In this large-scale observational study recruiting 11,042 participants in America, the study investigated the associations between lipid biomarkers, CMI and gout, alongside their potential mediating pathways. It was found that TG was directly associated with gout. HDL was inversely associated with gout. Besides, it was witnessed that BMI, WHtR, and HOMA-IR mediated the association between TG and gout at 20.42%, 26.09%, and 5.39%, respectively. While BMI, WHtR, leukocytes, GGT, and HOMA-IR functioned as mediators at 57.81%, 68.80%, 8.62%, and 5.35% (in individuals with HDL levels below 56 mmol/L), and 9.12%, respectively. Subsequently, CMI was implemented to demonstrate better the association between blood lipid biomarkers and gout. It was witnessed that CMI was significantly correlated with gout. Further post-matching multivariate logistic regression also supported the mentioned outcomes.

Previous observational investigations have reported that dyslipidaemia is a prevalent condition in individuals with gout [19, 20]. HUA is a critical determinant in the development of gout, as most gout patients exhibit elevated uric acid levels. However, the two conditions are not synonymous. Some individuals with HUA remain asymptomatic and never develop gout, while a subset of gout patients may present with normouricemia [1, 21, 22]. Several cross-sectional studies conducted in China and South Korea have suggested a correlation between dyslipidaemia and HUA [10,11,12,13]. Two retrospective studies in China have found that TG emerged as a standalone determinant in the development of HUA.

Drawing on our research, which includes a large American sample, the development and progression of gout are intertwined with disturbances in lipid metabolism. In multivariable logistic regression analysis of TG, HDL, and gout, discrepancies were observed between Model 1 and Model 2 outcomes. When assessing Model 3 in relation to Model 2, the OR for the association between TG and gout was 0.32 lower, while the OR for the association between HDL and gout was 0.09 higher. This difference may be attributed to the additional adjustment for HOMA-IR and BMI in Model 3. To further explore the mediating roles of HOMA-IR, BMI, and other potential factors, the study conducted a mediation analysis. The analysis provided deeper insights into the complex interplay between lipid profiles, obesity, insulin resistance, and inflammatory processes in gout development. Specifically, BMI and WHtR, as markers of obesity, were significant mediators of the relationship between TG and gout, explaining 20.42% and 26.09% of the association, respectively. The mediating effect size of WHtR exceeded that of BMI, indicating that central obesity, in comparison to general adiposity, had a more pronounced influence on the pathogenesis of gout. HOMA-IR, a marker of insulin resistance, also mediated 5.39% of the association, underscoring the metabolic disturbances that link lipid abnormalities and gout. These outcomes align with prior studies demonstrating that insulin resistance and obesity lead to increased serum urate levels and heighten the likelihood of gout. In the context of HDL, BMI, and WHtR accounted for 57.81% and 68.80% of the association between HDL and gout, revealing the significance of central obesity. In terms of analyzing the role of GGT, it was hypothesized that an increase in HDL levels would reduce the concentration of GGT, thereby mitigating oxidative stress and subsequently lowering the risk of gout. However, contrary to this hypothesis, GGT was found to mediate the association between HDL and gout at a negative proportion of -7.10%. This unexpected result prompted further investigation, leading to the stratification of participants based on an HDL threshold of 56 mmol/L, which was used as a critical point to assess differential mediation effects. In individuals with HDL concentrations below 56 mmol/L, GGT mediated the association at 5.18%, aligning with our hypothesis. Conversely, this mediating effect was not statistically significant in those with HDL levels above 55 mmol/L, and the indirect mediation of GGT was β = 0.0008 (95% CI = 0.0003–0.0012, P < 0.001). The anticipated protective influence of HDL on GGT may diminish or even reverse. This may be attributable to the increased particle sizes and detrimental subspecies in the demographic exhibiting elevated HDL levels. Under these conditions, changes in the protein makeup of HDL particles may result in functional impairment, potentially leading to a shift from an anti-inflammatory to a pro-inflammatory state [23,24,25,26]. In the multiple logistic regression analysis of the top quartile of HDL and gout, a similar pattern emerged, with the protective effect of HDL no longer observed. The quandary may stem from statistical imbalances within the study population. In the baseline characteristics, it could be found that there were pronounced disparities in anthropometric measurements, demographics, and concurrent health issues. It was thought that age emerged as the predominant confounding variable. In the geriatric population, a propensity for reduced levels of HDL may ensue, potentially culminating in an insufficient HDL-mediated protective effect against gout. Discrepancies were also observed in the multivariable logistic regression results before and after PSM, likely attributable to the influence of confounders.

Mechanically, the correlation between hyperlipidemia and gout is complex. As shown in Fig. 2, TG and HDL may exert convergent effects on the pathogenesis of gout. Both of them are pivotal in modulating obesity. It is one of the most vital factors influencing the progression of gout. Elevated TG levels increase the circulation of free fatty acid (FFA), which are re-esterified in the liver and released via VLDL, promoting fat accumulation, particularly in the abdominal region, contributing to obesity. HDL, by contrast, can contribute to helping maintain healthy lipid profiles and reducing excessive fat buildup. Obesity can enhance nucleic acid metabolism, increasing uric acid synthesis. Additionally, cytokines associated with obesity, including adiponectin and leptin, play a role in the onset of HUA [27, 28]. Elevated TG levels or diminished HDL can precipitate insulin resistance, thereby augmenting serum urate concentrations [29,30,31]. Insulin resistance also plays a particularly essential and multifaceted role in blood lipid profiles and gout. It can upregulate xanthine oxidoreductase production in fatty tissue and augment the excretion of serum urate by modulating metabolic processes, thereby contributing to the pathogenesis of HUA [32]. It also activates the renin-angiotensin-aldosterone system, exacerbating renal dysfunction and impairing uric acid regulation [33]. Regarding distinct TG and HDL mechanisms, FFA, the lipolysis products of TG, can contribute to acute gouty arthritis by upregulating pro-IL-1β transcription, resulting in increased IL-1β in monosodium urate crystal-induced joint inflammation [34, 35]. HDL’s inverse association with gout may be attributed to its role in inflammation reduction and oxidative stress mitigation. HDL downregulates pro-inflammatory cytokines and diminishes the levels of adhesion molecules such as vascular cell adhesion molecule 1, intercellular adhesion molecule 1, and E-selectin, thereby inhibiting leukocyte activation and endothelial adhesion. HDL also contains antioxidant enzymes, including paraoxonase-1 and glutathione peroxidase, which protect against LDL oxidation and foam cell formation [36,37,38,39].

Potential mechanisms of TG and HDL leading to gout. TG broke down into free fatty acids and glycerol. Free fatty acids contribute to the disease mechanism by influencing the transcription of pro-IL-1β. This process may result in the substantial production of bioactive IL-1β molecules, particularly in the context of joint inflammation induced by monosodium urate crystals. TG can contribute to the development of gout by promoting obesity and insulin resistance. In terms of HDL, it may reduce the risk of gout by mitigating obesity and decreasing insulin resistance. Meanwhile, HDL acted as roles in anti-inflammatory and antioxidant functions which reduce the risk of gout. MSU: monosodium urate

Based on current findings, this research represents the inaugural attempt to assess the association between CMI and gout. CMI is currently a clinical indicator that combines TG/HDL and WHtR. It reflected visceral adipose tissue distribution and individual blood lipid levels [40]. TG/HDL ratio was considered the predictor for insulin resistance assessment [41]. Prior studies have documented a correlation between the TG/HDL ratio and HUA [13, 42, 43]. WHtR is an efficient predictor of abdominal obesity. Research has demonstrated that obesity is significantly associated with a heightened gout risk among men [44]. Besides, Body measurement indices, including BMI, WC, and WHtR, are found to be highly associated with the likelihood of gout [45, 46]. Both ratios are closely related to serum uric acid content and gout. Besides, earlier investigations also identified an association between CMI and HUA [47, 48]. Given that CMI integrates TG/HDL and WHtR, it may serve as a novel predictor for elucidating the association and mediators between blood lipid levels and gout.

The study had some noteworthy advantages. First, the study extended the associations to a more extensive and diverse American sample group, broadening the applicability of the results and strengthening the evidence linking lipid profiles with gout and HUA. Moreover, the study further explores the potential mediators and proportions between blood lipid biomarkers and gout, which helped to gain a deeper insight into the intricate associations between lipid files and gout. Based on these analyses, CMI, which serves as an indicator of metabolic dysregulation and its application in predicting gout risk, offered a crucial foundation for health promotion and gout prevention strategies.

The study also had several limitations. First of all, as a cross-sectional study, it was not capable of determining causal associations between HDL, TG, CMI, and gout. Second, Specific data were gathered through self-reported questionnaires by participants. This bias could potentially distort the accuracy of the data collected and impact the reliability of our findings. The potential for recall bias must be acknowledged in this study. Third, the study could not incorporate data on all covariates influencing gout and lipid biomarkers to maintain an adequately sized sample due to database constraints. Thus, PSM was utilized to address confounding factors in the analysis. The results are consistent, thereby substantiating the reliability of the conclusions drawn. Nevertheless, the current associations between lipid biomarkers, CMI, and gout remain robust enough to address the impact of unaccounted variables.