Association between the triglyceride-glucose-body mass index and the risk of early vascular aging in young and middle-aged Chinese adults: a cross-sectional study

Association between the triglyceride-glucose-body mass index and the risk of early vascular aging in young and middle-aged Chinese adults: a cross-sectional study | 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 Association between the triglyceride-glucose-body mass index and the risk of early vascular aging in young and middle-aged Chinese adults: a cross-sectional study Chaomin Kong, Caixia Lyu, Wenjin Lu, Xuedong Bai, Hongyang Dong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8662500/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 18 You are reading this latest preprint version Abstract Background Insulin resistance is closely associated with arterial stiffness and vascular aging. The triglyceride–glucose–body mass index (TyG-BMI) is a validated surrogate marker of insulin resistance. However, evidence regarding the association between TyG-BMI and early vascular aging (EVA) remains limited, particularly among young and middle-aged populations. Methods This cross-sectional study included 1,272 Chinese adults aged 30–59 years who underwent brachial–ankle pulse wave velocity (baPWV) assessment. EVA was defined using the Framingham vascular age score. Multivariable logistic and linear regression models, restricted cubic spline analyses, and threshold analyses were applied to evaluate the association between TyG-BMI and EVA. Receiver operating characteristic (ROC) curve analysis was conducted to assess the predictive value of TyG-BMI for EVA. Results Each one-unit increase in TyG-BMI was associated with a 2.9% higher risk of EVA (odds ratio 1.029, 95% confidence interval 1.024–1.034) after full adjustment. Restricted cubic spline analysis revealed a nonlinear association, with an inflection point at TyG-BMI of 220.90. Below this threshold, EVA risk increased modestly, whereas above the threshold, the risk increased substantially. TyG-BMI was also positively associated with baPWV and other vascular aging indicators. ROC analysis suggested a moderate discriminatory ability of TyG-BMI for identifying individuals with EVA.(area under the curve 0.812). Conclusions TyG-BMI is independently and nonlinearly associated with the risk of early vascular aging in young and middle-aged Chinese adults. TyG-BMI may serve as a practical biomarker for identifying individuals at high risk of vascular aging. Early vascular aging Triglyceride–glucose–body mass index Insulin resistance Arterial stiffness Framingham vascular age score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The global demographic shift toward an aging population has been accompanied by a substantial increase in the prevalence of vascular aging–related conditions, including atherosclerosis, hypertension, and diabetes mellitus (DM) [ 1 ]. Vascular aging is characterized by progressive structural and functional alterations in the vascular wall, manifested primarily as increased arterial stiffness resulting from endothelial dysfunction, elastin fragmentation, collagen accumulation, and vascular smooth muscle cell remodeling within the tunica media [ 2 ]. These pathological changes form the biological foundation for the development of cardiovascular diseases (CVDs) and contribute significantly to their growing morbidity and mortality worldwide [ 3 ]. Epidemiological projections suggest that by 2030, vascular aging–related conditions will account for approximately 27 million cases of hypertension, 8 million cases of coronary artery disease, and 4 million cases of stroke globally [ 4 , 5 ]. Accordingly, early identification and effective management of vascular aging risk factors are critical for the primary prevention of CVDs and for reducing their associated socioeconomic burden. Early vascular aging (EVA) refers to a condition in which vascular structural alterations, increased arterial stiffness, and endothelial dysfunction occur prematurely, resulting in a predicted vascular age that exceeds an individual’s chronological age [ 6 , 7 ]. Predicted vascular age is commonly estimated using the Framingham vascular age score developed by D’Agostino et al., which is derived from established cardiovascular risk factors, including sex, age, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure (treated or untreated), smoking status, and diabetes mellitus [ 8 ]. Since its introduction, the Framingham vascular age score has been widely applied in clinical practice for cardiovascular risk assessment. However, despite its utility, this score primarily reflects traditional risk factors and provides limited information regarding vascular structural integrity and functional status. In contrast, brachial–ankle pulse wave velocity (baPWV) has emerged as a practical and noninvasive indicator of arterial stiffness, offering quantitative assessment of pressure wave propagation along the arterial tree, with higher values indicating increased arterial rigidity [ 9 ]. Given the importance of delaying EVA to reduce future CVD risk—particularly among young and middle-aged adults—identifying reliable biomarkers for early detection is essential to facilitate timely intervention and inform personalized prevention strategies. In this study, we adopted an integrated assessment approach combining the Framingham vascular age score with baPWV measurements to improve the precision of EVA evaluation. Insulin resistance (IR), a core pathophysiological feature of type 2 diabetes mellitus (T2DM), is characterized by reduced cellular responsiveness to insulin, resulting in impaired glucose uptake and metabolic dysregulation. IR is a well-established risk factor for the development of CVDs [ 10 ]. The triglyceride–glucose–body mass index (TyG-BMI) has recently emerged as a simple and practical surrogate marker of insulin resistance, integrating glycometabolic and adiposity-related information into a single index [ 11 ]. Growing evidence has demonstrated significant associations between elevated TyG-BMI levels and increased risks of heart failure, cardiovascular events, atrial fibrillation, and hypertension [ 12 – 15 ]. However, the potential role of TyG-BMI as a predictive biomarker for early vascular aging has not been sufficiently investigated. Therefore, the present study aimed to examine the association between TyG-BMI and EVA risk in young and middle-aged adults, with the goal of providing evidence for early identification of individuals at increased risk of vascular aging. Methods Data source and study population This cross-sectional study was conducted at Hebei General Hospital, adhering to the ethical principles established in the Declaration of Helsinki. The Institutional Review Board of Hebei General Hospital granted ethical approval for this research. Clinical trial number: not applicable. Given the retrospective design of the study, the requirement for informed consent was waived. Participants were recruited from a routine health examination population at Hebei General Hospital, reflecting a real-world screening setting for early cardiometabolic risk assessment. This study enrolled 1,572 participants who underwent baPWV measurements between January 2021 and December 2024. Following the application of exclusion criteria, which comprised individuals aged 59 years, as well as those presenting with hepatic insufficiency, renal insufficiency, cardiac insufficiency, coronary heart disease, stroke, malignant hematological disorders, rheumatic immune system diseases, malignant tumors, or pregnancy status, a final cohort of 1,272 eligible participants was established for subsequent analysis (Fig. 1 ). Data measurement and definitions Demographic and clinical parameters were systematically extracted from the hospital's electronic medical record system. Anthropometric measurements, including body weight and height, were obtained for all participants using standardized protocols, with body mass index (BMI) subsequently calculated using the formula: BMI = weight (kg)/height (m)². Smoker was operationally defined as the daily consumption of ≥ 1 cigarette for a minimum duration of six consecutive months. Drinker was categorized based on regular intake (≥ 2 times per week) sustained for at least six months. Fasting venous blood samples were collected following an overnight fasting period of ≥ 8 hours, with all biochemical analyses performed using a Beckman Coulter AU5800 automated biochemical analyzer. The measurements included TC, triglycerides (TG), HDL-C, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), and homocysteine (Hcy) levels. The triglyceride-glucose (TyG) index was calculated using the established formula: Ln [TG (mg/dL) × FPG (mg/dL)/2], while the TyG-BMI was derived through the multiplication of the TyG index by BMI. Blood pressure measurements were obtained bilaterally, with the mean value utilized for SBP assessment. Predicted vascular age was determined utilizing the Framingham vascular age score, which integrate key clinical parameters including sex, chronological age, HDL-C, TC, SBP (untreated/treated ), smoker, and diabetic. To facilitate computational efficiency in vascular age estimation, threshold values of "> 80" and "< 30" were standardized to 80 years and 30 years, respectively. Based on the comparative analysis between predicted vascular age and chronological age, participants were stratified into two distinct groups: the EVA group (predicted vascular age > chronological age) and the non-EVA group (predicted vascular age ≤ chronological age) [ 16 ]. In addition, the predicted cardiovascular disease (CVD) risk was derived from the Framingham CVD risk score. Arterial stiffness was evaluated utilizing the Vascular Profiler BP-203RPEIII (Omron, Japan), a validated non-invasive device for pulse wave velocity measurement. Following a standardized protocol, participants maintained a supine position for a minimum of 10 minutes to achieve hemodynamic stability. Subsequently, certified technicians positioned four pneumatic cuffs on the bilateral upper extremities and lower extremities. The device simultaneously measured baPWV through the calculation of pulse wave propagation distance, which was automatically derived from anthropometric parameters (specifically, body height), divided by the pulse transit time between the brachial and posterior tibial arteries. The mean baPWV values obtained from bilateral measurements were utilized for subsequent statistical analyses [ 17 ]. Statistical analysis Statistical analyses were performed utilizing R software (version 4.5.0), SPSS Statistics (version 27.0), and GraphPad Prism (version 9.5), with a predefined significance threshold of P < 0.05 (two-tailed). Participants were stratified into EVA and non-EVA groups based on the Framingham vascular age score. Baseline characteristics were subsequently compared between these groups. Continuous variables were expressed as either median (interquartile range) or mean ± standard deviation (SD), while categorical variables were presented as frequencies (percentages). Normality assumptions were assessed, with normally distributed continuous variables analyzed using independent samples t-tests, non-normally distributed continuous variables evaluated via Mann-Whitney U tests, and categorical variables compared using chi-square tests. To evaluate the association between TyG-BMI and the risk of EVA, multivariate logistic regression analyses were performed utilizing three progressively adjusted models to account for potential confounding variables. The covariates incorporated in the analysis encompassed chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Study participants were categorized into quartiles (Q1-Q4) based on TyG-BMI values to facilitate the examination of dose-response relationships with EVA risk. The strength and precision of these associations were quantified through the calculation of odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Spearman's rank correlation analysis was employed to assess the linear relationships between the TyG-BMI index and continuous variables, including predicted vascular age, predicted vascular age -chronological age difference, predicted CVD risk, and baPWV. Subsequently, multivariate linear regression analyses were performed across three distinct models, with each model incorporating progressive adjustments for potential confounders. The dependent variables in these models were predicted vascular age, the predicted vascular age-chronological age difference, predicted CVD risk, and baPWV. The confounding factors adjusted for in the models included chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Restricted cubic spline (RCS) analyses were performed to investigate non-linear relationships between TyG-BMI and EVA, baPWV, predicted vascular age, predicted vascular age–chronological age difference, and predicted CVD risk, with adjustment for chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Where nonlinearity emerged, a recursive algorithm pinpointed the inflection point. We then formulated a piecewise regression model on either side of these inflection points. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive efficacy of TyG-BMI for EVA, with the area under the curve (AUC) used to quantify accuracy. Analyses were stepwise adjusted for chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Results Baseline characteristics The present study enrolled 1,272 participants (744 with EVA and 528 with non-EVA), aged 30–59 years, for comprehensive analysis (Table 1 ). Comparative analysis revealed that the EVA group demonstrated significantly elevated levels of multiple parameters, including TG, FPG, HbA1c, BMI, TyG index, TyG-BMI, and baPWV (all P < 0.001), along with a higher prevalence of alcohol consumption. Table 2 delineates the distribution of variables derived from the Framingham vascular age score utilized for EVA estimation. Consistent with expectations, the EVA group exhibited significantly higher SBP and TC levels, reduced HDL-C concentrations, and increased prevalence of smoking, SBP treatment history, and diabetes mellitus (all P < 0.001). Notably, no significant intergroup difference in chronological age was observed ( P = 0.214). Stratified analysis demonstrated significant proportional distribution disparities across all related factors between the two groups ( P < 0.001, Fig. 2 ). Based on the Framingham vascular age and CVD risk score, we calculated predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk. The EVA group manifested significantly higher values across all these parameters (all P < 0.001), as shown in Table 2 . Table 1 Baseline characteristics of the EVA and non-EVA groups in people aged 30–59 years Characteristics Overall (n = 1272) EVA(n = 744) non-EVA(n = 528) P value Drinker, n (%) 552 (43.40) 390 (52.42) 162 (30.68) < 0.001 Height (cm, mean ± SD) 173.00 ± 8.36 173.94 ± 7.51 171.69 ± 9.28 < 0.001 Weight (kg, mean ± SD) 76.41 ± 13.36 80.94 ± 12.47 70.02 ± 11.90 < 0.001 BMI (kg/m 2 , mean ± SD) 25.42 ± 3.53 26.72 ± 3.58 23.60 ± 2.49 < 0.001 TG (mmol/L, mean ± SD) 1.66 ± 0.99 1.91 ± 1.14 1.31 ± 0.58 < 0.01 FPG (mmol/L, mean ± SD) 5.74 ± 1.74 6.05 ± 2.17 5.31 ± 0.60 < 0.001 HbA1c (%, mean ± SD) 5.84 ± 0.89 6.01 ± 1.10 5.62 ± 0.31 < 0.001 Hcy (µmol/L, quartile) 11.81 (9.84, 15.30) 12.15 (10.15, 16.19) 11.45 (9.45, 13.34) < 0.01 BaPWV (cm·s − 1 , mean ± SD) 1300.48 ± 205.11 1348.76 ± 214.63 1232.44 ± 169.08 < 0.001 TyG index (mean ± SD) 8.77 ± 0.61 8.95 ± 0.65 8.53 ± 0.45 < 0.001 TyG-BMI (mean ± SD) 223.87 ± 40.14 239.73 ± 40.80 201.51 ± 26.15 < 0.001 Q1 (< 192.69) 318 (25.00) 120 (16.13) 198 (37.50) 249.01) 318 (25.00) 300 (40.32) 18 (3.41) EVA, early vascular aging; BMI, body mass index; TG, triglyceride; FPG, fasting plasma glucose; HbA1c, hlycosylated hemoglobin; BaPWV, brachial-ankle pulse wave velocity; TyG, triglyceride-glucose; TyG-BMI, Triglyceride glucose-body mass index Table 2 The distribution of components of the Framingham vascular age score and the result of the Framingham CVD risk score between the EVA and non-EVA groups in people aged 30–59 years Characteristics Overall (n = 1272) EVA (n = 744) non-EVA (n = 528) P value Male, n (%) 1002 (78.77) 666 (89.52) 336 (63.64) < 0.001 Chronological age (years, mean ± SD) 44.03 ± 8.54 43.76 ± 9.22 44.42 ± 7.48 0.173 30–34 204 (16.04) 144 (19.35) 60 (11.36) < 0.001 35–39 270 (21.23) 150 (20.16) 120 (22.73) 40–44 198 (15.57) 138 (18.55) 60 (11.36) 45–49 180 (14.15) 48 (6.45) 132 (25.00) 50–54 252 (19.81) 132 (17.74) 120 (22.73) 55–59 168 (13.21) 132 (17.74) 36 (6.82) HDL-C (mmol/L, mean ± SD) 1.30 ± 0.26 1.25 ± 0.24 1.39 ± 0.27 < 0.001 ≥ 1.55 198 (15.57) 60 (8.06) 138 (26.14) < 0.001 1.29–1.54 396 (31.13) 198 (26.61) 198 (37.50) 1.16–1.28 282 (22.17) 204 (27.42) 78 (14.77) 0.91–1.15 360 (28.30) 252 (33.87) 108 (20.45) < 0.91 36 (2.83) 30(4.03) 6 (1.14) TC (mmol/L, mean ± SD) 5.21 ± 0.96 5.31 ± 1.06 5.09 ± 0.80 < 0.001 < 4.14 120 (9.43) 54 (7.26) 66 (12.50) < 0.001 4.14–5.16 492 (38.68) 258 (34.68) 234 (44.32) 5.17–6.20 474 (37.26) 306 (41.13) 168 (31.82) 6.21–7.23 156 (12.26) 96 (12.90) 60 (11.6) ≥ 7.23 30 (2.36) 30 (4.03) 0 (0) SBP treated history, n (%) 258 (20.28) 252 (33.87) 6 (1.14) < 0.001 SBP (mmHg, mean ± SD) 124.73 ± 15.76 132.34 ± 15.33 114.00 ± 8.40 < 0.001 < 120 546 (42.92) 138 (18.55) 408 (77.27) < 0.001 120–129 324 (25.47) 222 (29.84) 102 (19.32) 130–139 222 (17.45) 204 (27.42) 18 (3.41) 140–149 90 (7.08) 90 (12.10) 0 (0) 150–159 42 (3.30) 42 (5.65) 0 (0) ≥ 160 48 (3.77) 48 (6.45) 0 (0) Smoker, n (%) 246 (19.34) 234 (31.45) 12 (2.27) < 0.001 Diabetic, n (%) 90 (7.08) 90 (12.10) 0 (0) < 0.001 Predicted vascular age (years, median, quartile) 45 (36, 59) 52.5 (40, 68) 37 (34, 42) < 0.001 Predicted vascular age–chronological age difference (years, median, quartile) 1 (-4, 12) 10 (3,17) -5 (-9, -2) < 0.001 Predicted CVD risk (%, median, quartile) 4.70(2.33, 10.75) 7.9 (3.9, 15.6) 2.4 (2.0, 3.9) < 0.001 EVA, early vascular aging; HDL-C, high density lipoprotein cholesterol; TC, total cholesterol; SBP, systolic blood pressure; CVD, cardiovascular disease Logistic regression analysis for the association between TyG-BMI and EVA To examine the association between TyG-BMI levels and EVA, logistic regression analyses were performed, as illustrated in Fig. 3 . In Model 1, a 1-unit increase in TyG-BMI was significantly associated with a 3.3% elevated risk of EVA (OR: 1.033, 95% CI: 1.029–1.038). Similarly, Model 2 revealed a 3.0% increase in EVA risk per 1-unit increment in TyG-BMI (OR: 1.030, 95% CI: 1.025–1.034). In Model 3, each 1-unit rise in TyG-BMI was associated with a 2.9% higher risk of EVA (OR: 1.029, 95% CI: 1.024–1.034). Furthermore, TyG-BMI was categorized into quartiles to assess its impact as a categorical variable. In the multivariable-adjusted model, with the lowest quartile (Q1) as the reference, the ORs for EVA risk in the other quartiles were as follows: Q3 exhibited an OR of 1.634 (95% CI: 1.135–2.352), while Q4 demonstrated a markedly elevated OR of 15.947 (95% CI: 9.120–27.885). This indicates that, compared to participants in Q1, those in Q3 had a 63.4% increased risk of EVA, whereas those in Q4 had a 1494.7% higher risk. No statistically significant association was observed between Q2 and Q1 ( P > 0.05), suggesting that the risk elevation became significant starting from Q3. Spearman correlation analysis between TyG-BMI and vascular aging related indicators As demonstrated in Table 3 , TyG-BMI exhibited significant positive correlations with baPWV (r = 0.228), predicted vascular age (correlation coefficient [r] = 0.385), predicted vascular age-chronological age difference (r = 0.588), and predicted CVD risk (r = 0.478), all P < 0.001. Table 3 Correlation analysis between TyG-BMI and vascular aging related indicators TyG-BMI Predicted vascular age Predicted vascular age-chronological age Predicted CVD risk BaPWV r 0.385 0.588 0.478 0.228 P value < 0.001 < 0.001 < 0.001 < 0.001 TyG-BMI, triglyceride glucose-body mass index; CVD, cardiovascular disease; BaPWV, brachial-ankle pulse wave velocity; r, coefficient of association Linear regression analysis for the relationship between TyG-BMI and vascular aging related indicators To assess the independent associations between TyG-BMI and baPWV, predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk, a linear regression analysis was conducted (Fig. 4 ). The analysis revealed statistically significant associations between all investigated predictors and TyG-BMI ( P < 0.001 for all variables). In the fully adjusted Model 3, which accounted for potential confounding factors, each 1-unit increment in TyG-BMI was associated with significant changes in the following parameters: a 1.002-unit increase in baPWV (95% CI: 0.713–1.291), a 0.121-unit increase in predicted vascular age (95% CI: 0.107–0.136), a corresponding 0.121-unit elevation in the predicted vascular age-chronological age difference (95% CI: 0.107–0.136), and a 0.044-unit rise in predicted CVD risk (95% CI: 0.037–0.052). RCS analysis between the TyG-BMI, with EVA and other related indicators The non-linear relationship between TyG-BMI and EVA risk was quantitatively assessed using RCS analysis, as illustrated in Fig. 5 . The analysis revealed a significant inflection point at 220.90 mmol/L for TyG-BMI. Subsequent application of a two-piecewise logistic regression model enabled the estimation of ORs and corresponding 95% CIs for EVA risk on either side of this inflection point (Table 4 ). Specifically, the OR was calculated as 1.013 (95% CI: 1.002–1.024) below the inflection point, while it increased to 1.088 (95% CI: 1.067–1.109) above this threshold. Table 4 The result of the two-piecewise linear regression model Outcome Inflection points of TyG-BMI OR / B (95% CI) P value EVA < 220.90 1.013 (1.002–1.024) 0.023 ≥ 220.90 1.088 (1.067–1.109) < 0.001 BaPWV < 220.90 1.229 (0.490–1.969) 0.001 ≥ 220.90 2.606 (2.035–3.177) < 0.001 Predicted vascular age < 220.90 0.099 (0.058–0.140) < 0.001 ≥ 220.90 0.131 (0.104–0.158) < 0.001 Predicted vascular age-chronological age < 220.90 0.099 (0.058–0.140) < 0.001 ≥ 220.90 0.131 (0.104–0.158) < 0.001 Predicted CVD risk < 220.90 0.019 (0.003–0.034) 0.021 ≥ 220.90 0.054 (0.038–0.071) < 0.001 Adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, and diabetic. Furthermore, additional RCS analyses demonstrated non-linear associations between TyG-BMI and several vascular parameters, including baPWV, predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk, as presented in Fig. 6 . Linear regression analyses of both segments revealed that the unstandardized beta coefficient (B) for these vascular parameters were significantly steeper compared to their pre-inflection counterparts (Table 4 ). ROC curve of TyG-BMI for predicting EVA The predictive performance of TyG-BMI for EVA was quantitatively assessed through ROC curve analysis, wherein the AUC served as a critical metric for evaluation. The AUC demonstrates a direct correlation with predictive accuracy, with higher values indicating superior diagnostic performance. As illustrated in Fig. 7 , after adjusted for multiple factors the ROC analysis of continuous TyG-BMI measurements yielded a statistically significant AUC of 0.8115 (95% confidence interval: 0.7886–0.8344, P < 0.001). The optimal diagnostic threshold was determined to be 180.69 mmol/L, with this cutoff value demonstrating a sensitivity of 75.0% and specificity of 72.7% in predicting EVA occurrence (Model 3). Discussion In this study, we investigated the association between triglyceride–glucose–body mass index (TyG-BMI) and early vascular aging (EVA) among young and middle-aged adults aged 30–59 years. Our findings demonstrated that higher TyG-BMI levels were significantly associated with an increased risk of EVA. Notably, a significant inflection point was identified, indicating distinct associations between TyG-BMI and EVA below and above this threshold. These results suggest that TyG-BMI may serve as a useful biomarker for the early identification of vascular aging risk in this population. Vascular aging is a complex biological process characterized by progressive structural and functional deterioration of the vascular system, which often manifests as subclinical changes during its early stages [ 18 ]. This process begins during fetal development and persists throughout the lifespan, with its progression potentially accelerated by multiple pro-aging factors, particularly cardiovascular risk factors, thereby increasing susceptibility to EVA [ 19 ]. In the present study, vascular age was estimated using the Framingham vascular age score, a validated composite index that integrates multiple cardiovascular risk factors into a single predictive measure, including sex, age, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure (treated or untreated), smoking status, and diabetes status [ 8 ]. EVA was defined as a condition in which the estimated vascular age exceeds the chronological age, representing a preclinical marker of cardiovascular disease that reflects early arterial deterioration before the onset of overt clinical events [ 16 ]. The triglyceride–glucose (TyG) index has been extensively validated as a reliable surrogate marker of insulin resistance (IR). A growing body of evidence has demonstrated significant associations between elevated TyG levels and vascular aging–related outcomes [ 20 – 22 ]. For example, a large retrospective cohort study conducted in China reported that higher TyG levels were independently associated with an increased incidence of arterial stiffness after comprehensive adjustment for confounding factors [ 22 ]. Meanwhile, body mass index (BMI), as a conventional anthropometric indicator of adiposity, has also been consistently implicated in the pathogenesis of vascular aging [ 23 – 25 ]. Longitudinal evidence from the German Health Interview and Examination Survey for Children and Adolescents cohort demonstrated that obesity during childhood and adolescence substantially contributes to the development of subclinical atherosclerosis and arterial stiffness in early adulthood [ 24 ]. However, both TyG and BMI have inherent limitations when used as independent predictors. TyG primarily reflects glycometabolic dysfunction but does not capture adiposity-related risk, whereas BMI fails to account for underlying metabolic abnormalities such as insulin resistance. By integrating glycometabolic and adiposity-related information, TyG-BMI may provide a more comprehensive assessment of vascular aging risk. TyG-BMI, a composite biomarker derived from the product of the TyG index and BMI, has emerged as a promising indicator of insulin resistance with improved diagnostic performance and clinical applicability [ 12 ]. Given the well-established associations of both TyG and BMI with vascular aging and the central role of insulin resistance in vascular pathophysiology, it is biologically plausible that TyG-BMI is positively associated with EVA risk. Although direct evidence linking TyG-BMI to EVA remains limited, accumulating studies have demonstrated robust associations between TyG-BMI and cardiovascular diseases, which represent advanced manifestations of vascular aging [ 10 , 26 – 28 ]. A recent meta-analysis including more than 870,000 participants reported significantly higher risks of cardiovascular disease, coronary artery disease, and stroke among individuals with elevated TyG-BMI levels [ 26 ]. Similar associations have been observed in population-based surveys and clinical cohorts, further supporting the potential relevance of TyG-BMI in cardiovascular risk stratification [ 10 , 27 , 28 ].Collectively, these findings support the hypothesis that TyG-BMI may be closely linked to early vascular aging processes. In the present study, TyG-BMI was analyzed as both a continuous variable and a categorical variable stratified into quartiles to comprehensively characterize its association with EVA. Multivariable logistic regression analyses revealed a dose-dependent increase in EVA risk with increasing TyG-BMI levels, and sensitivity analyses confirmed the robustness of these associations after adjustment for potential confounders. Notably, the lack of a statistically significant difference in EVA risk between the lowest and second quartiles suggests the presence of a threshold effect, whereby a certain magnitude of TyG-BMI elevation may be required before a measurable impact on vascular aging becomes evident. To further validate these findings, we examined multiple EVA-related markers, including predicted vascular age, the difference between predicted vascular age and chronological age, predicted cardiovascular disease risk, and brachial–ankle pulse wave velocity. Linear regression analyses demonstrated that higher TyG-BMI levels were independently associated with adverse changes in all these indicators, providing consistent evidence for a significant relationship between TyG-BMI and early vascular aging. The biological mechanisms underlying the association between TyG-BMI and EVA are likely multifactorial and remain incompletely elucidated. As a composite marker of insulin resistance, TyG-BMI captures both adiposity and glycometabolic dysfunction. Insulin resistance, a core feature of metabolic syndrome, has been implicated in dyslipidemia, endothelial dysfunction, vascular inflammation, and atherosclerotic progression, all of which contribute to vascular aging [ 29 ]. Insulin resistance promotes hypertriglyceridemia, reduces high-density lipoprotein cholesterol levels, and increases the production of small dense low-density lipoprotein particles, thereby exacerbating endothelial injury [ 30 ]. In addition, insulin resistance impairs endothelial function through increased oxidative stress, activation of proinflammatory signaling pathways, reduced nitric oxide bioavailability, and disruption of cyclic guanosine monophosphate signaling in vascular smooth muscle cells. These alterations collectively lead to reduced arterial compliance, increased arterial stiffness, and accelerated vascular aging [ 31 – 33 ]. An important and novel finding of this study is the identification of a nonlinear association between TyG-BMI and EVA risk, with a significant inflection point at 220.90. Below this threshold, increases in TyG-BMI were associated with a modest elevation in EVA risk, whereas above the threshold, the risk increased substantially, indicating a dose-dependent relationship with more pronounced vascular consequences at higher TyG-BMI levels. Consistent nonlinear associations were also observed between TyG-BMI and multiple vascular aging–related indicators, including predicted vascular age, predicted vascular age–chronological age difference, predicted cardiovascular disease risk, and brachial–ankle pulse wave velocity. These findings are in line with previous studies reporting nonlinear relationships between TyG-BMI and cardiovascular outcomes [ 10 , 34 ]. although some investigations have reported linear associations [ 26 ]. The discrepancies across studies may be attributable to differences in study design, population characteristics, and outcome definitions. Importantly, our focus on subclinical vascular aging markers in a relatively young population may partly explain the observed threshold effects, as metabolic disturbances may exert more pronounced nonlinear influences during the early stages of vascular damage. The identification of these nonlinear associations has important clinical implications, suggesting that TyG-BMI may be particularly useful for identifying individuals at high risk of early vascular aging who may benefit most from targeted preventive interventions. Longitudinal studies are warranted to confirm these findings and to further elucidate the temporal and mechanistic relationships between TyG-BMI and vascular aging progression. In addition, receiver operating characteristic curve analyses demonstrated that TyG-BMI exhibited good discriminatory performance for identifying EVA after adjustment for confounding factors. Previous studies have reported moderate predictive value of TyG-BMI for cardiovascular outcomes in different clinical settings [ 35 – 38 ]. In the present study, the optimal TyG-BMI cut-off value for EVA decreased after adjustment for confounders, suggesting that maintaining TyG-BMI below this level may be particularly relevant for the prevention of early vascular aging. This analysis does not imply diagnostic utility but rather reflects the potential role of TyG-BMI as a screening and risk stratification indicator. From a public health perspective, these findings provide evidence supporting the use of TyG-BMI as a low-cost and easily accessible indicator for large-scale screening of early vascular aging. Given that TyG-BMI can be readily derived from routine anthropometric and biochemical measurements, its implementation in community-based or occupational health settings may facilitate early identification of individuals at increased vascular aging risk and enable timely preventive interventions. This study has several strengths. First, TyG-BMI was examined in both continuous and categorical forms, allowing for comprehensive evaluation of dose–response relationships while minimizing information loss. Second, by incorporating nonlinear analytical approaches, this study is, to our knowledge, the first to report a nonlinear association between TyG-BMI and EVA. Third, multiple sensitivity analyses were conducted to assess the stability and robustness of the findings. Finally, the focus on a relatively young population highlights the importance of early identification and intervention to improve long-term vascular health. Several limitations should also be acknowledged. The cross-sectional design precludes causal inference and limits conclusions regarding temporal relationships. Although extensive adjustments were made for potential confounders, Lifestyle factors such as physical activity and dietary patterns were not available in the electronic medical records and could not be adjusted for, which may result in residual confounding. In addition, the single-center nature of the study may limit the generalizability of the findings. Future multicenter prospective studies with larger and more diverse populations are needed to validate these results and to further clarify the role of TyG-BMI in early vascular aging. Conclusion Higher TyG-BMI levels were significantly associated with an increased risk of early vascular aging in young and middle-aged adults. A nonlinear relationship was observed, with a markedly higher risk above a TyG-BMI threshold of 220.90. These findings suggest that TyG-BMI may be a useful marker for early identification of individuals at increased risk of vascular aging. Abbreviations IR Insulin resistance TyG-BMI Triglyceride-glucose body mass index EVA Early vascular aging baPWV Brachial-ankle pulse wave velocity RCS Restricted cubic spline ROC Receiver operating characteristic OR Odds ratio CI Confidential interval AUC Area under the curve DM Diabetes mellitus CVDs Cardiovascular diseases HDL-C High-density lipoprotein cholesterol TC Total cholesterol SBP Systolic blood pressure T2DM Type 2 diabetes mellitus BMI Body mass index TG Triglyceride FPG Fasting plasma glucose HbA1c Glycosylated hemoglobin Hcy Homocysteine TyG Triglyceride-glucose CVD Cardiovascular disease SD Standard deviation HR Hazard ratio CAD Coronary artery disease ACS Acute coronary syndrome Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Hebei General Hospital. Clinical trial number: not applicable. The requirement for informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Hebei Province 2025 Medical Science Research Program Plan (Grant No. 20250008), the Hebei Provincial Key Medical Research Project (Grant No. 20220871), the 2023 Government-funded Clinical Medicine Outstanding Talent Project (Grant No. ZF2023202), and the Natural Science Foundation of Hebei Province (Grant No. H2016307015). The funders had no role in the study design, data collection, analysis, interpretation, or manuscript preparation. Authors’ contributions CK performed the data analysis and drafted the manuscript. CL and CK conceived and designed the study. 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Yang X, Li K, Wen J, Yang C, Li Y, Xu G, Ma Y: Association of the triglyceride glucose-body mass index with the extent of coronary artery disease in patients with acute coronary syndromes . Cardiovascular diabetology 2024, 23 (1):24. Tian J, Dong Y, Xu Z, Ke J, Xu H: Association between triglyceride glucose-body mass index and 365-day mortality in patients with critical coronary heart disease . Frontiers in endocrinology 2025, 16 :1513898. Powell-Wiley TM, Poirier P, Burke LE, Després JP, Gordon-Larsen P, Lavie CJ, Lear SA, Ndumele CE, Neeland IJ, Sanders P et al : Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association . Circulation 2021, 143 (21):e984-e1010. Achari AE, Jain SK: Adiponectin, a Therapeutic Target for Obesity, Diabetes, and Endothelial Dysfunction . International journal of molecular sciences 2017, 18 (6). Engin A: Endothelial Dysfunction in Obesity . Advances in experimental medicine and biology 2017, 960 :345-379. Horton WB, Love KM, Gregory JM, Liu Z, Barrett EJ: Metabolic and vascular insulin resistance: partners in the pathogenesis of cardiovascular disease in diabetes . American journal of physiology Heart and circulatory physiology 2025, 328 (6):H1218-h1236. Li C, Lin Q, Wan C, Li L: Nonlinear relationships between the triglyceride glucose-body mass index and cardiovascular disease in middle-aged and elderly women from NHANES (1999-2018) . Scientific reports 2025, 15 (1):10953. Yadegar A, Mohammadi F, Seifouri K, Mokhtarpour K, Yadegar S, Bahrami Hazaveh E, Seyedi SA, Rabizadeh S, Esteghamati A, Nakhjavani M: Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration . Lipids in health and disease 2025, 24 (1):96. Wang L, Li Z, Qiu R, Luo L, Yan X: Triglyceride glucose index-body mass index as a predictor of coronary artery disease severity in patients with H-type hypertension across different glucose metabolic states . Diabetology & metabolic syndrome 2025, 17 (1):15. Wang J, Tang H, Tian J, Xie Y, Wu Y: Non-insulin-based insulin resistance indices predict early neurological deterioration in elderly and middle-aged acute ischemic stroke patients in Northeast China . Scientific reports 2024, 14 (1):16138. Sun Y, Hu Y: Association of triglyceride-glucose-body mass index with all-cause mortality among individuals with cardiovascular disease: results from NHANES . Frontiers in endocrinology 2025, 16 :1529004. Additional Declarations No competing interests reported. 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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-8662500","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581782150,"identity":"1443972f-0fe7-4668-a68f-ec8fc1343f35","order_by":0,"name":"Chaomin Kong","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chaomin","middleName":"","lastName":"Kong","suffix":""},{"id":581782151,"identity":"4cf2b5be-0a1a-49e2-a2a8-b57b980a2aa5","order_by":1,"name":"Caixia Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPhCRwCDBwM/MfPABUVrYYFok29mSDYjXAgIG53nMBIjTIpH88MPDNgt548MMZgwMNTbRRGhJM5ZIOCNhuO0wQ9oDhmNpuQ0EtfCcYZBIqJBgBGo5bsDYcJgoLcw/Egwk7Dc3M7ZJEKeFvYcNZEviBmZmNmK1tJlZAP2SPOMwG7NBAjF+Acbg45s/2+ps+/vPf3zwocaGsBZUkECa8lEwCkbBKBgFuAAAab42hHWjS2AAAAAASUVORK5CYII=","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Caixia","middleName":"","lastName":"Lyu","suffix":""},{"id":581782153,"identity":"d74b2dc0-c4dd-43fa-8ae8-45775687c162","order_by":2,"name":"Wenjin Lu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Lu","suffix":""},{"id":581782154,"identity":"9f9c9ace-d70a-4591-ab1e-36d0cc61247c","order_by":3,"name":"Xuedong Bai","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuedong","middleName":"","lastName":"Bai","suffix":""},{"id":581782155,"identity":"2f63227a-87fd-4aa2-bfce-1bff9a2b22ea","order_by":4,"name":"Hongyang Dong","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyang","middleName":"","lastName":"Dong","suffix":""},{"id":581782156,"identity":"f00a0b7d-8de5-480b-917b-8ad8444a0a69","order_by":5,"name":"Feifei Zhang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Zhang","suffix":""},{"id":581782157,"identity":"e61f00d1-643f-49b8-9fef-95d7bc8cc15a","order_by":6,"name":"Lixia Yao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Yao","suffix":""},{"id":581782162,"identity":"82b856a6-975c-41d4-aeac-cc02d460f32f","order_by":7,"name":"Shuchun Chen","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuchun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-21 17:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8662500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8662500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101439855,"identity":"0e0e9ec9-0d5a-47aa-8515-1ed970eb4d2e","added_by":"auto","created_at":"2026-01-29 16:50:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":261073,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of the aim of the study and participants selection process. TyG-BMI, Triglyceride glucose-body mass index; EVA, early vascular aging; TG, triglyceride; FPG, fasting plasma glucose; BMI, body mass index; HDL-C, high density lipoprotein cholesterol; TC, total cholesterol; SBP, systolic blood pressure; BaPWV, brachial-ankle pulse wave velocity\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/422382d85469d7c6d551943d.png"},{"id":101439852,"identity":"f211b7fc-edfc-4778-9684-ec514f6fc454","added_by":"auto","created_at":"2026-01-29 16:50:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163228,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of components of the Framingham vascular age score between the EVA and non-EVA groups in people aged 30-59 years. EVA, early vascular aging; SBP, systolic blood pressure\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/ea7ef3d614ed4d3fcbb7f5b5.png"},{"id":101439853,"identity":"d093c27e-a91e-4449-b3c3-ad58f6fe74e2","added_by":"auto","created_at":"2026-01-29 16:50:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178357,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression analysis for the relationship between TyG-BMI and EVA in different models of people aged 30-59 years. Model 1: unadjusted. Model 2: adjusted for chronological age, sex. Model 3: adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, diabetic. EVA, early vascular aging; TyG-BMI, triglyceride glucose-body mass index; OR, odds ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/ec22a34ddd50957a3d21422f.png"},{"id":101439848,"identity":"ab015d2d-c770-4cf3-8891-2b80261bc553","added_by":"auto","created_at":"2026-01-29 16:50:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154922,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis for the relationship between TyG-BMI and vascular aging related indicators in different models of people aged 30-59 years. Model 1: unadjusted. Model 2: adjusted for chronological age, sex. Model 3: adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, diabetic. EVA, early vascular aging; TyG-BMI, Triglyceride glucose-body mass index; CVD, cardiovascular disease; BaPWV, brachial-ankle pulse wave velocity\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/dee1ba3154e19c3c2e540385.png"},{"id":101439847,"identity":"45fb8487-a426-427a-8701-d77089887a23","added_by":"auto","created_at":"2026-01-29 16:50:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73775,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the association between TyG-BMI and EVA in participants aged 30–59 Years. Adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, diabetic. EVA, early vascular aging; TyG-BMI, triglyceride glucose-body mass index\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/5f4e0d680e10d0979d3095db.png"},{"id":101439851,"identity":"f4e277cb-96f9-4e40-b2c6-2449581a89ce","added_by":"auto","created_at":"2026-01-29 16:50:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136874,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the association between TyG-BMI with baPWV and indexes calculated by the vascular age score in participants aged 30-59 years. Model (a-d): adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, diabetic. EVA, early vascular aging; TyG-BMI, triglyceride glucose-body mass index; CVD, cardiovascular disease; BaPWV, brachial-ankle pulse wave velocity\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/4c7070e7fcb1f28d4ce59dd7.png"},{"id":101439856,"identity":"95a16d7d-0f67-4458-b152-51aa8887f545","added_by":"auto","created_at":"2026-01-29 16:50:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":116273,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of TyG-BMI for predicting EVA. Model 1: unadjusted. Model 2: adjusted for chronological age, sex. Model 3: adjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, diabetic. ROC, receiver operator characteristic; AUC, area under the curve; EVA, early vascular aging; TyG-BMI, triglyceride glucose-body mass index\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/797f5f225dffeeff59439e63.png"},{"id":101751824,"identity":"87a8ea2d-e07f-45e2-97ca-0109195d8e77","added_by":"auto","created_at":"2026-02-03 10:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3894489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8662500/v1/87baeec7-b005-4a9a-8d53-e180120ffdd3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the triglyceride-glucose-body mass index and the risk of early vascular aging in young and middle-aged Chinese adults: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global demographic shift toward an aging population has been accompanied by a substantial increase in the prevalence of vascular aging\u0026ndash;related conditions, including atherosclerosis, hypertension, and diabetes mellitus (DM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Vascular aging is characterized by progressive structural and functional alterations in the vascular wall, manifested primarily as increased arterial stiffness resulting from endothelial dysfunction, elastin fragmentation, collagen accumulation, and vascular smooth muscle cell remodeling within the tunica media [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These pathological changes form the biological foundation for the development of cardiovascular diseases (CVDs) and contribute significantly to their growing morbidity and mortality worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological projections suggest that by 2030, vascular aging\u0026ndash;related conditions will account for approximately 27\u0026nbsp;million cases of hypertension, 8\u0026nbsp;million cases of coronary artery disease, and 4\u0026nbsp;million cases of stroke globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Accordingly, early identification and effective management of vascular aging risk factors are critical for the primary prevention of CVDs and for reducing their associated socioeconomic burden.\u003c/p\u003e \u003cp\u003eEarly vascular aging (EVA) refers to a condition in which vascular structural alterations, increased arterial stiffness, and endothelial dysfunction occur prematurely, resulting in a predicted vascular age that exceeds an individual\u0026rsquo;s chronological age [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Predicted vascular age is commonly estimated using the Framingham vascular age score developed by D\u0026rsquo;Agostino et al., which is derived from established cardiovascular risk factors, including sex, age, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure (treated or untreated), smoking status, and diabetes mellitus [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Since its introduction, the Framingham vascular age score has been widely applied in clinical practice for cardiovascular risk assessment. However, despite its utility, this score primarily reflects traditional risk factors and provides limited information regarding vascular structural integrity and functional status. In contrast, brachial\u0026ndash;ankle pulse wave velocity (baPWV) has emerged as a practical and noninvasive indicator of arterial stiffness, offering quantitative assessment of pressure wave propagation along the arterial tree, with higher values indicating increased arterial rigidity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given the importance of delaying EVA to reduce future CVD risk\u0026mdash;particularly among young and middle-aged adults\u0026mdash;identifying reliable biomarkers for early detection is essential to facilitate timely intervention and inform personalized prevention strategies. In this study, we adopted an integrated assessment approach combining the Framingham vascular age score with baPWV measurements to improve the precision of EVA evaluation.\u003c/p\u003e \u003cp\u003eInsulin resistance (IR), a core pathophysiological feature of type 2 diabetes mellitus (T2DM), is characterized by reduced cellular responsiveness to insulin, resulting in impaired glucose uptake and metabolic dysregulation. IR is a well-established risk factor for the development of CVDs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The triglyceride\u0026ndash;glucose\u0026ndash;body mass index (TyG-BMI) has recently emerged as a simple and practical surrogate marker of insulin resistance, integrating glycometabolic and adiposity-related information into a single index [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Growing evidence has demonstrated significant associations between elevated TyG-BMI levels and increased risks of heart failure, cardiovascular events, atrial fibrillation, and hypertension [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the potential role of TyG-BMI as a predictive biomarker for early vascular aging has not been sufficiently investigated. Therefore, the present study aimed to examine the association between TyG-BMI and EVA risk in young and middle-aged adults, with the goal of providing evidence for early identification of individuals at increased risk of vascular aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003e This cross-sectional study was conducted at Hebei General Hospital, adhering to the ethical principles established in the Declaration of Helsinki. The Institutional Review Board of Hebei General Hospital granted ethical approval for this research. Clinical trial number: not applicable. Given the retrospective design of the study, the requirement for informed consent was waived.\u003c/p\u003e \u003cp\u003eParticipants were recruited from a routine health examination population at Hebei General Hospital, reflecting a real-world screening setting for early cardiometabolic risk assessment. This study enrolled 1,572 participants who underwent baPWV measurements between January 2021 and December 2024. Following the application of exclusion criteria, which comprised individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;30 years or \u0026gt;\u0026thinsp;59 years, as well as those presenting with hepatic insufficiency, renal insufficiency, cardiac insufficiency, coronary heart disease, stroke, malignant hematological disorders, rheumatic immune system diseases, malignant tumors, or pregnancy status, a final cohort of 1,272 eligible participants was established for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData measurement and definitions\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical parameters were systematically extracted from the hospital's electronic medical record system. Anthropometric measurements, including body weight and height, were obtained for all participants using standardized protocols, with body mass index (BMI) subsequently calculated using the formula: BMI\u0026thinsp;=\u0026thinsp;weight (kg)/height (m)\u0026sup2;. Smoker was operationally defined as the daily consumption of \u0026ge;\u0026thinsp;1 cigarette for a minimum duration of six consecutive months. Drinker was categorized based on regular intake (\u0026ge;\u0026thinsp;2 times per week) sustained for at least six months. Fasting venous blood samples were collected following an overnight fasting period of \u0026ge;\u0026thinsp;8 hours, with all biochemical analyses performed using a Beckman Coulter AU5800 automated biochemical analyzer. The measurements included TC, triglycerides (TG), HDL-C, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), and homocysteine (Hcy) levels. The triglyceride-glucose (TyG) index was calculated using the established formula: Ln [TG (mg/dL) \u0026times; FPG (mg/dL)/2], while the TyG-BMI was derived through the multiplication of the TyG index by BMI. Blood pressure measurements were obtained bilaterally, with the mean value utilized for SBP assessment.\u003c/p\u003e \u003cp\u003ePredicted vascular age was determined utilizing the Framingham vascular age score, which integrate key clinical parameters including sex, chronological age, HDL-C, TC, SBP (untreated/treated ), smoker, and diabetic. To facilitate computational efficiency in vascular age estimation, threshold values of \"\u0026gt; 80\" and \"\u0026lt; 30\" were standardized to 80 years and 30 years, respectively. Based on the comparative analysis between predicted vascular age and chronological age, participants were stratified into two distinct groups: the EVA group (predicted vascular age\u0026thinsp;\u0026gt;\u0026thinsp;chronological age) and the non-EVA group (predicted vascular age\u0026thinsp;\u0026le;\u0026thinsp;chronological age) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, the predicted cardiovascular disease (CVD) risk was derived from the Framingham CVD risk score.\u003c/p\u003e \u003cp\u003eArterial stiffness was evaluated utilizing the Vascular Profiler BP-203RPEIII (Omron, Japan), a validated non-invasive device for pulse wave velocity measurement. Following a standardized protocol, participants maintained a supine position for a minimum of 10 minutes to achieve hemodynamic stability. Subsequently, certified technicians positioned four pneumatic cuffs on the bilateral upper extremities and lower extremities. The device simultaneously measured baPWV through the calculation of pulse wave propagation distance, which was automatically derived from anthropometric parameters (specifically, body height), divided by the pulse transit time between the brachial and posterior tibial arteries. The mean baPWV values obtained from bilateral measurements were utilized for subsequent statistical analyses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed utilizing R software (version 4.5.0), SPSS Statistics (version 27.0), and GraphPad Prism (version 9.5), with a predefined significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). Participants were stratified into EVA and non-EVA groups based on the Framingham vascular age score. Baseline characteristics were subsequently compared between these groups. Continuous variables were expressed as either median (interquartile range) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables were presented as frequencies (percentages). Normality assumptions were assessed, with normally distributed continuous variables analyzed using independent samples t-tests, non-normally distributed continuous variables evaluated via Mann-Whitney U tests, and categorical variables compared using chi-square tests.\u003c/p\u003e \u003cp\u003eTo evaluate the association between TyG-BMI and the risk of EVA, multivariate logistic regression analyses were performed utilizing three progressively adjusted models to account for potential confounding variables. The covariates incorporated in the analysis encompassed chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Study participants were categorized into quartiles (Q1-Q4) based on TyG-BMI values to facilitate the examination of dose-response relationships with EVA risk. The strength and precision of these associations were quantified through the calculation of odds ratios (ORs) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eSpearman's rank correlation analysis was employed to assess the linear relationships between the TyG-BMI index and continuous variables, including predicted vascular age, predicted vascular age -chronological age difference, predicted CVD risk, and baPWV. Subsequently, multivariate linear regression analyses were performed across three distinct models, with each model incorporating progressive adjustments for potential confounders. The dependent variables in these models were predicted vascular age, the predicted vascular age-chronological age difference, predicted CVD risk, and baPWV. The confounding factors adjusted for in the models included chronological age, sex, drinker, HbA1c, Hcy, and diabetic.\u003c/p\u003e \u003cp\u003eRestricted cubic spline (RCS) analyses were performed to investigate non-linear relationships between TyG-BMI and EVA, baPWV, predicted vascular age, predicted vascular age\u0026ndash;chronological age difference, and predicted CVD risk, with adjustment for chronological age, sex, drinker, HbA1c, Hcy, and diabetic. Where nonlinearity emerged, a recursive algorithm pinpointed the inflection point. We then formulated a piecewise regression model on either side of these inflection points. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive efficacy of TyG-BMI for EVA, with the area under the curve (AUC) used to quantify accuracy. Analyses were stepwise adjusted for chronological age, sex, drinker, HbA1c, Hcy, and diabetic.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe present study enrolled 1,272 participants (744 with EVA and 528 with non-EVA), aged 30\u0026ndash;59 years, for comprehensive analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Comparative analysis revealed that the EVA group demonstrated significantly elevated levels of multiple parameters, including TG, FPG, HbA1c, BMI, TyG index, TyG-BMI, and baPWV (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with a higher prevalence of alcohol consumption. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e delineates the distribution of variables derived from the Framingham vascular age score utilized for EVA estimation. Consistent with expectations, the EVA group exhibited significantly higher SBP and TC levels, reduced HDL-C concentrations, and increased prevalence of smoking, SBP treatment history, and diabetes mellitus (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, no significant intergroup difference in chronological age was observed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.214). Stratified analysis demonstrated significant proportional distribution disparities across all related factors between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the Framingham vascular age and CVD risk score, we calculated predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk. The EVA group manifested significantly higher values across all these parameters (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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 the EVA and non-EVA groups in people aged 30\u0026ndash;59 years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;1272)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEVA(n\u0026thinsp;=\u0026thinsp;744)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-EVA(n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e552 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390 (52.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162 (30.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHeight (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173.94\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171.69\u0026thinsp;\u0026plusmn;\u0026thinsp;9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWeight (kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.41\u0026thinsp;\u0026plusmn;\u0026thinsp;13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.94\u0026thinsp;\u0026plusmn;\u0026thinsp;12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.02\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eTG (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHbA1c (%, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHcy (\u0026micro;mol/L, quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.81 (9.84, 15.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.15 (10.15, 16.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.45 (9.45, 13.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaPWV (cm\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1300.48\u0026thinsp;\u0026plusmn;\u0026thinsp;205.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1348.76\u0026thinsp;\u0026plusmn;\u0026thinsp;214.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1232.44\u0026thinsp;\u0026plusmn;\u0026thinsp;169.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eTyG index (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eTyG-BMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223.87\u0026thinsp;\u0026plusmn;\u0026thinsp;40.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239.73\u0026thinsp;\u0026plusmn;\u0026thinsp;40.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201.51\u0026thinsp;\u0026plusmn;\u0026thinsp;26.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eQ1 (\u0026lt;\u0026thinsp;192.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120 (16.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198 (37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\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\u003eQ2 (192.69\u0026ndash;220.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (16.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192 (36.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (220.97\u0026ndash;249.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 (26.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120 (22.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (\u0026gt;\u0026thinsp;249.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300 (40.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (3.41)\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\u003eEVA, early vascular aging; BMI, body mass index; TG, triglyceride; FPG, fasting plasma glucose; HbA1c, hlycosylated hemoglobin; BaPWV, brachial-ankle pulse wave velocity; TyG, triglyceride-glucose; TyG-BMI, Triglyceride glucose-body mass index\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\u003eThe distribution of components of the Framingham vascular age score and the result of the Framingham CVD risk score between the EVA and non-EVA groups in people aged 30\u0026ndash;59 years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;1272)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEVA (n\u0026thinsp;=\u0026thinsp;744)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-EVA (n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e1002 (78.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e666 (89.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336 (63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eChronological age (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204 (16.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (19.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (11.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\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\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270 (21.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (20.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (22.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (15.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (11.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (14.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (25.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (19.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (17.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (22.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (17.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (6.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026ge;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (15.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (26.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\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\u003e1.29\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396 (31.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (26.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (37.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.16\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (22.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (27.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (14.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.91\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (28.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (33.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (20.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\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\u003e4.14\u0026ndash;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (38.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (34.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234 (44.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.17\u0026ndash;6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474 (37.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (41.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (31.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.21\u0026ndash;7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (12.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP treated history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (20.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (33.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSBP (mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.73\u0026thinsp;\u0026plusmn;\u0026thinsp;15.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.34\u0026thinsp;\u0026plusmn;\u0026thinsp;15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e546 (42.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408 (77.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\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\u003e120\u0026ndash;129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324 (25.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (29.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (19.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e130\u0026ndash;139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (17.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (27.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (3.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e140\u0026ndash;149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (12.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u0026ndash;159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (5.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246 (19.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (31.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDiabetic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (12.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePredicted vascular age (years, median, quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (36, 59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5 (40, 68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (34, 42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePredicted vascular age\u0026ndash;chronological age difference (years, median, quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (-4, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5 (-9, -2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePredicted CVD risk (%, median, quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.70(2.33, 10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9 (3.9, 15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (2.0, 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eEVA, early vascular aging; HDL-C, high density lipoprotein cholesterol; TC, total cholesterol; SBP, systolic blood pressure; CVD, cardiovascular disease\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression analysis for the association between TyG-BMI and EVA\u003c/h2\u003e \u003cp\u003eTo examine the association between TyG-BMI levels and EVA, logistic regression analyses were performed, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In Model 1, a 1-unit increase in TyG-BMI was significantly associated with a 3.3% elevated risk of EVA (OR: 1.033, 95% CI: 1.029\u0026ndash;1.038). Similarly, Model 2 revealed a 3.0% increase in EVA risk per 1-unit increment in TyG-BMI (OR: 1.030, 95% CI: 1.025\u0026ndash;1.034). In Model 3, each 1-unit rise in TyG-BMI was associated with a 2.9% higher risk of EVA (OR: 1.029, 95% CI: 1.024\u0026ndash;1.034). Furthermore, TyG-BMI was categorized into quartiles to assess its impact as a categorical variable. In the multivariable-adjusted model, with the lowest quartile (Q1) as the reference, the ORs for EVA risk in the other quartiles were as follows: Q3 exhibited an OR of 1.634 (95% CI: 1.135\u0026ndash;2.352), while Q4 demonstrated a markedly elevated OR of 15.947 (95% CI: 9.120\u0026ndash;27.885). This indicates that, compared to participants in Q1, those in Q3 had a 63.4% increased risk of EVA, whereas those in Q4 had a 1494.7% higher risk. No statistically significant association was observed between Q2 and Q1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the risk elevation became significant starting from Q3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpearman correlation analysis between TyG-BMI and vascular aging related indicators\u003c/h3\u003e\n\u003cp\u003eAs demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, TyG-BMI exhibited significant positive correlations with baPWV (r\u0026thinsp;=\u0026thinsp;0.228), predicted vascular age (correlation coefficient [r]\u0026thinsp;=\u0026thinsp;0.385), predicted vascular age-chronological age difference (r\u0026thinsp;=\u0026thinsp;0.588), and predicted CVD risk (r\u0026thinsp;=\u0026thinsp;0.478), all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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\u003eCorrelation analysis between TyG-BMI and vascular aging related indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted vascular age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted vascular age-chronological age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted CVD risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaPWV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eTyG-BMI, triglyceride glucose-body mass index; CVD, cardiovascular disease; BaPWV, brachial-ankle pulse wave velocity; r, coefficient of association\u003c/p\u003e\n\u003ch3\u003eLinear regression analysis for the relationship between TyG-BMI and vascular aging related indicators\u003c/h3\u003e\n\u003cp\u003eTo assess the independent associations between TyG-BMI and baPWV, predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk, a linear regression analysis was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis revealed statistically significant associations between all investigated predictors and TyG-BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all variables). In the fully adjusted Model 3, which accounted for potential confounding factors, each 1-unit increment in TyG-BMI was associated with significant changes in the following parameters: a 1.002-unit increase in baPWV (95% CI: 0.713\u0026ndash;1.291), a 0.121-unit increase in predicted vascular age (95% CI: 0.107\u0026ndash;0.136), a corresponding 0.121-unit elevation in the predicted vascular age-chronological age difference (95% CI: 0.107\u0026ndash;0.136), and a 0.044-unit rise in predicted CVD risk (95% CI: 0.037\u0026ndash;0.052).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRCS analysis between the TyG-BMI, with EVA and other related indicators\u003c/h2\u003e \u003cp\u003eThe non-linear relationship between TyG-BMI and EVA risk was quantitatively assessed using RCS analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The analysis revealed a significant inflection point at 220.90 mmol/L for TyG-BMI. Subsequent application of a two-piecewise logistic regression model enabled the estimation of ORs and corresponding 95% CIs for EVA risk on either side of this inflection point (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, the OR was calculated as 1.013 (95% CI: 1.002\u0026ndash;1.024) below the inflection point, while it increased to 1.088 (95% CI: 1.067\u0026ndash;1.109) above this threshold.\u003c/p\u003e \u003cp\u003e \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\u003eThe result of the two-piecewise linear regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\" colname=\"c2\"\u003e \u003cp\u003eInflection points of TyG-BMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR / B (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013 (1.002\u0026ndash;1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.088 (1.067\u0026ndash;1.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaPWV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.229 (0.490\u0026ndash;1.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.606 (2.035\u0026ndash;3.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted vascular age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099 (0.058\u0026ndash;0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131 (0.104\u0026ndash;0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted vascular age-chronological age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099 (0.058\u0026ndash;0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131 (0.104\u0026ndash;0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted CVD risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019 (0.003\u0026ndash;0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;220.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054 (0.038\u0026ndash;0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eAdjusted for chronological age, sex, drinker, glycosylated hemoglobin, homocysteine, and diabetic.\u003c/p\u003e \u003cp\u003eFurthermore, additional RCS analyses demonstrated non-linear associations between TyG-BMI and several vascular parameters, including baPWV, predicted vascular age, predicted vascular age-chronological age difference, and predicted CVD risk, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Linear regression analyses of both segments revealed that the unstandardized beta coefficient (B) for these vascular parameters were significantly steeper compared to their pre-inflection counterparts (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eROC curve of TyG-BMI for predicting EVA\u003c/h2\u003e \u003cp\u003eThe predictive performance of TyG-BMI for EVA was quantitatively assessed through ROC curve analysis, wherein the AUC served as a critical metric for evaluation. The AUC demonstrates a direct correlation with predictive accuracy, with higher values indicating superior diagnostic performance. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, after adjusted for multiple factors the ROC analysis of continuous TyG-BMI measurements yielded a statistically significant AUC of 0.8115 (95% confidence interval: 0.7886\u0026ndash;0.8344, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal diagnostic threshold was determined to be 180.69 mmol/L, with this cutoff value demonstrating a sensitivity of 75.0% and specificity of 72.7% in predicting EVA occurrence (Model 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the association between triglyceride\u0026ndash;glucose\u0026ndash;body mass index (TyG-BMI) and early vascular aging (EVA) among young and middle-aged adults aged 30\u0026ndash;59 years. Our findings demonstrated that higher TyG-BMI levels were significantly associated with an increased risk of EVA. Notably, a significant inflection point was identified, indicating distinct associations between TyG-BMI and EVA below and above this threshold. These results suggest that TyG-BMI may serve as a useful biomarker for the early identification of vascular aging risk in this population.\u003c/p\u003e \u003cp\u003eVascular aging is a complex biological process characterized by progressive structural and functional deterioration of the vascular system, which often manifests as subclinical changes during its early stages [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This process begins during fetal development and persists throughout the lifespan, with its progression potentially accelerated by multiple pro-aging factors, particularly cardiovascular risk factors, thereby increasing susceptibility to EVA [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the present study, vascular age was estimated using the Framingham vascular age score, a validated composite index that integrates multiple cardiovascular risk factors into a single predictive measure, including sex, age, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure (treated or untreated), smoking status, and diabetes status [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. EVA was defined as a condition in which the estimated vascular age exceeds the chronological age, representing a preclinical marker of cardiovascular disease that reflects early arterial deterioration before the onset of overt clinical events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe triglyceride\u0026ndash;glucose (TyG) index has been extensively validated as a reliable surrogate marker of insulin resistance (IR). A growing body of evidence has demonstrated significant associations between elevated TyG levels and vascular aging\u0026ndash;related outcomes [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, a large retrospective cohort study conducted in China reported that higher TyG levels were independently associated with an increased incidence of arterial stiffness after comprehensive adjustment for confounding factors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, body mass index (BMI), as a conventional anthropometric indicator of adiposity, has also been consistently implicated in the pathogenesis of vascular aging [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Longitudinal evidence from the German Health Interview and Examination Survey for Children and Adolescents cohort demonstrated that obesity during childhood and adolescence substantially contributes to the development of subclinical atherosclerosis and arterial stiffness in early adulthood [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, both TyG and BMI have inherent limitations when used as independent predictors. TyG primarily reflects glycometabolic dysfunction but does not capture adiposity-related risk, whereas BMI fails to account for underlying metabolic abnormalities such as insulin resistance. By integrating glycometabolic and adiposity-related information, TyG-BMI may provide a more comprehensive assessment of vascular aging risk.\u003c/p\u003e \u003cp\u003eTyG-BMI, a composite biomarker derived from the product of the TyG index and BMI, has emerged as a promising indicator of insulin resistance with improved diagnostic performance and clinical applicability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given the well-established associations of both TyG and BMI with vascular aging and the central role of insulin resistance in vascular pathophysiology, it is biologically plausible that TyG-BMI is positively associated with EVA risk. Although direct evidence linking TyG-BMI to EVA remains limited, accumulating studies have demonstrated robust associations between TyG-BMI and cardiovascular diseases, which represent advanced manifestations of vascular aging [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A recent meta-analysis including more than 870,000 participants reported significantly higher risks of cardiovascular disease, coronary artery disease, and stroke among individuals with elevated TyG-BMI levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similar associations have been observed in population-based surveys and clinical cohorts, further supporting the potential relevance of TyG-BMI in cardiovascular risk stratification [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Collectively, these findings support the hypothesis that TyG-BMI may be closely linked to early vascular aging processes.\u003c/p\u003e \u003cp\u003eIn the present study, TyG-BMI was analyzed as both a continuous variable and a categorical variable stratified into quartiles to comprehensively characterize its association with EVA. Multivariable logistic regression analyses revealed a dose-dependent increase in EVA risk with increasing TyG-BMI levels, and sensitivity analyses confirmed the robustness of these associations after adjustment for potential confounders. Notably, the lack of a statistically significant difference in EVA risk between the lowest and second quartiles suggests the presence of a threshold effect, whereby a certain magnitude of TyG-BMI elevation may be required before a measurable impact on vascular aging becomes evident. To further validate these findings, we examined multiple EVA-related markers, including predicted vascular age, the difference between predicted vascular age and chronological age, predicted cardiovascular disease risk, and brachial\u0026ndash;ankle pulse wave velocity. Linear regression analyses demonstrated that higher TyG-BMI levels were independently associated with adverse changes in all these indicators, providing consistent evidence for a significant relationship between TyG-BMI and early vascular aging.\u003c/p\u003e \u003cp\u003eThe biological mechanisms underlying the association between TyG-BMI and EVA are likely multifactorial and remain incompletely elucidated. As a composite marker of insulin resistance, TyG-BMI captures both adiposity and glycometabolic dysfunction. Insulin resistance, a core feature of metabolic syndrome, has been implicated in dyslipidemia, endothelial dysfunction, vascular inflammation, and atherosclerotic progression, all of which contribute to vascular aging [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Insulin resistance promotes hypertriglyceridemia, reduces high-density lipoprotein cholesterol levels, and increases the production of small dense low-density lipoprotein particles, thereby exacerbating endothelial injury [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, insulin resistance impairs endothelial function through increased oxidative stress, activation of proinflammatory signaling pathways, reduced nitric oxide bioavailability, and disruption of cyclic guanosine monophosphate signaling in vascular smooth muscle cells. These alterations collectively lead to reduced arterial compliance, increased arterial stiffness, and accelerated vascular aging [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn important and novel finding of this study is the identification of a nonlinear association between TyG-BMI and EVA risk, with a significant inflection point at 220.90. Below this threshold, increases in TyG-BMI were associated with a modest elevation in EVA risk, whereas above the threshold, the risk increased substantially, indicating a dose-dependent relationship with more pronounced vascular consequences at higher TyG-BMI levels. Consistent nonlinear associations were also observed between TyG-BMI and multiple vascular aging\u0026ndash;related indicators, including predicted vascular age, predicted vascular age\u0026ndash;chronological age difference, predicted cardiovascular disease risk, and brachial\u0026ndash;ankle pulse wave velocity. These findings are in line with previous studies reporting nonlinear relationships between TyG-BMI and cardiovascular outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. although some investigations have reported linear associations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The discrepancies across studies may be attributable to differences in study design, population characteristics, and outcome definitions. Importantly, our focus on subclinical vascular aging markers in a relatively young population may partly explain the observed threshold effects, as metabolic disturbances may exert more pronounced nonlinear influences during the early stages of vascular damage.\u003c/p\u003e \u003cp\u003eThe identification of these nonlinear associations has important clinical implications, suggesting that TyG-BMI may be particularly useful for identifying individuals at high risk of early vascular aging who may benefit most from targeted preventive interventions. Longitudinal studies are warranted to confirm these findings and to further elucidate the temporal and mechanistic relationships between TyG-BMI and vascular aging progression.\u003c/p\u003e \u003cp\u003eIn addition, receiver operating characteristic curve analyses demonstrated that TyG-BMI exhibited good discriminatory performance for identifying EVA after adjustment for confounding factors. Previous studies have reported moderate predictive value of TyG-BMI for cardiovascular outcomes in different clinical settings [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In the present study, the optimal TyG-BMI cut-off value for EVA decreased after adjustment for confounders, suggesting that maintaining TyG-BMI below this level may be particularly relevant for the prevention of early vascular aging. This analysis does not imply diagnostic utility but rather reflects the potential role of TyG-BMI as a screening and risk stratification indicator.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, these findings provide evidence supporting the use of TyG-BMI as a low-cost and easily accessible indicator for large-scale screening of early vascular aging. Given that TyG-BMI can be readily derived from routine anthropometric and biochemical measurements, its implementation in community-based or occupational health settings may facilitate early identification of individuals at increased vascular aging risk and enable timely preventive interventions.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, TyG-BMI was examined in both continuous and categorical forms, allowing for comprehensive evaluation of dose\u0026ndash;response relationships while minimizing information loss. Second, by incorporating nonlinear analytical approaches, this study is, to our knowledge, the first to report a nonlinear association between TyG-BMI and EVA. Third, multiple sensitivity analyses were conducted to assess the stability and robustness of the findings. Finally, the focus on a relatively young population highlights the importance of early identification and intervention to improve long-term vascular health.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. The cross-sectional design precludes causal inference and limits conclusions regarding temporal relationships. Although extensive adjustments were made for potential confounders, Lifestyle factors such as physical activity and dietary patterns were not available in the electronic medical records and could not be adjusted for, which may result in residual confounding. In addition, the single-center nature of the study may limit the generalizability of the findings. Future multicenter prospective studies with larger and more diverse populations are needed to validate these results and to further clarify the role of TyG-BMI in early vascular aging.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigher TyG-BMI levels were significantly associated with an increased risk of early vascular aging in young and middle-aged adults. A nonlinear relationship was observed, with a markedly higher risk above a TyG-BMI threshold of 220.90. These findings suggest that TyG-BMI may be a useful marker for early identification of individuals at increased risk of vascular aging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eInsulin resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eTyG-BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eTriglyceride-glucose body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eEVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eEarly vascular aging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003ebaPWV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eBrachial-ankle pulse wave velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eRestricted cubic spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eConfidential interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eCVDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eTotal cholesterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eFasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eGlycosylated hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eHcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eHomocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eTriglyceride-glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8475%;\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89.1525%;\"\u003e\n \u003cp\u003eAcute coronary syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Hebei General Hospital. Clinical trial number: not applicable. The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Hebei Province 2025 Medical Science Research Program Plan (Grant No. 20250008), the Hebei Provincial Key Medical Research Project (Grant No. 20220871), the 2023 Government-funded Clinical Medicine Outstanding Talent Project (Grant No. ZF2023202), and the Natural Science Foundation of Hebei Province (Grant No. H2016307015). The funders had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCK performed the data analysis and drafted the manuscript. CL and CK conceived and designed the study. CL, LY, and SC reviewed and revised the manuscript. WL, XB, HD, and FZ collected and curated the data. CK and CL obtained funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang S, Xia B, Kalionis B, Li H, Zhang X, Zhang X, Xia S: \u003cstrong\u003eThe Role and Mechanism of Vascular Aging in Geriatric Vascular Diseases\u003c/strong\u003e. \u003cem\u003eAging and disease \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(4):2237-2249.\u003c/li\u003e\n\u003cli\u003eDorogovtsev V, Yankevich D, Martyushev-Poklad A, Borisov I, Grechko AV: \u003cstrong\u003eThe Importance of Orthostatic Increase in Pulse Wave Velocity in the Diagnosis of Early Vascular Aging\u003c/strong\u003e. \u003cem\u003eJournal of clinical medicine \u003c/em\u003e2024, 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\u003cstrong\u003e17\u003c/strong\u003e(1):15.\u003c/li\u003e\n\u003cli\u003eWang J, Tang H, Tian J, Xie Y, Wu Y: \u003cstrong\u003eNon-insulin-based insulin resistance indices predict early neurological deterioration in elderly and middle-aged acute ischemic stroke patients in Northeast China\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2024, \u003cstrong\u003e14\u003c/strong\u003e(1):16138.\u003c/li\u003e\n\u003cli\u003eSun Y, Hu Y: \u003cstrong\u003eAssociation of triglyceride-glucose-body mass index with all-cause mortality among individuals with cardiovascular disease: results from NHANES\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2025, \u003cstrong\u003e16\u003c/strong\u003e:1529004.\u003c/li\u003e\n\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":"Early vascular aging, Triglyceride–glucose–body mass index, Insulin resistance, Arterial stiffness, Framingham vascular age score","lastPublishedDoi":"10.21203/rs.3.rs-8662500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8662500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInsulin resistance is closely associated with arterial stiffness and vascular aging. The triglyceride\u0026ndash;glucose\u0026ndash;body mass index (TyG-BMI) is a validated surrogate marker of insulin resistance. However, evidence regarding the association between TyG-BMI and early vascular aging (EVA) remains limited, particularly among young and middle-aged populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 1,272 Chinese adults aged 30\u0026ndash;59 years who underwent brachial\u0026ndash;ankle pulse wave velocity (baPWV) assessment. EVA was defined using the Framingham vascular age score. Multivariable logistic and linear regression models, restricted cubic spline analyses, and threshold analyses were applied to evaluate the association between TyG-BMI and EVA. Receiver operating characteristic (ROC) curve analysis was conducted to assess the predictive value of TyG-BMI for EVA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEach one-unit increase in TyG-BMI was associated with a 2.9% higher risk of EVA (odds ratio 1.029, 95% confidence interval 1.024\u0026ndash;1.034) after full adjustment. Restricted cubic spline analysis revealed a nonlinear association, with an inflection point at TyG-BMI of 220.90. Below this threshold, EVA risk increased modestly, whereas above the threshold, the risk increased substantially. TyG-BMI was also positively associated with baPWV and other vascular aging indicators. ROC analysis suggested a moderate discriminatory ability of TyG-BMI for identifying individuals with EVA.(area under the curve 0.812).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTyG-BMI is independently and nonlinearly associated with the risk of early vascular aging in young and middle-aged Chinese adults. TyG-BMI may serve as a practical biomarker for identifying individuals at high risk of vascular aging.\u003c/p\u003e","manuscriptTitle":"Association between the triglyceride-glucose-body mass index and the risk of early vascular aging in young and middle-aged Chinese adults: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:50:38","doi":"10.21203/rs.3.rs-8662500/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-03T06:14:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T15:51:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64794520491155035689885303871772362603","date":"2026-02-02T05:00:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T04:57:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163453129258850403266396456754689165761","date":"2026-01-30T04:20:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T02:05:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7389968026680589430007721313951360897","date":"2026-01-29T21:57:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14455553773927487297706061649678999626","date":"2026-01-29T12:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299251336584572589565285509030690921682","date":"2026-01-29T08:04:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280671126640884142244001659212828403691","date":"2026-01-28T11:52:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74711232285845879567913438449418290295","date":"2026-01-28T10:20:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235503460122584536178986765858645659683","date":"2026-01-28T02:10:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15272273513061530104138781012909825512","date":"2026-01-27T19:09:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T12:15:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T11:14:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T11:53:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T11:51:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-21T17:20:59+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":"fd3ee492-ea01-4d12-b411-41f665b51957","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T06:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:50:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8662500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8662500","identity":"rs-8662500","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}