elevated circulating triglycerides lead to increased cellular uptake of free fatty acids (FFAs), particularly saturated fatty acids such as palmitate. These FFAs can directly disrupt ER membrane lipid composition, activate protein kinase C (PKC) and c-Jun N-terminal kinase (JNK) signaling pathways, and induce reactive oxygen species (ROS) production, all of which converge to trigger or exacerbate ER stress (31, 32). Thus, we propose a “two-hit” model wherein the genetic background (TAP2 variants) provides the first hit by impairing peptide loading, while metabolic stress from hypertriglyceridemia delivers the second hit by independently inducing ER stress, together creating a synergistic amplification loop that drives pathological Th17 responses in metabolic syndrome-associated gout. Electronic validation based on single-cell database further supported this hypothesis. We utilized an independent gout single-cell RNA sequencing dataset (GSE217561) to validate the cell-type-specific expression patterns of core hub genes. Dot plot analysis showed that IFNGR1 and TAP2 exhibited the highest expression levels in monocytes and dendritic cells, supporting their functional localization in antigen-presenting cells. Further comparison between gout patients and healthy controls revealed that JUN and FOS were significantly downregulated, while IFNGR1 was significantly upregulated in monocytes of gout patients (all p < 0.0001). The downregulation of JUN and FOS may reflect negative feedback regulation or functional exhaustion of the AP-1 pathway under chronic inflammatory conditions, whereas the upregulation of IFNGR1 is consistent with sustained activation of the IFN-γ signaling pathway. Notably, TAP2 showed no significant difference in expression between the two groups (p = 0.39), a finding highly consistent with our MR analysis conclusions—suggesting that the pathogenic effect of TAP2 stems from protein functional defects caused by genetic variants rather than changes in expression levels. Single-cell validation confirmed the myeloid cell specificity of core hub genes at the cellular level, suggesting that monocytes/macrophages serve as the core origin of this pathological cascade (Figure 10).

Integrating multi-dimensional research findings, we propose a molecular mechanism framework for MetS and gout comorbidity: at the genetic susceptibility level, ABC transporter-related gene variants lead to uric acid transport dysfunction, while antigen presentation-related gene (such as TAP2) variants cause immune regulatory defects; at the metabolic disorder level, insulin resistance upregulates URAT1 expression to increase uric acid reabsorption (7), high triglycerides compete with uric acid for renal excretion pathways (33), and visceral fat secretes hypoxanthine as a substrate for uric acid synthesis (34); at the inflammatory amplification level, metabolic stress activates the NLRP3 inflammasome, free fatty acids enhance its response to MSU crystals (35), and Th17 cell overactivation maintains chronic inflammatory states. This mechanistic framework integrates the two main lines of uric acid metabolic abnormalities and immune inflammatory activation, providing a systematic theoretical explanation for understanding comorbidity.

This study has several methodological strengths. First, the multi-dimensional validation strategy (clinical cohort → genetic causality → molecular mechanism) constructed a complete evidence chain, enhancing the reliability of conclusions. Second, large-sample real-world data improved statistical power, and PSM method effectively controlled measured confounding factors. Third, MR analysis utilized the random allocation characteristic of genetic variants to maximize avoidance of reverse causation and unmeasured confounding influences. Fourth, multi-omics integration analysis validated findings at different biological levels (genome → transcriptome), revealing upstream-downstream associations of comorbidity mechanisms. Fifth, trajectory modeling captured the impact of dynamic evolution of metabolic indicators on disease risk, compensating for limitations of cross-sectional analysis. However, this study also has several limitations. First, the retrospective design of real-world study may have information bias and selection bias, and missing some important variables (such as lifestyle details) limits the completeness of confounding control. Second, GWAS data used in MR analysis mainly come from European ancestry populations, and the applicability of conclusions to other ethnicities remains to be verified. Third, although multiple sensitivity analyses were used, violation of MR assumptions (such as horizontal pleiotropy) may still affect results. Fourth, sample size for transcriptomic analysis was relatively limited, and being based on peripheral blood rather than joint synovial tissue may not fully reflect local pathological features. Fifth, this study is an association and causal inference study, and the proposed mechanistic framework still requires functional experimental verification. Future research should focus on: (1) validating MR findings in multi-ethnic populations; (2) conducting prospective cohort studies to clarify temporal relationships; (3) performing functional experiments to verify biological roles of key genes; (4) developing clinical risk prediction tools based on these findings.

In conclusion, this study systematically established the causal association between metabolic syndrome and gout through multi-dimensional evidence chains, revealing the molecular mechanism of comorbidity centered on “antigen presentation-immune response.” These findings emphasize the importance of monitoring and controlling metabolic factors in gout patient management, and provide new evidence-based foundation for developing risk stratification tools and targeted intervention strategies based on metabolic phenotypes.