This study utilized the baseline data from the China H-type Hypertension Registry Study (Registration number: ChiCTR1800017274), which was a prospective, real-world, and observational study. Data collection methods and exclusion criteria have been described previously (16). A protocol for the study was approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University (No. CH1059), in accordance with the Declaration of Helsinki. Forms of informed consent were completed by all participants before enrollment.

A total of 10, 923 participants completed the ankle brachial index (ABI) measurement in this study. After excluding 17 individuals without hypertension, 6 cases without serum uric acid data, 1,056 cases with eGFR ≤ 60 ml/min/1.73m², and 5 individuals with ABI >1.4 (17, 18), finally 9, 839 participants were enrolled in our analysis ( Figure 1 ).

In this study, researchers utilized carefully compiled questionnaires to collect data on a variety of participant characteristics, including demographic information such as age and gender, lifestyle habits like smoking and drinking, medical history (atrial fibrillation, hypertension, diabetes mellitus, and dyslipidemia), as well as medication use including antihypertensive, hypoglycemic, lipid-lowering, and antiplatelet agents. Current smokers were defined as participants who smoked at least 20 packets of cigarettes in their lifetime and currently smoke cigarettes. Former smokers were participants who had smoked at least 20 packets of cigarettes in their lifetime, and quit smoking for at least 1 month. A drinker was defined as someone who has consumed at least one alcoholic beverage per week in the past month (19).

Additionally, we collected participants’ anthropometric measurements, including weight, height, waist circumference, hip circumference, systolic blood pressure (SBP), diastolic blood (DBP), and heart rate (HR), as well as their height and waist circumference measurements. A validated upper arm medical electronic sphygmomanometer (HBP-1300; Omron; Kyoto, Japan) was utilized with the appropriate cuff size for the upper right arm to measure blood pressure. For each participant, four consecutive office blood pressure measurements were performed, and the mean of the last three readings was analyzed. A 1.5-m-long inelastic measuring tape with 0.1 cm resolution was used to measure waist circumference and hip circumference. Body weight without heavy clothing was measured using a body fat and weight measurement device (V-body HBF-371, Omron; Kyoto, Japan). BMI was calculated by multiplying weight (in kilograms) by height (in meters squared: kg/m2). The waist-hip ratio (WHR) was calculated by dividing the waist circumference by the hip circumference. The mean values of the last three readings were analyzed.

Participants were classified into three groups based on their BMI values: underweight (<24 kg/m2), overweight (24-28 kg/m2), and obese (≥28 kg/m2), using published standards for the Chinese population. Central obesity was defined as male participants with a WHR of ≥0.9 or female participants with a WHR of ≥0.85 (20).

After an overnight fast of at least 12 hours, blood samples were collected by venipuncture. Blood biochemical tests for plasma total homocysteine (tHcy), fasting blood glucose (FBG), total cholesterol (TC), total triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), SUA, serum creatinine, blood urea nitrogen (BUN), total and direct bilirubin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were measured using automatic clinical analyzers (Beckman Coulter) at the core laboratory of the National Clinical Research Center for Kidney Disease, Guangzhou, China. In our study, DM was defined as FBG levels above 7.0 mmol/L or treatment for diagnosed DM (21). Dyslipidemia was defined as having TG >2.3 mmol/L, TC >6.2 mmol/L, LDL >4.1 mmol/L, HDL <1.0 mmol/L in men and <1.2mmol/L in women (22), or being treated with appropriate lipid-lowering agents. HUA was defined as SUA levels >420 mol/L in men and >360 mol/L in women by most studies among Chinese populations (23, 24). In some studies, HUA was designated with lower cutoff points (25, 26).CKD-EPI’s (Chronic Kidney Disease Epidemiology Collaboration) equation was used to estimate glomerular filtration rate (eGFR) (27).

The ABI was performed using a BP-203 RPE III networked arteriosclerosis detection device (VP-2000, Omron Health Care, Kyoto, Japan) following the guidelines recommended by the American College of Cardiology/American Heart Association (28). Participants rested in a quiet room for at least five minutes in a supine position before undergoing ABI measurements. Consumption of coffee, tea, cigarettes, or alcohol was prohibited for 30 minutes prior to the test. Electrodes were attached to the participant’s forearms to measure an electrocardiogram (ECG). Both arms (brachial artery) and ankles (posterior tibial artery) were fitted with a blood pressure cuff and a Doppler ultrasound device. The ABI was measured for a period of 10-30 seconds, and the lowest ABI value between right and left was used as a normality parameter. Patients with an ABI ≤0.9 was diagnosed as PAD (17, 18, 28–30). Patients with diabetes and/or advanced chronic kidney disease are more likely to have ABI values >1.40, indicating that the arteries cannot be compressed. Only five patients with ABI values higher than 1.4 were excluded at the entrance.

The clinical characteristics of participants were determined based on their baseline SUA levels. A continuous variable was expressed as its mean ± standard deviation (SD) or median (Q1-Q3). In both groups, variables with normal distributions and homoscedasticity were analyzed using a one-way analysis of variance. The Kruskal-Wallis test was performed on variables that showed skewed distribution. A chi-square or Fisher’s exact probability test was used to compare groups based on categorical variables expressed as counts (percentages).

The association between SUA levels and PAD was evaluated using multivariable logistic regression models based on odds ratios (ORs) and their 95% confidence intervals (CIs). Covariates were not taken into account in the crude model. Age was the only adjustment made to Model I. Model II was a covariate model. The covariate model screened for covariates including age, SBP, DBP, BMI, smoking and drinking status, homocysteine, TC, TG, HDL-C, LDL-C, AST, ALT, serum creatinine, DM, antihypertensive agents, lipid-lowering agents, and antiplatelet agents. Supplementary Table 1 showed the associations of each covariate with PAD. Covariates were the main model. Subjects were categorized into SUA tertiles, and SUA predictive capacity was explored for SUA, with either considered as a continuous or a categorical variable.

Finally, to ensure the robustness between SUA levels and PAD in different genders, the subgroup analysis was presented in tabular form with a forest plot using stratified multivariate regression and interaction analyses.

The statistical package R (The R Foundation; http://www.r-project.org; version 4.2.0) was used to perform all statistical analyses and EmpowerStats (R; www.empowerstats.com; X&Y Solutions, Inc, Boston, MA, USA; version 4.2). Statistical significance was denoted by a P value of <0.05, and each P value is two-tailed.

This study enrolled 9,839 Chinese adults with hypertension, with ages ranging from 27 to 93 years and a mean age of 63.14 ± 8.99. Table 1 presents the clinical characteristics of the study participants, grouped by HUAAs the schematics revealed, the proportion of male participants was 46.22%, and the prevalence of HUA was 50.72%.

Study participants were grouped by sex and their clinical characteristics are summarized in Supplementary Table 2 . The overall prevalence of PAD was 2.67%, with male participants exhibiting a higher proportion (3.17%) than female participants (2.25%). The proportion of HUA was higher in male (56.82%) than in female participants (45.47%). Furthermore, as shown in Supplementary Table 2 , we found significant differences between male and female participants with regard to current smoking (50.53% vs. 5.43%) and drinking habits (43.68% vs. 6.03%). No significant difference was observed in medication use, including antihypertensive agents and lipid-lowering agents, between the two genders. Supplementary Table 2 also illustrates that women had a higher use rate of hypoglycemic agents (5.44%) compared to men (4.27%), while men had a higher use rate of antiplatelet agents (4.20%) compared to women (3.35%), with statistical significance (P = 0.007 and P = 0.026, respectively). Specifically, male patients exhibited a higher likelihood of having elevated levels of DBP, WHR, Hcy, SUA, BUN, serum creatinine, eGFR, total and direct bilirubin, AST, ALT, and ABI. Conversely, female patients exhibited lower levels of SBP, BMI, FBG, TC, TG, HDL-C, and LDL-C.

To assess the association between SUA levels and PAD, multivariable logistic regression was conducted after adjusting for covariates that could impact the outcome by more than 10%. Results showed no significant association between SUA levels and PAD risk when SUA was analyzed as a continuous variable in the whole sample (adjusted OR per SD increase: 1.00, 95% CI 1.00 to 1.00, P = 0.055; Supplementary Table 3 ).

Multivariable logistic regression analyses were then performed separately for male and female particiants. There was no significant association between SUA levels and PAD risk when SUA levels were viewed as continuous variables in both men (adjusted OR per SD increase: 1.19, 95% CI 0.98 to 1.45, P = 0.087; Table 2 ) and women (adjusted OR per SD increase: 1.15, 95% CI 0.91 to 1.46, P = 0.232; Table 2 ). Furthermore, multivariable logistic regression analyses were conducted to examine the relationship between PAD and hyperuricemia, which was defined as SUA > 420 μmol/L in men and > 360 μmol/L in women. A positive association was found between hyperuricemia and PAD risk in men (adjusted OR per SD increase: 1.72, 95% CI 1.17 to 2.53, P = 0.006; Table 2 ). However, when HUA was defined as SUA > 5.1 mg/dL for female and 5.6 mg/dL for male participants, we did not observe a significant relationship between HUA and PAD in the overall population (adjusted OR per SD increase:1.30, 95% CI: 0.91 to 1.87, p = 0.147, Supplementary Table 4 ). This lack of association was consistent when analyzing male participants separately (adjusted OR per SD increase: 1.78, 95% CI: 0.97 to 3.25, p = 0.062; Supplementary Table 5 ) and female participants separately (adjusted OR per SD increase: 1.00, 95% CI: 0.63 to 1.60, p = 0.991; Supplementary Table 5 ). The highest SUA tertile was associated with a significantly higher prevalence of PAD compared to the lowest tertile in men (adjusted OR: 2.63, 95% CI 1.42 to 4.86, P = 0.002; P for trend = 0.001). However, this relationship was not significant in women (adjusted OR: 1.29, 95% CI: 0.77 to 2.17, P = 0.327; P for trend = 0.347).

To explore whether the association between SUA levels and the prevalence of PAD was still stable in different subgroups, subgroup analysis was presented in tabular form with a forest plot using stratified multivariate regression and interaction analyses.

In any subgroup of male patients, there were no statistically significant interactions, including age (<60 vs. ≥60 years), SBP dichotomy (≤146.67 vs. ≥ 147.00 mmHg), DBP dichotomy (≤89.33 vs. ≥89.67 mmHg), different BMI group, smoking habit (no vs. yes), drinking habit (no vs. yes), antihypertensive agents (no vs. yes), homocysteine (<15 vs. ≥15 μmol/L), LDL dichotomy (< 2.93 vs. ≥2.94 mmol/L), and serum creatinine (≤62.0 vs. ≥63.0 mmol/L) (all P for interactions >0.05; Supplementary Figure 1 ).

Based on the subgroup analysis, none of the subgroups had statistically significant interactions for females, including age (<60 vs. ≥60 years), the SBP dichotomy (≤146.67 vs. ≥ 147.00 mmHg), the DBP dichotomy (≤89.33 vs. ≥89.67 mmHg), different BMI group, antihypertensive agents (no vs. yes), antiplatelet agents (no vs. yes), homocysteine (<15 vs. ≥15 μmol/L), LDL dichotomy (< 2.94 vs. ≥2.94 mmol/L), and AST dichotomy (≤23.00 vs. ≥24.00 U/L) (all P for interactions >0.05; Supplementary Figure 2 ).