Carotid intima-media thickness stratifies early and supernormal vascular aging phenotypes in UK biobank: Implications for cardiovascular risk prediction

Carotid intima-media thickness stratifies early and supernormal vascular aging phenotypes in UK biobank: Implications for cardiovascular risk prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carotid intima-media thickness stratifies early and supernormal vascular aging phenotypes in UK biobank: Implications for cardiovascular risk prediction Zhichen Dong, Fangfang Fan, Hongyu Chen, Tianhui Dong, Leyuan Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8822772/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background Chronological age is central to cardiovascular risk assessment but may inadequately capture biological vascular aging. Carotid intima-media thickness (cIMT), a non-invasive marker of subclinical atherosclerosis, may provide additional insight into individualized cardiovascular risk. Methods In this prospective cohort study, We analyzed 31,311 UK Biobank participants with cIMT measurements. Vascular age and Δ-age were derived using multivariable models incorporating cIMT and conventional risk factors, and extreme Δ-age deciles were used to define early vascular aging (EVA) and supernormal vascular aging (SUPERNOVA). Associations with major adverse cardiovascular events (MACE) were assessed using Cox models, and incremental predictive value was evaluated using discrimination and reclassification metrics. Results During a median follow-up of 4.18 years, 682 MACE cases were recorded. After multivariable adjustment, EVA was associated with a twofold increase in MACE risk (HR 2.00, 95% CI 1.45–2.75), whereas SUPERNOVA demonstrated a significant protective effect (HR 0.70, 95% CI 0.54–0.91). Stratified analyses revealed that these associations were most pronounced in the high-risk group (Framingham Risk Score > 20%), where EVA further increased MACE risk (HR 2.69, 95% CI 1.72–4.20) and SUPERNOVA remained protective (HR 0.64, 95% CI 0.47–0.85). Incorporating these vascular aging phenotypes into traditional risk models significantly improved predictive performance, yielding a Net Reclassification Improvement (NRI) of 13.1% (95% CI 8.7–16.8). Conclusion cIMT-derived vascular age effectively stratifies extreme vascular aging phenotypes and is associated with future cardiovascular events. Incorporation of vascular age into conventional risk models provides incremental prognostic value beyond traditional risk scores. carotid intima-media thickness vascular age early vascular aging cardiovascular risk atherosclerosis prospective cohort Figures Figure 1 Figure 2 Figure 3 1 Introduction Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, placing a considerable burden on global health systems [ 1 ] . Early identification of individuals at elevated cardiovascular risk is essential for effective prevention strategies. While conventional risk prediction models provide practical estimates based on traditional factors such as age, sex, blood pressure, and lipid profiles, they often fail to reflect the biological complexity and heterogeneity of vascular aging at the individual level [ 2 ] . Chronological age is a key component of most risk models, yet it does not always correspond to the true physiological state of the vascular system. Increasing evidence highlights that individuals of the same chronological age can exhibit markedly different vascular conditions [ 3 , 4 ] . This discrepancy may lead to risk underestimation in younger individuals with advanced vascular damage and overestimation in older adults with preserved vascular health. To address these limitations, the concept of vascular age has been proposed to capture cumulative structural and functional changes in the arteries over time [ 5 ] . Combining traditional cardiovascular risk factors with vascular biomarkers has shown promise in improving the accuracy of risk prediction beyond traditional models alone [ 2 , 6 ] . Among available vascular biomarkers, pulse wave velocity (PWV) and carotid intima-media thickness (cIMT) are commonly used to assess vascular aging. While PWV is a sensitive indicator of arterial stiffness, it is influenced by acute hemodynamic changes, limiting its reproducibility. In contrast, cIMT provides a stable structural measure of arterial remodeling and is widely recognized as a marker of subclinical atherosclerosis and a predictor of future cardiovascular events [ 7 , 8 ] . Building upon these measurements, the concept of vascular Δ-age—defined as the difference between vascular age and chronological age—has been introduced to quantify deviations from normal vascular aging. Furthermore, phenotypes such as early vascular aging (EVA) and supernormal vascular aging (SUPERNOVA) have been proposed to identify individuals with accelerated or delayed vascular aging [ 9 , 10 ] . However, the application of cIMT-based Δ-age in defining these phenotypes and its prognostic relevance remains underexplored. In this study, we leveraged data from the UK Biobank, a large population-based cohort, to construct a vascular age model derived from cIMT and conventional cardiovascular risk factors. Based on deviations between vascular and chronological age, we identified EVA and SUPERNOVA phenotypes. We further examined their associations with major cardiovascular events and evaluated whether incorporating vascular age could improve risk prediction beyond traditional models. 2 Methods 2.1 Study Population The UK Biobank (UKB) is a prospective cohort study that collected questionnaire data, physical measurements, and biological samples from individuals across the United Kingdom. The UK Biobank received ethical approval from the North West Multicentre Research Ethics Committee (REC reference: 11/NW/03820) [ 11 ] . All participants provided written informed consent. In this study, we included participants from the UKB who underwent carotid ultrasound examinations from 2014. As shown in the flowchart in Fig. 1 , we excluded participants with myocardial infarction or stroke at baseline and cleared participants with important missing baseline data for the final analysis. 2.2 Measurements of cIMT and other covariates at baseline In the UK Biobank study, carotid intima-media thickness (cIMT) was automatically measured at the far wall of the distal common carotid artery, approximately 1 cm proximal to the carotid bifurcation, at two predefined insonation angles on both the left and right sides (150° and 120° on the right; 210° and 240° on the left). For each angle, the maximum, mean, and minimum cIMT values were obtained across three cardiac cycles using a validated automated software (UKB data fields 22670–22681). In order to better reflect the average level of aging in the arteries, the overall mean of the maximum cIMT values at different angles was utilized to determine the effect size. Detailed information regarding the cIMT measurement is available in the referenced protocol [ 12 ] . Anthropometric measurements and questionnaire data collected concurrently with the carotid ultrasound examination served as baseline information. Age and gender information were obtained from the central registry of recruitment. Body mass index (BMI) and waist circumference were measured during the assessment center visit. Current smoking and alcohol use status were determined based on self-reported data collected via a touchscreen questionnaire. Systolic and diastolic blood pressure were automatically measured using an electronic monitor, and the average of two measurements was recorded as the final blood pressure value. In light of the lack of contemporaneous serological data accompanying the imaging examinations in the UKB dataset, laboratory measurements of blood glucose and cholesterol levels were substituted with clinical histories of diabetes and hypercholesterolemia, as well as information regarding the use of insulin and cholesterol-lowering medications. Data concerning current treatments, including cholesterol-lowering medications, blood pressure medications, and insulin use, were collected through self-reports on the touchscreen questionnaire. Hypertension was defined as a self-reported medical history confirmed through oral interviews, the use of antihypertensive medications, or elevated blood pressure (systolic ≥ 140 mmHg or diastolic ≥ 90 mmHg). Diabetes mellitus and hypercholesterolemia were identified based on a combination of self-reported history and corresponding medication use. 10-year cardiovascular risk was estimated using a simplified version of the Framingham Risk Score, in which BMI was used in place of HDL-C due to missing laboratory lipid data. According to the risk of 10% and 20%, the population can be divided into low, medium and high risk. The sex-specific algorithm was derived from the Framingham Heart Study cohort [ 13 ] . 2.3 Vascular age modeling and definitions of vascular aging phenotypes Vascular age was defined as the predicted age derived from a multivariable regression model that included classic cardiovascular risk factors and cIMT. The Variance Inflation Factor (VIF) was utilized to assess multicollinearity, ensuring that no significant collinearity issues were present among the variables. Subsequently, lasso regression was employed for variable selection. The final models incorporated sex, smoking status, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, comorbidities of hypertension, diabetes, hypercholesterolemia, and the use of antihypertensive, cholesterol-lowering, and insulin medications. Since multiple continuous variables exhibited nonlinear relationships with age, we employed smoothing splines within a generalized additive model framework to enhance the predictive performance of the model. Δ-age was calculated by subtracting chronological age from vascular age. To define early EVA and SUPERNOVA, we used the 10th and 90th percentiles of ∆-age as the cut-offs. Individuals with ∆-age above the 90th percentile are defined as EVA, while individuals below the 10th percentile are considered as SUPERNOVA. 2.4 Outcome ascertainment The primary outcome of our analysis was the occurrence of major adverse cardiovascular events (MACE), defined as a composite endpoint comprising the first non-fatal stroke, the first non-fatal myocardial infarction (MI), or cardiovascular mortality. We identified the components of MACE from Hospital Episode Statistics (HES) data and death registry data, employing International Classification of Diseases (ICD-9 and ICD-10) codes. A list of codes used to define outcomes is attached for details. 2.5 Statistical Analyses In the baseline characteristics of the participants, categorical variables are represented as frequencies (percentages), while continuous variables are reported as medians with interquartile ranges (IQRs), due to the non-normal distribution of variables. Chi-square tests were used for categorical variables, and Kruskal-Wallis tests were applied to continuous variables to compare intergroup differences among the three vascular aging categories: EVA, normal vascular aging, and SUPERNOVA. To investigate the association between EVA/SUPERNOVA and cardiovascular events, a Cox proportional hazards regression analysis was conducted. After checking the assumption of proportional hazards (Schoenfeld residuals), two models were constructed. Model 1 adjusted for age (as a continuous variable) and sex (as a categorical variable, male vs. female). Model 2 further incorporated the Framingham cardiovascular risk score. Model 3 included additional covariates reflecting lifestyle behaviors and social deprivation. Additionally, we evaluated the risk of cardiovascular outcomes when cIMT and Δ-age was treated as a continuous variable. Lastly, we examined the incremental value of vascular age and cIMT in comparison to traditional cardiovascular risk models by employing the C-index, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). All statistical analyses were conducted using R software version 4.3.3, with a two-sided P-value of less than 0.05 considered statistically significant. 3 Results 3.1 Characteristics of the Study Population and Vascular Aging Distribution From the initially enrolled 52,886 participants with carotid ultrasound data, we excluded 3,147 participants with myocardial infarction or stroke, and 18,428 participants due to missing critical variables. Eventually, 31,311 patients were included in the final analysis. The median age of the population was 64.0 (IQR: 58.0–70.0) years, and 49.2% were male. The median of vascular age was 63.75 (IQR: 60.44–67.12) years old. The median cIMT was 779.25 (IQR: 686.75–885.25) µm. In terms of baseline comorbidities, the population included 22.6% with hypertension, 14.3% with hyperlipidemia, and 2.7% with diabetes mellitus (Table 1 ). Participants were classified into three phenotypes based on Δ-age—EVA, Normal VA, and SUPERNOVA. EVA was defined as Δ-age greater than 8.09 years, and SUPERNOVA as Δ-age less than − 8.08 years. Table 1 Baseline Characteristics of Study Participants According to Vascular Aging Phenotypes. Values are median [IQR] or n (%). VA = vascular aging, Δ-age = chronological age minus vascular age; cIMT = carotid intima-media thickness; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure. Variable Overall EVA Normal VA SUPERNOVA P value Number of participants 31311 3131 25049 3131 Chronological age, y (median [IQR]) 64.00 [58.00, 70.00] 53.00 [50.00, 56.00] 64.00 [59.00, 69.00] 74.00 [71.00, 77.00] < 0.001 Male, n (%) 15397 (49.2) 1683 (53.8) 12104 (48.3) 1610 (51.4) < 0.001 Vascular age, y (median [IQR]) 63.75 [60.44, 67.12] 63.53 [61.05, 66.40] 63.86 [60.33, 67.41] 63.19 [60.34, 65.90] < 0.001 Δ-age, y (median [IQR]) -0.06 [-4.36, 4.35] 10.05 [8.97, 11.74] -0.06 [-3.43, 3.37] -10.21 [-12.03, -8.99] < 0.001 cIMT, mm (median [IQR]) 779.25 [686.75, 885.25] 789.00 [702.50, 885.25] 779.25 [683.25, 885.25] 760.25 [683.25, 857.88] < 0.001 Current smoking, n (%) 1057 (3.4) 100 (3.2) 843 (3.4) 114 (3.6) 0.606 Current alcohol consumption, n (%) 29383 (93.8) 2915 (93.1) 23549 (94.0) 2919 (93.2) 0.044 MET per week, ×10 3 (median [IQR]) 2.19 [1.12, 3.90] 2.03 [1.04, 3.67] 2.20 [1.13, 3.92] 2.22 [1.125, 3,98] < 0.001 Townsend Deprivation Index (median [IQR]) -2.63 [-3.89, -0.54] -2.38 [-3.67, -0.06] -2.64 [-3.90, -0.58] -2.76 [-3.98, -0.75] < 0.001 BMI, kg/m2 (median [IQR]) 25.78 [23.42, 28.63] 25.81 [23.43, 28.63] 25.77 [23.41, 28.63] 25.85 [23.48, 28.68] 0.735 Waist circumference, cm (median [IQR]) 88.00 [79.00, 96.00] 88.00 [80.00, 96.00] 88.00 [79.00, 96.00] 88.00 [79.00, 96.00] 0.012 SBP, mmHg (median [IQR]) 137.50 [126.00, 151.00] 137.50 [126.50, 150.50] 138.00 [126.00, 151.00] 135.50 [125.00, 148.50] < 0.001 DBP, mmHg (median [IQR]) 78.50 [72.00, 85.50] 79.00 [72.00, 85.50] 78.50 [72.00, 85.50] 79.00 [72.50, 86.00] 0.036 Diabetes mellitus, n (%) 830 (2.7) 84 (2.7) 674 (2.7) 72 (2.3) 0.435 Hypertension, n (%) 7063 (22.6) 673 (21.5) 5768 (23.0) 622 (19.9) < 0.001 Hypercholesterolemia, n (%) 4463 (14.3) 411 (13.1) 3667 (14.6) 385 (12.3) < 0.001 Lipid-lowering medication, n (%) 6986 (22.3) 639 (20.4) 5806 (23.2) 541 (17.3) < 0.001 Antihypertensive medication, n (%) 7041 (22.5) 682 (21.8) 5750 (23.0) 609 (19.5) < 0.001 Insulin use (%) 226 (0.7) 32 (1.0) 170 (0.7) 24 (0.8) 0.097 Framingham Risk Score (median [IQR]) 17.81 [10.14, 29.47] 11.48 [6.89, 17.47] 18.05 [10.19, 29.45] 26.57 [15.85, 38.42] < 0.001 As expected, cIMT levels varied across the three groups, consistent with the vascular aging phenotypes. The median cIMT in the EVA group was 789.00 µm (IQR: 702.50–885.25), compared to 760.25 µm (IQR: 683.25–857.88) in the SUPERNOVA group. In contrast, conventional cardiovascular risk factors, including BMI, blood pressure, and baseline comorbidities, showed only modest differences across groups. Interestingly, the traditional ASCVD risk score, measured by the Framingham Risk Score, exhibited a paradoxical pattern: individuals in the EVA group had significantly lower scores, while those in the SUPERNOVA group had significantly higher scores, compared with the Normal VA group. This counterintuitive trend may be partly attributed to differences in chronological age among the three groups. 3.2 Association Between Continuous Vascular Aging Metrics and Cardiovascular Risk We next assessed the predictive value of continuous vascular aging metrics, including cIMT and Δ-age. In the fully adjusted model, each 1-year increase in Δ-age was associated with a 7% higher risk (HR = 1.07, 95% CI: 1.05–1.10, P < 0.001; Table 2 ). Similar significant associations were also observed for myocardial infarction and stroke, reinforcing the robustness of Δ-age in predicting a range of cardiovascular outcomes. Table 2 Hazard Ratios (HRs) for Δ-age for Cardiovascular Outcomes. Values are HR (95%CI). Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, and Framingham Risk Score; Model 3: Model 2 plus smoke status, drinking status, MET per week, and Townsend Deprivation Index. HR: hazard ratio, CI: confidence interval, MACE: major adverse cardiovascular events, MI: Myocardial infarction, CVD: Cardiovascular disease. Outcome Model 1 Model 2 Model 3 HRs (95% CI) P value HRs (95% CI) P value HRs (95% CI) P value MACE 1.08 (1.06, 1.11) p < 0.001 1.07 (1.05, 1.10) p < 0.001 1.07 (1.04, 1.09) p < 0.001 `MI 1.11 (1.08, 1.14) p < 0.001 1.11 (1.08, 1.15) p < 0.001 1.09 (1.06, 1.12) p < 0.001 Stroke 1.06 (1.03, 1.10) p < 0.001 1.03 (0.99, 1.07) 0.122 1.05 (1.01, 1.08) 0.005 CVD Mortality 1.02 (0.96, 1.09) 0.435 1.05 (0.98, 1.13) 0.176 1.01 (0.94, 1.07) 0.864 3.3 Prognostic Value of Vascular Aging Phenotypes During a median follow-up of 4.18 years (IQR: 3.36–6.12), a total of 682 MACE (2.2%) were documented, including 371 MI (1.2%), 290 stroke events (0.9%), and 69 cardiovascular deaths (0.2%). Throughout the follow-up period, individuals in the EVA group consistently exhibited a higher risk of MACE compared to those in the other two groups (Fig. 2 ). After adjustment for age, sex, and Framingham Risk Score (Table 3 , Model 2), the EVA group showed a 97% increased risk of MACE (HR = 1.98, 95% CI: 1.44–2.73, P < 0.001), while the SUPERNOVA group had a 28% reduced risk (HR = 0.71, 95% CI: 0.54–0.92, P = 0.010). These associations remained robust after further adjustment for lifestyle and socioeconomic factors (Table 3 , Model 3). The endpoint of cardiovascular events was a composite of myocardial infarction, stroke, and cardiovascular mortality. Estimated cumulative cardiovascular events were stratified by vascular aging (VA) phenotypes from multivariable Cox proportional hazards regressions after adjustments for age and sex. Table 3 Hazard Ratios (HRs) for Vascular Aging Phenotypes for Clinical Outcomes. Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, and Framingham Risk Score; Model 3: Model 2 plus smoking status, drinking status, MET per week, and Townsend Deprivation Index. VA: vascular aging, HR: hazard ratio, CI: confidence interval, MACE: major adverse cardiovascular events, MI: Myocardial infarction, CVD: Cardiovascular disease, EVA: early vascular aging, SUPERNOVA: Supernormal vascular aging. Outcome VA phenotypes Model 1 Model 2 Model 3 HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value MACE EVA 2.28 (1.66, 3.13) p < 0.001 1.98 (1.44, 2.73) p < 0.001 2.00 (1.45, 2.75) p < 0.001 Normal VA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) SUPERNOVA 0.61 (0.47, 0.79) p < 0.001 0.71 (0.54, 0.92) 0.010 0.70 (0.54, 0.91) 0.009 MI EVA 2.34 (1.54, 3.56) p < 0.001 2.03 (1.33, 3.10) 0.001 2.06 (1.35, 3.16) p < 0.001 Normal VA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) SUPERNOVA 0.54 (0.37, 0.78) p < 0.001 0.63 (0.43, 0.91) 0.015 0.61 (0.42, 0.89) 0.011 Stroke EVA 2.10 (1.24, 3.57) 0.006 1.86 (1.09, 3.17) 0.022 1.82 (1.07, 3.11) 0.027 Normal VA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) SUPERNOVA 0.66 (0.45, 0.96) 0.029 0.75 (0.51, 1.09) 0.135 0.76 (0.52, 1.12) 0.162 CVD Mortality EVA 3.96 (1.47, 10.64) 0.006 3.49 (1.29, 9.43) 0.014 3.52 (1.29, 9.61) 0.014 Normal VA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) SUPERNOVA 1.07 (0.54, 2.10) 0.847 1.21 (0.61, 2.43) 0.582 1.21 (0.60, 2.45) 0.590 A similar pattern was observed for MI and stroke, with EVA associated with a significantly elevated risk and SUPERNOVA showing a protective association. For cardiovascular mortality, the EVA group also had a significantly increased risk, whereas the association for SUPERNOVA was not statistically significant. 3.4 Stratified Analyses by Baseline Cardiovascular Risk To evaluate the consistency of vascular aging phenotypes across different risk profiles, we performed stratified analyses based on Framingham Risk Score (FRS) categories (Fig. 3 ). The prognostic associations were most pronounced in the high-risk stratum (FRS > 20%), where both extreme phenotypes were strongly and significantly associated with MACE risk. In this cohort, the EVA phenotype was associated with a 2.69-fold increased risk (HR 2.69, 95% CI: 1.72–4.20; P < 0.001), while the SUPERNOVA phenotype conferred a significant 36% risk reduction (HR 0.64, 95% CI: 0.47–0.85; P = 0.003). Consistent point estimates were observed in the mid-risk stratum for EVA (HR 1.59, 95% CI: 0.93–2.71; P = 0.088) and SUPERNOVA (HR 0.74, 95% CI: 0.36–1.51; P = 0.405). In the low-risk stratum, the EVA phenotype also showed a trend toward increased MACE risk (HR 2.29, 95% CI: 0.93–5.63; P = 0.070), which did not reach statistical significance, likely attributable to the limited number of recorded events (n = 9). Hazard ratios and 95% confidence intervals are derived from the fully adjusted Cox proportional hazards model (Model 3), which controlled for chronological age, sex, BMI, lifestyle behaviors, and socioeconomic status. Reference lines represent Normal Vascular Aging within each FRS stratum. P-values are shown for each phenotype comparison. 3.5 Incremental Predictive Value of Vascular Age We evaluated whether incorporating vascular age into a conventional risk model—including age, sex, and the Framingham Risk Score—could enhance cardiovascular risk prediction. The addition of VA achieved a ΔC-statistic of 0.010 (95% CI: 0.003–0.017, P = 0.003) and an NRI of 13.10% (95% CI: 8.68–16.81% P < 0.001) for MACE (Table 4 ). Similar and statistically significant improvements were observed for myocardial infarction and stroke. These findings support the role of imaging-based biomarkers in refining risk stratification beyond traditional risk scores. Table 4 Incremental Predictive Value of Vascular Age in Risk Models: Difference in C-statistic, NRI, and IDI for Cardiovascular Outcomes. Conventional model: age, sex, and Framingham risk score at baseline. IDI: integrated discrimination improvement, NRI: net reclassification index. Outcome Difference in C-statistics IDI NRI (Continuous) Estimate (95% CI) P value Estimate (95% CI) P value Estimate (95% CI) P value MACE 0.010 (0.003, 0.017) 0.003 0.16 (0.06, 0.29) p < 0.001 13.10 (8.68, 16.81) p < 0.001 MI 0.013 (0.002, 0.023) 0.014 0.17 (0.06, 0.34) p < 0.001 15.20 (9.14, 20.14) p < 0.001 Stoke 0.006 (-0.002, 0.014) 0.116 0.05 (0.01, 0.13) p < 0.001 9.58 (3.25, 16.06) p < 0.001 CVD Mortality 0.000 (-0.012, 0.007) 0.976 0.00 (-0.01, 0.16) 0.468 3.52 (-9.74, 16.02) 0.587 4 Discussion 4.1 Summary of Major Findings In this large-scale prospective cohort study, we demonstrate that vascular age derived from cIMT is a robust and independent predictor of major adverse cardiovascular events (MACE). The most pivotal finding of our investigation is the identification of a significant discordance between biological vascular age and chronological age, which traditional risk scores such as the Framingham Risk Score (FRS) often fail to capture. Specifically, our data reveals a "risk score paradox," where individuals with the Early Vascular Aging (EVA) phenotype displayed significantly lower FRS compared to the Normal VA group yet bore a twofold increased risk of MACE. Conversely, the SUPERNOVA phenotype identified a subset of individuals who, despite having higher traditional risk scores, exhibited a significant reduction in MACE risk. Crucially, our stratified analysis by the Framingham Risk Score demonstrated that vascular age had markedly enhanced discriminative power in high-risk populations, effectively identifying those protected by intact vascular health while also capturing an elevated risk trend in low-risk individuals with the EVA phenotype. These findings suggest that integrating cIMT-derived vascular age into risk prediction models can effectively reclassify residual risk, particularly in populations where chronological age alone misrepresents true biological risk,, which align with several previous studies [ 9 , 10 , 14 , 15 ] . 4.2 Precision Risk Stratification Beyond Chronological Age Cardiovascular risk scores play a crucial role in assessing cardiovascular risk, predicting cardiovascular outcomes, and guiding personalized treatment. Since the introduction of the Framingham Risk Score, numerous cardiovascular scores have been developed and applied to different populations using various methods, providing essential support for clinical practice [ 13 , 16 – 18 ] . In these models, chronological age has consistently been a dominant determinant of risk, reflecting the progressive nature of atherosclerosis and arterial stiffness with advancing age. However, vascular aging does not always parallel chronological aging, and this discordance can lead to misclassification [ 19 ] . Some individuals classified as low risk may still experience cardiovascular events, while others with a high burden of conventional risk factors may never develop overt disease [ 20 – 22 ] . In our general population analysis, cIMT-derived vascular age phenotypes demonstrated strong independent predictive value. Even after adjusting for traditional risk factors and FRS, individuals with the EVA phenotype exhibited a two-fold increased risk of MACE, while those with the SUPERNOVA phenotype showed a significant risk reduction. This confirms that biological vascular age captures residual risk information that chronological age–based models fail to identify. Notably, the discriminative power of vascular age was most pronounced in the high-risk stratum (FRS > 20%). In this group—typically characterized by advanced age and multiple comorbidities—traditional scores often reach a "ceiling effect," limiting their ability to distinguish individuals who will actually develop events. Our cIMT-derived model effectively breaks this ceiling by identifying two distinct biological trajectories. We identified a subset of individuals who, despite their high estimated risk scores, exhibited the SUPERNOVA phenotype and a significant risk reduction (HR 0.64, 95% CI: 0.47–0.85). This suggests a remarkable "Vascular Resilience" to the detrimental effects of aging, providing an evidence base for potentially avoiding overtreatment in the elderly who remain structurally healthy. In sharp contrast, the EVA phenotype within this same high-risk stratum revealed a critical vulnerability, conferring a staggering 2.69-fold increased risk (95% CI: 1.72–4.20). This indicates a "synergistic amplification" of risk: when advanced chronological age intersects with accelerated structural aging, the cardiovascular burden exceeds the sum of its parts. Clinically, these individuals represent the "sickest of the sick" who face substantial residual risk, signaling an urgent need for aggressive therapeutic escalation. Conversely, in the low-risk stratum, a different form of discordance was observed. Although the association did not reach statistical significance (P = 0.070) likely due to the limited number of events, individuals with EVA showed a concerning trend toward increased risk (HR 2.29, 95% CI: 0.93–5.63). This signal suggests that structural vascular damage often precedes the elevation of clinical risk factors. In these "hidden high-risk" individuals, physiological youth masks early biological decay. Therefore, cIMT monitoring may offer a critical early warning for these individuals, facilitating a shift toward primordial prevention before traditional risk factors trigger an alarm. 4.3 Distinct Value of cIMT in Characterizing Extreme Vascular Phenotypes and Risk Stratification To better capture these extreme vascular phenotypes, our study utilized cIMT rather than PWV as a morphological biomarker. While PWV reflects arterial stiffness and indirect functional impacts of arterial elasticity, cIMT offers a more direct, stable measure of structural vascular changes—critical for understanding vascular aging [ 23 , 24 ] . Clinically, cIMT is a routine, non-invasive ultrasound screening tool for subclinical carotid damage, with advantages in accessibility, standardization, and reproducibility [ 25 ] . As an early indicator of carotid atherosclerosis distinct from advanced plaques, cIMT thickening links to metabolic and inflammatory processes driving endothelial dysfunction, fibrosis, and vascular aging [ 8 , 26 ] . Although cIMT is widely recognized as a surrogate endpoint for cardiovascular events and correlates strongly with traditional risk factors, its incremental prognostic value has been a subject of debate [ 27 , 28 ] . While some studies suggest limited added benefit over traditional models, others support its utility in risk classification [ 20 , 29 , 30 ] . Our findings resolve this ambiguity in the context of vascular aging phenotypes. Integrating cIMT-derived vascular age into conventional models significantly enhanced predictive accuracy, yielding a Net Reclassification Improvement (NRI) of 13.10% (95% CI: 8.68, 16.81, P < 0.001) for MACE. 4.4 Limitations This study has several limitations. First, the UK Biobank lacks concurrent laboratory measurements at the time of imaging acquisition, which limited our ability to incorporate biochemical markers into the model. However, we mitigated this by incorporating self-reported medical history and medication use as proxies for metabolic conditions. Second, due to the absence of HDL-C and other laboratory variables, we used a simplified version of the Framingham Risk Score that substitutes BMI for lipid measures. Previous studies have shown that this alternative version performs comparably for primary care [ 13 ] . Third, the relatively short median follow-up period may have led to an underestimation of long-term associations, particularly in the SUPERNOVA group, where event rates were lower and the delayed onset of cardiovascular events may occur. 5 Conclusion In this large, prospective cohort study, we demonstrated that cIMT-derived vascular aging phenotypes—EVA and SUPERNOVA—offer substantial incremental prognostic value beyond traditional cardiovascular risk models. While the vascular age showed limited improvement in predicting cardiovascular mortality, they significantly enhanced the risk stratification for MACE and non-fatal events, including myocardial infarction and stroke.Crucially, our stratified analyses revealed that the prognostic power of these phenotypes is most pronounced in individuals already classified as high-risk by the Framingham Risk Score. In this population, the EVA phenotype identifies a subset with a nearly 2.7-fold increased risk, while the SUPERNOVA phenotype identifies those with significant vascular resilience and lower-than-expected event rates. These findings highlight the utility of imaging-derived vascular age as a practical and interpretable tool for refining primary prevention strategies. By identifying individuals whose biological vascular age significantly deviates from their chronological age, clinicians can better guide early intervention for early vascular aging individuals and avoid potential over-treatment in structurally healthy older adults. Future research should evaluate the cost-effectiveness of this approach and its integration into routine clinical workflows. Declarations Disclosures The authors report no conflicts of interest. Funding This study was supported by Beijing Natural Science Foundation (7252190, L246054) and National Key Research and Development Program of China (2021YFC2500503). Author Contribution Z.D. conducted the primary statistical analyses, prepared the figures, and drafted the initial manuscript. F.F. contributed to methodological development and assisted with manuscript revision. H.C. and T.D. supported the development of analytical methods and provided input during model refinement. Z.Y. assisted with data management and project coordination. L.Y. and Y.L. contributed to data organization and cleaning. X.Q. and Y.Z. supervised all stages of the study, provided critical intellectual guidance, and oversaw manuscript development. All authors critically reviewed the manuscript, contributed intellectual content, approved the final version, and agreed to its submission. Acknowledgement This study was conducted using data from the UK Biobank Resource under application number 73201. We thank the UK Biobank participants and investigators for their invaluable contributions. Data Availability The data that support the findings of this study are available from UK Biobank. Restrictions apply to the availability of these data, which were used under license for the present study.Data are available for bona fide researchers upon reasonable request and successful application to UK Biobank (www.ukbiobank.ac.uk). This study was conducted under UK Biobank application number 73201. Clinical trial number: not applicable. References Mensah GA, Fuster V, Murray CJL, Roth GA. Global Burden of Cardiovascular Diseases and Risks Collaborators. Global burden of cardiovascular diseases and risks, 1990–2022. J Am Coll Cardiol. 2023;82:2350–473. https://doi.org/10.1016/j.jacc.2023.11.007 . Nilsson PM. Early Vascular Aging in Hypertension. Front Cardiovasc Med. 2020;7. https://doi.org/10.3389/fcvm.2020.00006 . Nie C, Li Y, Li R, Yan Y, Zhang D, Li T, et al. Distinct biological ages of organs and systems identified from a multi-omics study. Cell Rep. 2022;38:110459. https://doi.org/10.1016/j.celrep.2022.110459 . Lorenzatti D, Piña P, Huang D, Apple SJ, Fernandez-Hazim C, Ippolito P, et al. Interaction between risk factors, coronary calcium, and CCTA plaque characteristics in patients aged 18–45 years. Eur Heart J Cardiovasc Imaging. 2024;25:1071–82. https://doi.org/10.1093/ehjci/jeae094 . Xu T, Zhang Y, Zhou Y, Yin L, Min X, Wu S, et al. Association of vascular aging phenotypes with adverse clinical outcomes in the Chinese population: A multicentre study. Clin Interv Aging. 2025;20:403–14. https://doi.org/10.2147/CIA.S485597 . Ungvari Z, Tarantini S, Sorond F, Merkely B, Csiszar A. Mechanisms of Vascular Aging, A Geroscience Perspective: JACC Focus Seminar. J Am Coll Cardiol. 2020;75:931–41. https://doi.org/10.1016/j.jacc.2019.11.061 . Liu L, Xu Q, Xu X, Peng J, Lin M, Chen C-E, et al. Evaluation of the ultrasonic arterial measurement and analysis system for predicting 10-year atherosclerotic cardiovascular disease risk in patients with type 2 diabetes. Quant Imaging Med Surg. 2025;15:4921–34. https://doi.org/10.21037/qims-24-1620 . Pewowaruk RJ, Tedla Y, Korcarz CE, Tattersall MC, Stein JH, Chesler NC, et al. Carotid artery stiffening with aging: Structural versus load-dependent mechanisms in MESA (the multi-ethnic study of atherosclerosis). Hypertens Dallas Tex 1979. 2022;79:150–8. https://doi.org/10.1161/HYPERTENSIONAHA.121.18444 . Bruno RM, Nilsson PM, Engström G, Wadström BN, Empana J-P, Boutouyrie P, et al. Early and Supernormal Vascular Aging Clinical Characteristics and Association With Incident Cardiovascular Events. Hypertens Dallas Tex 1979. 2020;76:1616–24. https://doi.org/10.1161/HYPERTENSIONAHA.120.14971 . Zuo Y, Chen S, Tian X, Wang P, Wu S, Wang A. Association of Vascular Aging With Cardiovascular Disease in Middle-Aged Chinese People: A Prospective Cohort Study. JACC Asia. 2023;3:895–904. https://doi.org/10.1016/j.jacasi.2023.07.014 . Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779 . Coffey S, Lewandowski AJ, Garratt S, Meijer R, Lynum S, Bedi R, et al. Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment. Eur J Prev Cardiol. 2017;24:1799–806. https://doi.org/10.1177/2047487317732273 . D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743–53. https://doi.org/10.1161/CIRCULATIONAHA.107.699579 . Cao F, Xu Z, Li X-X, Fu Z-Y, Han R-Y, Zhang J-L, et al. Trends and cross-country inequalities in the global burden of osteoarthritis, 1990–2019: A population-based study. Ageing Res Rev. 2024;99:102382. https://doi.org/10.1016/j.arr.2024.102382 . Dong T, Fan F, Chen H, Dong Z, Yang L, Luo Y, et al. Comparison between vascular age based on brachial-ankle pulse wave velocity or carotid-femoral pulse wave velocity. Hypertens Res Off J Jpn Soc Hypertens. 2025. https://doi.org/10.1038/s41440-025-02281-1 . Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S1–45. https://doi.org/10.1161/01.cir.0000437738.63853.7a . SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42:2439–54. https://doi.org/10.1093/eurheartj/ehab309 . Yang X, Li J, Hu D, Chen J, Li Y, Huang J, et al. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China). Circulation. 2016;134:1430–40. https://doi.org/10.1161/CIRCULATIONAHA.116.022367 . DeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med. 2015;162:266–75. https://doi.org/10.7326/M14-1281 . Willeit P, Tschiderer L, Allara E, Reuber K, Seekircher L, Gao L, et al. Carotid Intima-Media Thickness Progression as Surrogate Marker for Cardiovascular Risk: Meta-Analysis of 119 Clinical Trials Involving 100 667 Patients. Circulation. 2020;142:621–42. https://doi.org/10.1161/CIRCULATIONAHA.120.046361 . Dhindsa DS, Sandesara PB, Shapiro MD, Wong ND. The evolving understanding and approach to residual cardiovascular risk management. Front Cardiovasc Med. 2020;7:88. https://doi.org/10.3389/fcvm.2020.00088 . Naqvi TZ, Mendoza F, Rafii F, Gransar H, Guerra M, Lepor N, et al. High prevalence of ultrasound detected carotid atherosclerosis in subjects with low framingham risk score: Potential implications for screening for subclinical atherosclerosis. J Am Soc Echocardiogr Off Publ Am Soc Echocardiogr. 2010;23:809–15. https://doi.org/10.1016/j.echo.2010.05.005 . Sigl M, Winter L, Schumacher G, Helmke SC, Shchetynska-Marinova T, Amendt K, et al. Comparison of Functional and Morphological Estimates of Vascular Age. Vivo Athens Greece. 2023;37:2178–87. https://doi.org/10.21873/invivo.13317 . Cuspidi C, Sala C, Tadic M, Grassi G, Mancia G. Carotid intima-media thickness and anti-hypertensive treatment: Focus on angiotensin II receptor blockers. Pharmacol Res. 2018;129:20–6. https://doi.org/10.1016/j.phrs.2018.01.007 . Zhang L, Fan F, Qi L, Jia J, Yang Y, Li J, et al. The association between carotid intima-media thickness and new-onset hypertension in a Chinese community-based population. BMC Cardiovasc Disord. 2019;19:269. https://doi.org/10.1186/s12872-019-1266-1 . Cheng DCY, Climie RE, Shu M, Grieve SM, Kozor R, Figtree GA. Vascular aging and cardiovascular disease: Pathophysiology and measurement in the coronary arteries. Front Cardiovasc Med. 2023;10:1206156. https://doi.org/10.3389/fcvm.2023.1206156 . Bots ML, Groenewegen KA, Anderson TJ, Britton AR, Dekker JM, Engström G, et al. Common carotid intima-media thickness measurements do not improve cardiovascular risk prediction in individuals with elevated blood pressure: The USE-IMT collaboration. Hypertens Dallas Tex 1979. 2014;63:1173–81. https://doi.org/10.1161/HYPERTENSIONAHA.113.02683 . den Ruijter HM, Peters SaE, Groenewegen KA, Anderson TJ, Britton AR, Dekker JM, et al. Common carotid intima-media thickness does not add to framingham risk score in individuals with diabetes mellitus: The USE-IMT initiative. Diabetologia. 2013;56:1494–502. https://doi.org/10.1007/s00125-013-2898-9 . Hald EM, Lijfering WM, Mathiesen EB, Johnsen SH, Løchen M-L, Njølstad I, et al. Carotid atherosclerosis predicts future myocardial infarction but not venous thromboembolism: The tromsø study. Arterioscler Thromb Vasc Biol. 2014;34:226–30. https://doi.org/10.1161/ATVBAHA.113.302162 . Baldassarre D, Veglia F, Hamsten A, Humphries SE, Rauramaa R, De Faire U, et al. Progression of carotid intima-media thickness as predictor of vascular events: Results from the IMPROVE study. Arterioscler Thromb Vasc Biol. 2013;33:2273–9. https://doi.org/10.1161/ATVBAHA.113.301844 . Zanchetti A, Crepaldi G, Bond MG, Gallus GV, Veglia F, Ventura A, et al. Systolic and pulse blood pressures (but not diastolic blood pressure and serum cholesterol) are associated with alterations in carotid intima-media thickness in the moderately hypercholesterolaemic hypertensive patients of the plaque hypertension lipid lowering italian study. PHYLLIS study group. J Hypertens. 2001;19:79–88. https://doi.org/10.1097/00004872-200101000-00011 . Mancusi C, Gerdts E, Losi MA, D’Amato A, Manzi MV, Canciello G, et al. Differential effect of obesity on prevalence of cardiac and carotid target organ damage in hypertension (the campania salute network). Int J Cardiol. 2017;244:260–4. https://doi.org/10.1016/j.ijcard.2017.06.045 . Holewijn S, den Heijer M, Swinkels DW, Stalenhoef AFH, de Graaf J. The metabolic syndrome and its traits as risk factors for subclinical atherosclerosis. J Clin Endocrinol Metab. 2009;94:2893–9. https://doi.org/10.1210/jc.2009-0084 . Willeit P, Tschiderer L, Allara E, Reuber K, Seekircher L, Gao L, et al. Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: Meta-analysis of 119 clinical trials involving 100 667 patients. Circulation. 2020;142:621–42. https://doi.org/10.1161/CIRCULATIONAHA.120.046361 . Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 08 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8822772","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592930410,"identity":"9c20f564-a9cb-4081-9560-7758062d3835","order_by":0,"name":"Zhichen Dong","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhichen","middleName":"","lastName":"Dong","suffix":""},{"id":592930411,"identity":"4a675931-013b-4a72-b850-97f0798e2a45","order_by":1,"name":"Fangfang Fan","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Fan","suffix":""},{"id":592930412,"identity":"a20d9d80-ed28-4e04-a072-13d519f57c8d","order_by":2,"name":"Hongyu Chen","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Chen","suffix":""},{"id":592930413,"identity":"3060e5e9-3d11-4107-bb62-1109219e1c1b","order_by":3,"name":"Tianhui Dong","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianhui","middleName":"","lastName":"Dong","suffix":""},{"id":592930414,"identity":"f7db5732-11f9-4b3e-8b57-1da0d8552cdb","order_by":4,"name":"Leyuan Yang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leyuan","middleName":"","lastName":"Yang","suffix":""},{"id":592930415,"identity":"91308e1c-8861-4914-87a5-f0a2ba76d0ac","order_by":5,"name":"Yiming Luo","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Luo","suffix":""},{"id":592930416,"identity":"5d4282a4-041f-4c41-9db5-b7a3faa2e2df","order_by":6,"name":"Ziliang Ye","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziliang","middleName":"","lastName":"Ye","suffix":""},{"id":592930417,"identity":"7de0d8d4-8061-4f86-947f-940738f92286","order_by":7,"name":"Xianhui Qin","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianhui","middleName":"","lastName":"Qin","suffix":""},{"id":592930418,"identity":"b9d536f1-8671-4605-8bb5-30fb3e158215","order_by":8,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDCCAwxsDAwGEjz87I2NDz8Qr6XAQk6y53CzsQTxWj5UGBvcSG8T4CFGB9/t488e8xhIJM6c+bCNQYLBTk63gYAWyXMJ6cYgLf3SiW0PChiSjc0OENBicIbhmDTYltmJ7QYSDAcStxHWwtgG1rLh5sE2CR7itDCzgbQAvc9IpBbJM2xsknMMJICBnAgMZAMi/MJ3hv2ZxJs/dcCoPP7w4YcKOzmCWkCACREdBkQoBwHGH0QqHAWjYBSMghEKADmVQPwV286yAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-08 15:39:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8822772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8822772/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504378,"identity":"b54ed76a-bdc3-444c-8b17-9e0e7b2723a1","added_by":"auto","created_at":"2026-02-26 13:19:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130479,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flowchart Illustrating Participant Selection from the UK Biobank for Vascular Aging Analysis Based on cIMT Measurements\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8822772/v1/6e484fa1a2884bf043cef8d5.jpg"},{"id":103504730,"identity":"f79c9452-0011-4d3c-890f-b546b52df41b","added_by":"auto","created_at":"2026-02-26 13:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79210,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of Major Adverse Cardiovascular Events (MACE) Stratified by Vascular Aging Phenotypes.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-8822772/v1/2ddb5b06b55652cc3eb29790.png"},{"id":103167090,"identity":"8ce2e005-317f-472c-8f2b-f5de6a0d234a","added_by":"auto","created_at":"2026-02-22 12:43:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131238,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between MACE and vascular aging phenotypes and stratified by Framingham Risk Score.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8822772/v1/9fbb15d4cc9155c43d77dbc4.jpg"},{"id":103510755,"identity":"322bf743-2008-4f8f-bc37-a99b3e7b9e83","added_by":"auto","created_at":"2026-02-26 14:06:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1409721,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8822772/v1/65cce5d8-bb3a-4595-b26f-3f04f6f028a6.pdf"},{"id":103167087,"identity":"389c6709-b771-481b-bec8-1204b63b3534","added_by":"auto","created_at":"2026-02-22 12:43:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26951,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8822772/v1/9829a381ba932b8f0678c7cd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carotid intima-media thickness stratifies early and supernormal vascular aging phenotypes in UK biobank: Implications for cardiovascular risk prediction","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, placing a considerable burden on global health systems\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Early identification of individuals at elevated cardiovascular risk is essential for effective prevention strategies. While conventional risk prediction models provide practical estimates based on traditional factors such as age, sex, blood pressure, and lipid profiles, they often fail to reflect the biological complexity and heterogeneity of vascular aging at the individual level\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronological age is a key component of most risk models, yet it does not always correspond to the true physiological state of the vascular system. Increasing evidence highlights that individuals of the same chronological age can exhibit markedly different vascular conditions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may lead to risk underestimation in younger individuals with advanced vascular damage and overestimation in older adults with preserved vascular health. To address these limitations, the concept of vascular age has been proposed to capture cumulative structural and functional changes in the arteries over time\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Combining traditional cardiovascular risk factors with vascular biomarkers has shown promise in improving the accuracy of risk prediction beyond traditional models alone\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Among available vascular biomarkers, pulse wave velocity (PWV) and carotid intima-media thickness (cIMT) are commonly used to assess vascular aging. While PWV is a sensitive indicator of arterial stiffness, it is influenced by acute hemodynamic changes, limiting its reproducibility. In contrast, cIMT provides a stable structural measure of arterial remodeling and is widely recognized as a marker of subclinical atherosclerosis and a predictor of future cardiovascular events\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBuilding upon these measurements, the concept of vascular Δ-age\u0026mdash;defined as the difference between vascular age and chronological age\u0026mdash;has been introduced to quantify deviations from normal vascular aging. Furthermore, phenotypes such as early vascular aging (EVA) and supernormal vascular aging (SUPERNOVA) have been proposed to identify individuals with accelerated or delayed vascular aging\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, the application of cIMT-based Δ-age in defining these phenotypes and its prognostic relevance remains underexplored.\u003c/p\u003e \u003cp\u003eIn this study, we leveraged data from the UK Biobank, a large population-based cohort, to construct a vascular age model derived from cIMT and conventional cardiovascular risk factors. Based on deviations between vascular and chronological age, we identified EVA and SUPERNOVA phenotypes. We further examined their associations with major cardiovascular events and evaluated whether incorporating vascular age could improve risk prediction beyond traditional models.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThe UK Biobank (UKB) is a prospective cohort study that collected questionnaire data, physical measurements, and biological samples from individuals across the United Kingdom. The UK Biobank received ethical approval from the North West Multicentre Research Ethics Committee (REC reference: 11/NW/03820) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. All participants provided written informed consent. In this study, we included participants from the UKB who underwent carotid ultrasound examinations from 2014. As shown in the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we excluded participants with myocardial infarction or stroke at baseline and cleared participants with important missing baseline data for the final analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurements of cIMT and other covariates at baseline\u003c/h2\u003e \u003cp\u003eIn the UK Biobank study, carotid intima-media thickness (cIMT) was automatically measured at the far wall of the distal common carotid artery, approximately 1 cm proximal to the carotid bifurcation, at two predefined insonation angles on both the left and right sides (150\u0026deg; and 120\u0026deg; on the right; 210\u0026deg; and 240\u0026deg; on the left). For each angle, the maximum, mean, and minimum cIMT values were obtained across three cardiac cycles using a validated automated software (UKB data fields 22670\u0026ndash;22681). In order to better reflect the average level of aging in the arteries, the overall mean of the maximum cIMT values at different angles was utilized to determine the effect size. Detailed information regarding the cIMT measurement is available in the referenced protocol \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnthropometric measurements and questionnaire data collected concurrently with the carotid ultrasound examination served as baseline information. Age and gender information were obtained from the central registry of recruitment. Body mass index (BMI) and waist circumference were measured during the assessment center visit. Current smoking and alcohol use status were determined based on self-reported data collected via a touchscreen questionnaire. Systolic and diastolic blood pressure were automatically measured using an electronic monitor, and the average of two measurements was recorded as the final blood pressure value. In light of the lack of contemporaneous serological data accompanying the imaging examinations in the UKB dataset, laboratory measurements of blood glucose and cholesterol levels were substituted with clinical histories of diabetes and hypercholesterolemia, as well as information regarding the use of insulin and cholesterol-lowering medications. Data concerning current treatments, including cholesterol-lowering medications, blood pressure medications, and insulin use, were collected through self-reports on the touchscreen questionnaire. Hypertension was defined as a self-reported medical history confirmed through oral interviews, the use of antihypertensive medications, or elevated blood pressure (systolic\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg). Diabetes mellitus and hypercholesterolemia were identified based on a combination of self-reported history and corresponding medication use. 10-year cardiovascular risk was estimated using a simplified version of the Framingham Risk Score, in which BMI was used in place of HDL-C due to missing laboratory lipid data. According to the risk of 10% and 20%, the population can be divided into low, medium and high risk. The sex-specific algorithm was derived from the Framingham Heart Study cohort\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Vascular age modeling and definitions of vascular aging phenotypes\u003c/h2\u003e \u003cp\u003eVascular age was defined as the predicted age derived from a multivariable regression model that included classic cardiovascular risk factors and cIMT. The Variance Inflation Factor (VIF) was utilized to assess multicollinearity, ensuring that no significant collinearity issues were present among the variables. Subsequently, lasso regression was employed for variable selection. The final models incorporated sex, smoking status, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, comorbidities of hypertension, diabetes, hypercholesterolemia, and the use of antihypertensive, cholesterol-lowering, and insulin medications. Since multiple continuous variables exhibited nonlinear relationships with age, we employed smoothing splines within a generalized additive model framework to enhance the predictive performance of the model.\u003c/p\u003e \u003cp\u003eΔ-age was calculated by subtracting chronological age from vascular age. To define early EVA and SUPERNOVA, we used the 10th and 90th percentiles of ∆-age as the cut-offs. Individuals with ∆-age above the 90th percentile are defined as EVA, while individuals below the 10th percentile are considered as SUPERNOVA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcome ascertainment\u003c/h2\u003e \u003cp\u003eThe primary outcome of our analysis was the occurrence of major adverse cardiovascular events (MACE), defined as a composite endpoint comprising the first non-fatal stroke, the first non-fatal myocardial infarction (MI), or cardiovascular mortality. We identified the components of MACE from Hospital Episode Statistics (HES) data and death registry data, employing International Classification of Diseases (ICD-9 and ICD-10) codes. A list of codes used to define outcomes is attached for details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analyses\u003c/h2\u003e \u003cp\u003eIn the baseline characteristics of the participants, categorical variables are represented as frequencies (percentages), while continuous variables are reported as medians with interquartile ranges (IQRs), due to the non-normal distribution of variables. Chi-square tests were used for categorical variables, and Kruskal-Wallis tests were applied to continuous variables to compare intergroup differences among the three vascular aging categories: EVA, normal vascular aging, and SUPERNOVA.\u003c/p\u003e \u003cp\u003eTo investigate the association between EVA/SUPERNOVA and cardiovascular events, a Cox proportional hazards regression analysis was conducted. After checking the assumption of proportional hazards (Schoenfeld residuals), two models were constructed. Model 1 adjusted for age (as a continuous variable) and sex (as a categorical variable, male vs. female). Model 2 further incorporated the Framingham cardiovascular risk score. Model 3 included additional covariates reflecting lifestyle behaviors and social deprivation. Additionally, we evaluated the risk of cardiovascular outcomes when cIMT and Δ-age was treated as a continuous variable.\u003c/p\u003e \u003cp\u003eLastly, we examined the incremental value of vascular age and cIMT in comparison to traditional cardiovascular risk models by employing the C-index, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). All statistical analyses were conducted using R software version 4.3.3, with a two-sided P-value of less than 0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the Study Population and Vascular Aging Distribution\u003c/h2\u003e \u003cp\u003eFrom the initially enrolled 52,886 participants with carotid ultrasound data, we excluded 3,147 participants with myocardial infarction or stroke, and 18,428 participants due to missing critical variables. Eventually, 31,311 patients were included in the final analysis. The median age of the population was 64.0 (IQR: 58.0\u0026ndash;70.0) years, and 49.2% were male. The median of vascular age was 63.75 (IQR: 60.44\u0026ndash;67.12) years old. The median cIMT was 779.25 (IQR: 686.75\u0026ndash;885.25) \u0026micro;m. In terms of baseline comorbidities, the population included 22.6% with hypertension, 14.3% with hyperlipidemia, and 2.7% with diabetes mellitus (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were classified into three phenotypes based on Δ-age\u0026mdash;EVA, Normal VA, and SUPERNOVA. EVA was defined as Δ-age greater than 8.09 years, and SUPERNOVA as Δ-age less than \u0026minus;\u0026thinsp;8.08 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Study Participants According to Vascular Aging Phenotypes. Values are median [IQR] or n (%). VA\u0026thinsp;=\u0026thinsp;vascular aging, Δ-age\u0026thinsp;=\u0026thinsp;chronological age minus vascular age; cIMT\u0026thinsp;=\u0026thinsp;carotid intima-media thickness; BMI\u0026thinsp;=\u0026thinsp;body mass index; SBP\u0026thinsp;=\u0026thinsp;systolic blood pressure; DBP\u0026thinsp;=\u0026thinsp;diastolic blood pressure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal VA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSUPERNOVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronological age, y (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.00 [58.00, 70.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.00 [50.00, 56.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.00 [59.00, 69.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.00 [71.00, 77.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15397 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1683 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12104 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1610 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular age, y (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.75 [60.44, 67.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.53 [61.05, 66.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.86 [60.33, 67.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.19 [60.34, 65.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ-age, y (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06 [-4.36, 4.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.05 [8.97, 11.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06 [-3.43, 3.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.21 [-12.03, -8.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecIMT, mm (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e779.25 [686.75, 885.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e789.00 [702.50, 885.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e779.25 [683.25, 885.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e760.25 [683.25, 857.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e843 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent alcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29383 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2915 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23549 (94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2919 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMET per week, \u0026times;10\u003csup\u003e3\u003c/sup\u003e (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19 [1.12, 3.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03 [1.04, 3.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20 [1.13, 3.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.22 [1.125, 3,98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend Deprivation Index (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.63 [-3.89, -0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.38 [-3.67, -0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.64 [-3.90, -0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.76 [-3.98, -0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2 (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.78 [23.42, 28.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.81 [23.43, 28.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.77 [23.41, 28.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.85 [23.48, 28.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.00 [79.00, 96.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.00 [80.00, 96.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.00 [79.00, 96.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.00 [79.00, 96.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.50 [126.00, 151.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.50 [126.50, 150.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.00 [126.00, 151.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.50 [125.00, 148.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.50 [72.00, 85.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.00 [72.00, 85.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.50 [72.00, 85.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.00 [72.50, 86.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e830 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e674 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7063 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5768 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e622 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4463 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3667 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e385 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6986 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e639 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5806 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e541 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7041 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5750 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e609 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin use (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramingham Risk Score (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.81 [10.14, 29.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.48 [6.89, 17.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.05 [10.19, 29.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.57 [15.85, 38.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs expected, cIMT levels varied across the three groups, consistent with the vascular aging phenotypes. The median cIMT in the EVA group was 789.00 \u0026micro;m (IQR: 702.50\u0026ndash;885.25), compared to 760.25 \u0026micro;m (IQR: 683.25\u0026ndash;857.88) in the SUPERNOVA group. In contrast, conventional cardiovascular risk factors, including BMI, blood pressure, and baseline comorbidities, showed only modest differences across groups. Interestingly, the traditional ASCVD risk score, measured by the Framingham Risk Score, exhibited a paradoxical pattern: individuals in the EVA group had significantly lower scores, while those in the SUPERNOVA group had significantly higher scores, compared with the Normal VA group. This counterintuitive trend may be partly attributed to differences in chronological age among the three groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association Between Continuous Vascular Aging Metrics and Cardiovascular Risk\u003c/h2\u003e \u003cp\u003eWe next assessed the predictive value of continuous vascular aging metrics, including cIMT and Δ-age. In the fully adjusted model, each 1-year increase in Δ-age was associated with a 7% higher risk (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.05\u0026ndash;1.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar significant associations were also observed for myocardial infarction and stroke, reinforcing the robustness of Δ-age in predicting a range of cardiovascular outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard Ratios (HRs) for Δ-age for Cardiovascular Outcomes. Values are HR (95%CI). Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, and Framingham Risk Score; Model 3: Model 2 plus smoke status, drinking status, MET per week, and Townsend Deprivation Index. HR: hazard ratio, CI: confidence interval, MACE: major adverse cardiovascular events, MI: Myocardial infarction, CVD: Cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRs (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRs (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHRs (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.06, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.05, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (1.04, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e`MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.08, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.08, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (1.06, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.03, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.99, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.01, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.96, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.98, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.94, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Prognostic Value of Vascular Aging Phenotypes\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 4.18 years (IQR: 3.36\u0026ndash;6.12), a total of 682 MACE (2.2%) were documented, including 371 MI (1.2%), 290 stroke events (0.9%), and 69 cardiovascular deaths (0.2%). Throughout the follow-up period, individuals in the EVA group consistently exhibited a higher risk of MACE compared to those in the other two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjustment for age, sex, and Framingham Risk Score (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 2), the EVA group showed a 97% increased risk of MACE (HR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.44\u0026ndash;2.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the SUPERNOVA group had a 28% reduced risk (HR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.54\u0026ndash;0.92, P\u0026thinsp;=\u0026thinsp;0.010). These associations remained robust after further adjustment for lifestyle and socioeconomic factors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe endpoint of cardiovascular events was a composite of myocardial infarction, stroke, and cardiovascular mortality. Estimated cumulative cardiovascular events were stratified by vascular aging (VA) phenotypes from multivariable Cox proportional hazards regressions after adjustments for age and sex.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard Ratios (HRs) for Vascular Aging Phenotypes for Clinical Outcomes. Model 1: adjusted for age and sex; Model 2: adjusted for age, sex, and Framingham Risk Score; Model 3: Model 2 plus smoking status, drinking status, MET per week, and Townsend Deprivation Index. VA: vascular aging, HR: hazard ratio, CI: confidence interval, MACE: major adverse cardiovascular events, MI: Myocardial infarction, CVD: Cardiovascular disease, EVA: early vascular aging, SUPERNOVA: Supernormal vascular aging.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVA phenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28 (1.66, 3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98 (1.44, 2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00 (1.45, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal VA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPERNOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.47, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (0.54, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.54, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34 (1.54, 3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.03 (1.33, 3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.06 (1.35, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal VA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPERNOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54 (0.37, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.43, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61 (0.42, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10 (1.24, 3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86 (1.09, 3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82 (1.07, 3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal VA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPERNOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.45, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75 (0.51, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.52, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCVD Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96 (1.47, 10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.49 (1.29, 9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.52 (1.29, 9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal VA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPERNOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.54, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (0.61, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21 (0.60, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA similar pattern was observed for MI and stroke, with EVA associated with a significantly elevated risk and SUPERNOVA showing a protective association. For cardiovascular mortality, the EVA group also had a significantly increased risk, whereas the association for SUPERNOVA was not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Stratified Analyses by Baseline Cardiovascular Risk\u003c/h2\u003e \u003cp\u003eTo evaluate the consistency of vascular aging phenotypes across different risk profiles, we performed stratified analyses based on Framingham Risk Score (FRS) categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The prognostic associations were most pronounced in the high-risk stratum (FRS\u0026thinsp;\u0026gt;\u0026thinsp;20%), where both extreme phenotypes were strongly and significantly associated with MACE risk. In this cohort, the EVA phenotype was associated with a 2.69-fold increased risk (HR 2.69, 95% CI: 1.72\u0026ndash;4.20; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the SUPERNOVA phenotype conferred a significant 36% risk reduction (HR 0.64, 95% CI: 0.47\u0026ndash;0.85; P\u0026thinsp;=\u0026thinsp;0.003). Consistent point estimates were observed in the mid-risk stratum for EVA (HR 1.59, 95% CI: 0.93\u0026ndash;2.71; P\u0026thinsp;=\u0026thinsp;0.088) and SUPERNOVA (HR 0.74, 95% CI: 0.36\u0026ndash;1.51; P\u0026thinsp;=\u0026thinsp;0.405). In the low-risk stratum, the EVA phenotype also showed a trend toward increased MACE risk (HR 2.29, 95% CI: 0.93\u0026ndash;5.63; P\u0026thinsp;=\u0026thinsp;0.070), which did not reach statistical significance, likely attributable to the limited number of recorded events (n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHazard ratios and 95% confidence intervals are derived from the fully adjusted Cox proportional hazards model (Model 3), which controlled for chronological age, sex, BMI, lifestyle behaviors, and socioeconomic status. Reference lines represent Normal Vascular Aging within each FRS stratum. P-values are shown for each phenotype comparison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Incremental Predictive Value of Vascular Age\u003c/h2\u003e \u003cp\u003eWe evaluated whether incorporating vascular age into a conventional risk model\u0026mdash;including age, sex, and the Framingham Risk Score\u0026mdash;could enhance cardiovascular risk prediction. The addition of VA achieved a ΔC-statistic of 0.010 (95% CI: 0.003\u0026ndash;0.017, P\u0026thinsp;=\u0026thinsp;0.003) and an NRI of 13.10% (95% CI: 8.68\u0026ndash;16.81% P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for MACE (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar and statistically significant improvements were observed for myocardial infarction and stroke. These findings support the role of imaging-based biomarkers in refining risk stratification beyond traditional risk scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental Predictive Value of Vascular Age in Risk Models: Difference in C-statistic, NRI, and IDI for Cardiovascular Outcomes. Conventional model: age, sex, and Framingham risk score at baseline. IDI: integrated discrimination improvement, NRI: net reclassification index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDifference in C-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNRI (Continuous)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010 (0.003, 0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.06, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.10 (8.68, 16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013 (0.002, 0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.06, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.20 (9.14, 20.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006 (-0.002, 0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (0.01, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.58 (3.25, 16.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (-0.012, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (-0.01, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52 (-9.74, 16.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary of Major Findings\u003c/h2\u003e \u003cp\u003eIn this large-scale prospective cohort study, we demonstrate that vascular age derived from cIMT is a robust and independent predictor of major adverse cardiovascular events (MACE). The most pivotal finding of our investigation is the identification of a significant discordance between biological vascular age and chronological age, which traditional risk scores such as the Framingham Risk Score (FRS) often fail to capture. Specifically, our data reveals a \"risk score paradox,\" where individuals with the Early Vascular Aging (EVA) phenotype displayed significantly lower FRS compared to the Normal VA group yet bore a twofold increased risk of MACE. Conversely, the SUPERNOVA phenotype identified a subset of individuals who, despite having higher traditional risk scores, exhibited a significant reduction in MACE risk. Crucially, our stratified analysis by the Framingham Risk Score demonstrated that vascular age had markedly enhanced discriminative power in high-risk populations, effectively identifying those protected by intact vascular health while also capturing an elevated risk trend in low-risk individuals with the EVA phenotype. These findings suggest that integrating cIMT-derived vascular age into risk prediction models can effectively reclassify residual risk, particularly in populations where chronological age alone misrepresents true biological risk,, which align with several previous studies \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Precision Risk Stratification Beyond Chronological Age\u003c/h2\u003e \u003cp\u003eCardiovascular risk scores play a crucial role in assessing cardiovascular risk, predicting cardiovascular outcomes, and guiding personalized treatment. Since the introduction of the Framingham Risk Score, numerous cardiovascular scores have been developed and applied to different populations using various methods, providing essential support for clinical practice\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In these models, chronological age has consistently been a dominant determinant of risk, reflecting the progressive nature of atherosclerosis and arterial stiffness with advancing age. However, vascular aging does not always parallel chronological aging, and this discordance can lead to misclassification\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Some individuals classified as low risk may still experience cardiovascular events, while others with a high burden of conventional risk factors may never develop overt disease\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our general population analysis, cIMT-derived vascular age phenotypes demonstrated strong independent predictive value. Even after adjusting for traditional risk factors and FRS, individuals with the EVA phenotype exhibited a two-fold increased risk of MACE, while those with the SUPERNOVA phenotype showed a significant risk reduction. This confirms that biological vascular age captures residual risk information that chronological age\u0026ndash;based models fail to identify.\u003c/p\u003e \u003cp\u003eNotably, the discriminative power of vascular age was most pronounced in the high-risk stratum (FRS\u0026thinsp;\u0026gt;\u0026thinsp;20%). In this group\u0026mdash;typically characterized by advanced age and multiple comorbidities\u0026mdash;traditional scores often reach a \"ceiling effect,\" limiting their ability to distinguish individuals who will actually develop events. Our cIMT-derived model effectively breaks this ceiling by identifying two distinct biological trajectories. We identified a subset of individuals who, despite their high estimated risk scores, exhibited the SUPERNOVA phenotype and a significant risk reduction (HR 0.64, 95% CI: 0.47\u0026ndash;0.85). This suggests a remarkable \"Vascular Resilience\" to the detrimental effects of aging, providing an evidence base for potentially avoiding overtreatment in the elderly who remain structurally healthy. In sharp contrast, the EVA phenotype within this same high-risk stratum revealed a critical vulnerability, conferring a staggering 2.69-fold increased risk (95% CI: 1.72\u0026ndash;4.20). This indicates a \"synergistic amplification\" of risk: when advanced chronological age intersects with accelerated structural aging, the cardiovascular burden exceeds the sum of its parts. Clinically, these individuals represent the \"sickest of the sick\" who face substantial residual risk, signaling an urgent need for aggressive therapeutic escalation.\u003c/p\u003e \u003cp\u003eConversely, in the low-risk stratum, a different form of discordance was observed. Although the association did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.070) likely due to the limited number of events, individuals with EVA showed a concerning trend toward increased risk (HR 2.29, 95% CI: 0.93\u0026ndash;5.63). This signal suggests that structural vascular damage often precedes the elevation of clinical risk factors. In these \"hidden high-risk\" individuals, physiological youth masks early biological decay. Therefore, cIMT monitoring may offer a critical early warning for these individuals, facilitating a shift toward primordial prevention before traditional risk factors trigger an alarm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.3 Distinct Value of cIMT in Characterizing Extreme Vascular Phenotypes and Risk Stratification\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo better capture these extreme vascular phenotypes, our study utilized cIMT rather than PWV as a morphological biomarker. While PWV reflects arterial stiffness and indirect functional impacts of arterial elasticity, cIMT offers a more direct, stable measure of structural vascular changes\u0026mdash;critical for understanding vascular aging \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Clinically, cIMT is a routine, non-invasive ultrasound screening tool for subclinical carotid damage, with advantages in accessibility, standardization, and reproducibility \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. As an early indicator of carotid atherosclerosis distinct from advanced plaques, cIMT thickening links to metabolic and inflammatory processes driving endothelial dysfunction, fibrosis, and vascular aging \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough cIMT is widely recognized as a surrogate endpoint for cardiovascular events and correlates strongly with traditional risk factors, its incremental prognostic value has been a subject of debate \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. While some studies suggest limited added benefit over traditional models, others support its utility in risk classification \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Our findings resolve this ambiguity in the context of vascular aging phenotypes. Integrating cIMT-derived vascular age into conventional models significantly enhanced predictive accuracy, yielding a Net Reclassification Improvement (NRI) of 13.10% (95% CI: 8.68, 16.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for MACE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the UK Biobank lacks concurrent laboratory measurements at the time of imaging acquisition, which limited our ability to incorporate biochemical markers into the model. However, we mitigated this by incorporating self-reported medical history and medication use as proxies for metabolic conditions. Second, due to the absence of HDL-C and other laboratory variables, we used a simplified version of the Framingham Risk Score that substitutes BMI for lipid measures. Previous studies have shown that this alternative version performs comparably for primary care\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Third, the relatively short median follow-up period may have led to an underestimation of long-term associations, particularly in the SUPERNOVA group, where event rates were lower and the delayed onset of cardiovascular events may occur.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this large, prospective cohort study, we demonstrated that cIMT-derived vascular aging phenotypes\u0026mdash;EVA and SUPERNOVA\u0026mdash;offer substantial incremental prognostic value beyond traditional cardiovascular risk models. While the vascular age showed limited improvement in predicting cardiovascular mortality, they significantly enhanced the risk stratification for MACE and non-fatal events, including myocardial infarction and stroke.Crucially, our stratified analyses revealed that the prognostic power of these phenotypes is most pronounced in individuals already classified as high-risk by the Framingham Risk Score. In this population, the EVA phenotype identifies a subset with a nearly 2.7-fold increased risk, while the SUPERNOVA phenotype identifies those with significant vascular resilience and lower-than-expected event rates.\u003c/p\u003e \u003cp\u003eThese findings highlight the utility of imaging-derived vascular age as a practical and interpretable tool for refining primary prevention strategies. By identifying individuals whose biological vascular age significantly deviates from their chronological age, clinicians can better guide early intervention for early vascular aging individuals and avoid potential over-treatment in structurally healthy older adults. Future research should evaluate the cost-effectiveness of this approach and its integration into routine clinical workflows.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosures\u003c/h2\u003e \u003cp\u003eThe authors report no conflicts of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Beijing Natural Science Foundation (7252190, L246054) and National Key Research and Development Program of China (2021YFC2500503).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.D. conducted the primary statistical analyses, prepared the figures, and drafted the initial manuscript. F.F. contributed to methodological development and assisted with manuscript revision. H.C. and T.D. supported the development of analytical methods and provided input during model refinement. Z.Y. assisted with data management and project coordination. L.Y. and Y.L. contributed to data organization and cleaning. X.Q. and Y.Z. supervised all stages of the study, provided critical intellectual guidance, and oversaw manuscript development. All authors critically reviewed the manuscript, contributed intellectual content, approved the final version, and agreed to its submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThis study was conducted using data from the UK Biobank Resource under application number 73201. We thank the UK Biobank participants and investigators for their invaluable contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from UK Biobank. Restrictions apply to the availability of these data, which were used under license for the present study.Data are available for bona fide researchers upon reasonable request and successful application to UK Biobank (www.ukbiobank.ac.uk). This study was conducted under UK Biobank application number 73201. Clinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMensah GA, Fuster V, Murray CJL, Roth GA. Global Burden of Cardiovascular Diseases and Risks Collaborators. Global burden of cardiovascular diseases and risks, 1990\u0026ndash;2022. J Am Coll Cardiol. 2023;82:2350\u0026ndash;473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2023.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2023.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson PM. Early Vascular Aging in Hypertension. Front Cardiovasc Med. 2020;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2020.00006\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2020.00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie C, Li Y, Li R, Yan Y, Zhang D, Li T, et al. Distinct biological ages of organs and systems identified from a multi-omics study. Cell Rep. 2022;38:110459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.celrep.2022.110459\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2022.110459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorenzatti D, Pi\u0026ntilde;a P, Huang D, Apple SJ, Fernandez-Hazim C, Ippolito P, et al. Interaction between risk factors, coronary calcium, and CCTA plaque characteristics in patients aged 18\u0026ndash;45 years. Eur Heart J Cardiovasc Imaging. 2024;25:1071\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ehjci/jeae094\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeae094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu T, Zhang Y, Zhou Y, Yin L, Min X, Wu S, et al. Association of vascular aging phenotypes with adverse clinical outcomes in the Chinese population: A multicentre study. Clin Interv Aging. 2025;20:403\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/CIA.S485597\u003c/span\u003e\u003cspan address=\"10.2147/CIA.S485597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUngvari Z, Tarantini S, Sorond F, Merkely B, Csiszar A. Mechanisms of Vascular Aging, A Geroscience Perspective: JACC Focus Seminar. J Am Coll Cardiol. 2020;75:931\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2019.11.061\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2019.11.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Xu Q, Xu X, Peng J, Lin M, Chen C-E, et al. Evaluation of the ultrasonic arterial measurement and analysis system for predicting 10-year atherosclerotic cardiovascular disease risk in patients with type 2 diabetes. Quant Imaging Med Surg. 2025;15:4921\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/qims-24-1620\u003c/span\u003e\u003cspan address=\"10.21037/qims-24-1620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePewowaruk RJ, Tedla Y, Korcarz CE, Tattersall MC, Stein JH, Chesler NC, et al. Carotid artery stiffening with aging: Structural versus load-dependent mechanisms in MESA (the multi-ethnic study of atherosclerosis). Hypertens Dallas Tex 1979. 2022;79:150\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/HYPERTENSIONAHA.121.18444\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.121.18444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruno RM, Nilsson PM, Engstr\u0026ouml;m G, Wadstr\u0026ouml;m BN, Empana J-P, Boutouyrie P, et al. Early and Supernormal Vascular Aging Clinical Characteristics and Association With Incident Cardiovascular Events. Hypertens Dallas Tex 1979. 2020;76:1616\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/HYPERTENSIONAHA.120.14971\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.120.14971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo Y, Chen S, Tian X, Wang P, Wu S, Wang A. Association of Vascular Aging With Cardiovascular Disease in Middle-Aged Chinese People: A Prospective Cohort Study. JACC Asia. 2023;3:895\u0026ndash;904. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacasi.2023.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jacasi.2023.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1001779\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1001779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoffey S, Lewandowski AJ, Garratt S, Meijer R, Lynum S, Bedi R, et al. Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment. Eur J Prev Cardiol. 2017;24:1799\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2047487317732273\u003c/span\u003e\u003cspan address=\"10.1177/2047487317732273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.107.699579\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.107.699579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao F, Xu Z, Li X-X, Fu Z-Y, Han R-Y, Zhang J-L, et al. Trends and cross-country inequalities in the global burden of osteoarthritis, 1990\u0026ndash;2019: A population-based study. Ageing Res Rev. 2024;99:102382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2024.102382\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2024.102382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong T, Fan F, Chen H, Dong Z, Yang L, Luo Y, et al. Comparison between vascular age based on brachial-ankle pulse wave velocity or carotid-femoral pulse wave velocity. Hypertens Res Off J Jpn Soc Hypertens. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41440-025-02281-1\u003c/span\u003e\u003cspan address=\"10.1038/s41440-025-02281-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S1\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.cir.0000437738.63853.7a\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.0000437738.63853.7a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42:2439\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehab309\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehab309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Li J, Hu D, Chen J, Li Y, Huang J, et al. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China). Circulation. 2016;134:1430\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.116.022367\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.116.022367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med. 2015;162:266\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7326/M14-1281\u003c/span\u003e\u003cspan address=\"10.7326/M14-1281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilleit P, Tschiderer L, Allara E, Reuber K, Seekircher L, Gao L, et al. Carotid Intima-Media Thickness Progression as Surrogate Marker for Cardiovascular Risk: Meta-Analysis of 119 Clinical Trials Involving 100 667 Patients. Circulation. 2020;142:621\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.120.046361\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.120.046361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhindsa DS, Sandesara PB, Shapiro MD, Wong ND. The evolving understanding and approach to residual cardiovascular risk management. Front Cardiovasc Med. 2020;7:88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2020.00088\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2020.00088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaqvi TZ, Mendoza F, Rafii F, Gransar H, Guerra M, Lepor N, et al. High prevalence of ultrasound detected carotid atherosclerosis in subjects with low framingham risk score: Potential implications for screening for subclinical atherosclerosis. J Am Soc Echocardiogr Off Publ Am Soc Echocardiogr. 2010;23:809\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.echo.2010.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.echo.2010.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSigl M, Winter L, Schumacher G, Helmke SC, Shchetynska-Marinova T, Amendt K, et al. Comparison of Functional and Morphological Estimates of Vascular Age. Vivo Athens Greece. 2023;37:2178\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21873/invivo.13317\u003c/span\u003e\u003cspan address=\"10.21873/invivo.13317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuspidi C, Sala C, Tadic M, Grassi G, Mancia G. Carotid intima-media thickness and anti-hypertensive treatment: Focus on angiotensin II receptor blockers. Pharmacol Res. 2018;129:20\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phrs.2018.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2018.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Fan F, Qi L, Jia J, Yang Y, Li J, et al. The association between carotid intima-media thickness and new-onset hypertension in a Chinese community-based population. BMC Cardiovasc Disord. 2019;19:269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12872-019-1266-1\u003c/span\u003e\u003cspan address=\"10.1186/s12872-019-1266-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng DCY, Climie RE, Shu M, Grieve SM, Kozor R, Figtree GA. Vascular aging and cardiovascular disease: Pathophysiology and measurement in the coronary arteries. Front Cardiovasc Med. 2023;10:1206156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcvm.2023.1206156\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1206156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBots ML, Groenewegen KA, Anderson TJ, Britton AR, Dekker JM, Engstr\u0026ouml;m G, et al. Common carotid intima-media thickness measurements do not improve cardiovascular risk prediction in individuals with elevated blood pressure: The USE-IMT collaboration. Hypertens Dallas Tex 1979. 2014;63:1173\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/HYPERTENSIONAHA.113.02683\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.113.02683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eden Ruijter HM, Peters SaE, Groenewegen KA, Anderson TJ, Britton AR, Dekker JM, et al. Common carotid intima-media thickness does not add to framingham risk score in individuals with diabetes mellitus: The USE-IMT initiative. Diabetologia. 2013;56:1494\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-013-2898-9\u003c/span\u003e\u003cspan address=\"10.1007/s00125-013-2898-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHald EM, Lijfering WM, Mathiesen EB, Johnsen SH, L\u0026oslash;chen M-L, Nj\u0026oslash;lstad I, et al. Carotid atherosclerosis predicts future myocardial infarction but not venous thromboembolism: The troms\u0026oslash; study. Arterioscler Thromb Vasc Biol. 2014;34:226\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/ATVBAHA.113.302162\u003c/span\u003e\u003cspan address=\"10.1161/ATVBAHA.113.302162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldassarre D, Veglia F, Hamsten A, Humphries SE, Rauramaa R, De Faire U, et al. Progression of carotid intima-media thickness as predictor of vascular events: Results from the IMPROVE study. Arterioscler Thromb Vasc Biol. 2013;33:2273\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/ATVBAHA.113.301844\u003c/span\u003e\u003cspan address=\"10.1161/ATVBAHA.113.301844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanchetti A, Crepaldi G, Bond MG, Gallus GV, Veglia F, Ventura A, et al. Systolic and pulse blood pressures (but not diastolic blood pressure and serum cholesterol) are associated with alterations in carotid intima-media thickness in the moderately hypercholesterolaemic hypertensive patients of the plaque hypertension lipid lowering italian study. PHYLLIS study group. J Hypertens. 2001;19:79\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00004872-200101000-00011\u003c/span\u003e\u003cspan address=\"10.1097/00004872-200101000-00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMancusi C, Gerdts E, Losi MA, D\u0026rsquo;Amato A, Manzi MV, Canciello G, et al. Differential effect of obesity on prevalence of cardiac and carotid target organ damage in hypertension (the campania salute network). Int J Cardiol. 2017;244:260\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijcard.2017.06.045\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2017.06.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolewijn S, den Heijer M, Swinkels DW, Stalenhoef AFH, de Graaf J. The metabolic syndrome and its traits as risk factors for subclinical atherosclerosis. J Clin Endocrinol Metab. 2009;94:2893\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1210/jc.2009-0084\u003c/span\u003e\u003cspan address=\"10.1210/jc.2009-0084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilleit P, Tschiderer L, Allara E, Reuber K, Seekircher L, Gao L, et al. Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: Meta-analysis of 119 clinical trials involving 100 667 patients. Circulation. 2020;142:621\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.120.046361\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.120.046361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"carotid intima-media thickness, vascular age, early vascular aging, cardiovascular risk, atherosclerosis, prospective cohort","lastPublishedDoi":"10.21203/rs.3.rs-8822772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8822772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronological age is central to cardiovascular risk assessment but may inadequately capture biological vascular aging. Carotid intima-media thickness (cIMT), a non-invasive marker of subclinical atherosclerosis, may provide additional insight into individualized cardiovascular risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, We analyzed 31,311 UK Biobank participants with cIMT measurements. Vascular age and Δ-age were derived using multivariable models incorporating cIMT and conventional risk factors, and extreme Δ-age deciles were used to define early vascular aging (EVA) and supernormal vascular aging (SUPERNOVA). Associations with major adverse cardiovascular events (MACE) were assessed using Cox models, and incremental predictive value was evaluated using discrimination and reclassification metrics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 4.18 years, 682 MACE cases were recorded. After multivariable adjustment, EVA was associated with a twofold increase in MACE risk (HR 2.00, 95% CI 1.45\u0026ndash;2.75), whereas SUPERNOVA demonstrated a significant protective effect (HR 0.70, 95% CI 0.54\u0026ndash;0.91). Stratified analyses revealed that these associations were most pronounced in the high-risk group (Framingham Risk Score\u0026thinsp;\u0026gt;\u0026thinsp;20%), where EVA further increased MACE risk (HR 2.69, 95% CI 1.72\u0026ndash;4.20) and SUPERNOVA remained protective (HR 0.64, 95% CI 0.47\u0026ndash;0.85). Incorporating these vascular aging phenotypes into traditional risk models significantly improved predictive performance, yielding a Net Reclassification Improvement (NRI) of 13.1% (95% CI 8.7\u0026ndash;16.8).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ecIMT-derived vascular age effectively stratifies extreme vascular aging phenotypes and is associated with future cardiovascular events. Incorporation of vascular age into conventional risk models provides incremental prognostic value beyond traditional risk scores.\u003c/p\u003e","manuscriptTitle":"Carotid intima-media thickness stratifies early and supernormal vascular aging phenotypes in UK biobank: Implications for cardiovascular risk prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:43:46","doi":"10.21203/rs.3.rs-8822772/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T05:38:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T20:08:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T04:25:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187604459179879630334773279081707503804","date":"2026-03-31T08:45:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T23:32:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181686323225177571277111576131585705774","date":"2026-03-26T12:12:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319915672939247920522348022326226993856","date":"2026-03-24T19:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142071281966776970682577651893568846868","date":"2026-03-24T15:29:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T09:00:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T10:27:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T04:13:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T04:10:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-08T15:22:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92a2d19e-27cb-4f41-9630-f701d3733e6b","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T05:38:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T20:08:35+00:00","index":66,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T05:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:43:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8822772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8822772","identity":"rs-8822772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}