Three cohorts accounting for 4,802 individuals from different European Risk Regions based on SCORE2 and SCORE2-OP (4, 19) were analyzed. SCORE2 evolved to predict 10-year risk of first-onset CVD and incorporates sex- and country-specific approaches to CV risk estimation (4). SCORE2-OP provides calibrated risk prediction for elderly populations (19). Based on these models, European countries are categorized into four risk regions, where Spain is classified as a low risk region, Sweden as a moderate risk region, and Lithuania as a very high-risk region. The description of each study sample is presented below and summarized in Supplementary Table S1.

SCAPIS – The Swedish CArdioPulmonary bioImage Study (20) is a population-based study which randomly recruited Swedish residents in the 50–64 age group at six university hospitals. Nationwide, 30,154 individuals participated. 9,079 underwent measurement of arterial stiffness. For 3,518 of these individuals, examined at the Malmö site between January 2016 and March 2018, measurement of both SUA and hs-CRP was part of the screening program. Of those, 3,255 had complete information on lipid status, HbA1c, plasma glucose, plasma creatinine, anthropometric data, and blood pressure.

The Sagunto Cohort was a prospective cohort study conducted in Sagunto, a city in the Valencia Region of Spain. The study aimed to investigate the relationship between CV risk factors and CVD. The Sagunto Cohort study included a sample of 1,500 participants aged 18–90 years, with suspected untreated primary hypertension at Sagunto Hospital (Sagunto, Spain), who were recruited between 2003 and 2006. Out of 900 individuals, 838 underwent arterial stiffness measurement and had serum uric acid and hs-CRP data.

LitHiR – The Lithuanian High Cardiovascular Risk Prevention Program (21) attendees who met inclusion criteria were included at primary care centers and examined at Vilnius University Hospital Santaros Klinikos (Vilnius, Lithuania). Age 40–54 years for men and 50–64 years for women, no previous history of CVD, but presence of metabolic syndrome according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) guidelines were inclusion criteria for this study. Nationwide, 52,012 adults had been examined between 2006 and 2019. However, serum uric acid and hs-CRP were available for 709 individuals who were included in this analysis.

Anthropometric measurements were taken in all three countries using a calibrated scale. Body weight (kg), waist circumference (WC, cm), and body height (cm) were measured. Body mass index (BMI) was calculated as weight divided by height square, kg/m².

Different calibrated equipment was used for BP measurement. An automatic device (Omron M10-IT, Omron Health Care Co, Kyoto, Japan) was utilized in Sweden. BP was measured twice in each arm, and the mean value was used. In Spain validated, semiautomatic devices (OMRON M5-1, OMRON 705IT, Omron Health Care Co, Kyoto, Japan) were used and a manual sphygmomanometer was used in Lithuania. The mean BP value was calculated from three measurements on the left arm according to European Society of Hypertension (ESH) guidelines (22).

The same methodological approach was used in all participants. Aortic stiffness was measured with Sphygmocor (v.7.01) (AtCor Medical Pty. Ltd. 1999–2002, Sydney, Australia) applanation tonometry, using the foot-to-foot method in the right carotid and femoral arteries for visualizing carotid-femoral pulse wave velocity (cfPWV). The simultaneous ECG registration was performed. The only difference was that in Sweden, the cuff-based device was used (23). Participants were advised to avoid caffeine, nicotine and alcohol intake several hours before the examination. The pulse travel distance was multiplied by 0.8.

Fasting venous blood samples were collected. Serum uric acid (SUA), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), serum creatinine, hs-CRP, glycated hemoglobin (HbA1c, available in Swedish and in Spanish participants), urinary albumin-to-creatinine ratio (UACR) (in Lithuanians and Spanish subjects) were analyzed.

Blood test results, including SUA measurements, obtained using the Abbott Architect ci8200 PLUS in Lithuania and the Roche Cobas 6000/8000 in Spain and Sweden, revealed comparable results, enabling reliable cross-platform analysis. Both analyzers meet standards for calibration with a certified reference material.

Kidney function was estimated by creatinine-based estimated glomerular filtration equations (eGFR) EKFC (The new creatinine-based European Kidney Function Consortium equation) (24). Conversion factors were applied to unify measurement values across countries.

For this work, we utilized data gathered through questionnaires and clinical registries. Diabetes was defined as the use of antidiabetic drugs or previous known diagnosis or plasma glucose level above 7 mmoL/L and/or HbA1c level above 6.5%. Hypertension was defined as using antihypertensive medications or office systolic blood pressure (BP) ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg. Smoking was determined as “current smoker,” “former smoker” if smoking cessation happened 1 year before assessment, or “never” smoker.

Hyperuricemia is defined differently across countries, with varying SUA thresholds considered abnormal. In Sweden, SUA > 350 μmol/L in women 18–49 years old; >400 μmol/L in women 50 years or older; >480 μmol/L in adult men. In Spain, SUA ≥ 7.0 mg/dL (~416 μmol/L) for men and > = 6,5 mg/dL (~387 μmol/L) for women. In Lithuania, SUA values >357 μmol/L in women and >428 μmol/L in men.

Swedish participants represented the largest subgroup and exhibited the lowest SUA levels; therefore, their sex-specific SUA quartiles were used as a reference value for grouping Lithuanian and Spanish patients. 1st quartile (SUAq1) values < 225 μmol/L (women) or < 301 μmol/L (men); 2nd quartile (SUAq2) – 225-265 μmol/L (women) or 301–342 μmol/L (men); 3rd quartile (SUAq3) – 265-309 μmol/L (women) or 342–387 μmol/L (men); 4th quartile (SUAq4) – values >309 μmol/L (women) or >387 μmol/L (men). The distribution of sex-specific SUA quartiles is presented in Supplementary Figure 1.

Elevated UACR excretion was determined as ≥3 mg/mmoL in Lithuanians and Spaniards.

cfPWV was considered as an outcome variable. In linear models, crude values were used. In logistic regression, we utilized cfPWV values above 10 m/s, as per the guidelines of the ESH (22).

SUA and hs-CRP were exposure variables.

Normally distributed variables are presented as means and standard deviations, non-parametric – as medians and interquartile ranges and categorical – as percentages and case numbers. The hs-CRP values did not follow a normal distribution; therefore, a natural logarithmic transformation (ln) of hs-CRP was applied, with 1-unit increase in ln(hs-CRP) corresponding approximately to a 2.72-fold increase in hs-CRP. Additionally, due to the high variability in SUA, the values were rescaled, and SUA was analyzed per 50 μmol/L increase (SUA₅₀). The transformed data were analyzed using both linear and logistic regression models. Analyses were stratified by sex. cfPWV z-scores (cfPWVz) were calculated to plot cross-country comparisons of aortic stiffness, using recently published reference values derived from white male and female participants in the longitudinal Austrian Lung, hEart, sociAl, boDy (LEAD) study (25). cfPWVz use the average cfPWV of healthy individuals of the same age and sex as a comparator. The equation for cfPWVz:

Where cfPWV(measured) is a raw value, cfPWV(expected) is the reference value, and SD (expected) is the population standard deviation published by Azizzadeh et al. (25).

Sex disaggregated linear regression analysis defined the association between cfPWV (outcome), hs-CRP (exposure) and SUA (exposure). Interaction analyses were performed to assess potential interactions between SUA and cohort, hs-CRP and cohort, and the three-way interaction among SUA, hs-CRP, and cohort. For this purpose, centralized values of SUA and hs-CRP were calculated by subtracting the overall mean value from each data point (SUA_C and hs-CRP_C). Variables SUA_C, hs-CRP_C, SUA_C*cohort, hs-CRP_C*cohort, and SUA_C*hs-CRP_C*cohort were then entered into a linear regression model with cfPWV as the outcome. Both SUA and hs-CRP showed significant interactions with cohort (SUA_C*cohort: β = 0.002, SE = 0.0004, p < 0.001; hs-CRP_C*cohort: β = 0.134, SE = 0.028, p < 0.001; SUA_C*hs-CRP_C*cohort: β = 0.0004, SE = 0.0001, p < 0.001). Therefore, data were not pooled, and each country was analyzed separately. No collinearity between SUA and hs-CRP was detected, as the Variance Inflation Factor (VIF) in the unadjusted model was 1.08 for women and 1.02 for men. VIF’s were further employed in nested linear models (Models 1 to 3), incorporating the covariates listed below. This approach helped identify factors mediating or mitigating the relationship between outcome and exposure.

Model 1: Exposure variables adjusted for age and BMI.

Model 2: Model 1 adjusted for eGFR, heart rate (HR), pulse pressure (PP).

Model 3: Model 2 adjusted for diabetes, smoking, FPG, TG, antihypertensive and lipid-lowering treatment.

A sensitivity analysis was performed using cfPWVz as the outcome to assess the robustness of the results. These results are presented in Supplemental materials.

Sex specific bivariate logistic regression with an outcome variable included similar nested Models 1 to 3 as in linear regression. The outcome and exposure variables as defined previously in the paper.

SPSS 29.0 software was used for statistical analysis. A p-value below 0.05 was considered statistically significant.

ANOVA revealed a “J”-shaped association, with the highest cfPWV in the highest SUA quartile (Q4) among Swedish, Lithuanian, and Spanish women (p < 0.001) (Supplementary Figure 1). In men, no differences in cfPWV across SUA quartiles were observed, except for a near “U”-shaped pattern in Spaniards (p < 0.001). hs-CRP distribution was more complex, with increasing values across SUA quartiles in Swedish, Lithuanian, and Spanish women, but with a less clear trend in men. Younger Spanish (p = 0.026) and Lithuanian men (p < 0.001), as well as older Spanish and Swedish women, were more frequently in the highest SUA quartile (p < 0.001 for both groups). Age did not differ across SUA quartiles in Lithuanian women (p = 0.840) and Swedish men (p = 0.669). BMI increased stepwise (p < 0.001), and pulse pressure showed a right-peaked distribution (all p < 0.001) (Supplementary Figure 1). Kidney function, determined by creatinine-based eGFR, was lower in the highest SUA quartile across countries and sexes (all p < 0.001; in Spanish men p = 0.002), except in Lithuanian men (p = 0.315).

Sex-stratified linear regression analyses showed country-specific associations between cfPWV, hs-CRP, and SUA (Table 2). In unadjusted models, cfPWV was significantly associated with both hs-CRP and SUA in Swedish women and men (p = 0.014 and p = 0.005, respectively), and in Lithuanian women and men (p < 0.001 and p = 0.046, respectively). In contrast, cfPWV was associated only with SUA in Spanish women (p < 0.001), and with hs-CRP in Spanish men (p < 0.001). In Model 1, associations were attenuated: a ~ 2.7 fold increase in hs-CRP was associated with 0.17 m/s higher cfPWV in Lithuanian women and a 0.11 m/s higher cfPWV in Swedish women (p = 0.003 and p = 0.023, respectively), as well as a 0.14 m/s higher cfPWV in Swedish men (p < 0.001). Meanwhile, each 50 μmol/L increase in SUA remained significantly associated with a 0.14 m/s higher cfPWV in Lithuanian women (p = 0.001) and a 0.05 m/s higher cfPWV in Swedish men (p = 0.023). In Models 2 and 3, a ~ 2.7 fold increase in hs-CRP was associated with a 0.09–0.10 m/s higher cfPWV only in Swedish men (p = 0.007, Model 3), while each 50 μmol/L increase in SUA was associated with 0.07 m/s higher cfPWV in Swedish men (p = 0.001, Model 3) and a 0.09 m/s higher cfPWV in Lithuanian women (p = 0.029, Model 3). Complete information on regression coefficients is shown in Supplementary Table S3. The heat maps of p-values are presented in Figure 2.

Sensitivity analyses with cfPWVz as the outcome yielded identical p-values, although the associations were weaker (Supplementary Table S4).

In unadjusted logistic regression, each 50 μmol/L increase in SUA and 2.7 fold increase in hs-CRP predicted higher odds (OR) of cfPWV >10 m/s in Lithuanian women (OR 1.272, p = 0.002 for SUA; OR 1.378, p = 0.021 for hs-CRP, Figures 3A,B). Associations were also observed with higher hs-CRP levels in Spanish men (OR 1.506, p = 0.001) and were borderline significant in Swedish men (OR 1.182, p = 0.057), see Figure 3A. In Spanish women, only 50 μmol/L increase in SUA could predict 27% increase in cfPWV (OR 1.274, p = 0.001), see Figure 3B. After adjustment for age and BMI, hs-CRP remained significant in Lithuanian women (OR 1.443, p = 0.016, Figure 3B). Interestingly, each 50 μmol/L decrease in SUA was associated with 19% higher cfPWV in Spanish women (OR 0.815, p = 0.043), whereas each 50 μmol/L increase in SUA predicted 27% higher cfPWV in Lithuanian women (OR 1.270, p = 0.004) (Model 1; Table 2; Figures 3A,B). With further adjustments (Models 2–3; Table 2; Figure 3A), SUA predicted cfPWV >10 m/s in Swedish men (Model 2: OR 1.156, p = 0.034; Model 3: OR 1.155, p = 0.041) and in Lithuanian women (Model 2: OR 1.216, p = 0.047; Model 3: OR 1.205, p = 0.065). In Spanish women, the association with SUA remained negative and of borderline significance (OR 0.766, p = 0.067). hs-CRP was not associated with cfPWV >10 m/s in adjusted models.

In Model 3, additional independent predictors of cfPWV >10 m/s included age, BMI, and heart rate in Swedes and Spaniards, and pulse pressure in Swedes. Sex-specific associations were observed for higher glucose levels and lipid-lowering therapy in Swedish women; eGFR in Swedish men; heart rate, pulse pressure, and smoking in Lithuanian women; and higher glucose in Spanish men. No significant predictors were found in Lithuanian men. Detailed coefficients are provided in Supplementary Table S5.

This study has several limitations that should be acknowledged. First, its cross-sectional exploratory design, in which heterogeneous cohorts with different inclusion criteria were compared, may introduce selection, measurement, confounding, temporal, and social desirability biases. The Swedish cohort represented a random sample from the general population, whereas the Lithuanian and Spanish cohorts comprised individuals already identified as being at higher cardiovascular risk. Inter-operator variability in central hemodynamic assessment might also contributed to measurement bias. In addition, differences in equipment and measurement units required the use of conversion factors for dataset harmonization, which may have introduced minor inaccuracies. Social desirability bias may have distorted the reported prevalence of smoking across the cohorts. Finally, information on dietary habits, alcohol consumption, and the use of uric acid–lowering medication was not available.

A key strength of our study is that we analyzed a comprehensive dataset encompassing individuals with diverse demographic characteristics and varying cardiovascular risk profiles. Vascular aging was assessed using cfPWV, the gold standard measurement of arterial stiffness. Finally, the study’s findings have clear translational relevance to clinical practice, as the biomarkers investigated are inexpensive, widely available, and easily implemented in routine care.

The clinical implications of this cross-sectional study should be interpreted with caution, as longitudinal outcome data are not available. Although many previous works showed that anti-inflammatory and uric acid–lowering therapies in individuals with hypertension have been associated with improved CV outcomes, the present findings might suggest that this approach may not be uniformly beneficial across all populations. For example, in Spanish women, lower SUA levels were associated with increased cfPWV, raising the possibility that SUA reduction may not always be advantageous. Similarly, it remains uncertain whether intervening in otherwise healthy individuals, such as Swedish men, based on cfPWV levels alone, prior to the development of gout, kidney disease, or markedly elevated SUA, would lead to meaningful CV benefit. Future studies in large, population-based cohorts are needed to replicate these findings and to better identify subgroups with low to moderate cardiovascular risk in whom very high or very low SUA levels, in conjunction with inflammation, may warrant targeted intervention.