Introduction
Gout is the most common form of inflammatory arthritis, characterized by acute pain, redness, swelling, and increased skin temperature. Recurrent attacks can lead to joint destruction and deformities1 Recent studies indicate a continued rise in global gout prevalence, with approximately 55.8 million affected individuals worldwide and an age-standardized prevalence rate (ASPR) of 659.3 per 100,000.2 In 2019, China had around 16.2 million gout patients, with prevalence rates above the global average and showing an upward trend. By 2029, ASPR in men and women is expected to reach 1170 and 400 per 100,000, respectively.3 Gout prevalence among young adults in China is also concerning, with rates of 1000, 2300, and 3400 per 100,000 in the 18–29, 30–39, and 40–49 age groups, respectively, exceeding those of the same age groups in the United States.3 Among young adults, the primary labor force—gout limits mobility and severely affects productivity,4,5 leading to direct and indirect economic losses of $172 to $6179 per person,6,7 imposing an increasing burden on patients and society. Identifying risk factors for gout in young populations and implementing early prevention strategies hold critical importance.
The TyG index, calculated from triglyceride and glucose levels, was initially recognized as a novel indicator of insulin resistance (IR).8 In recent years, research has increasingly linked the TyG index to multiple metabolic diseases like coronary heart disease, heart failure, ischemic stroke, and hypertension. Studies have identified the TyG index as an independent risk factor for heart failure and an independent predictor of coronary heart disease severity, adverse outcomes, and all-cause mortality.9–12 Tao et al showed that higher TyG was linked to worse prognosis in hypertension, including increased stroke risk, especially ischemic stroke.13 The data indicate that the TyG index holds promise as a biomarker for disease prognosis and evaluation.
The pathogenesis of gout is closely linked to metabolic syndrome. Biologically, the TyG index reflects triglyceride-glucose coupling and serves as a validated surrogate of insulin resistance,14,15 a state that can elevate serum urate by reducing renal urate excretion and up-regulating urate transporters such as URAT1 (SLC22A12) and GLUT9 (SLC2A9).16,17 TyG elevation also co-occurs with low HDL-C and systemic inflammation, both implicated in gout susceptibility and activity.18,19 Prior studies have linked TyG to gout in general populations: Cao et al reported that lower TyG was associated with lower gout odds,20 and Li et al suggested TyG may help flag early gout rather than establish prediction or causation.21 However, most TyG-gout studies have focused on general or older populations, and it remains unclear whether those findings apply to young adults.21,22 Compared with older individuals, young adults tend to develop insulin resistance earlier, have shorter exposure to metabolic abnormalities, and exhibit more clustered metabolic risks (eg, central adiposity and dyslipidemia with low HDL-C); these features may modify both the effect size and clinical utility of TyG in this age stratum.23,24 Focusing on this group may therefore refine risk stratification and identify opportunities for earlier preventive strategies. Accordingly, we hypothesized that higher TyG would be independently associated with the presence of gout among young adults.
Methods
Study Population
This retrospective case-control study received approval from the Medical Ethics Committee of Meizhou People’s Hospital (No.: MPH-2024-C-127) and was conducted in full compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants. We consecutively collected data from all patients diagnosed with gout at Meizhou People’s Hospital between 2019 and 2023, designating them as the case group. We obtained all variables from the Meizhou People’s Hospital electronic medical record (EMR) and its linked laboratory information system for 2019–2023. Collected data included complete blood counts, assessments of liver and kidney function, fasting blood glucose, and lipid profile measurements. Inclusion criteria were: (1) diagnosis consistent with the 2015 ACR/EULAR gout classification criteria;25 (2) age ranging from 18 to 44 years. Exclusion criteria included: (1) secondary gout diagnosis; (2) severe conditions such as liver, kidney, and heart diseases, or cancer; (3) missing data. Simultaneously, individuals undergoing routine health examinations at Meizhou People’s Hospital were collected as the control cohort during the same time frame. Data included complete blood counts, liver and kidney function assessments, fasting blood glucose (FBG), and lipid profiles. Inclusion criteria required individuals aged 18–44 years, while exclusion criteria included (1) severe conditions such as liver, kidney, or heart diseases, or cancer, and (2) missing data. Cases and controls were frequency-matched 1:4 on age and sex using SPSS 26.0 (IBM Corp., Armonk, NY, USA).
Definitions
In this study, the TyG index is defined as the natural logarithm of the product of fasting triglycerides (TG) and FBG, divided by 2, represented as TyG = ln [fasting TG (mg/dL) * FBG (mg/dL)/2]. The Systemic immune-inflammation index (SII) is determined by the multiplying platelet and neutrophil counts and then dividing by the lymphocyte count, represented as SII = platelets (*109/L) * neutrophils (*109/L) / lymphocytes (*109/L).
Statistical Analysis
Statistical analysis was performed utilizing SPSS 26.0 software. The Shapiro–Wilk test was used to assess normality. For normally distributed variables such as age, data were presented as mean ± standard deviation and inter-group comparisons were conducted utilizing the Student’s t-test. Total cholesterol (TC), TyG index, uric acid (UA), TG, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), FBG, creatinine, alanine aminotransferase (ALT), glutamate aminotransferase (AST), white blood cell (WBC) count, lymphocyte count, neutrophil count, platelet count, and the SII did not follow a normal distribution. These variables were presented as medians (interquartile ranges) and compared between groups utilizing the Mann–Whitney U-test. Male sex, history of hypertension, and diabetes history were reported as frequencies (percentages) and compared between groups utilizing the chi-square test. For the multivariable logistic regression analysis, variables were categorized as follows: (1) known factors associated with gout outcomes, including age, hypertension, diabetes, and UA levels; and (2) variables showing P < 0.05 in the univariate regression, including HDL-C, creatinine, ALT, AST, WBC count, and the SII. Since the TyG index is derived from TG and FBG, and SII is calculated from neutrophil, lymphocyte and platelet counts, these individual components were excluded from the multivariate regression model to avoid collinearity. Because sex distribution was highly imbalanced by design (97% male in both groups), sex was handled through restriction/frequency matching and was not entered as a covariate in multivariable models. A multivariable logistic regression model (enter method: simultaneous forced entry of prespecified covariates in one block and retained regardless of univariable significance) was applied to assess the association between the TyG index and the presence of gout in young patients. We assessed multicollinearity among all covariates entered into the multivariable model using variance inflation factors (VIF) and tolerance; VIF < 5 and tolerance > 0.20 were considered acceptable The diagnostic performance of TyG index for gout was assessed using receiver operating characteristic (ROC) analysis. The diagnostic performance of TyG was quantified using the area under the ROC curve (AUC), and the optimal cutoff was determined by maximizing Youden’s J. Spearman’s rank correlation coefficient was utilized to investigate the relationships among the TyG index, UA, WBC, SII, and HDL-C. A P-value below 0.05 was regarded as indicative of statistical significance.
Results
Baseline Analysis of Gout and Healthy Control Groups
From 2,572 gout candidates, 102 with secondary gout and 463 with serious disease were excluded; an additional 1,560 were excluded for missing key variables (ALT, n=321; creatinine, n=389; hypertension/diabetes, n=289; SII, n=219; TyG, n=342), leaving 447 cases. Among 32,920 potential controls, 177 had serious disease and 8,634 had missing data (ALT, n=1,796; creatinine, n=2,125; hypertension/diabetes, n=1,569; SII, n=1,105; TyG, n=2,039), yielding 24,109 eligible controls, from which 1,788 were frequency-matched 1:4 to the cases by age and sex for analysis (Figure 1). Because TyG components were ordered selectively in routine care, TyG missingness may not have been completely at random; thus, potential selection bias is noted. The final analytic sample comprised 2,235 individuals (447 cases and 1,788 controls; mean age 34.5 ± 6.9 years). Males accounted for 97% (2175 individuals). The gout group consisted of 447 patients, averaging 34.6 ± 6.8 years in age, with 435 (97%) being male. The healthy control group included 1788 individuals, averaging 34.6 ± 6.8 years in age, with 1740 (97%) males.
Figure 1 The flowchart of the study participants.
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At baseline, TG, TC, FBG, creatinine, ALT, AST, WBC count, platelet count, and the SII were notably elevated in the gout group versus the healthy control group. Conversely, individuals with gout exhibited significantly lower levels of HDL-C (P < 0.05). The median TyG index was 4.86 (0.44) in the gout cohort, compared to 4.63 (0.40) in the healthy control group (P < 0.001, Table 1).
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Table 1 Clinical Characteristics of Gout Individuals and Healthy Controls
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Association and discriminative performance of the TyG index (logistic regression and ROC analyses) Univariate logistic regression analysis indicated a significant association between the TyG index and gout (OR = 8.841, 95% CI: 6.379–12.253, P < 0.001); HDL-C was inversely related to gout (OR = 0.061, 95% CI: 0.041–0.091, P < 0.001); and WBC count showed a positive association with gout (OR = 1.694, 95% CI: 1.594–1.799, P < 0.001). Additional factors – including hypertension, diabetes mellitus, UA, creatinine, ALT, AST, WBC count, and the SII – were significantly associated with gout (all P < 0.001, Table 2).
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Table 2 Logistic Regression Analysis Between TyG Index and Gout
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After adjustment for age, hypertension, diabetes mellitus, UA, HDL-C, creatinine, ALT, AST, WBC count, and the SII, multivariate logistic regression analysis identified the TyG index as an independent associated factor for gout among young adults (OR = 2.454, 95% CI: 1.496–4.026, P < 0.001). Other independent associated factors included hypertension (OR = 1.886, 95% CI: 1.053–3.380, P = 0.033), HDL-C (OR = 0.247, 95% CI: 0.145–0.420, P < 0.001), WBC count (OR = 1.222, 95% CI: 1.129–1.324, P < 0.001), creatinine (OR = 1.020, 95% CI: 1.010–1.030, P < 0.001), ALT (OR = 1.012, 95% CI: 1.002–1.019, P = 0.017), UA (OR = 1.006, 95% CI: 1.004–1.007, P < 0.001), and the SII (OR = 1.003, 95% CI: 1.002–1.003, P < 0.001, Table 2). In multivariable models, collinearity checks showed no problematic multicollinearity (all VIFs < 5, all tolerances > 0.20; Table S1).
We also evaluated the diagnostic performance of TyG in gout using receiver operating characteristic (ROC) analysis. TyG yielded an AUC of 0.709 (95% CI, 0.683–0.736), with an optimal cutoff of 4.735 (sensitivity 0.669; specificity 0.638; Figure 2).
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Figure 2 ROC curve of the TyG index for discriminating gout among young adults.
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Correlation Analysis Between TyG and Clinical Baseline Data
We performed a correlation analysis to investigate the associations among the TyG index, UA, WBC, SII, and HDL-C. The results revealed that the TyG index exhibited significant positive correlations with UA (r = 0.3393, P < 0.001), WBC (r = 0.3250, P < 0.001), and SII (r = 0.2069, P < 0.001; Figure 3A–C). Conversely, a significant negative correlation was observed between the TyG index and HDL-C (r = −0.4913, P < 0.001, Figure 3D).
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Figure 3 Correlations between the TyG index and clinical variables. (A) TyG vs UA; (B) TyG vs WBC; (C) TyG vs SII; (D) TyG vs HDL-C.
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Discussion
In this single-center case-control study, young patients with gout at Meizhou People’s Hospital had higher TyG values than controls. In multivariable logistic regression, higher TyG was independently associated with the presence of gout. Beyond association, TyG showed diagnostic value with an AUC of 0.709. TyG correlated positively with UA, WBC, and SII and inversely with HDL-C, supporting its role as a readily available metabolic marker to aid risk stratification and case-finding in young adults.
A study based on the KNHANES database reported that among gout patients over the age of 19, males accounted for 90.7% (733,915/808,778), with 54.9% having comorbid hypertension (444,290/808,778) and 18.4% having diabetes (149,184/808,778).26 Another study based on the NHANES database found that males comprised 69.4% (504/726) of gout patients, with 73.0% having hypertension (530/726) and 34.7% having diabetes (236/726).16 In our study of 447 young gout patients, 12% had hypertension and 5.2% had diabetes. The mean UA level among young gout patients was 522.25 (217.10) μmol/L. Because the sex distribution was highly imbalanced by design (97% male in both groups), internal comparability was improved. However, this limits generalizability to women; future studies with more balanced sex distributions are warranted. Given that our study focused on young adults aged 18–45, the percentage of gout patients with hypertension and diabetes was relatively lower. Overall, our study population demonstrates a certain level of representativeness.
In our study, multivariable logistic regression showed that higher TyG was independently associated with the presence of gout (adjusted OR = 2.454). Similarly, NHANES-based analyses in US adults reported an association between higher TyG and prevalent gout, with adjusted ORs ranging from 1.43 to 1.64.14–16 The larger effect size in our young Chinese cohort may reflect differences in age structure and ethnicity. Beyond association, TyG also demonstrated acceptable discriminative performance for case-finding. These results suggest that the TyG-gout association may be stronger in young adults than in the general population, potentially due to (i) population differences between China and the US; (ii) greater work-related stress and irregular schedules in younger individuals;27,28 and (iii) unhealthy dietary patterns, including higher consumption of sugar-sweetened beverages.29,30 While the observed discrimination supports potential case-finding value, prospective work is needed to define actionable thresholds and assess incremental clinical utility.
The potential connection between the TyG index and gout likely centers around IR, as the TyG index serves as an important marker for this condition. IR is characterized by a diminished response to insulin, influenced by various factors, which leads to compensatory and secondary metabolic adaptations. This condition is frequently associated with metabolic disorders like obesity and metabolic syndrome, both of which are closely linked to gout.31 Natalie McCormick et al found a causal relationship between IR, hyperuricemia (HUA), and gout.32 Sun et al highlighted a bidirectional relationship between IR and HUA.33 There are several theoretical mechanisms linking IR with gout. Yoo et al suggested that HUA observed in gout patients may be due to increased adiposity associated with IR.34 Additionally, research has shown that IR enhances sodium reabsorption in renal tubules, thus reducing UA excretion.35,36 Kottgenet et al proposed that IR elevates serum UA levels by promoting the generation of intermediates, such as pyrophosphate and ribose-5-phosphate, in glycolysis and the pentose phosphate pathway.37 Conversely, elevated UA levels can worsen IR by reducing nitric oxide availability, intensifying mitochondrial oxidative stress, and initiating inflammation through multiple pathways.35,36 Chang JB et al reported that overproduction of uric acid and reactive oxygen species is implicated in IR.38 Further research is required to determine whether additional pathways, beyond those mediated by IR, link the TyG index with gout.
Interestingly, this study also suggests a potential association between HDL-C levels and the incidence of gout in young adults. A Mendelian randomization study by Yang et al revealed a significant causal link between elevated HDL-C and a reduced risk of gout.39 Another Mendelian randomization study reported that each standard deviation increase in HDL-C was linked to an approximately 25% reduction in gout risk and a reduction in serum UA by 0.09 mg/dL.40 Mendelian randomization analyses suggest a causal link consistent with a protective role of higher HDL-C. Mechanistically, HDL exerts anti-inflammatory and antioxidant actions relevant to gout pathophysiology: experimental work shows that HDL attenuates monosodium urate (MSU) crystal-induced inflammation by limiting leukocyte recruitment and chemokine (eg, CCL2) production in synovial cells and in vivo models; HDL can also dampen crystal-triggered cytokine responses.41,42 In parallel, HDL particles possess inflammasome-modulating and antioxidant capacities (eg, via ApoA-I-dependent signaling and paraoxonase-1 activity), which may reduce oxidative stress and blunt downstream NLRP3 activation central to gout flares.43 Taken together with our data, these observations are consistent with HDL-C acting as an associated protective marker in this cross-sectional design.
To further characterize the clinical milieu associated with TyG, we conducted a correlation analysis. The findings revealed a positive correlation between the TyG index and UA, which is closely associated with gout.44 Furthermore, the TyG index demonstrated a positive correlation with WBC count and the SII. Yu et al reported higher WBC counts in acute gout compared with remission, while Yi et al observed higher SII in acute gouty arthritis than in healthy controls.45,46 Conversely, the TyG index demonstrated a negative correlation with HDL-C levels. Prior research has indicated that the anti-inflammatory properties of HDL-C are diminished in individuals with gout.47 Therefore, we hypothesized that the TyG index may have a potential association with gout and the precise underlying mechanisms connecting TyG to gout require further investigation.
The strength of our research: this study included 447 young patients diagnosed with gout over a five-year span, matched by sex and age, with a 1:4 ratio between case and control groups, resulting in 1788 healthy individuals as controls. The study design is robust, enhancing the reliability of the findings. This study represents one of the few investigations exploring the link between the TyG index and gout, specifically within a young Chinese population.
This study has several limitations. First, case ascertainment and control selection may introduce selection bias: cases were hospital patients, whereas controls were health-checkup attendees who may differ in health-seeking behavior and metabolic profiles. In addition, TyG availability depended on routine test ordering, making missingness unlikely to be completely at random; bias in either direction is possible. We therefore interpret the TyG-gout association with appropriate caution. Second, we did not capture lifestyle and anthropometric variables (eg, BMI/waist circumference, alcohol intake, consumption of purine- or fructose-rich foods, overall diet quality, physical activity). These factors are biologically linked to both TyG and serum urate and may introduce residual confounding. Third, this single-center study -predominantly male (97%)-limits generalizability to women and to other settings or regions. Future work should include multicenter, community-based cohorts and prospective designs to validate thresholds, assess incremental clinical utility, and clarify temporality.
Conclusion
Our findings indicate that the TyG index is independently associated with the presence of gout among young adults, tracks a pro-inflammatory/low-HDL milieu, and exhibits moderate discriminative value for case-finding in this demographic. As a routinely available metric, TyG may support clinical risk stratification; further research is needed to establish actionable thresholds, assess incremental clinical utility, and clarify temporality and causal pathways in prospective studies, while interpreting the present results in light of missing TyG data and unmeasured lifestyle factors.
Abbreviations
ASPR, age-standardized prevalence rate; IR, insulin resistance; VIF, variance inflation factors; ROC, receiver operating characteristic; AUC, area under the ROC curve; TG, triglycerides; FBG, fasting blood glucose; SII, systemic immune-inflammation index; TC, total cholesterol; UA, uric acid; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, glutamate aminotransferase; WBC, white blood cell.
Data Sharing Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Ethics Approval and Consent to Participate
The research presented in this work strictly adheres to the ethical standards outlined in the Declaration of Helsinki as endorsed by the World Medical Association. This study is granted by the Medical Ethics Committee of Meizhou People’s Hospital (No.: MPH-2024-C-127). Written informed consent was obtained from all participants.
Acknowledgments
We would like to thank the patients for their valuable participation in this study and the staff of the Department of General Medicine for their unwavering support.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This research was supported by Medical Research Foundation of Guangdong Province (A2023324), State Key Laboratory of Neurology and Oncology Drug Development (SKLSIM-F-202412), Medical and Health Research Project of Meizhou City (2024-B-50).
Disclosure
The authors declare no competing interests in this work.
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