Higher serum uric acid levels are associated with an increased risk of carotid atherosclerosis in 67,256 adults

Background: The association between serum uric acid (SUA) and carotid atherosclerosis (CAS) remains controversial, particularly regarding its independence from traditional risk factors. This cross-sectional study aimed to investigate this relationship and its dose–response pattern in 67,256 adults.

Methods: We conducted a cross-sectional analysis of 67,256 participants who underwent health check-up at Beijing MJ Healthcare Center between 2012 and 2022. Data collected included demographics, physical exams, laboratory tests, and carotid ultrasound. Participants were divided into quartiles based on baseline SUA levels. Multivariate logistic regression analysis was performed with four models to assess the association between SUA and CAS, as well as its subtypes, including increased carotid intima-media thickness (cIMT) and carotid plaque (CP). Additionally, SUA was treated as a continuous variable, and a Restricted Cubic Spline (RCS) model was fitted to explore the dose–response relationship. Subgroup and sensitivity analyses were conducted to examine the heterogeneity and robustness of our findings.

Results: The prevalence of CAS was 28.8%. Each standard deviation (per-SD) increase in SUA was significantly associated with CAS risk (OR = 1.22, 95% CI: 1.20–1.24, Model 1), (OR = 1.07, 95%CI: 1.04–1.10, Model 4). When SUA was analyzed by quartiles, the Q4 group showed a higher CAS risk (OR = 1.85, 95%CI: 1.76–1.95, Model 1), (OR = 1.19, 95%CI: 1.10–1.28, Model 4). Consistently, across different CAS subtypes, higher levels of SUA were associated with an increased risk of both increased cIMT and CP. The RCS model revealed a linear dose–response relationship in the total population, with CAS risk increasing when SUA exceeded 324 μmol/L. Subgroup analysis demonstrated sex-specific associations: higher SUA (Q4) was significantly linked to CAS in females (OR = 1.34, 95%CI: 1.14–1.58) but not in males (OR = 1.04, 95%CI: 0.92–1.17). There is a linear dose–response relationship for females and a nonlinear trend for males. Both age groups (<60 and ≥60 years) showed increased CAS risk with elevated SUA. Sensitivity analyses excluding individuals with hypertension, diabetes, or dyslipidemia yielded consistent results (per-SD: OR = 1.08, 95%CI: 1.04–1.12; Q4 group: OR = 1.19, 95%CI: 1.06–1.33).

Conclusion: The prevalence of CAS was high in the Beijing health check-up population, and high levels of SUA were significantly associated with an increased risk of CAS, particularly in females.

1 Introduction

Cardiovascular diseases (CVDs) remain a leading global health burden, with projections indicating substantial increases in prevalence and mortality by 2050 (1). In China, atherosclerotic CVD accounts for over 40% of deaths (2). As a window to systemic atherosclerosis, the occurrence and development of carotid atherosclerosis (CAS) are closely related to a variety of metabolic factors. Current research primarily focuses on various subtypes of CAS, including increased carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stenosis (CS). In 2020, the global prevalence of increased cIMT in people aged 30–79 years was estimated to be 27.6%, CP was estimated to be 21.1%, and CS was estimated to be 1.5% (3). In the Chinese population aged 20 years and older, the weighted prevalence of all subtypes was 26.2% for increased cIMT, 21% for CP, 0.56% for CS, and 0.15% for moderate to severe CS (4). However, the various subtypes of CAS exhibit a continuum in pathological progression, and their occurrence and development may be influenced by partially shared etiological factors and risk factors. Among these, the role of serum uric acid (SUA) has garnered significant attention in recent years.

SUA is the end product of purine nucleotide metabolism, and its abnormal levels are closely associated with various CVDs (5–8). Studies indicate that SUA contributes to the atherosclerotic process by promoting oxidative stress, inflammatory responses, and endothelial dysfunction (5, 9, 10). Notably, even when SUA levels are at the upper limit of the normal range (309–357 μmol/L), elevated levels remain associated with the onset and progression of CVDs (11).

Current research on the association between SUA and different subtypes of CAS yields inconsistent conclusions. Some studies suggested an association between elevated SUA and increased cIMT (12, 13), and a cross-sectional study conducted in multiple provinces in Chinese indicated that elevated SUA levels were associated with an increased risk of developing all three subtypes of CAS (4). However, a multi-rural community-based cohort study in South Korea found no association between increased cIMT and SUA levels in both sexes (14). Overall, existing evidence primarily focuses on middle-aged and elderly populations, lacking systematic evaluation across broader adult age groups (≥18 years). Furthermore, most relevant studies have been conducted in Western, Japanese, and Korean populations, with limited high-quality data available for the Chinese population.

Therefore, we aimed to investigate the association between SUA and CAS and its subtypes, and to test a dose–response relationship in adults based on a large health check-up population in Beijing. Additionally, we performed a series of subgroup and sensitivity analyses to test the heterogeneity and robustness of our results.

2 Materials and methods

2.1 Study design and participants

We used a cross-sectional study design and included study subjects who participated in medical check-up and completed serum uric acid tests and carotid ultrasound examinations at the Beijing MJ Healthcare Center from 2012 to 2022. For those who participated in health check-up several times during the 11 years, only the latest health check-up of the same participants were included to ensure that the data analyzed better reflect the current health status of the population. In total, 69,594 participants were eligible for this study. After excluding those aged less than 18 years (N = 20), those with unclear diagnosis of CAS (N = 1755), and those with outliers of covariates such as height, BMI, and blood pressure exceeding Mean±3SD (N = 563), this study finally included 67,256 participants for analysis.

We collected information about the study population through questionnaires, including general demographic information (age, sex, height, weight, etc.) and lifestyle (smoking status, alcohol drinking, etc.). All physical examinations, laboratory tests, and ancillary investigations were performed by qualified healthcare professionals using certified medical equipment in strict accordance with standardized operating procedures. CAS was examined by carotid ultrasound using a Philips EPIQ7 color Doppler ultrasound system (equipped with a C5-1 convex array probe, scanning frequency of 3.5 MHz). The diagnostic criteria for CAS subtypes are as follows: cIMT of 1.0 mm or greater is defined as thickening (15). CP is defined as meeting any of the following morphological criteria: a focal structure within a specific arterial segment exhibiting intima-media thickness ≥1.5 mm, or thickening ≥0.5 mm or exceeding 50% compared to the surrounding vessel wall (16). The severity of CS is graded based on the percentage reduction in lumen diameter: less than 50% is mild stenosis, 50 to 69% is moderate stenosis, and 70% or greater is classified as severe stenosis (17). For the primary analysis, a composite binary endpoint of CAS was defined as the presence of any of the above conditions (increased cIMT, CP, or CS of any degree).

2.2 Statistical analysis

Categorical variables were described using numbers (%), and comparisons between groups were made using the Chi-square test or Fisher’s exact test. Continuous variables were described using mean ± standard deviation (Mean ± SD), and comparisons between groups were made using the independent T-test or analysis of variance.

SUA levels were standardized using the per-standard deviation (per-SD) method. Specifically, each SUA measurement was centered by subtracting the population mean and then scaled by dividing by the standard deviation. Participants were categorized according to the quartiles of the baseline SUA level. The subjects were divided into Q1 (n = 16,646), Q2 (n = 16,875), Q3 (n = 16,846), and Q4 (n = 16,889). Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for risk of CAS and its subtypes (increased cIMT and CP) across different SUA categories with four models: Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, smoking status, and alcohol drinking; Model 4: adjusted for age, sex, smoking status, alcohol drinking, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), creatinine and estimated glomerular filtration rate (eGFR).

To assess the dose–response relationship between SUA and CAS, a logistic regression model with restricted cubic splines (RCS) was fitted, treating SUA as a continuous variable. Subgroup analyses were stratified by sex and age (<60 years vs. ≥ 60 years) to examine the heterogeneity of the associations between SUA and CAS. To clarify the role of SUA in different pathological stages of CAS, we categorized CAS into two independent binary outcome variables: increased cIMT and CP, then analyzed their respective associations with SUA. To assess the robustness of the findings and rule out potential reverse causality, we conducted a sensitivity analysis based on first health check-up data from the total population. Meanwhile, Previous studies had identified hypertension, diabetes, and dyslipidemia as risk factors for CAS (3, 4, 13). To assess the robustness of the results, we conducted sensitivity analyses, excluding: (1) participants with hypertension (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, or a documented history of hypertension with ongoing antihypertensive medication use) (18); (2) participants with diabetes (FBG ≥ 7 mmol/L or a documented history of diabetes with ongoing hypoglycemic medication use) (19); (3) participants with dyslipidemia (TG ≥ 2.3 mmol/L, or TC ≥ 6.2 mmol/L or HDL-c < 1.0 mmol/L or LDL-c ≥ 4.1 mmol/L, or ongoing use of lipid-lowering medications) (20); and (4) participants with hypertension, diabetes, or dyslipidemia.

All statistical analyses were performed using R (version 4.2.2). Two-sided p < 0.05 was considered statistically significant.

3 Result

A total of 67,256 participants were included, and the overall prevalence of CAS was 28.8% (19,394/67,256). The mean age of participants was 47 ± 12 years. The baseline characteristics of the study population stratified by CAS status are presented in Table 1. Significant differences were observed in all variables between the two groups (all p < 0.001). Individuals with CAS tended to be older and were more likely to be males. They also had higher rates of current smoking and drinking compared to those without CAS. In addition, those with CAS generally exhibited higher levels of BMI, SBP, DBP, creatinine, SUA, FBG, TG, TC, and LDL-c, while having lower HDL-c levels and eGFR. The overall prevalence of CAS increased with age and was higher in males than in females (Figure 1).

Table 1

Table 1. Baseline characteristics of study participants.

Figure 1

Figure 1. Prevalence trend of carotid atherosclerosis by age and sex groups among study participants (males in green, females in red).

This study found a significant association between SUA levels and CAS risk in all models. The risk of CAS increased with per-SD change in SUA: Model 1 (OR = 1.22, 95%CI: 1.20–1.24), Model 2 (OR = 1.12, 95%CI: 1.09–1.15), Model 3 (OR = 1.12, 95%CI: 1.09–1.15) and Model 4 (OR = 1.07, 95%CI: 1.04–1.10). Compared to the reference group (Q1), SUA levels in Q2, Q3, and Q4 were associated with an increased risk of CAS when unadjusted with ORs of 1.56 (95%CI: 1.49–1.64), 1.82 (95%CI: 1.74–1.92), and 1.86 (95%CI: 1.76–1.95), respectively. After adjusting for age, sex, smoking status, alcohol drinking, BMI, SBP, DBP, FBG, TG TC, creatinine and eGFR in Model 4, SUA levels in Q4 were associated with a higher CAS risk (OR = 1.19, 95%CI: 1.10–1.28) (Table 2). Similar associations were observed across different CAS subtypes, with higher SUA levels being significantly associated with increased risks of CP (Model4 OR: 1.21, 95% CI: 1.10–1.32) and increased cIMT (Model4 OR: 1.18, 95% CI: 1.08–1.30) (Supplementary Table S1). The RCS model showed a linear dose–response relationship between SUA and CAS after adjusting for all covariates (P for nonlinearity = 0.919). When SUA levels exceeded 324 μmol/L, the risk of CAS increased with increasing SUA levels (Figure 2A). Compared to those with an SUA level of 324 μmol/L, the adjusted OR (95% CI) was 1.16 (1.08–1.25) and 1.38 (1.11–1.69) for SUA levels of 500 μmol/L and 700 μmol/L, respectively (Figure 2B).

Table 2

Table 2. Logistic regression analyses of associations between serum uric acid and carotid atherosclerosis.

Figure 2

Figure 2. Dose–response association between serum uric acid and carotid atherosclerosis. Adjusted for sex, age, smoking status, alcohol drinking, BMI, SBP, DBP, FBG, TG, TC, creatinine, and eGFR. (A) RCS curve showing the adjusted OR for CAS across SUA levels. (B) Selected point estimates.

Subgroup analyses showed a sex-specific pattern between SUA and CAS (P for interaction = 0.039), with high levels of SUA (Q4) increasing the risk of CAS in females (OR = 1.34, 95%CI: 1.14–1.58) but not males (OR = 1.04, 95%CI: 0.92–1.17). In different age groups, high levels of SUA (Q4) significantly increased the risk of CAS in both <60 years (OR = 1.17, 95%CI: 1.07–1.28) and ≥60 years (OR = 1.37, 95%CI: 1.14–1.65) (P for interaction = 0.030) (Figure 3). Meanwhile, the RCS model showed that after adjusting for all covariates, there was a significant linear dose–response relationship between SUA and CAS in females (P for nonlinearity = 0.534). When the SUA level exceeded 268 μmol/L, the risk of CAS increased with increasing SUA level (Figure 4B). However, in males, there was a nonlinear dose–response relationship between SUA and CAS (P for nonlinearity = 0.020), and the association between SUA and CAS existed only at SUA levels between 372 and 590 μmol/L, while the association was not statistically significant when SUA was at higher or lower levels (Figure 4A). High levels of SUA significantly increased the risk of CAS in both <60 years and ≥60 years. When SUA levels exceeded 412 μmol/L in those <60 years and 325 μmol/L in those ≥60 years, the risk of CAS increased with increasing SUA in both age groups (Figures 4C,D).

Figure 3

Figure 3. Subgroup analysis of the association between serum uric acid and carotid atherosclerosis by sex and age. Adjusted for sex, age, smoking status, alcohol drinking, BMI, SBP, DBP, FBG, TG, TC, creatinine, and eGFR.

Figure 4

Figure 4. Subgroup analysis of the dose–response association between serum uric acid and carotid atherosclerosis. (A) Male. (B) Female. (C) Age <60 years. (D) Age ≥60 years. Adjusted for sex, age, smoking status, alcohol drinking, BMI, SBP, DBP, FBG, TG, TC, creatinine and eGFR.

Sensitivity analysis results based on first health check-up data indicated that for each standard deviation increase in SUA, the risk of CAS increased by 4% (Model 4: OR: 1.04, 95% CI: 1.01–1.07). Compared with the lowest SUA level, the highest level was associated with a 9% increased risk of CAS (Model 4: OR: 1.09, 95% CI: 1.00–1.18) (Supplementary Table S2). RCS results indicated a dose–response relationship between SUA and CAS (P for nonlinearity = 0.121), the risk of CAS increases when SUA levels exceed 516 μmol/L (Supplementary Figure S1A). Similarly, sensitivity analysis, which excluded patients with hypertension, diabetes, dyslipidemia, and any of the three diseases, showed results consistent with the main findings (Supplementary Table S2; Supplementary Figure S1).

4 Discussion

In this large cross-sectional analysis, we found that higher levels of SUA were significantly and positively associated with the risk of CAS and its subtypes in the Beijing health check-up population. Subgroup analyses revealed a sex-specific difference in the association between SUA levels and CAS, with high levels of SUA significantly increasing the risk of CAS in females, but not in males. Various sensitivity analyses showed that the association between SUA and CAS remained robust.

The prevalence of CAS and the association between SUA levels and CAS and its subtypes (CP and increased cIMT) in our study are consistent with previous studies. Jingzhu Fu et al. enrolled 10,733,975 participants across 31 provinces in China. The findings indicated that the prevalence of CAS was higher in older adults and males, and elevated SUA levels were a risk factor for CP and increased cIMT (4). We observed a sex-specific difference in the association between SUA and CAS, with higher levels of SUA increasing the risk of CAS in females but not in males. Previous studies reported similar sex differences. For example, one study found an association between UA and CAS, which was more significant in females than in males (21); another also reported that the effect of SUA on atherosclerosis and CAS was higher in females than in males (22). In addition, Li L et al. found that SUA level predicted the development of CAS independently of traditional risk factors only in females, but not in males (23). The sex differences may arise from estrogen in females promotes renal excretion of SUA and maintains low levels of SUA in the body (22). Therefore, females are more sensitive to elevated SUA levels than males. High SUA levels can stimulate oxidative stress and promote the development of CAS. Our study found that after excluding patients with hypertension, diabetes, and dyslipidemia, the association between SUA levels and CAS remained significant, which is consistent with previous studies. A study found that higher SUA levels were associated with increased cIMT in non-hypertensive patients (5). Furthermore, Shuzo Takayama et al. found that there was a significant correlation between UA levels and increased cIMT in the Japanese population aged 60 years and older, and this association was also significant in males and females without metabolic syndrome (12).

The mechanisms underlying the association between SUA and CAS have not been fully elucidated. However, several hypotheses and studies had suggested that high SUA levels may contribute to the onset and development of CAS through multiple pathways: (1) Oxidative stress: oxidative stress is a key factor in the development of CAS (24, 25). Studies had indicated that SUA can promote oxidative activity and induce intracellular oxidative stress. Oxidative stress promotes CAS by inducing endothelial cell dysfunction, inflammation in inflammatory cells (e.g., macrophages), platelet aggregation, and low-density lipoprotein oxidation (26). (2) Endothelial dysfunction: hyperuricemia disrupts the asymmetric dimethylarginine (ADMA)/dimethylarginine dimethylaminohydrolase-2 (DDAH-2) axis in endothelial cells (ECs), leading to endothelial cell dysfunction and accelerating atherosclerosis (27). (3) Lipid metabolic disorders: SUA levels are significantly associated with dyslipidemia (28–30). Patients with hyperuricemia are often accompanied by hypercholesterolemia, hypertriglyceridemia, and low HDL cholesterol, and these dyslipidemias promote CAS (29). These multiple mechanisms interact and accelerate CAS progression.

This study’s strength lies in its use of the Beijing MJ Healthcare Center Database, which is a large-scale health check-up database with the advantages of a large sample size (67,256). We not only used SUA as a categorical variable to investigate the association between different SUA groups and CAS, but also included SUA as a continuous variable in the RCS model to further investigate the dose–response relationship between SUA and CAS. In addition, subgroup and sensitivity analyses were conducted to evaluate the heterogeneity and robustness of our findings. However, our study has several limitations. First, as a cross-sectional study, our ability to infer causality is limited. This raises the possibility of reverse causation or unmeasured confounding factors influencing the observed associations. To mitigate this, we employed robust statistical methods, including multivariate logistic regression with comprehensive adjustment for confounders (e.g., age, sex, lifestyle factors, and cardiometabolic variables) and RCS models to explore dose–response relationships. Second, SUA levels in this study were based on is a single measurement, which may be inaccurate due to influences from short-term factors, such as diet, dehydration, medication, or physical activity, and future studies should use mean values from multiple measurements. Thirdly, because Beijing has a high level of economic development and health literacy, and the study population consisted of individuals actively undergoing health check-up, caution should be exercised when generalizing the results to other populations. To address this, we conducted sensitivity analyses and detailed subgroup analyses. These findings generate hypotheses for future longitudinal cohort studies to confirm the temporal relationship and explore underlying mechanisms, thereby building on the strengths of this cross-sectional design.

5 Conclusion

This study found that the overall prevalence of CAS was high among Chinese adults, and a high level of SUA was significantly associated with the risk of CAS, especially in females. Our findings suggest that SUA levels should be actively monitored, and that timely intervention in high-risk groups may reduce the risk of CAS.

Data availability statement

The data were derived from the database of Beijing MJ Health Screening Center and could not be shared publicly due to patient privacy and confidentiality agreements. Data access is available upon reasonable request by contacting the corresponding author, subject to ethical approval. Requests to access these datasets should be directed to bmluZ3lpQG11aG4uZWR1LmNu; cWlvbmdndWl6QG11aG4uZWR1LmNu.

Ethics statement

The studies involving humans were approved by Hainan Medical University Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the study was conducted in accordance with the Declaration of Helsinki and was approved by the Hainan Medical University Ethics Committee. The committee waived the requirement for individual consent, as the research utilized retrospective, anonymized health checkup data (containing only patient codes and test results) (Approval No.: HYLL-2023-449). All data were handled confidentially.

Author contributions

RC: Formal analysis, Data curation, Writing – original draft, Conceptualization. YG: Writing – original draft, Data curation, Methodology. ML: Data curation, Writing – original draft, Investigation. LQ: Writing – review & editing. YN: Project administration, Writing – review & editing, Supervision, Conceptualization, Resources, Funding acquisition. QZ: Supervision, Funding acquisition, Conceptualization, Formal analysis, Writing – review & editing, Methodology, Writing – original draft, Project administration, Data curation.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Hainan Province High-level Talent Program (Grant No. HNT20240055), Hainan Medical University Distinguished Scholar Fund (Grant No. XRC220005), and Talent Program of Hainan Medical University (Grant No. RZ2400002321).

Acknowledgments

We gratefully acknowledge the participants of Beijing MJ Healthcare Center for their contributions to this study. Without their cooperation and support, this study would not have been possible. We also extend our sincere thanks to the medical professionals involved in the collection and collation of health check-up data. Their expertise and dedication have been instrumental in ensuring the accuracy and reliability of the data.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2026.1752079/full#supplementary-material

References

1. Chong, B, Jayabaskaran, J, Jauhari, SM, Chan, SP, Goh, R, Kueh, MTW, et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Prev Cardiol. (2024) 32:1001–15. doi: 10.1093/eurjpc/zwae281

Crossref Full Text | Google Scholar

2. National Center for Cardiovascular Diseases the Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2023: an updated summary. Biomed Environ Sci. (2024) 37:949–92. doi: 10.3967/bes2024.162

Crossref Full Text | Google Scholar

3. Song, P, Fang, Z, Wang, H, Cai, Y, Rahimi, K, Zhu, Y, et al. Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study. Lancet Glob Health. (2020) 8:e721–9. doi: 10.1016/S2214-109X(20)30117-0,

PubMed Abstract | Crossref Full Text | Google Scholar

4. Fu, J, Deng, Y, Ma, Y, Man, S, Yang, X, Yu, C, et al. National and provincial-level prevalence and risk factors of carotid atherosclerosis in Chinese adults. JAMA Netw Open. (2024) 7:e2351225–5. doi: 10.1001/jamanetworkopen.2023.51225,

PubMed Abstract | Crossref Full Text | Google Scholar

5. Du, L, Zong, Y, Li, H, Wang, Q, Xie, L, Yang, B, et al. Hyperuricemia and its related diseases: mechanisms and advances in therapy. Signal Transduct Target Ther. (2024) 9:212. doi: 10.1038/s41392-024-01916-y,

PubMed Abstract | Crossref Full Text | Google Scholar

6. Copur, S, Demiray, A, and Kanbay, M. Uric acid in metabolic syndrome: does uric acid have a definitive role? Eur J Intern Med. (2022) 103:4–12. doi: 10.1016/j.ejim.2022.04.022

Crossref Full Text | Google Scholar

7. Freilich, M, Arredondo, A, Zonnoor, SL, and McFarlane, IM. Elevated serum uric acid and cardiovascular disease: a review and potential therapeutic interventions. Cureus. (2022) 14:e23582. doi: 10.7759/cureus.23582,

PubMed Abstract | Crossref Full Text | Google Scholar

8. Wang, JJ, Yi, JK, Zhou, LR, Chen, J, Zhang, BQ, Huang, HM, et al. A systematic review of the connection between serum uric acid levels and the risk of cardiovascular disease. Front Cardiovasc Med. (2025) 12:1577952. doi: 10.3389/fcvm.2025.1577952,

PubMed Abstract | Crossref Full Text | Google Scholar

9. Nishizawa, H, Maeda, N, and Shimomura, I. Impact of hyperuricemia on chronic kidney disease and atherosclerotic cardiovascular disease. Hypertens Res. (2022) 45:635–40. doi: 10.1038/s41440-021-00840-w

Crossref Full Text | Google Scholar

10. Nardi, V, Franchi, F, Prasad, M, Fatica, EM, Alexander, MP, Bois, MC, et al. Uric acid expression in carotid atherosclerotic plaque and serum uric acid are associated with cerebrovascular events. Hypertension. (2022) 79:1814–23. doi: 10.1161/hypertensionaha.122.19247,

PubMed Abstract | Crossref Full Text | Google Scholar

11. Piani, F, Annesi, L, and Borghi, C. New insights into uric acid metabolism in the pathophysiology of Ischaemic heart disease. Eur Cardiol. (2025) 20:e18. doi: 10.15420/ecr.2024.30,

PubMed Abstract | Crossref Full Text | Google Scholar

12. Takayama, S, Kawamoto, R, Kusunoki, T, Abe, M, and Onji, M. Uric acid is an independent risk factor for carotid atherosclerosis in a Japanese elderly population without metabolic syndrome. Cardiovasc Diabetol. (2012) 11:2. doi: 10.1186/1475-2840-11-2,

PubMed Abstract | Crossref Full Text | Google Scholar

13. Mingzhu, M, Liangxu, W, Wenjing, H, Xiaoni, Z, Longfei, L, Huan, W, et al. Meta-analysis of the correlation between serum uric acid level and carotid intima-media thickness. PLoS One. (2021) 16:e0246416–6. doi: 10.1371/journal.pone.0246416

Crossref Full Text | Google Scholar

14. Bae, JS, Shin, DH, Park, PS, Choi, BY, Kim, MK, Shin, MH, et al. The impact of serum uric acid level on arterial stiffness and carotid atherosclerosis: the Korean multi-rural communities cohort study. Atherosclerosis. (2013) 231:145–51. doi: 10.1016/j.atherosclerosis.2013.08.017,

PubMed Abstract | Crossref Full Text | Google Scholar

15. Stein, JH, Korcarz, CE, Hurst, RT, Lonn, E, Kendall, CB, Mohler, ER, et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography carotid intima-media thickness task force endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr. (2008) 21:93–111. doi: 10.1016/j.echo.2007.11.011,

PubMed Abstract | Crossref Full Text | Google Scholar

16. Touboul, PJ, Hennerici, MG, Meairs, S, Adams, H, Amarenco, P, Desvarieux, M, et al. Mannheim intima-media thickness consensus. Cerebrovasc Dis. (2004) 18:346–9. doi: 10.1159/000081812,

PubMed Abstract | Crossref Full Text | Google Scholar

17. Grant, EG, Benson, CB, Moneta, GL, Alexandrov, AV, Baker, JD, Bluth, EI, et al. Carotid artery stenosis: gray-scale and Doppler US diagnosis—Society of Radiologists in ultrasound consensus conference. Radiology. (2003) 229:340–6. doi: 10.1148/radiol.2292030516,

PubMed Abstract | Crossref Full Text | Google Scholar

18. Wang, JG. Chinese guidelines for the prevention and treatment of hypertension (2024 revision). J Geriatr Cardiol. (2025) 22:1–149. doi: 10.26599/1671-5411.2025.01.008,

PubMed Abstract | Crossref Full Text | Google Scholar

19. Guo, L, and Xiao, X. Guideline for the Management of Diabetes Mellitus in the elderly in China (2024 edition). Aging Med (Milton). (2024) 7:5–51. doi: 10.1002/agm2.12294,

PubMed Abstract | Crossref Full Text | Google Scholar

20. Joint Committee on the Chinese Guidelines for Lipid Management. Chinese guidelines for lipid management (2023). Zhonghua Xin Xue Guan Bing Za Zhi. (2023) 51:221–55. doi: 10.3760/cma.j.cn112148-20230119-00038

Crossref Full Text | Google Scholar

21. Ma, M, Wang, L, Zhong, X, Zhong, L, Chen, R, Li, L, et al. Age and gender differences between carotid intima-media thickness and serum uric acid. Am J Cardiol. (2022) 172:137–43. doi: 10.1016/j.amjcard.2022.02.023,

PubMed Abstract | Crossref Full Text | Google Scholar

22. Sugiura, T, Dohi, Y, Takagi, Y, Yokochi, T, Yoshikane, N, Suzuki, K, et al. Increased impact of serum uric acid on arterial stiffness and atherosclerosis in females. J Atheroscler Thromb. (2022) 29:1672–91. doi: 10.5551/jat.63368,

PubMed Abstract | Crossref Full Text | Google Scholar

23. Li, L, Tian, X, Gao, Y, Gu, Y, Zhang, K, Li, L, et al. Serum uric acid predicts the development of atherosclerosis in women but not in men: a ten-year cohort study in China. Nutr Metab Cardiovasc Dis. (2024) 34:198–205. doi: 10.1016/j.numecd.2023.10.005,

PubMed Abstract | Crossref Full Text | Google Scholar

24. Priya Dharshini, LC, Vishnupriya, S, Sakthivel, KM, and Rasmi, RR. Oxidative stress responsive transcription factors in cellular signalling transduction mechanisms. Cell Signal. (2020) 72:109670. doi: 10.1016/j.cellsig.2020.109670

Crossref Full Text | Google Scholar

25. Yang, Y, and Dong, M. Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning. Biol Direct. (2025) 20:15. doi: 10.1186/s13062-025-00600-7,

PubMed Abstract | Crossref Full Text | Google Scholar

26. Kimura, Y, Tsukui, D, and Kono, H. Uric acid in inflammation and the pathogenesis of atherosclerosis. Int J Mol Sci. (2021) 22:12394. doi: 10.3390/ijms222212394,

PubMed Abstract | Crossref Full Text | Google Scholar

27. Lee, TS, Lu, TM, Chen, CH, Guo, BC, and Hsu, CP. Hyperuricemia induces endothelial dysfunction and accelerates atherosclerosis by disturbing the asymmetric dimethylarginine/dimethylarginine dimethylaminotransferase 2 pathway. Redox Biol. (2021) 46:102108. doi: 10.1016/j.redox.2021.102108,

PubMed Abstract | Crossref Full Text | Google Scholar

28. Chen, S, Yang, H, Chen, Y, Wang, J, Xu, L, Miao, M, et al. Association between serum uric acid levels and dyslipidemia in Chinese adults: a cross-sectional study and further meta-analysis. Medicine (Baltimore). (2020) 99:e19088. doi: 10.1097/md.0000000000019088,

PubMed Abstract | Crossref Full Text | Google Scholar

29. Zhang, S, Zhang, Y, Lin, S, Zhang, S, and Qiu, M. Hyperuricemia as a possible risk factor for abnormal lipid metabolism in the Chinese population: a cross-sectional study. Ann Palliat Med. (2021) 10:11454–63. doi: 10.21037/apm-21-2734,

PubMed Abstract | Crossref Full Text | Google Scholar

30. Kuwabara, M, Borghi, C, Cicero, AFG, Hisatome, I, Niwa, K, Ohno, M, et al. Elevated serum uric acid increases risks for developing high LDL cholesterol and hypertriglyceridemia: a five-year cohort study in Japan. Int J Cardiol. (2018) 261:183–8. doi: 10.1016/j.ijcard.2018.03.045,

PubMed Abstract | Crossref Full Text | Google Scholar